code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase_ ( UpperCAmelCase_):
"""simple docstring"""
def __init__( self , _UpperCAmelCase , ... | 483 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase__ ( ) -> Any:
"""simple docstring"""
UpperCAmelCase = ... | 373 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __UpperCamelCase ( _lowerCAmelCase = "isbn/0140328726" ) -> dict:
"""simple docstring"""
A : Optional[Any] = olid.strip().strip("""/""" ) # Remove le... | 662 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 0 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params im... | 47 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,... | 505 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 0 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
Bert... | 448 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_lowerCAmelCa... | 569 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_deter... | 690 | 0 |
def _snake_case (__lowercase = 50):
UpperCamelCase_ = [1] * (length + 1)
for row_length in range(length + 1):
for tile_length in range(2 , 5):
for tile_start in range(row_length - tile_length + 1):
ways_number[row_length] += ways_number[
... | 23 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 360 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
_lowercase = tuple[int, int]
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : set[int] ,lowerCAmelCase__ : Mapping[EdgeT, int] ... | 659 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
A : List[Any] = version.parse(version.p... | 15 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A_ : List[str] ={
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNex... | 483 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 | 0 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig... | 373 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Union[str, Any] = []
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> bool:
"""simple docstring"""
for i in range(len(UpperCamelCase__ ) ):
if board[row][i] == ... | 662 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 0 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _UpperCamelCase( unittest.TestCase ):
def __lowerCAmelCase ( self : ... | 47 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.fl... | 690 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {"vocab_file": "spie... | 505 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 0 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format... | 448 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ... | 690 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@re... | 569 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 23 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 690 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE: Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE: List[Any] = "T5Config"
class... | 360 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Unio... | 690 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class __snake_case ( UpperCAmelCase_ ):
... | 659 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[... | 690 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_en... | 15 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 0 |
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
a_ = [0 for i in range(r + 1 )]
# nc0 = 1
a_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previo... | 483 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in pos... | 690 | 0 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ... | 373 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 690 | 0 |
def __UpperCamelCase ( _lowerCAmelCase = 10**9 ) -> int:
"""simple docstring"""
A : Optional[int] = 1
A : Optional[int] = 2
A : str = 0
A : Dict = 0
A : Optional[int] = 0
while perimeter <= max_perimeter:
p... | 662 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ... | 690 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNA... | 47 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase ... | 690 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowercase (_snake_cas... | 505 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcess... | 448 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class _UpperCAmelCase ( UpperCAmelCase_ ):
# `task` is not a ClassVar since we want it to be part of the `as... | 569 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_deter... | 690 | 0 |
import argparse
from collections import defaultdict
import yaml
snake_case__ : Optional[Any] = "docs/source/en/_toctree.yml"
def _snake_case (__lowercase):
UpperCamelCase_ = defaultdict(UpperCamelCase__)
UpperCamelCase_ = []
Upper... | 23 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.c... | 690 | 0 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE: List[str] = Lock()
def _a ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ... | 360 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 0 |
import functools
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : int = len(UpperCamelCase__)
lowerCAmelCase_ : Optional[int] = len(UpperCamelCase__)
@functools.cache
def min_distance(snake_case__ , snake_case__) -> int:
... | 659 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 690 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import lo... | 15 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={p... | 690 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_... | 483 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 690 | 0 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
SCREAMING_SNAKE_CASE_ = False
class _UpperC... | 373 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
SCREAMING_SNAKE_CASE_:Optional[int] = HfApi()
SCREAMING_SNAKE_CASE_:Optional[Any] = {}
# fmt: off
SCREAMING_SNAKE_CASE_:Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.... | 662 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 0 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
f... | 47 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.fl... | 690 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils im... | 505 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = ... | 690 | 0 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedul... | 448 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ... | 690 | 0 |
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
_lowerCAmelCase = get_logg... | 569 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class A__ :
def __init__( self : Any , _a : int ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =value
_SCREAMING_SNAKE_CASE =None
... | 691 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : List[Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConf... | 691 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 691 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 1 |
import math
import qiskit
def lowerCamelCase( a__ = 1 ,a__ = 1 ,a__ = 1):
if (
isinstance(a__ ,a__)
or isinstance(a__ ,a__)
or isinstance(a__ ,a__)
):
raise TypeError('''inputs must be integers.''')
