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 |
|---|---|---|---|---|
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def __SCREAMING_SNA... | 620 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 0 |
'''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,... | 442 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transfor... | 204 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __snake_case ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'''
lowercase... | 441 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 0 |
'''simple docstring'''
import numpy as np
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
a_ = (0, 0)
a_ = None
a_ = 0
a_ = 0
... | 536 |
'''simple docstring'''
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 :... | 689 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
... | 636 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
els... | 178 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 0 |
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 .tokenization... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a: int = logg... | 162 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 0 |
'''simple docstring'''
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
_UpperCamelCase = logging.get_logger(__name__)
def _lowercase (SCREAMING_SNAKE_CASE... | 111 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 0 |
'''simple docstring'''
from math import isqrt, loga
def __UpperCAmelCase (lowercase__ ) -> list[int]:
'''simple docstring'''
a_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
fo... | 685 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
... | 620 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
a_ : ... | 442 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 0 |
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_comma... | 204 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 0 |
import sys
_snake_case : Any = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6689664... | 441 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 0 |
'''simple docstring'''
from collections import deque
def __UpperCamelCase ( lowercase_ : Any ):
"""simple docstring"""
a_ = len(lowercase_ )
a_ = deque()
a_ = [False for _ in range(lowercase_ )]
a_ ... | 536 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 0 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class _UpperCamelCase ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self , *__a , **__a ):
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAM... | 636 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : str , lowercase : int ) -> list[int]:
"""simple docstring"""
snake_case : str = int(lowercase )
# Initialize Result
snake_case : Dict = []
# Traverse through all... | 178 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import i... | 252 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __lowerCAmelCase ( A ):
if not is_accelerate_available():
return method
UpperCAmelCase_ = version.parse(accelerate.__version__ ).base_versio... | 162 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 0 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers... | 111 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 0 |
'''simple docstring'''
import re
import subprocess
import sys
a_ = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
a_ = subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode('utf-8').split()
a_ = """|""".join(sys.argv[1:])
a_ = ... | 685 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFM... | 620 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 0 |
'''simple docstring'''
import numpy as np
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : List[str] ):
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 442 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""facebook/xlm-roberta-xl""": """https://hug... | 204 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 0 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_snake_case : str = {
# 1536-bit
5: {
"""prime""": int(... | 441 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ ... | 536 |
'''simple docstring'''
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 :... | 689 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _lowerCamelCase ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0... | 636 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : Optional[int] ) -> str:
"""simple docstring"""
snake_case : Optional[int] = len(lowercase )
for i in range(length - 1 ):
snake_case : Tuple = i
for k in range(i ... | 178 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
import string
import numpy
def __lowerCAmelCase ( A , A ):
return b if a == 0 else greatest_common_divisor(b % a , A )
class __UpperCamelCase :
SCREAMING_SNAKE_CASE__ = string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e.... | 162 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 0 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _lowercase (SCREAMING_SNAKE_CASE = True , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
... | 111 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=lowerCAmelCase_ ):
_UpperCAmelCase =["""onnx"""]
def __init__( self: Any , *a: int , **a: Union[str, Any]) ->Dict:
'''simple docs... | 685 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 0 |
'''simple docstring'''
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( _U... | 620 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase_ : Tuple = """\
"""
lowerCAmelCase_ : Tuple = ... | 442 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 0 |
def UpperCamelCase ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int ):
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ... | 204 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelin... | 441 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( lowercase_ : int ):
"""simple docstring"""
create_state_space_tree(lowercase_ , [] , 0 , [0 for i in range(len(lowercase_ ) )] )
def __UpperCam... | 536 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Union[str, Any] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""":... | 636 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 0 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [rang... | 178 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfi... | 252 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_a: List[str] = 10
def __lowerCAmelCase ( A , A , A , A ):
for i in range(A , A ... | 162 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 0 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _lowercase ():
'''simple docstring'''
__A : str = 9
__A : List[str] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7,... | 111 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 0 |
'''simple docstring'''
import datasets
a_ = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
... | 685 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 0 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = 10**12 ):
"""simple docstring"""
lowercase_ : List[str] = 1
lowercase_ : List[Any] = 0
lowercase_ : List[str] = 1
lowercase_ : Dict ... | 620 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
class SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , lowercase__ : Tuple ):
'''simple docstring'''
a_ : str ... | 442 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 0 |
import warnings
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
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
... | 204 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_snake_case : str = logging.get_logger(__name__)
_snak... | 441 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowerCAmelCase = 4
__lowerCAmelCase = 3
class __SC... | 536 |
'''simple docstring'''
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 :... | 689 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = []
for data in source_data:
for i, el in enumerate(_UpperCamelCase ):
if len(_UpperCamelCase ) < i + 1:
data_lists.append([] )
data_... | 636 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 0 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class _lowerCA... | 178 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
