code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import sys
import turtle
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmel... | 353 |
from __future__ import annotations
from typing import Any
class __A :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int ):
lowerCAmelCase : Tuple = num_of_nodes
lowerCAmelCase : list[list[int]] = []
... | 323 | 0 |
__A : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
lowerCAmelCase : Optional[int] = 0
while number:
# Increased Speed Slightly b... | 354 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 323 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 1_000_000 ) -> int:
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = limit + 1
lowerCAmelCase : str = [0] * limit
for first_term in range(1, _UpperCAmelCase ):
for n in range(_U... | 355 |
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
lowerCAmelCase : Any = sum(i * i for i in range(1, n + 1 ) )
lowerCAmelCase : str = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
return s... | 323 | 0 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 356 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Any = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedde... | 357 |
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, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : List[Any] = {
... | 358 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 323 | 0 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config... | 359 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('String lengths must match!' )
lowerCAmelCase : Tuple = 0
for chara, chara in zi... | 323 | 0 |
__A : Optional[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : int = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if not is_tor... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''shi-labs/dinat-mini-... | 323 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class __A :
def __init__( self : List[str] ):
lowerCAmelCase : Any = {}
def lowercase__ ( self : Optional[int] , UpperCAmelCase... | 362 |
from manim import *
class __A ( lowerCAmelCase ):
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 , width=... | 323 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__A : str = 3
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
print('Generating primitive root of p' )
while True:
... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError('Input must be a positive integer' )
lowerCAmelCase : List[str] = [True] * (num + 1)
lowerCAmelCase : int = 2
whi... | 364 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : Dict = models.Sequential()
# Step 1 - Convolution
# ... | 323 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueEr... | 365 |
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,
... | 323 | 0 |
"""simple docstring"""
import re
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if len(re.findall('[ATCG]', lowerCAmelCase__ ) ) != len(lowerCAmelCase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dn... | 366 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A ... | 323 | 0 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeac... | 367 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 323 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechCo... | 323 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytess... | 369 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from... | 323 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 370 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 323 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[Any] = {
'''configuration_xlm_roberta'''... | 323 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ) -> Tuple:
'''simple docstring'''
... | 350 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sma... | 323 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
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_configuration_common import ConfigTester
from ...test_mod... | 351 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return x + 2
class __A ( unittest.TestCase ):
... | 323 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A ( __lo... | 352 |
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def SCREAMING_SNAKE_CASE__ ( ... | 323 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__A : List[str] = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value network
... | 353 |
from __future__ import annotations
from typing import Any
class __A :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int ):
lowerCAmelCase : Tuple = num_of_nodes
lowerCAmelCase : list[list[int]] = []
... | 323 | 0 |
class __A ( lowercase_ ):
pass
class __A ( lowercase_ ):
pass
class __A :
def __init__( self : Tuple ):
lowerCAmelCase : List[str] = [
[],
[],
[],
]
def lowercase_... | 354 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 323 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__A : Optional[int] = ... | 355 |
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
lowerCAmelCase : Any = sum(i * i for i in range(1, n + 1 ) )
lowerCAmelCase : str = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
return s... | 323 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__A : List[Any] = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': 1000,
... | 356 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : List[str] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCAmelCase : Optional[Any] = ''
lowerCAmelCase : str ... | 357 |
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, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
from collections.abc import Sequence
from queue import Queue
class __A :
def __init__( self : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Any=Non... | 358 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 323 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = None ) -> Union[str, Any]:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
lowerCAmelCase : ... | 359 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('String lengths must match!' )
lowerCAmelCase : Tuple = 0
for chara, chara in zi... | 323 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def SCREAMING_SNAKE_CASE__ ( ) -> int:
'''... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __A ( ... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''shi-labs/dinat-mini-... | 323 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
'''simple docstring'''
if isinstance(__snake_case, __snake_case ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(__snake_case, __snake_case ):
raise TypeError('\'str\' ob... | 362 |
from manim import *
class __A ( lowerCAmelCase ):
