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 os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
C... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ = 2_000_000 )-> int:
lowerCAmelCase_ : List[Any] = [0 for i in range(n + 1 )]
lowerCAmelCase_ : List[str] = 1
lowerCAmelCase_ : Optional[int] = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
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 BackboneTest... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> List[Any]:
lowerCAmelCase_ : Tuple = data
lowerCAmelC... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
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 Ac... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : List[Any] ={
"""vocab_file""": """vocab.json""",
... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
_UpperCAmelCase ="""\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Lang... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCas... | 707 |
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, slow, torc... | 619 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self , __lowercase ) -> fl... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : List[str] ={
... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> str:
lowerCAmelCase_ : List[str] ... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCAmelCase : Tuple ={
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extrac... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
def lowerCAmelCase ( )-> List[Any]:
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : int = 1
while len(lowerCAmelCase_ ) < 1e6:
constant.append(str(lowerCAmelCase_ ) )
i += 1
lowerCAmelCase_ : Tuple = '''... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : Tuple ={
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/co... | 713 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_UpperCAmelCase : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> list[list]:
lowerCAmelCase_ : Optional[int] = current_set.copy()
for row_index, row in enumerate(lowerCAmelCase_ ):
lowerCAmelCase_ : Dict = row[0]
for column_index, column in enumerate(lowerCAmelCase_ ):
i... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
import os
import unicodedata
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
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : D... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = ["""image_processor""", """tokenizer"""]
... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCas... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase : List[Any] =False
class snake_case__( unittest.TestCase ):
'''simpl... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : List[Any] ={
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}
try:
if not is... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ ):
# authorize twitter, initialize tweepy
lowerCAmelC... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
_UpperCAmelCase : Optional[int] =HfArgumentParser(InitializationArguments)
_UpperCAmelCase : Optional[int] =parser.parse_args()
# Load codeparrot ... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : List[Any] ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mode... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNe... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as o... | 707 |
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, slow, torc... | 619 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : Tuple ={name: getattr(transformers, name + """Fast""") for name ... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
cl... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : Tu... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
from __future__ import annotations
from math import pow, sqrt
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance == ... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
import argparse
import os
import re
_UpperCAmelCase : Optional[Any] ="""src/diffusers"""
# Pattern that looks at the indentation in a line.
_UpperCAmelCase : Tuple =re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCAmelCase : ... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def lowerCAmelCase ( lowerC... | 713 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_UpperCAmelCase : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_UpperCAmelCase : Tuple =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("""""", """|""", """|"""),
... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
from math import ceil
def lowerCAmelCase ( lowerCAmelCase_ = 1_001 )-> int:
lowerCAmelCase_ : int = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
lowerCAmelCase_ : Union[str, Any] = 2 * i + 1
lowerCAmelCase_ : List[str] = 2 * i
low... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCAmelCase : Tuple ={
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
_UpperCAmelCase : int ={
"""gwf-440k""": {
... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import math
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
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 primes
return False
# All primes n... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM i... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase ( )-> Dict:
lowerCAmelCase_ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=lowerCAmelCase_ , default='''biencoder-nq-dev.json''' , hel... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> None:
lowerCAmelCase_ : Dict = len(lowerCAmelCase_ )
# If row is equal to the size of the board it means there are a queen in ... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000 )-> int:
lowerCAmelCase_ : List[Any] = 2**power
lowerCAmelCase_ : Any = 0
while n:
lowerCAmelCase_ : List[str] = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(in... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Optional[Any] ={"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetC... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
from datetime import datetime
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> bytes:
lowerCAmelCase_ : Dict = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
lowerCAmelCase_ : List[str] = requests.get(base_url + url ).json()[0]['''urls... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
from itertools import product
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> list[int]:
lowerCAmelCase_ : int = sides_number
lowerCAmelCase_ : List[str] = max_face_number * dice_number
lowerCAmelCase_ : Any = [0] * (max_total + 1... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : int = [int(lowerCAmelCase_ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(lowerCAmelCase_ ) == 4 and all(0 <= int(lowerCAmelCase_ ) <= 254 for octet in octets )
if __name__ == "__main__":
_UpperCAme... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
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 primes
... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 707 |
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, slow, torc... | 619 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
from itertools import permutations
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowerCAmelCase_ : Union[str, Any]... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils ... | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
import argparse
import gc
import json
import os
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 Accelerat... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] =logging.get_logger(__name__)
_UpperCAmelCase : Tuple ={}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hugg... | 713 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_UpperCAmelCase : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
# Copyright 2023 The HuggingFace Inc. 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 required by a... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000 )-> int:
return sum(e for e in range(3 , lowerCAmelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""") | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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():
... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
