code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import requests
_snake_case = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited g... | 36 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiff... | 101 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 67 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 1 |
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_ = False
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ (... | 12 | '''simple docstring'''
from __future__ import annotations
__a = list[tuple[int, int]]
__a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0... | 145 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Any = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFI... | 365 | '''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class UpperCamelCase ( a_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any):
"""s... | 345 | 0 |
'''simple docstring'''
import random
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ = False ):
__UpperCamelCase : dict = {i: [] for i in range(snake_case__ )}
# if probability is greater or equal than 1, then g... | 298 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/l... | 298 | 1 |
from math import isqrt, loga
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _lowercase , _lowerc... | 258 | from typing import Union
import fire
import torch
from tqdm import tqdm
def A ( _lowercase , _lowercase = "cpu" , _lowercase = None ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.load(_lowercase , map_location=_lowercase )
for k, v in tqdm(state_dict... | 258 | 1 |
'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transf... | 56 |
import unittest
from knapsack import greedy_knapsack as kp
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self : Any ) -> str:
"""simple docstring"""
snake_case_ ... | 159 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def _a( ):
'''simple docstring'''
assert... | 358 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : List[str] ):
'''simple docstring''... | 222 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def ... | 191 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {}
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : List[Any] ... | 191 | 1 |
def snake_case ( A__ ,A__ ):
UpperCAmelCase_ : Union[str, Any] = word.split()
def justify(A__ ,A__ ,A__ ) -> str:
UpperCAmelCase_ : Any = max_width - width
UpperCAmelCase_ : Optional[int] = len(__UpperCamelCase ... | 369 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..ut... | 253 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 99 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
__SCREAMING_SNAKE_CASE : float
__SCREAMING_SNAKE_CASE : TreeNode | None = None
__SCREAMING_SNAKE_CASE : TreeNode | None = None
... | 353 |
"""simple docstring"""
import importlib
import inspect
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_config_docstrings.py
_lowerCAmelCase : Dict = "src/transformers"
# This is to make sure the... | 202 | 0 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_availab... | 93 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self ) -> Dict:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase ,... | 68 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig... | 14 |
import baseaa
def lowerCamelCase ( a_ ) -> bytes:
return baseaa.baaencode(string.encode('utf-8' ) )
def lowerCamelCase ( a_ ) -> str:
return baseaa.baadecode(a_ ).decode('utf-8' )
if __name__ == "__main__":
lowerCamelC... | 14 | 1 |
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_S... | 228 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 314 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( _snake_case : np.ndarray , _snake_case : int , _snake_case : int ):
lowerCAmelCase : Dict = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ... | 314 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import ... | 90 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''EncodecFeatureExtractor'''
... | 90 | 1 |
import math
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase = 0, _UpperCAmelCase = 0 ) -> list:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = end or len(_UpperCAmelCase )
for i in range(_UpperCAmelCase, _UpperCAmelCase ):
lower... | 323 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, random_attention_mask
from... | 323 | 1 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : List[str] = {
'configuration_convbert': ['CONVBERT... | 361 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from... | 164 | 0 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 appl... | 99 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : List[Any] = get_tests_dir("fixtures/test_sentencepiec... | 218 | 0 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeli... | 315 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
... | 315 | 1 |
from math import sqrt
def A ( _lowerCamelCase ):
'''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 multip... | 36 | """simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSW... | 221 | 0 |
'''simple docstring'''
import math
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
UpperCAmelCase = [True] * n
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = True
for i i... | 350 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollato... | 91 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case_ = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
... | 78 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
... | 78 | 1 |
UpperCamelCase__ ={
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : ... | 325 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ... | 325 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : str = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig"""],
... | 14 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 1 |
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,
WavaVe... | 194 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE... | 194 | 1 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
if length <= 0 or not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(UpperCAmelCase_ )]
if __name__ == "__ma... | 83 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Optional[Any] = logging.get_logger(__name__)
snake_case_ : int = {
'microsoft/xprophetnet-large-wiki100-cased': (
'http... | 83 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
... | 354 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/c... | 241 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
'''simple docstring'''... | 46 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTeste... | 170 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
... | 81 | import cva
