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 |
|---|---|---|---|---|
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def a__ ( ):
'''simple docstring'''
UpperCAmelCase_ =HfArgumentParser(lowercase__ )
UpperCAmelCase_ =parser.parse_args_into_dataclasses()... | 54 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S ... | 54 | 1 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transfor... | 54 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_d... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A :
_snake_case =field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} )
_snake_case =field(
d... | 54 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.mode... | 54 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : List[Any] =logging.get_logger(__name__)
__lowercase : List[str] ={
"""roberta-base""": "... | 54 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 1 |
class A :
def __init__( self: List[str] , _lowerCAmelCase: Dict , _lowerCAmelCase: int , _lowerCAmelCase: str ) -> str:
'''simple docstring'''
UpperCAmelCase_ =None
UpperCAmelCase_ =None
Upper... | 54 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 54 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize... | 54 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 1 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__lowercase : Optional[Any] =numpy.array([0, 0])
__lowercase : str =numpy.array([0.5, 0.866_0254])
__lowercase : Optional[Any] =numpy.array([1, 0])... | 54 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a__ ( lowercase__ ):
'''s... | 54 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( __lowercase ):
_snak... | 54 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__lowercase : Optional[Any] ="""src/diffusers"""
# Matches is_xxx_available()
__lowercase : int =re.compile(R... | 54 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 1 |
from __future__ import annotations
class A :
def __init__( self: Dict , _lowerCAmelCase: int ) -> None:
'''simple docstring'''
UpperCAmelCase_ =order
# a_{0} ... a_{k}
UpperCAmelCase_ =[1.0] + [0... | 54 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =FileLock(str(tmpdir / "foo.lock" ) )
UpperCAmelCase_ =FileLock(str(t... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation impo... | 54 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : int =logging.get_logger(__name__)
__lowercase : Optional[Any] ={
"""kakaobrain/a... | 54 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =""
for i in table:
res += inp[i - 1]
return res
def a__ ( lowercase__ ):
'''simple docstring'''
retur... | 54 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 1 |
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_availab... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class A ( __lowercase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` ... | 54 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):... | 54 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 1 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ , lowercase__ , ):
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 valu... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_na... | 54 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S ... | 54 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__lowercase : Optional[int] =logging.getLogger()
@unittest.skip('''Temporarily dis... | 54 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 1 |
import math
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if (
not isinstance(lowercase__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a val... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import C... | 54 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] =logging.get_logger(__name__)
__lowercase : Optional[Any] ={
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""h... | 54 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 1 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 54 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 1 |
from __future__ import annotations
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =0.00
UpperCAmelCase_ =0
for resistor in resistors:
if resistor <= 0:
UpperCAmelCase_ =F'Resistor at... | 54 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__lowercase : Optional[Any] =TypeVar("""T""")
class A ( Generic[T] ):
def __init__( self: List[Any] , _lowerCAmelCase: T ) -> Unio... | 54 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
__lowercase : List[str] =tuple[int, int]
class A :
def __init__( self: Any , _lowerCAmelCase: set[int] , _lowerCAmelCase: Mapping[EdgeT, int] ) -> None:
... | 54 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 1 |
from __future__ import annotations
class A :
def __init__( self: Union[str, Any] , _lowerCAmelCase: int = 0 ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ =key
def lowerCAmelCase__ ( se... | 54 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 54 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ , lowercase__ = 0 , lowercase__ = 0 ):
'''simple docstring'''
UpperCAmelCase_ =right or len(lowercase__ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
e... | 54 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common i... | 54 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 1 |
class A :
def __init__( self: List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ ={}
def lowerCAmelCase__ ( self: str ) -> None:
'''simple docstring'''
... | 54 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ ):
'''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
... | 54 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 1 |
def a__ ( lowercase__ = 1 , lowercase__ = 1_0_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =1
UpperCAmelCase_ =0
for divide_by_number in range(lowercase__ , digit + 1 ):
UpperCAmelCase_ =[]
UpperCAmel... | 54 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =[0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase_ =1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
Up... | 54 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 1 |
def a__ ( ):
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =1
UpperCAmelCase_ ... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S ... | 54 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] =logging.get_logger(__name__)
__lowercase : Dict ={
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/c... | 54 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 1 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =[int(lowercase__ ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(lowercase__ ) == 4 and all(0 <= int(lowercase__ ) <= 2_5_4 for octet in octets )
if __name__ == "_... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
from math import ceil
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =list(range(0 , lowercase__ ) )
UpperCAmelCase_ =[item for sublist in list(device_map.values() ) for item in sublist]
... | 54 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
impor... | 54 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class A ( nn.Module ):
def __init__( self: Dict , _lowerCAmelCase: int = 16 , _lowerCAmelCase: int = 88 , _lowerCAmelCase: Optio... | 54 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase_ =_modexpt(lowercase__ , exponent // 2 , lowercase__ ) % modulo_value
... | 54 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import... | 54 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 1 |
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 warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Tuple =logging.get_logger(__name__)
__lowercase : str ={
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/reso... | 54 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_we... | 54 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 1 |
from math import sqrt
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =0
for i in range(1 , int(sqrt(lowercase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase__ ):
total += i + n // i
elif i ==... | 54 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A ( __lowercase ):
_snake_case ='''Wav2Vec... | 54 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 1 |
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
print("The following activities are selected:" )
# The first activity is always selected
UpperCAmelCase_ =0
print(lowercase_... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A ( __lowercase ):
_snake_case =['''image_processor''', '''tokenizer''']
_snake_case ='''AutoImageProcessor'''
_snake_case ='''AutoTokenizer'''
... | 54 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 54 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =os.path.join(args.tf_model_dir , "par... | 54 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 1 |