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 , width=... | 323 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A : int = pd.read_csv('''sample_data.csv''', header=None)
__A : Dict ... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__A : ... | 364 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : Dict = models.Sequential()
# Step 1 - Convolution
# ... | 323 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__A : List[str] = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
... | 365 |
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,
... | 323 | 0 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
lowerCAmel... | 366 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A ... | 323 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.num... | 367 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 323 | 0 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A : Any = importlib.util.find_spec('''s3fs''') is not None
if _has_safs:
from .safilesystem import SaFil... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechCo... | 323 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__A : Any = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_P... | 369 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from... | 323 | 0 |
import qiskit
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> qiskit.result.counts.Counts:
'''simple docstring'''
lowerCAmelCase : Tuple = qiskit.Aer.get_backend('aer_simulator' )
lowerCAmelCase : Union[str, Any] = qiskit.Qua... | 370 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 323 | 0 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impor... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[Any] = {
'''configuration_xlm_roberta'''... | 323 | 0 |
class __A :
def __init__( self : Optional[int] , UpperCAmelCase_ : Union[str, Any] ):
# we need a list not a string, so do something to change the type
lowerCAmelCase : Optional[int] = arr.split(',' )
def lowercase__ ( se... | 350 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sma... | 323 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...featu... | 351 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return x + 2
class __A ( unittest.TestCase ):
... | 323 | 0 |
"""simple docstring"""
import argparse
import gc
import json
import os
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_schedule_with_warmup, set_seed
from a... | 352 |
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def SCREAMING_SNAKE_CASE__ ( ... | 323 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available(... | 353 |
from __future__ import annotations
from typing import Any
class __A :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int ):
lowerCAmelCase : Tuple = num_of_nodes
lowerCAmelCase : list[list[int]] = []
... | 323 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torc... | 354 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 323 | 0 |
__A : str = '''Tobias Carryer'''
from time import time
class __A :
def __init__( self : int , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union... | 355 |
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
lowerCAmelCase : Any = sum(i * i for i in range(1, n + 1 ) )
lowerCAmelCase : str = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
return s... | 323 | 0 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowerCAmelCase : Any = first_str.lower().strip()
lowerCAmelCase : List[str] = second_str.lower().strip()
# Rem... | 356 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 0 |
# 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 ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet impor... | 357 |
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, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 358 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 323 | 0 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_avai... | 359 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('String lengths must match!' )
lowerCAmelCase : Tuple = 0
for chara, chara in zi... | 323 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowerCAmelCase : Any = int(_UpperCAmelCase )
# Initialize Result
lowerCAmelCase : Dict = []
# Traverse through all denomination
fo... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_i... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''shi-labs/dinat-mini-... | 323 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__A : Optional[int] = TypeVar('''_T''')
class __A ( Generic[_T] ):
def __init__( self : Any , UpperCAmelCase_ : Iterable[_T] | None = None ):
lowerCAmelCase ... | 362 |
from manim import *
class __A ( lowerCAmelCase ):
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 , width=... | 323 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : Optional[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[Any] = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if... | 364 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : Dict = models.Sequential()
# Step 1 - Convolution
# ... | 323 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Tuple = (CMStochasticIterativeScheduler,)
lowerCAmelCase_ : List[Any] = 10
... | 365 |
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,
... | 323 | 0 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torc... | 366 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A ... | 323 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Union[str, Any] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
... | 367 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 323 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechCo... | 323 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTester... | 369 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from... | 323 | 0 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from tra... | 370 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 323 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[Any] = {
'''configuration_xlm_roberta'''... | 323 | 0 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
lowerCAmelCase : List[str] = 1
lowerCAmelCase : str = True
for v in tree[start]:
if v not in visited:
ret += ... | 350 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sma... | 323 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 351 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return x + 2
class __A ( unittest.TestCase ):
... | 323 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCa... | 352 |
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def SCREAMING_SNAKE_CASE__ ( ... | 323 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : str = {
'''facebook/xlm-roberta-xl''': '''http... | 353 |
from __future__ import annotations
from typing import Any
class __A :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int ):
