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
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 DUMMY_UNKNOWN_ID... | 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 655 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from tr... | 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("""fixtures/test_sentencepiece_... | 655 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( ... | 655 | 1 |
def _A ( __magic_name__ ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
lowercase__ = gray_code_sequence_string(__magic_name__ )
#
# convert them to in... | 655 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 655 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.... | 655 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelT... | 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConf... | 655 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
... | 655 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 655 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ['image_processor', 'tokenizer']
__lowerCamelCase = 'CLIPImageProcessor'
__lowerCamelCase = ('... | 655 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _i... | 655 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from... | 655 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 655 | 1 |
def _A ( __magic_name__ ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("Input value must be a 'int' type" )
return bin(__magic_name__ ).count("1" )
if __name__ == "__main__":... | 655 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( ... | 655 | 1 |
import sys
_snake_case = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""668966489504452445231... | 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 655 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase_ )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = field(default='automatic-speech-rec... | 655 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 655 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_s... | 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case ... | 655 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase_ )
class lowerCAmelCase ( lowercase_ ):
# `task` is not a ClassVar since we want it to be part of ... | 655 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ ... | 655 | 1 |
from __future__ import annotations
from typing import Any
class lowerCAmelCase ( lowercase_ ):
pass
class lowerCAmelCase :
def __init__( self :Any , _lowercase :Any ):
'''simple docstring'''
lowercase__ = data
lowercase__... | 655 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging... | 655 | 1 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = ... | 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = ... | 655 | 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 ( __magic_name__ , __magic_name__ , __magic_name__=1024 , __magic_name__=1024 , __magic_name__=False , **__magic_... | 655 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
... | 655 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRCo... | 655 |
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 lowerCAmelCase ( enum.Enum ):
__low... | 655 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_snake_case = parse(importlib.metadata.version("""torch"""))
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if operat... | 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""",... | 655 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( lowerCamelCase ):
a__ = ['''image_processor''', '''tokenizer''']
a__ = '''CLIPImageProcessor'''
a__ = ('''XLM... | 0 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ... | 655 | 0 |
class __lowerCamelCase :
def __init__( self: Union[str, Any],A_: Tuple ):
'''simple docstring'''
__UpperCamelCase = val
__UpperCamelCase = None
__UpperCamelCase = None
def snake_cas... | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
... | 655 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase__ ( datasets.BeamBasedBuilder):
"""simple docstring"""
def snak... | 2 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 | 0 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from t... | 3 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 655 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identifi... | 4 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 0 |
'''simple docstring'''
def A (__lowerCamelCase :Any , __lowerCamelCase :str , __lowerCamelCase :List[Any] , __lowerCamelCase :int , __lowerCamelCase :Tuple , __lowerCamelCase :Any ):
if index == r:
for j in range(__lowerCamelCase ):
... | 5 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( ... | 655 | 0 |
import sys
import turtle
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: tuple[float, float] , UpperCamelCase__: tuple[float, float] ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: tuple[float, float] ... | 6 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 655 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DD... | 7 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.... | 655 | 0 |
'''simple docstring'''
import sys
lowercase__ : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715... | 8 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConf... | 655 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if ... | 9 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 655 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
imp... | 10 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _i... | 655 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowercase_ = get_logger(__name__)
class __A :
'''simple docstring'''
def __init__(self , A , A=None ) -> List[str]:
"""simple docstring"""
_a ... | 11 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 655 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import Feat... | 12 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( ... | 655 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : int = {
"""vocab_file""": """vocab.... | 13 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 655 | 0 |
import collections
import os
import re
from pathlib import Path
a__ = '''src/transformers'''
# Matches is_xxx_available()
a__ = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
a__ = re.compile(R'''^_import_struc... | 14 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 655 | 0 |
import qiskit
def UpperCamelCase ( __magic_name__ : int = 2 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
lowercase__ = qubits
# Using Aer's simulator
lowercase__ = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Q... | 15 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case ... | 655 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subproces... | 16 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ ... | 655 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ,len(a__ ) ... | 17 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging... | 655 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def __a(SCREAMING_SNAKE_CASE_ : bytes ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) != 32:
raise ValueError("Input must be of length 32" )
_lowerCAmelCase ... | 18 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = ... | 655 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils impor... | 19 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
... | 655 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase_ (lowercase__ ):
def __init__( self , lowercase_ , low... | 20 |
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 lowerCAmelCase ( enum.Enum ):
__low... | 655 | 0 |
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
... | 21 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""",... | 655 | 0 |
'''simple docstring'''
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 IterableDatase... | 22 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ... | 655 | 0 |
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 impo... | 23 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
... | 655 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_input... | 24 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 | 0 |
import os
from datetime import datetime as dt
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def lowerCamelCase__ ( ):
SCREAMING_SNAKE_CASE : Union[s... | 25 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 655 | 0 |
'''simple docstring'''
from __future__ import annotations
class _A :
def __init__( self : Optional[int] , __magic_name__ : list[list[int]] ) -> str:
"""simple docstring"""
__snake_case : str = TypeError(
... | 26 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
Bar... | 27 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( ... | 655 | 0 |
'''simple docstring'''
import numpy as np
def lowercase__( __UpperCamelCase: np.array ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 28 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 655 | 0 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_ = [ord(letter) for letter in string.ascii_lowercase]
A_ ... | 29 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.... | 655 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Any = {}