from scipy.stats import spearmanr
import datasets
__lowercase : 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 correlation.
Positive correl... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql impo... | 54 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 1 |
import argparse
import os
import re
import packaging.version
__lowercase : Tuple ="""examples/"""
__lowercase : Optional[Any] ={
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.c... | 54 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate mode... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 1 |
def a__ ( lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) <= 1:
return [tuple(lowercase__ )]
UpperCAmelCase_ =[]
def generate(lowercase__ , lowercase__ ):
if k == 1:
res.append(tuple(arr[:] ) )
... | 54 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S ... | 54 | 1 |
import operator
def a__ ( lowercase__ , lowercase__ = False , lowercase__ = None ):
'''simple docstring'''
UpperCAmelCase_ =operator.lt if reverse else operator.gt
UpperCAmelCase_ =solution or []
if not arr:
r... | 54 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProces... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
import numpy as np
class A :
def __init__( self: Tuple ) -> Any:
'''simple docstring'''
UpperCAmelCase_ =(0, 0)
UpperCAmelCase_ =None
UpperCAmelCase_ =0
UpperCAmelCase_ =0... | 54 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 1 |
import re
from filelock import FileLock
try:
import nltk
__lowercase : str =True
except (ImportError, ModuleNotFoundError):
__lowercase : Optional[Any] =False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=... | 54 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__lowerca... | 54 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 1 |
from manim import *
class A ( __lowercase ):
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ =Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ ... | 54 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 1 |
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 BackboneTesterM... | 54 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def a__ ( lowercase__ , lowercase__=1_0_0_0 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCAmelCase_ ... | 54 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 1 |
class A :
def __init__( self: Optional[Any] , _lowerCAmelCase: List[str] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ =val
UpperCAmelCase_ =None
UpperCAmelCase_ =None
... | 54 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 1 |
import csv
import tweepy
# Twitter API credentials
__lowercase : Any =""""""
__lowercase : int =""""""
__lowercase : Dict =""""""
__lowercase : List[Any] =""""""
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmel... | 54 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def a__ ( lowercase__ ):
'''simple docstring'''
return np.dot(lowercase__ , lowercase__ )
class A :
def __init__( ... | 54 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterM... | 54 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def a__ ( lowercase__ ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class A ... | 54 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] =logging.get_logger(__name__)
__lowercase : Optional[Any] ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/Br... | 54 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowercase : str =[
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 1 |
__lowercase : Tuple =8.314_4598
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot b... | 54 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] =logging.get_logger(__name__)
__lowercase : Tuple ={
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/ma... | 54 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( __lowercase ):
_snake_case ... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( __lowercase ):
_snake_... | 54 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S ... | 54 | 1 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowercase : List[Any] =datasets.utils.logging.get_logger(__name__)
@dataclass
class A ... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.... | 54 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 1 |
def a__ ( lowercase__ ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ =head.next, head
while fast and fast.next:
UpperCAmelCase_ =fast.next.n... | 54 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 1 |
import math
def a__ ( lowercase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are no... | 54 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Union[str, Any] ={
"""configuration_longformer""": [
"""LONGFORMER_PRETRAIN... | 54 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 1 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ = None ):
'''simple docstring'''
UpperCAmelCase_ =word_bank or []
# create a table
UpperCAmelCase_ =len(lowercase__ ) + 1
UpperCAmelCase_ ... | 54 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 1 |
def a__ ( lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be an 'int' type" )
UpperCAmelCase_ =0
while number:
position += 1
number >>= 1
ret... | 54 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 1 |
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()
__lowercase : str =logging.get_logger(__name__)
def a__ ( lowercase__ , ... | 54 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 1 |
from datetime import datetime as dt
import os
from github import Github
__lowercase : Optional[int] =[
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def a__ ( ... | 54 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 1 |
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