code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A_ : Optional[Any] = logging.get_logge... | 32 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ... | 32 | 1 |
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 _lowercase ... | 32 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''... | 32 | 1 |
import copy
import re
class _lowercase :
_UpperCAmelCase = '''hp'''
_UpperCAmelCase = {}
_UpperCAmelCase = None
@classmethod
def A ( cls : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/ma... | 32 | 1 |
import doctest
from collections import deque
import numpy as np
class _lowercase :
def __init__( self : Optional[Any] ) -> None:
"""simple docstring"""
a = [2, 1, 2, -1]
a = [1, 2, 3, 4]
def A ( s... | 32 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Tuple = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a ... | 32 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = {}
a ... | 32 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase ... | 32 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_UpperCAmelCase = [('''size''... | 32 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Any = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at ht... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': ... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persist... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = N... | 32 | 1 |
import json
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :Optional[int] ):
'''simple docstring'''
with open(UpperCAmelCase__ , encoding="utf-8" ) as f:
a = json.load(UpperCAmelCase__ )
a = ["<details>", "<s... | 32 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ..... | 32 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers... | 32 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""s... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Con... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 1 |
from collections.abc import Iterable
from typing import Any
class _lowercase :
def __init__( self : Optional[int] , __lowerCAmelCase : int | None = None ) -> Optional[Any]:
"""simple docstring"""
a = value
a =... | 32 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmel... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a , a = 1, 1
a = []
for i in range(1 , n + 1 ):
a = prev_numerator + 2 * prev_denominator
a = prev_numerator + prev_denominator
if len(str(UpperCAmelCase... | 32 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 32 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCa... | 32 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a ... | 32 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
... | 32 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': ... | 32 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Dict = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise Option... | 32 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 32 | 1 |
# Function to print upper half of diamond (pyramid)
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
for i in range(0 , UpperCAmelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-... | 32 | 1 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
... | 32 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List... | 32 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
A_ : List[str] = 2_99_79_24_58
# Symbols
A_ , A_ , A_ , A_ : Union[str, Any] = symbols('''ct x y z''')
def UpperCAmelCase__ ( UpperCAmelCase__ :... | 32 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision... | 32 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_to... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
de... | 32 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
def decorator(UpperCAmelCase__ :Any ):
a = getattr(UpperCAmelCase__ , "handle_key" , [] )
handle += [key]
... | 32 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :list ):
'''simple docstring'''
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]... | 32 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''... | 32 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/ma... | 32 | 1 |
import warnings
from functools import wraps
from typing import Callable
def UpperCAmelCase__ ( UpperCAmelCase__ :Callable ):
'''simple docstring'''
@wraps(UpperCAmelCase__ )
def _inner_fn(*UpperCAmelCase__ :List[str] , **UpperCAmelCase__ :str ):
warnings.... | 32 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ... | 32 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_ : Union[st... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a ... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a , a = 1, 1
a = 2
while True:
a = 0
a = fa + fa
a , a = fa, f
index += 1
for _ in str(UpperCAmelCase__ ):
i += 1
if i == n:
break
... | 32 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase ... | 32 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 32 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 32 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': ... | 32 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = N... | 32 | 1 |
import unittest
import numpy as np
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_inputs
if is_torch_available():
... | 32 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ..... | 32 | 1 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""s... | 32 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmel... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indic... | 32 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmel... | 32 | 1 |
from collections import defaultdict
from math import gcd
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 1_50_00_00 ):
'''simple docstring'''
a = defaultdict(UpperCAmelCase__ )
a = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range(... | 32 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 32 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''M-CLIP'''
def __init__( self : Tuple , __lowerCAmelCase : Tuple=1024 , __lowerCAmelCase : ... | 32 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a ... | 32 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (DDPMParallelScheduler,)
def A ( self : Optional[int] , **__lowerCAmelCase : ... | 32 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_... | 32 | 1 |
import socket
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
a = socket.gethostname()
a = 1_23_12
sock.connect((host, port) )
sock.send(b"Hello server!" )
with open("Receiv... | 32 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : List[A... | 32 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 32 | 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, rand... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-... | 32 | 1 |
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
A_ : Union[str,... | 32 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''ht... | 32 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision... | 32 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A_ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
de... | 32 | 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,
StableDiffusionPipeli... | 32 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ... | 32 | 1 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ..... | 32 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''... | 32 | 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
A_ : List[str] = logging.get_logger(__name__)
A_ : Dict = ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/ma... | 32 | 1 |
from math import isqrt, loga
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCAmelCase__ , UpperCAme... | 32 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ... | 32 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from tr... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a ... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a ... | 32 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase ... | 32 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common im... | 32 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 32 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''A... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': ... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Tuple = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcesso... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = N... | 32 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
