code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassi... | 101 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
... | 82 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {
"""microsoft/xprophetnet-larg... | 102 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 82 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 103 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
'''simple docstring'''
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_... | 104 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100_0000 ) ->int:
'''simple docstring'''
a : Dict = set(range(3 , _lowercase , 2 ) )
primes.add(2 )
for p in range(3 , _lowercase ... | 105 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase ... | 82 | 0 |
"""simple docstring"""
import math
import random
def __SCREAMING_SNAKE_CASE ( A_ , A_ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCamelCase : Any = 0.0_2
def __SCREAMING_SNAKE_CASE ( A_ , A_... | 106 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(... | 82 | 0 |
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
__lowerCAmelCase : Dict = {
'facebook/maskformer-swin-base-ade': (
... | 107 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def a__ ( ):
'''simple docstring'''
print("Truth Table of NOR Gate:" )
print("| ... | 108 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ear... | 82 | 0 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A: ... | 109 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_... | 110 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 82 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("_Datasets", ["train", "validation", "test"]... | 36 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 82 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a( A : int , A : Dict ) -> List[Any]:
"""simple docstring"""
a ... | 227 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lo... | 82 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowercase (lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , ... | 275 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _Uppe... | 82 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( __a , __a , __a=None ) -> Any:
"""s... | 10 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def... | 82 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=13_37 , num_examples=42 , dataset_name="my_dataset"... | 128 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDI... | 82 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.... | 17 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specifi... | 82 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils im... | 239 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https... | 82 | 0 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
f... | 346 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n ... | 82 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/mai... | 208 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, ... | 82 | 0 |
'''simple docstring'''
class __magic_name__ :
def __init__( self : List[Any] ):
_a : str = 0
_a : Dict = 0
_a : List[str] = {}
def __lowercase ( self : ... | 89 |
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
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowe... | 82 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
f... | 328 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_ca... | 82 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpToken... | 82 | 0 |
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
if is_torch_available():
import... | 227 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
... | 82 | 0 |
from __future__ import annotations
def _lowercase ( lowercase__ , lowercase__ ):
if b == 0:
return (1, 0)
((__lowerCAmelCase), (__lowerCAmelCase)) : Optional[int] = extended_euclid(lowercase__ , a % b )
__lowerCAmelCase : Union[... | 275 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 82 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.u... | 10 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCAmelCase : List[str] ={
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-lar... | 128 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : Optional[Any], UpperCamelCase_ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def _A ( ) -> Tuple:
'''simple docstring'''
assert nand_gate(0, 0) == 1
assert nand_... | 17 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase ... | 82 | 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,
)
_lowercase : Union[str, Any] = {
"conf... | 239 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(... | 82 | 0 |
'''simple docstring'''
UpperCAmelCase_ = 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_ = 'TRWAGMYFPDXBNJZSQVHLCKE'
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , S... | 346 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
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,
AutoModel... | 208 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ear... | 82 | 0 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> Dict:
_a : Union[str, Any] = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __lowerCamelCase ( lowerCAm... | 89 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
"google/pix2struct-textcaps-base": (
"http... | 328 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 82 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 36 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 82 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
_lowercase: int = datasets.logging.get_logger(__name__)
_lowercase: int = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel'... | 227 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lo... | 82 | 0 |
from PIL import Image
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : List[str] = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(lowercase__ ) -> int:
return int(1_2_8 + factor * (c - 1_2_8) )
return im... | 275 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _Uppe... | 82 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCAmelCase_ ( __a , __a , __a , __a , ) -> List[str]:
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__: Optional[int] =... | 10 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def... | 82 | 0 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def ... | 128 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDI... | 82 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a = '\\n\n'
_a = '\nPerplexity (PPL) is one of the most common metrics for evaluating... | 17 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specifi... | 82 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import float... | 239 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https... | 82 | 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, set_see... | 346 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n ... | 82 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_UpperCamelCase = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PR... | 208 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, ... | 82 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[int]:
_a : Optional[Any] =... | 89 |
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
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowe... | 82 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : Optional[Any] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
... | 328 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_ca... | 82 | 0 |
from scipy.stats import pearsonr
import datasets
_snake_case = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is nor... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpToken... | 82 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase: Dict = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available():
... | 227 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
... | 82 | 0 |
def _lowercase ( lowercase__ , lowercase__ , lowercase__ ):
return round(float(moles / volume ) * nfactor )
def _lowercase ( lowercase__ , lowercase__ , lowercase__ ):
return round(float((moles * 0.0_8_2_1 * temperature) / (volume) ) )
def ... | 275 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 82 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, Stabl... | 10 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 128 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeli... | 17 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoToken... | 239 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(... | 82 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_co... | 346 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
