code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
... | 80 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab... | 80 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 80 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__UpperCamelCase : Union[str, Any] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ... | 80 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests... | 80 | 1 |
__UpperCamelCase : Tuple = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" ... | 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lower... | 80 | 1 |
from manim import *
class __UpperCamelCase ( _lowerCAmelCase ):
def _a ( self : str ) -> Tuple:
"""simple docstring"""
__lowercase = Rectangle(height=0.5 , width=0.5 )
__lowercase = Rectangle(height=0.46 , width=0.46 ... | 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""facebook/xlm-rob... | 80 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 80 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__UpperCamelCas... | 80 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""sim... | 80 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 1 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density) ** 0.5
if __name__... | 80 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for f... | 80 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 80 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase... | 80 | 1 |
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... | 80 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_0... | 80 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 80 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
... | 80 | 1 |
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , lowerCamelCase , lowerCamelCase , lowerCamelCase )
move_disk(lowerCamelCase , lowerCamelCase )
... | 80 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_c... | 80 | 1 |
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if b == 0:
return (1, 0)
((__lowercase) , (__lowercase)) = extended_euclid(lowerCamelCase , a % b )
__lowercase = a // b
return... | 80 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -... | 80 | 1 |
def snake_case ( lowerCamelCase = 1_000 ):
'''simple docstring'''
__lowercase = -1
__lowercase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__lowercase = (n * n - 2 * a * n) // (2 * n - 2 ... | 80 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ... | 80 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ... | 80 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
... | 80 | 1 |
import math
def snake_case ( ):
'''simple docstring'''
__lowercase = input("""Enter message: """ )
__lowercase = int(input(F'Enter key [2-{len(lowerCamelCase ) - 1}]: ' ) )
__lowercase = input("""Encryption/Decryption [e/d]: """ )... | 80 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase ... | 80 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__Uppe... | 80 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ..... | 80 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mo... | 80 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] ... | 80 | 1 |
__UpperCamelCase : int = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
__UpperCamelCase : ... | 80 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__Upp... | 80 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
SCREAMING_SNAKE_CASE__ : List[str] = logging.getLogger(__name__)
if is_torch_tpu_avai... | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes... | 80 | 0 |
def _A ( _lowercase , _lowercase , _lowercase ) -> Dict:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_lowercase , n - 1 , _lowercase ) * a) % mod
else:
__UpperCamelCase = ... | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab... | 80 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[dict, list, tuple, torch.Tensor] ... | 2 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
... | 80 | 0 |
'''simple docstring'''
from typing import Any
def A_( A : list):
if not input_list:
return []
UpperCamelCase = [input_list.count(A) for value in input_list]
UpperCamelCase = max(A) # Gets the maximum count in the input list.
... | 3 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 80 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] ... | 4 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests... | 80 | 0 |
'''simple docstring'''
import cva
import numpy as np
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
if k in (0.04, 0.06):
_lowerCAmelCase = k
... | 5 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lower... | 80 | 0 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 6 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''... | 7 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 80 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...im... | 8 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""sim... | 80 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A ( __UpperCamelCase ) -> List[str]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/... | 9 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 0 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 10 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for f... | 80 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extracti... | 11 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase... | 80 | 0 |
def UpperCamelCase ( lowercase_ ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
lowercase__ : int = sum(lowercase_ ) / len(lowercase_ ) # Calculate the average
return sum(abs(x - ... | 12 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_0... | 80 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> str:
__lowerCamelCase : int = len(UpperCAmelCase_ )
__lowerCamelCase : int = len(UpperCAmelCase_ )
... | 13 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
... | 80 | 0 |
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
_a : Optional[Any] = sorted(string.lower() ... | 14 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_c... | 80 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class A :
'''simple docstring'''
A__ = None
A__ = False
A__ = False
A__ = False
A__ = ... | 15 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -... | 80 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __a ( A__ : List[Any] ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideogra... | 16 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ... | 80 | 0 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : str = logging.get_logger(__name__)
def ... | 17 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
... | 80 | 0 |
'''simple docstring'''
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_accelera... | 18 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase ... | 80 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils impo... | 19 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ..... | 80 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase: Dict = logging.get_logger(__name__)
_lowerCAmelCase: Tuple = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN ... | 20 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] ... | 80 | 0 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with... | 21 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__Upp... | 80 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : str ):
'''simple docstring'''
_a = int(UpperCamelCase )
# Initialize Result
_a = []
# Traverse through all den... | 22 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes... | 80 | 0 |
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ ... | 23 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab... | 80 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@r... | 24 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
... | 80 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def lowerCamelCase__ ( _a=None , _a... | 25 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 80 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _A :
def __init__( self : Optional[Any] , __magic_name__ : list[str] ) -> Tuple:
"""simple docstring"""
__snake_case : list[dic... | 26 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests... | 80 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncod... | 27 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lower... | 80 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def lowercase__( __UpperCamelCase: Callable[[float], float] ,__UpperCamelCase: float ,__UpperCamelCase: float ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : ... | 28 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 | 0 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1... | 29 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 80 | 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... | 30 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""sim... | 80 | 0 |
import colorsys