lowerCAmelCase : Tuple = num_of_nodes
lowerCAmelCase : list[list[int]] = []
... | 323 | 0 |
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def SCREAMING_SNAKE_CASE__ ( ... | 354 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 323 | 0 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_mi... | 355 |
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
lowerCAmelCase : Any = sum(i * i for i in range(1, n + 1 ) )
lowerCAmelCase : str = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
return s... | 323 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A : Optional[int] = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T... | 356 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers imp... | 357 |
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, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
from manim import *
class __A ( lowerCAmelCase ):
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 ,... | 358 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 323 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __A ( unittest.TestCase ):
def lowercase__ ( self... | 359 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('String lengths must match!' )
lowerCAmelCase : Tuple = 0
for chara, chara in zi... | 323 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A ( lowerCAmelCase ):
"""simple docstring... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase, _UpperCAmelCase ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_per... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''shi-labs/dinat-mini-... | 323 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase : Tuple = f"{sampling_rate}"
lowerCAmelCase : ... | 362 |
from manim import *
class __A ( lowerCAmelCase ):
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 , width=... | 323 | 0 |
from math import ceil
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 1_001 ) -> int:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = 1
for i in range(1, int(ceil(n / 2.0 ) ) ):
lowerCAmelCase : str = 2 * i + 1
lowerC... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : int = BeautifulSoup(requests.get(_UpperCAmelCase, params=_UpperCAmelCase ).content, 'html.parser' )
l... | 364 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : Dict = models.Sequential()
# Step 1 - Convolution
# ... | 323 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 365 |
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,
... | 323 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,... | 366 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A ... | 323 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__A : List[Any] = 0
__A : List[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[... | 367 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 323 | 0 |
from ..utils import DummyObject, requires_backends
class __A ( metaclass=lowerCAmelCase ):
lowerCAmelCase_ : List[str] = ["torch"]
def __init__( self : Tuple , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[int] ... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechCo... | 323 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {'''configuration_mbart''': ['''MB... | 369 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from... | 323 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : Tuple = {
'''kakaobrain/align-base''': '''... | 370 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 323 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipI... | 371 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[Any] = {
'''configuration_xlm_roberta'''... | 323 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ..... | 350 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sma... | 323 | 0 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
'''simple docstring'''
if num <= 0:
lowerCAmelCase : List[str] = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(_UpperCAmelCase ... | 351 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
return x + 2
class __A ( unittest.TestCase ):
... | 323 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmel... | 352 |
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def SCREAMING_SNAKE_CASE__ ( ... | 323 | 0 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __A ( nn.Module ):
lowerCAmelCase_ : int
lowerCAmelCase_ : int
lowerCAmelCase_ ... | 353 |
from __future__ import annotations
from typing import Any
class __A :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int ):
lowerCAmelCase : Tuple = num_of_nodes
lowerCAmelCase : list[list[int]] = []
... | 323 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_util... | 354 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 323 | 0 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_util... | 355 |
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
lowerCAmelCase : Any = sum(i * i for i in range(1, n + 1 ) )
lowerCAmelCase : str = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
return s... | 323 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
fr... | 356 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 0 |
import mpmath # for roots of unity
import numpy as np
class __A :
def __init__( self : Optional[int] , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : str=None ):
# Input as list
lowerCAmelCase : str = list(poly_a or... | 357 |
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, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 358 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 323 | 0 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__A : Optional[Any] = datasets.logging.get_logger(__name__)
__A : Tuple = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust ... | 359 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('String lengths must match!' )
lowerCAmelCase : Tuple = 0
for chara, chara in zi... | 323 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase : Optional[int] = os.path.join(ar... | 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''shi-labs/dinat-mini-... | 323 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class __A ( nn.Module ):
lowerCAmelCase_ : int
lowerCAmelCase_ : jnp.dtype = jnp.floataa
def lowercase__ ( self : int ):
lowerCAmelCase : List[Any] ... | 362 |
from manim import *
class __A ( lowerCAmelCase ):
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 , width=... | 323 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : Any = {
'''vocab_file''': '''vocab.json''',
'''tokenizer... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 364 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : Dict = models.Sequential()
# Step 1 - Convolution
# ... | 323 | 0 |
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