... | 208 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ear... | 82 | 0 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __lowerCamelCase ( lowerCAmelCase_ ) -> Dict:
_a : Optional[Any] = [
'decoder.version',
'de... | 89 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : Optional[int] = {}
try:
if not is_sentencepiece_available():
raise Op... | 328 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 82 | 0 |
import math
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or ... | 36 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 82 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
_lowercase: int = TypeVar("_T")
class _lowercase ( Generic[_T] ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ = None ):
"""simple docstring"""
a = list(iterable or... | 227 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lo... | 82 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
... | 275 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _Uppe... | 82 | 0 |
import csv
import tweepy
# Twitter API credentials
__A = ""
__A = ""
__A = ""
__A = ""
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =tweepy.OAuthHandler(__a , __a )
auth... | 10 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def... | 82 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None):
if version.parse(hfh.__version__).release < version.parse("0.11.0").release:
... | 128 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDI... | 82 | 0 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _A ( UpperCamelCase_ : Any, UpperCamelCase_ : Optional[int], UpperCamelCase_ : Union[str, Any], UpperCamelCase_ : ... | 17 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specifi... | 82 | 0 |
'''simple docstring'''
import re
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> Optional[int]:
if len(re.findall("""[ATCG]""" , UpperCAmelCase__ ) ) != len(UpperCAmelCase__ ):
raise ValueError("""Invalid Strand""" )
return dna.tran... | 239 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https... | 82 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict ):
'''simple docstring'''
UpperCAmelCase__ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] = 5000 ):
'''simple docstrin... | 346 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n ... | 82 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_UpperCamelCase ... | 208 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, ... | 82 | 0 |
'''simple docstring'''
import os
__lowerCAmelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
def __lowerCamelCase ( lowerCAmelCase_ ) -> Dict:
_a : Optional[Any] = 0
_a : str = ... | 89 |
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
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowe... | 82 | 0 |
import math
def A_ ( snake_case : Any ) -> int:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all m... | 328 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_ca... | 82 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerI... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpToken... | 82 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
fr... | 227 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
... | 82 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowercase (tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( ... | 275 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 82 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCamelCase__ ) , "Tat... | 10 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch... | 128 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _lowerCAmelCase ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 17 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : Optional[Any] = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resol... | 239 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(... | 82 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ = int(SCREAMING_SNAKE_CASE__ )
if n_element < 1:
UpperCAmelCase__ = ValueError("""a should be a positive number""" )
raise my_error... | 346 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_UpperCamelCase = logging.get_logger(__name__)
class lowerCamelCase_ ( lowerCamelCase__ ):
"""simple docstring""... | 208 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ear... | 82 | 0 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_... | 89 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowercase__ : Union[str, Any] = TypeVar("T")
lowercase__ : str = TypeVar("U")
class SCREAMING_SNAKE_CASE__ ( Generic[T, U] ):
"""simp... | 328 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 82 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional i... | 36 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 82 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerc... | 227 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lo... | 82 | 0 |
from collections.abc import Sequence
def _lowercase ( lowercase__ , lowercase__ ):
return sum(c * (x**i) for i, c in enumerate(lowercase__ ) )
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : Optional[int] = 0.0
for c... | 275 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _Uppe... | 82 | 0 |
import os
from math import logaa
def lowerCAmelCase_ ( __a = "base_exp.txt" ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: List[str] =0
lowerCamelCase__: str =0
for i, line in enumerate(open(os.path.join(os.path.dirname(__a ) , __a ) ... | 10 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def... | 82 | 0 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_z... | 128 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDI... | 82 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
_a = 'docs/source/en/_toctree.yml'
def _A ( UpperCamelCase_ : Tuple) -> int:
'''simple docstring'''
__lowercase = defaultdict(UpperCamelCase_)
__lowercase = ... | 17 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specifi... | 82 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin... | 239 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https... | 82 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 346 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n ... | 82 | 0 |
'''simple docstring'''
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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
... | 208 |
from __future__ import annotations
import math
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, ... | 82 | 0 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from t... | 89 |
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
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowe... | 82 | 0 |
import os
import sys
import unittest
lowercase__ : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mappi... | 328 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A__ = TypeVar("""T""")
A__ = TypeVar("""U""")
class __lowerCAmelCase ( Generic[T, U] ):
def __init__( self , _snake_case , _snake_ca... | 82 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpToken... | 82 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 227 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = 0
... | 82 | 0 |
_UpperCamelCase = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
... | 275 |
A__ = [0, 2, 4, 6, 8]
A__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 82 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
... | 10 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
from __future__ import annotations
def _lowerCAmelCase (_lowerCAmelCase):
UpperCamelCase_ = str(_lowerCAmelCase)
return n == n[::-1]
def _lowerCAmelCase (_lowerCAmelCase = 1_00_00_00):
UpperCamelCase_ = 0
for i in range(1 , _lowerCAmelCa... | 128 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
... | 17 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
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
_lowercase : Union[str, Any] = "src/transfor... | 239 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(... | 82 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
UpperCAmelCase_ = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, gl... | 346 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_Upper... | 208 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ear... | 82 | 0 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
M... | 89 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 328 |
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 82 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = "▁"
_snake_case = {"vocab_file": "spiece.mode... | 36 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 82 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaV... | 227 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lo... | 82 | 0 |
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