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 |
|---|---|---|---|---|
_UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Tuple =True
SCREAMING_SNAK... | 719 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 0 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
_UpperCAmelCase = {
"facebook/maskformer-swin-base-... | 720 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase , lowercase , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif st... | 721 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class a ( UpperCAmelCase__... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __magic_name__ ( lowercase ):
S... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase = 4 ):
SCREAMING_SNAKE_CASE_: List[Any] =abs(lowerCAmelCase_ ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase_ )] for y in range(lowerCAmelCase_ )]
def ... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metr... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration_maskfo... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/encodec_24khz""": """https://hugging... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 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 import Ge... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: List[Any] =ArgumentParser(
descript... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
"""simple docstring"""
from math import sqrt
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Dict =0
for i in range(1 , int(sqrt(snake_case_ ) + 1 ) ):
if n % i == 0 and i != sqrt(snake_case_ ):
total += i + n // i
elif i == sqrt(... | 709 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 0 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Any =('''dense.weight''', '''... | 710 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 0 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a ( lowerCAme... | 711 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return number | (1 << position)
def __magic_name__ ( lowercase , lowercase ):
return number & ~(1 << position)
def __magic_name__ ( lowercase , lo... | 712 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 0 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_fo... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
"""simple docstring"""
_UpperCAmelCase = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages"""... | 714 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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... | 715 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, Truncati... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 0 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...im... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
UpperCamelCase : Union[str, Any] = 4_2
UpperCamelCase : Optional[int]... | 718 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __magic_name__ ( lowercase ):
for param in module.parameters():
SCREAMING_SNAKE_CASE_: Union[str, Any] =False
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: ... | 719 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 0 |
"""simple docstring"""
from collections.abc import Callable
class a :
def __init__( self : int , lowerCAmelCase : Optional[int] = None ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
# Stores indexes of eac... | 720 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicat... | 721 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_stagin... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class a ( unittest.TestCase ):
def lowerCamelCase__ ( self : List[str] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Dict =0
SCREAMING_SNAKE... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
imp... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_config... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import flo... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
from typing import Any
def __magic_name__ ( lowercase ):
if not input_list:
return []
SCREAMING_SNAKE_CASE_: Tuple =[input_list.count(lowerCAmelCase__ ) for value in input_list]
SCREAMING_SNAKE_CASE_: Union[str, Any] =max(lowerCAm... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V an... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedul... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V an... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 709 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase = 10**12 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[int] =0
SCREAMING_SNAKE_CASE_: int =1
SCREAMING_SNAKE_CASE_: Optional[int] =1
while numerator <= 2 * min_... | 710 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
... | 711 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 0 |
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
_UpperCAmelCase = {
"""Acehnese Arabic""": """ace_Arab""",
"""Acehnese Latin""": """ace_Latn""",
"""Mesopotamian Arabic""": """acm_Arab""",
"""Ta'izzi-Adeni Arabi... | 712 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 0 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_par... | 714 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 0 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
_UpperCAmelCase = 6_3_7_8_1_3_7.0
_UpperCAmelCase = 6_3_5_6_7_5_2.3_1_4_2_4_5
_UpperCAmelCase = 6_3_7_8_1_3_7
def __magic_name__ ( lowercase , lowercase , lowercase ,... | 715 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 0 |
"""simple docstring"""
import argparse
import json
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... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 0 |
"""simple docstring"""
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 a ( unittest.TestCase ):
... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class a ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Optional[int] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str ) -> str:
'''simple docstring''... | 718 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.f... | 719 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 0 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
... | 720 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJob... | 721 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if n == 1 or not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE_: List[Any] =[0, 1]
for i in range(2 , n + 1 ):
sequence.... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import A... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import ... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def __magic_name__ ( lowerc... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
def __magic_name__ ( lowercase = 10**9 ):
SCREAMING_SNAKE_CASE_: Dict =1
SCREAMING_SNAKE_CASE_: Union[str, Any] =2
SCREAMING_SNAKE_CASE_: Optional[Any] =0
SCREAMING_SNAKE_CASE_: List[Any] =0
SCREAMING_SNAKE_CASE_: Optional[Any] =0
while ... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicat... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
from math import sqrt
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Any =0
for i in range(1 , int(sqrt(lowerCAmelCase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCAmelCase__ ):
total += i + n // i
elif i... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 709 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils ... | 710 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 0 |
"""simple docstring"""
A = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def __magic_name__ ( ... | 711 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_UpperCAmelCase = logging.get_logger(__name__... | 712 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
"""configuration_layoutlmv3""": [
... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_F... | 714 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 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 PreTrainedToke... | 715 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 0 |
"""simple docstring"""
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 impo... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARC... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvis... | 718 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 0 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( lowercase_ , unittest.TestCase ):
UpperCamelCase : List[Any] = Tr... | 719 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
_UpperCAmelCase = 3e8 # unit of c : m * s^-1
def ... | 720 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a ( snake_case__ ):
UpperCamelCase : Tuple = (IPNDMScheduler,)
UpperCamelCase : str = (('num_inference_st... | 721 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_prop... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if isinstance(lowercase , lowercase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(lowercase , lowercase ):
raise TypeError("""'str' object cannot be inter... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
from manim import *
class a ( UpperCAmelCase__ ):
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] =Rectangle(height=0.5 , width=0.5 )
SCREAMING_... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCa... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MA... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_UpperCAmelCase = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class a ( pl.LightningModule ):
def __init__( self : List[Any] , lowerCAmelCase : ... | 709 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegas... | 710 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 0 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 711 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if not isinstance(lowercase , lowercase ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(lowercase ) == 0:
raise ValueError("""Input list must be a non empty list""" )
... | 712 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 0 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( UpperCAmelCase__ ):
UpperCamelCase : Tuple = ['image_processor', 'tokenizer']
UpperCamelCase : Union[str, Any] ... | 714 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 0 |
"""simple docstring"""
from PIL import Image
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Dict =image.size
SCREAMING_SNAKE_CASE_: Union[str, Any] =0
SCREAMING_SNAKE_CASE_: Optional[int] =image.load()
for i in range(lowercase ):
for j in... | 715 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_av... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffuse... | 718 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_... | 719 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 0 |
"""simple docstring"""
from math import pi, sqrt
def __magic_name__ ( lowercase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(lowercase ) not in (0, 0.5):
raise NotImplemen... | 720 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 0 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 721 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: str =SwinConfig(image_size=192 )
if "base" in model... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a ( ... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_UpperCAmelCase = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
_UpperCAmelCase = ... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
import gc
import threading
import time
import psutil
import torch
class a :
def __init__( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Optional[int] =psutil.Process()
SCREAMING_SNAKE_CASE_: Tuple ... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
"""confi... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_UpperCAmelCase = False
class a ( unittest.TestCase )... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
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