import os
import sys
import unittest
_UpperCAmelCase : int =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backe... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 0 |
import math
import sys
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
if number != int(lowerCAmelCase_ ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number''' )
if number == 0:
... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_=28_123 )-> str:
lowerCAmelCase_ : List[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
lowerCAmelCase_ : ... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
import pytest
_UpperCAmelCase : List[str] ="""__dummy_dataset1__"""
_UpperCAmelCase : Dict ="""
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Union[str, Any] ={
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 619 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase ( lowerCAm... | 701 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 619 | 0 |
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, slow, torc... | 702 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 619 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] =logging.get_logger(__name__)
_UpperCAmelCase : Dict ={
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/re... | 703 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 619 | 0 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCas... | 704 |
import re
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
lowerCAmelCase_ : Tuple = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(lowerCAmelCase_ , lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 619 | 0 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> float:
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_interest_rate must ... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 619 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = """en... | 706 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] =logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] ={
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json"... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_UpperCAmelCase : Tuple ={"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_UpperCAmelCase : L... | 707 |
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, slow, torc... | 619 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 708 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
... | 619 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Tuple =logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] ={
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/... | 709 |
_UpperCAmelCase : int =frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
_UpperCAmelCase : List[Any]... | 619 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 710 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 )-> int:
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase_ ):
lowerCAmelCase_ : Tuple = ... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : int ={
"""configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],... | 711 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : str =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 619 | 0 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __low... | 712 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 0 |
import math
import qiskit
def lowerCAmelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCas... | 713 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_UpperCAmelCase : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.uti... | 714 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
rai... | 619 | 0 |
# Algorithm for the pigeonhole sorting
def lowerCAmelCase ( lowerCAmelCase_ )-> List[str]:
lowerCAmelCase_ : List[Any] = min(lowerCAmelCase_ ) # min() finds the minimum value
lowerCAmelCase_ : Optional[int] = max(lowerCAmelCase_ ) # max() finds the maximum value
... | 715 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase : Any ="""src/transformers"""
# This is to make sure the t... | 619 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : str ={
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConfig""",
... | 716 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class snake_case__:
... | 619 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase : Tuple ={"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not ... | 718 |
_UpperCAmelCase : Dict =[
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def lowerCAmelCase ( ... | 619 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 719 |
import csv
import tweepy
# Twitter API credentials
_UpperCAmelCase : int =""""""
_UpperCAmelCase : Optional[int] =""""""
_UpperCAmelCase : Dict =""""""
_UpperCAmelCase : str =""""""
def lowerCAmelCase ( lowerCAmelCase_ )-> None:
# authorize twitter, initialize tweepy
lowe... | 619 | 0 |
_UpperCAmelCase : str =tuple[float, float, float]
_UpperCAmelCase : Any =tuple[float, float, float]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Vectorad:
lowerCAmelCase_ : Optional[Any] = end_pointa[0] - end_pointa[0]
lowerCAmelCase_ : str = ... | 720 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : str = True
# 0 and 1 are none primes.
if number <= 1:
... | 619 | 0 |
import cva
import numpy as np
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase , __lowercase ) -> List[Any]:
if k in (0.04, 0.06):
lowerCAmelCase_ : Tuple = k
lowerCAmelCas... | 721 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase : Tuple =10
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_... | 619 | 0 |
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 __snake_case ( lowerCAmelCase__ ... | 620 |
from typing import List
import numpy as np
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = {key: len(_SCREAMING_SNAKE_CASE ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE , _SC... | 620 | 1 |
from __future__ import annotations
from math import pi, sqrt
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif... | 620 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models... | 620 | 1 |
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = len(_SCREAMING_SNAKE_CASE )
print('The following activities are selected:' )
# The first activity... | 620 |
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 __snake_case ( lower... | 620 | 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 __snake_case ( lower... | 620 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __snake_case :
def __init__( self , _A , _A , _A , _A , _A , _A=0.2 , _A=0.2):
SCREAMING_SNAKE_CASE_ = bp_numa
SCREAMING_SNAKE_CASE_ = bp_numa
... | 620 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ... | 620 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = Non... | 620 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
log... | 620 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Any = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"]... | 620 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __snake_case ( nn.Module ... | 620 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __snake_case ( unittest.TestCase ):
__lowerCAmelCase : Dict = inspec... | 620 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase__ : Dict = "src/diffusers"
# Matches is_xxx_available()
UpperCamelCase__ : List[Any] = re.compile(r"is\_... | 620 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCamelCase__ : Tuple = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
... | 620 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 620 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__ : int = Lock()
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE ... | 620 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_... | 620 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCamelCase__ : int = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teen... | 620 | 1 |
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return number | (1 << position)
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple d... | 620 |
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, prepare_image_inputs
if is_t... | 620 | 1 |
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