from PIL import Image # type: ignore
def UpperCAmelCase_ ( __UpperCAmelCase : float , __UpperCAmelCase : float , __UpperCAmelCase : int ) -> float:
SCREAMING_SNAKE_CASE_ = x
SCREAMING_SNAKE_CASE_ = y
for ste... | 31 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
... | 32 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for f... | 80 | 0 |
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,
MusicgenForConditionalGeneration,
Mus... | 33 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase... | 80 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils impo... | 34 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_0... | 80 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ :Union[str, Any] = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 35 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
... | 80 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : int = JukeboxTokenizer
__lowerCamelCase : int = {
'''artist''': '''... | 36 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_c... | 80 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCamelCase : List[str] = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
UpperCamelCase : Union[str, Any] = None
def UpperCamelCase_ ( ) -> List[str]... | 37 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -... | 80 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __snake_case :
'''simple docstring'''
lowerCamelCase__ = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of m... | 38 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ... | 80 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path impo... | 39 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
... | 80 | 0 |
from __future__ import annotations
import numpy as np
def UpperCamelCase ( snake_case__ : list[float] ) -> List[Any]:
return np.maximum(0 , snake_case__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 40 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase ... | 80 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowercase_ ... | 41 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ..... | 80 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> np.ndarray:
#... | 42 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] ... | 80 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import O... | 43 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__Upp... | 80 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tr... | 44 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes... | 80 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers... | 45 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab... | 80 | 0 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase = "cpu" , _lowerCamelCase = None ) -> None:
'''simple docstring'''
_lowerCamelCase : Any = torch.loa... | 46 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
... | 80 | 0 |
def UpperCAmelCase__ ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase_ , int(b / 2 ) ) * actual_power(lowerCamelCase_ , int(b / 2 ) )
else:
... | 47 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 80 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
UpperCAmelCase__ : List[str] = "Usage of script: script_name <size_of_canvas:int>"
UpperCAmelCase__ : Dict = [0] * 1_00 + [1] * 10
random.sh... | 48 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests... | 80 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
_lowercase : int = TypeVar('T')
_lowercase : str = Union[List[T], Tuple[T, ...]]
_lowercase : Optional[Any] = Union[T, List[T], Dict[str, T]]
_lowercase :... | 49 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lower... | 80 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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_tens... | 50 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 | 0 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#
... | 51 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 80 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list[int] , a_ :list[int] , a_ :list[int] , a_ :list[list[str]] , a_ :int , ) -> None:
__a : Tuple = len(a_)
# If row is equal to th... | 52 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""sim... | 80 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : int = {
'configuration_clip': [
... | 53 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTok... | 54 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for f... | 80 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : str ):
__A = ""
__A = ""
__A = []
__A = 0
__A = 2_56
... | 55 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase... | 80 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_u... | 56 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_0... | 80 | 0 |
def snake_case () -> str:
UpperCamelCase_: Optional[Any] = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
UpperCamelCase_: List[str] = 6
UpperCamelCase_: Optional[Any] = 1
UpperCamelCase_: Tuple = 1_9_0_1... | 57 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
... | 80 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDim... | 58 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_c... | 80 | 0 |
__A = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets... | 59 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -... | 80 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 60 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ... | 80 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = (KDPMaDiscreteSchedu... | 61 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
... | 80 | 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 is_xformers_ava... | 62 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase ... | 80 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here ... | 63 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ..... | 80 | 0 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowercase_ : int = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acce... | 64 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] ... | 80 | 0 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_v... | 65 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__Upp... | 80 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMSchedule... | 66 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes... | 80 | 0 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
snake_case = yaml.safe_load(
"""\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
- ... | 67 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab... | 80 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _A ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : ... | 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
... | 80 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
return round(float(moles / volume ) * nfactor )
def __UpperCAmelCase ( _UpperCAmelCase : float , _UpperCAmelCase ... | 69 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 80 | 0 |
from ... import PretrainedConfig
lowerCamelCase : Any = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = NEZHA_PRETRA... | 70 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests... | 80 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
i... | 71 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lower... | 80 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsk... | 72 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 | 0 |
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 lowerCamelCase__ (_UpperCAmelCase)... | 73 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 80 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
self.test()
def UpperCAme... | 74 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""sim... | 80 | 0 |
'''simple docstring'''
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> float:
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial intensity
if angle <... | 75 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase_ :
def __init__( self , UpperCamelCase_ = None ) -> None:
if components is N... | 76 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for f... | 80 | 0 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class a__ :
def __init__( self : Optional[Any] , UpperCamelCase_ : List[str]):
"""simple docstring"""
__UpperCAmelCase : Tuple = str(id_)
__U... | 77 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase... | 80 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenize... | 78 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_0... | 80 | 0 |
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCAmelCase__... | 79 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
... | 80 | 0 |
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