A_ : List[Any] = logging.get_logger(__name__... | 32 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ..... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[Any] = {
'''configuration_distilbert''': [
'''DIS... | 32 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""s... | 32 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
A_ : List[Any] = '''1'''
A_ : Tuple = '''0'''
A_ : Optional[int] = '''1'''
A_ : Union[str, Any] = ort.SessionOptions()
A_ : List[str] = ort.GraphOpti... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
a , a , a = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def UpperCA... | 32 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmel... | 32 | 1 |
from math import isclose, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float ):
'''simple docstring'''
a = point_y / 4 / point_x
a = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
a ... | 32 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 32 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Any = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/go... | 32 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a ... | 32 | 1 |
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''flax''', '''transformers''']
def __init__( self : int , *__lowerCAmelCase : Dict , **__lowerCAmelCase : str ... | 32 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_... | 32 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
A_ : Dict = False
A_ : List[str] = True
A_ : Dict = False
if __name__ == "__main__":
... | 32 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 32 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 32 | 1 |
import random
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :List[str] ):
'''simple docstring'''
a , a , a = [], [], []
for element in data:
if element < pivot:
less.append(UpperCAmelCase__ )
elif element > pivot:
greater... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :int ):
'''simple docstring'''
a = len(UpperCAmelCase__ )
a = [[0] * n for i in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
a = ... | 32 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ : int = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ... | 32 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :list[int] , UpperCAmelCase__ :list[int] , UpperCAmelCase__ :int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCAmelCase__ ) )
... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
de... | 32 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision... | 32 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ... | 32 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 32 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''... | 32 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
A_ : int = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/ma... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : List[str] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
... | 32 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ... | 32 | 1 |
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... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a ... | 32 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | 32 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase ... | 32 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase, UpperCAmelCase__ ):
def A ( self : List[Any] ) -> Tuple:
"""simple docstring"""
a ... | 32 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 32 | 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 UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :str , ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': ... | 32 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attentio... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = N... | 32 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
A_ : List[Any] = logging.get_logger(__name__)
A_ : List[str] = '''T5Config'''
class _lowercase ( ... | 32 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ..... | 32 | 1 |
def UpperCAmelCase__ ( UpperCAmelCase__ :list[int] , UpperCAmelCase__ :list[int] ):
'''simple docstring'''
if not len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == eq... | 32 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""s... | 32 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :Tuple , UpperCA... | 32 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmel... | 32 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _lowercase (... | 32 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 32 | 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,
StableDiffusionPanora... | 32 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a ... | 32 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingfa... | 32 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_... | 32 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..mo... | 32 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 1 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Tuple = logging.get_logger(__name__)
def ... | 32 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 32 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :int ):
'''simple docstring'''
a =... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-... | 32 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ : Any = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Dict , *__lowerCAmelCase : ... | 32 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List... | 32 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax impo... | 32 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision... | 32 | 1 |
A_ : str = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
de... | 32 | 1 |
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''torch''', '''scipy''']
def __init__( self : Tuple , *__lowerCAmelCase : Tuple , **__lowerCAmelCase : Union[str, ... | 32 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ... | 32 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILEN... | 32 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : str = {
'''configuration_ber... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/ma... | 32 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
A_ : Optional[int] = logging.getLogger()
def UpperCAmelCase_... | 32 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ... | 32 | 1 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def UpperCAmelCase__ ( ):
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path impo... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a ... | 32 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...ut... | 32 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase ... | 32 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transfo... | 32 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 32 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': ... | 32 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
A_ : Tuple = '''
import os
'''
A_ : str = '''
def foo():
import os
return False
'''
A_ : Optional[int] = '''
def foo():
def bar():
if True:
... | 32 |
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = N... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Any = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models a... | 32 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ..... | 32 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
... | 32 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""s... | 32 | 1 |
from math import sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 1 |
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