if (input_a < 0) or (input_a < 0) or... | 691 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
def lowerCamelCase( a__ ,a__):
_SCREAMING... | 691 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slo... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from tr... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
snake_case_ : Dict ... | 691 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__ ( unittest.TestCase ):
@property
def ... | 691 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
snake_case_ : str = {
'''iou_pr... | 691 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 691 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 1 |
from collections.abc import Generator
from math import sin
def lowerCamelCase( a__):
if len(a__) != 32:
raise ValueError('''Input must be of length 32''')
_SCREAMING_SNAKE_CASE =b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return littl... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE =[
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''mod... | 691 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
depreca... | 691 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 1 |
from ...configuration_utils import PretrainedConfig
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = "bert-generation"
def __init__( self : Union[str, Any] , _a : int=5_0358 , _a : List[str]=1024 , _a : Dict=24 , ... | 691 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 1 |
from math import factorial
def lowerCamelCase( a__ = 100):
return sum(map(a__ ,str(factorial(a__))))
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip()))) | 691 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_availabl... | 691 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class A__ :
UpperCAmelCase = 42
UpperCAmelCase = None
UpperCAmelCase = None
def lowerCamelCase( a__):
# Validation
def is_valid_tree(a__) -> bool:
... | 691 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case_ : int = ['''small''', '''medium''', '''large''']
snake_case_ : Optional[Any] = '''lm_head.decoder.weight'''
snake_case_ : Tuple = '''lm_head.weight'''
def ... | 691 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 691 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dumm... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 | 1 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=UpperCamelCase__ ):
UpperCAmelCase = ["torch", "scipy"]
def __init__( self : Optional[Any] , *_a : Union[str, Any] , **_a : Optional[Any] ) -> Union[str, Any... | 691 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 1 |
import enum
import shutil
import sys
snake_case_ , snake_case_ : Any = shutil.get_terminal_size()
snake_case_ : List[str] = {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}
class A__ ( enum.Enum ):
UpperCAmelCase ... | 691 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case_ : List[Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case_ : Optional[int] = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = ... | 691 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 1 |
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE =current_set.copy()
for row_index, row in enumerate(a__):
_SCREAMING_SNAKE_CASE =row[0]
for column_index, column in enumerate(a__):
if magnitude == 0:
_SCREAMING_SNAKE_CASE =column
... | 691 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 1 |
def lowerCamelCase( a__):
if len(a__) <= 1:
return [tuple(a__)]
_SCREAMING_SNAKE_CASE =[]
def generate(a__ ,a__):
if k == 1:
res.append(tuple(arr[:]))
return
generate(k - 1 ,a__)
for i in range(k - 1):
if k % 2 =... | 691 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 1 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
snake_case_ : List[Any] = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
a... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
snake_case_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def ... | 691 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
snake_case_ : Union[str, Any] = {
... | 691 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, t... | 691 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 1 |
import argparse
import os
import re
snake_case_ : List[Any] = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
snake_case_ : Dict = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
snake_case_ : Optional[Any] ... | 691 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 1 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCamelCase( a... | 691 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = (DPMSolver... | 691 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case_ : List[Any] = False
class A__ ( unittest.TestCase ):
def __UpperCamelCase ... | 691 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 1 |
import string
from math import logaa
def lowerCamelCase( a__ ,a__):
_SCREAMING_SNAKE_CASE =document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation)).replace('''\n''' ,'''''')
_SCREAMING_SNAKE_CASE =document_without_punctuation.split(''' ''') # ... | 691 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 1 |
import math
import unittest
def lowerCamelCase( a__):
assert isinstance(a__ ,a__) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
#... | 691 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class A__ ( enum.Enum ):
UpperCAmelCase ... | 691 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 1 |
from __future__ import annotations
class A__ :
def __init__( self : Tuple , _a : Tuple=None ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =data
_SCREAMING_SNAKE_CASE =None
def __repr__(... | 691 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.... | 691 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
snake_ca... | 691 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 691 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCamelCase( a__ ... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 | 1 |
from datetime import datetime
import requests
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE ='''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
_SCREAMING_SNAKE_CASE =requests.get(base_url + url).json()[0]['''urls'''][0]['''src''']
return requests.get(a__... | 691 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 1 |
import socket
def lowerCamelCase( ):
_SCREAMING_SNAKE_CASE =socket.socket(socket.AF_INET ,socket.SOCK_STREAM)
_SCREAMING_SNAKE_CASE =socket.gethostname()
_SCREAMING_SNAKE_CASE =1_2312
sock.connect((host, port))
sock.send(b'''Hello server!''')
with open('''R... | 691 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 1 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 1 |
def lowerCamelCase( a__ ,a__):
_SCREAMING_SNAKE_CASE =[1]
for i in range(2 ,a__):
factorials.append(factorials[-1] * i)
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =list(range(a__))
# Find perm... | 691 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 1 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
snake_case_ : Dict = logging.get_logger(__name__)... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
import torch
from torch import nn
class A__ ( nn.Module ):
def __init__( self : int , _a : Tuple , _a : Any , _a : Dict , _a : List[Any] , _a : Tuple=1 , _a : int=False ) -> str:
... | 691 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( UpperCamelCase__ ):
@staticmethod
@abstractmethod
def __UpperCamelCase ( _a : ArgumentParser ) -> List[Any]:
"""simple docstring"""
raise NotImplemen... | 691 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 1 |
def lowerCamelCase( a__ ,a__):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(a__ ,int(b / 2)) * actual_power(a__ ,int(b / 2))
else:
return a * actual_power(a__ ,int(b / 2)) * actual_power(a__ ,int(b / 2))
def lowerCamelCase( a__ ... | 691 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
snake_case_ : List[str] = logging.get_logger(__name__)
class A__ ( UpperCamelCase__ ):
def __init__( self : str , *_a : int , **_a... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
def lowerCamelCase( a__):
if not isinstance(a__ ,a__):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''')
if len(a__) == 0:
raise ValueError('''Input list must be a non empty list''')
if len(a__) == 1:
return True
_SCREAMING_SNAKE_CASE =... | 691 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 1 |
import numpy
# List of input, output pairs
snake_case_ : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
snake_case_ : List[str] = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
snake_case_ : Union[... | 691 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, loa... | 691 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 1 |
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