snake_case__ : List[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple doc... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def __lowerCAmelCase ( A )... | 162 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 0 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A ,__A : str = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
... | 111 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 0 |
'''simple docstring'''
from math import factorial
def __UpperCAmelCase (lowercase__ ,lowercase__ ,lowercase__ ) -> float:
'''simple docstring'''
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if tria... | 685 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if ... | 620 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __lt__( self : Union[str, An... | 442 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
i... | 204 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 0 |
def __snake_case ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
lowercase = 0
lowercase = len(__magic_name__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[l... | 441 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 0 |
'''simple docstring'''
import os
import sys
import unittest
__lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files,... | 536 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuratio... | 636 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : Optional[Any] , lowercase : List[str] , lowercase : List[str] ) -> int:
"""simple docstring"""
def count_of_possible_combinations(lowercase : Optional[int] ) -> int:
if target < 0:
... | 178 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 0 |
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class a ( TensorFormatter... | 252 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a: Optional[int] = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Bl... | 162 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 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
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCa... | 111 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 0 |
'''simple docstring'''
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_ ... | 685 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_devi... | 620 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 0 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
... | 442 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConf... | 204 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 0 |
def __snake_case ( __magic_name__ ):
'''simple docstring'''
lowercase = []
lowercase = set({"(", "[", "{"} )
lowercase = set({")", "]", "}"} )
lowercase = {"{": "}", "[": "]", "(": ")"}
for i in... | 441 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 0 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __SCREAMING_SNAKE_CASE (nn.Module ):
"""simple docstring"""
def __init__( self , UpperCamelCase__ = 16 , UpperCamelCase__... | 536 |
'''simple docstring'''
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 :... | 689 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 636 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _lowerCAmelCase :
__UpperCAmelCase : int
__UpperCAmelCase : TreeNode | None = None
__UpperCAmelCase : TreeNode | None ... | 178 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class a ( lowerCAmelCase_ ):
__lowerCAmelCase : Li... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
_a: List[Any] = 6_5521
def __lowerCAmelCase ( A ):
UpperCAmelCase_ = 1
UpperCAmelCase_ = 0
for plain_chr in plain_text:
UpperCAmelCase_ = (a + ord(A )) % MOD_ADLER
UpperCAmelCase_ = (b + a) % MOD_ADLER
return (b ... | 162 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _lowercase (SCREAMING_SNAKE_CASE = 100_0000 , SCREAMING_SNAKE_CASE = 10 ):
'''simple docstring'''
__A : Dict = defaultdict(SCREAMING_SNAKE_CASE ... | 111 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 0 |
'''simple docstring'''
def __UpperCAmelCase (lowercase__ ) -> int:
'''simple docstring'''
a_ = len(lowercase__ )
a_ = len(matrix[0] )
a_ = min(lowercase__ ,lowercase__ )
for row in range(lowercase__ ):
# ... | 685 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir... | 620 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __init__( self : str , *lowercase__ : Dict , **lowercase__ : int ):
... | 442 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no c... | 689 | 0 |
def UpperCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : int ):
snake_case : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case : str = n - k
# Calc... | 204 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_... | 689 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase_ ( unittest... | 441 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 689 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalG... | 536 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.... | 689 | 0 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 636 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers... | 689 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_... | 178 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase =... | 689 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase = 1000000 ) -> int:
"""simple docstring"""
snake_case__ : str = set(range(3 , __lowerCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , __lowerCAmelCase , 2 ):
if p not in primes:
... | 252 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 0 |
def __lowerCAmelCase ( A ):
UpperCAmelCase_ = int(A )
if n_element < 1:
UpperCAmelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ... | 162 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowe... | 689 | 0 |
'''simple docstring'''
from math import isclose, sqrt
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : Tuple = point_y / 4 / point_x
__A : Optional[Any... | 111 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCa... | 689 | 0 |
'''simple docstring'''
def __UpperCAmelCase (lowercase__ ,lowercase__ ,lowercase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
a_ = _modexpt(lowercase__ ,exponent // 2 ,lowercas... | 685 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is ... | 689 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __SCREAMING_SNAKE_CA... | 620 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 0 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasu... | 442 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartFor... | 689 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowerCamelCase = logging.getLogger(__name... | 204 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_du... | 689 | 0 |
def __snake_case ( __magic_name__ ):
'''simple docstring'''
lowercase = len(__magic_name__ )
for _ in range(__magic_name__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
lower... | 441 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
... | 689 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowerCAmelCase = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoO... | 536 |
'''simple docstring'''
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 :... | 689 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as n... | 636 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 0 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VAR... | 178 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_... | 689 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, i... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
import argparse
import os
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_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 162 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGe... | 689 | 0 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if n_term == "":
return []
__A : List[Any] = []
for temp in range(int(SCREAMING_SNAKE_CASE ) ):
series.append(f"1/{temp + ... | 111 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = ... | 689 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_co... | 685 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase ... | 689 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowercase_ : List[str] = Argument... | 620 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
... | 689 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.