UpperCAmelC... | 30 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConf... | 655 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_p... | 31 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 655 | 0 |
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,
UNetaDC... | 32 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _i... | 655 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fro... | 33 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 655 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForCon... | 34 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( ... | 655 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_config... | 35 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 655 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Any = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHI... | 36 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 655 | 0 |
def UpperCamelCase_ ( __a ) -> bool:
a__ : List[Any] = 0
for ch in input_str:
a__ : str = ord(__a )
a__ : Any = pow(2 , __a )
# If we already turned on bit for current character's unicode
if bitmap >> ch_uni... | 37 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case ... | 655 | 0 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : List[Any]=None ) -> Union[str, Any]:
''... | 38 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ ... | 655 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_M... | 39 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging... | 655 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checko... | 40 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = ... | 655 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTe... | 41 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
... | 655 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> list:
if len(__UpperCamelCase ) <= 1:
return [tuple(__UpperCamelCase )]
lowerCamelCase_ = []
def generate(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = [0] * n
... | 42 |
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 lowerCAmelCase ( enum.Enum ):
__low... | 655 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
for param in module.parameters():
lowercase__ = False
def _a ( ):
"""simple docstring"""
lowercase__ = '''cuda''... | 43 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""",... | 655 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(_lowerCAmelCase , x % y )
def A_ ( _lowerCAmelCase : int , _lowe... | 44 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ... | 655 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowercase )
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
_snake_case : ... | 45 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
... | 655 | 0 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase = "cpu" , _lowerCamelCase = None ) -> None:
'''simple docstring'''
_lowerCamelCase : Any = torch.loa... | 46 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 47 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 655 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A ( SCREAMING_SNAKE_CASE__ ):
snake_case__ :Tuple = ['image_processor', 'tokenizer']
snake_case__ :List[Any] = 'ChineseCLIPImageProcessor'
sna... | 48 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int ):
__UpperCAmelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowercase__ ( snake_case_ :int = 5_000 ):
__UpperCAmelCase = [(i * (3 * i - 1)) // 2 for i in r... | 49 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( ... | 655 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Optional[int] = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/face... | 50 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 655 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Tuple = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a__ :... | 51 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.... | 655 | 0 |
"""simple docstring"""
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_... | 52 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConf... | 655 | 0 |
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 _UpperCAmelCase ... | 53 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 655 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, sl... | 54 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _i... | 655 | 0 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_tor... | 55 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 655 | 0 |
'''simple docstring'''
def _a (lowercase__ : list , lowercase__ : list , lowercase__ : int ) -> int:
"""simple docstring"""
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError('The length of profit and weight must be same.' ... | 56 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( ... | 655 | 0 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
A_ : Tuple = 'scheduler_config.json'
class _lowerCAmelCase( UpperCAmelCase_... | 57 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 655 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : List[str] = {
'''MIT/ast-finetuned-audioset-10-10-... | 58 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 655 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def lowerCAmelCase_ ( __a ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(__a , 0 , __a , args=(__a) )[0]
... | 59 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case ... | 655 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase_ = HfApi()
lowerCAmelCase_ = {}
# fmt: off
lowerCAmelCase_ = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
... | 60 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ ... | 655 | 0 |
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _A ( lowerCAmelCase_ : s... | 61 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging... | 655 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""xlm-roberta-base""": """https://huggingface.co/xlm... | 62 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = ... | 655 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
... | 655 | 0 |
lowercase_ : Optional[Any] = tuple[float, float, float]
lowercase_ : Dict = tuple[float, float, float]
def A__ ( snake_case_ : Pointad , snake_case_ : Pointad ):
SCREAMING_SNAKE_CASE__: Tuple= end_pointa[0] - end_pointa[0]
SCREAMING_SNAKE_CASE__: Union[s... | 64 |
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 lowerCAmelCase ( enum.Enum ):
__low... | 655 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [... | 65 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""",... | 655 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 66 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ... | 655 | 0 |
snake_case = {
"""Pillow""": """Pillow""",
"""accelerate""": """accelerate>=0.11.0""",
"""compel""": """compel==0.1.8""",
"""black""": """black~=23.1""",
"""datasets""": """datasets""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1""",
"""hf-... | 67 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
... | 655 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase__ ( ) -> List[str]:
"""simple docstring"""
raise RuntimeError("""CUDA out ... | 68 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 655 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("Input value must be an 'int' type" )
__snake_case = 0
while number:
position += 1
num... | 69 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 655 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp,... | 70 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 0 |
'''simple docstring'''
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 _snake_case... | 71 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( ... | 655 | 0 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase... | 72 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_tor... | 655 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = int(number**0.5)
return number == sq * sq
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , ... | 73 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.... | 655 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ''''''
lowerCAmelCase_ ... | 74 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConf... | 655 | 0 |
'''simple docstring'''
import os
import sys
UpperCamelCase__ = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 75 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 655 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/b... | 76 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _i... | 655 | 0 |
"""simple docstring"""
import copy
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
from ..auto import CONFIG_MAPPING
A = logging.get_logger(... | 77 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 655 | 0 |
'''simple docstring'''
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 __A ( unittest.TestCase ):
de... | 78 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( ... | 655 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
... | 79 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 655 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.