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 json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer... | 33 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common i... | 33 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import D... | 33 | 1 |
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 to have ... | 33 |
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... | 33 | 1 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 33 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 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 __magic_name__ (snake_case_ ):
'''si... | 33 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ... | 33 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : List[str] = logging.get_logger(__name__)
lowerCamelCase__ : Dict = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/... | 33 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __magic_name__ (unittest.TestCase ):
'... | 33 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs... | 33 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ..... | 33 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 1 |
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
@require_torch_gpu
... | 33 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
""... | 33 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCamelCase__ : str = """src/transformers"""
# Matches is_xxx_available()
lowerCamelCase__ : List[str] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a... | 33 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 1 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase = False ) -> float:
if not arr:
return 0
snake_case__ = 0 if allow_empty_subarrays else float('''-inf''' )
snake_case__ = 0.0
for num in arr:
s... | 33 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = "laptop" ) -> DataFrame:
snake_case__ = F"""https://www.amazon.in/laptop/s?k={product}"""
snake_case__ = {
... | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Tuple = {
"""configuration_roberta""": ["""... | 33 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 33 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simpl... | 33 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 1 |
class __magic_name__ :
'''simple docstring'''
def __init__( self:Optional[Any] ):
snake_case__ = {} # Mapping from char to TrieNode
snake_case__ = False
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[str] ):
... | 33 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 1 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : int = logging.get_logger(__name__)
lowerCamelCase__ : List[Any] = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main... | 33 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = [1]
snake_case__ , snake_case__ , snake_case__ = 0, 0, 0
snake_case__ = ugly_nums[ia] * 2
snake_case__ = ugly_nums[ia] * 3
snake_case__ = ugly_nums[ia] * 5
for _ in range(1 ... | 33 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 1 |
def SCREAMING_SNAKE_CASE ( ) -> str:
snake_case__ = 0
for i in range(1 , 1001 ):
total += i**i
return str(__lowerCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 33 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( ... | 33 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import D... | 33 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ (... | 33 |
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... | 33 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Union[str, Any] = {"""configuration_xln... | 33 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 1 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs... | 33 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ... | 33 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> None:
snake_case__ , snake_case__ = analyze_text(__lowerCAmelCase )
snake_case__ = list(''' '''... | 33 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase__ : Optional[int] = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggin... | 33 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 1 |
import torch
from diffusers import StableDiffusionPipeline
lowerCamelCase__ : Any = """path-to-your-trained-model"""
lowerCamelCase__ : List[str] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
lowerCamelCase... | 33 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs... | 33 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common imp... | 33 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
... | 33 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list:
snake_case__ = []
snake_case__ , snake_case__ = input_list[low:mid], input_list[mid : high + 1]... | 33 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> List[str]... | 33 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers... | 33 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoToke... | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Tuple = {
"""configuration_roberta""": ["""... | 33 | 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 considered
# ... | 33 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
lowerCamelCase__ : Li... | 33 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
if len(__lowerCAmelCase ) < k or k < 0:
raise ValueError('''Invalid Input''' )
snake_case__ = snake_case__ = sum(array[:k] )
for i in range... | 33 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase__ : Dict = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
l... | 33 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 1 |
from __future__ import annotations
from typing import Any
class __magic_name__ :
'''simple docstring'''
def __init__( self:Tuple , _a:int ):
snake_case__ = num_of_nodes
snake_case__ = []
snake_case__ = {}
def SCREAMI... | 33 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 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_dimension... | 33 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanorama... | 33 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE... | 33 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __magic_name__ (enum.Enum ):
... | 33 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import D... | 33 | 1 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_... | 33 |
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... | 33 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = (n * (n + 1) // 2) ** 2
snake_case__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 33 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 1 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ... | 33 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __... | 33 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 33 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerC... | 33 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs... | 33 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ... | 33 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
snake_case__ = gri... | 33 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 1 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
snake_case__ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCAmelCase )
if number < 1:
snake_case__ = F"""... | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Tuple = {
"""configuration_roberta""": ["""... | 33 | 1 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
... | 33 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 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... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor... | 33 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 1 |
from collections.abc import Sequence
from queue import Queue
class __magic_name__ :
'''simple docstring'''
def __init__( self:str , _a:List[Any] , _a:Optional[Any] , _a:List[Any] , _a:Dict=None , _a:List[Any]=None ):
snake_case__ = s... | 33 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 1 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,... | 33 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ (unittest.TestCase ):
'''simple docstring'''
... | 33 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 1 |
lowerCamelCase__ : Any = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yott... | 33 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import D... | 33 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCamelCase__ : Optional[Any] = 2_0_4_8
lowerCamelCase__ : Tuple = 4_0_9_6
lowerCamelCase__ : Any = 4_2
lowerCamelCase__ : str = os.en... | 33 |
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... | 33 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
'''simple docstring'''
def __init__( self:List[str] , _a:str , _a:Optional[Any] , _a:Union[str, Any] , _a:Union[str, Any] , _a:Optional[Any] , _a:i... | 33 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lo... | 33 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ... | 33 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowerCamelCase__ : List[str] = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any mem... | 33 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ : int = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_... | 33 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertT... | 33 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs... | 33 | 1 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
return np.where(vector > 0 , __lowerCAmelCase , (alpha * (np.exp(__lowerCAmelCase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest... | 33 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : int = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""Jukeb... | 33 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase__ : List[Any] = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenizat... | 33 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 1 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 1 |
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def SCREAMING_SNAKE... | 33 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 1 |
import datasets
from .evaluate import evaluate
lowerCamelCase__ : Dict = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXi... | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Tuple = {
"""configuration_roberta""": ["""... | 33 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Generator[tuple[str, ...], None, None]:
snake_case__ = iter(__lowerCAmelCase )
while True:
snake_case__ = t... | 33 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Dict = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : Any = ... | 33 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __magic_name__ (snake_case_ ):
'''sim... | 33 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 1 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=snake_case_ ):
'''simple docstring'''
__lowercase : Union[str, Any] = ['flax']
def __init__( self:str , *_a:Tuple , **_a:str ):
requires_backe... | 33 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 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 transformers.m... | 33 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 1 |
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here... | 33 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ... | 33 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 1 |
lowerCamelCase__ : List[Any] = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusio... | 33 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 1 |
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 __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 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
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : str ... | 33 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import D... | 33 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase__ : int =... | 33 |
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... | 33 | 1 |
from cva import destroyAllWindows, imread, imshow, waitKey
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Any:
# getting number of pixels in the image
snake_case__ , snake_case__ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i... | 33 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case__ = mf_knapsack(i - 1 , __lowerCAmelCas... | 33 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ... | 33 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ... | 33 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = "" ) -> dict[str, float]:
snake_case__ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
snake_case__ = BeautifulSo... | 33 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : Optional[int] = {
"""microsoft/unispeech-large-1500h-cv""": (... | 33 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs... | 33 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __magic_name__ (yaml.SafeLoader ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] , _a:List[str] ):
snake_cas... | 33 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> tuple[str, float]:
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 val... | 33 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 1 |
import functools
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
rais... | 33 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str ... | 33 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 1 |
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