import numpy as np
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
if k in (0.04, 0.06):
_A = k
_A = window_size
else:
... | 81 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_I... | 280 |
import cva
import numpy as np
class _a :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : float , UpperCAmelCase : int ):
if k in (0.04, 0.06):
A_ = k
A_ ... | 312 | 0 |
"""simple docstring"""
import os
__snake_case = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_a = 0
_a = 0
while index < len(_lowerCAm... | 153 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ... | 153 | 1 |
"""simple docstring"""
_a = '\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a = [{'type': 'code', 'content': INSTALL_CONTENT}]
_a = {
... | 17 |
# 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 A__ :
def __init__( se... | 325 | 0 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> Tuple:
if i... | 349 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 349 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
def UpperCAmelCase ( a_=None , a_=None ) -> Any:
... | 15 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _A (__a , __a , __a ) -> Dict:
"""simple d... | 91 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
... | 367 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def lowercase (_A ):
"""simple docstring"""
if not postfix_notation:
return 0
_lowerCAmelCase : int = ... | 25 | 0 |
import random
from typing import Any
def __lowerCAmelCase ( a__ ) -> Any:
for _ in range(len(a__ ) ):
__a = random.randint(0 , len(a__ ) - 1 )
__a = random.randint(0 , len(a__ ) - 1 )
__a , __a = data[b]... | 6 |
'''simple docstring'''
def a__ ( a__ ):
"""simple docstring"""
if isinstance(a__ , a__ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(a__ , a__ ):
raise TypeError("""'str' object cann... | 267 | 0 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.util... | 165 | def lowercase( UpperCamelCase_ = 1000000 ) -> int:
'''simple docstring'''
UpperCamelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCamelCase_ ):
phi[j] -= phi[j] // ... | 165 | 1 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def __snake_case ( UpperCAmelCase_ : Iterable[str] , UpperCAmelCase_ : int ):
lowerCamelCase_ = iter(UpperCAmelCase_ )
while True:
lowerCamelCase... | 55 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
... | 55 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class __UpperCAmelCase :
def __init__( self ):
"""simple docstring"""
_snake_case = []
_snake_case = 0
_snake_case... | 160 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def SCREAMING_SNAKE_CASE__ ( __A , __A=1_000 ) -> str:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_snake_case = n - 1
_snake_case ... | 160 | 1 |
def _UpperCamelCase ( snake_case__ ) -> Union[str, Any]:
if not head:
return True
# split the list to two parts
__UpperCAmelCase , __UpperCAmelCase : str = head.next, head
while fast and fast.next:
__UpperCAm... | 157 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( UpperCamelCase_ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(UpperCamelCase_ , Up... | 340 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase ... | 25 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.ut... | 25 | 1 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowerCamelCase_ : Union[str... | 81 |
"""simple docstring"""
lowerCamelCase_ : int = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install gi... | 81 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCAmelCase = ['small', 'medium', 'large']
lowerCAmelCase = 'lm_head.decoder.weight'
lowerCAmelCase = 'lm_head.weight'
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ... | 364 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
_lowercase : int
_lowercase : TreeNode | None = None
_lowercase : TreeNode | None = None
lowerCAmelCase =... | 93 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
lowerCAmelCase__ = ... | 153 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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/LICEN... | 153 | 1 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __lowerCAmelCase ... | 365 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, Sched... | 11 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
p... | 221 | """simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_... | 221 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __lowercase ( snake_case_ : List[str] ) ->Any:
'''simple docstring'''
__A : str = ... | 291 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
snake_case_ : int = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
... | 83 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowercase_ ( _snake_case ):
... | 25 | 0 |
snake_case_ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
snake_case_ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowerCamelCase__ ( snake_case_ : dict[int, list[int]] , snake_case_ : int , snake_case_ : list[bool] ) ... | 238 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 238 | 1 |
import os
from distutils.util import strtobool
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
for e in env_keys:
a__ : Dict =int(os.environ.get(SCREAMING_SNAKE_CASE ... | 95 |
def _A ( SCREAMING_SNAKE_CASE : int = 50 ):
"""simple docstring"""
a__ : Any =[1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_st... | 95 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_... | 218 |
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 UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = args.log... | 218 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 213 | """simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCamelCase ( yaml.SafeLoader ):
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
... | 213 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytor... | 371 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
fr... | 5 | 0 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowercase_ ( _snake_case ,_snake_case ,_snake_case = False ):
if radian_mode:
return [magnitude * cos(_snake_case... | 25 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : str = logging.get_logger(__nam... | 25 | 1 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __l... | 302 |
"""simple docstring"""
import argparse
import os
# New Code #
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_sched... | 302 | 1 |
"""simple docstring"""
from __future__ import annotations
lowercase_ = 8.988e9 # units = N * m^s * C^-2
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ) -> L... | 45 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_lowercase : Union[str, Any] = yaml.safe_load(
"\\nname: \"\"\nallow_emp... | 93 | 0 |
import math
import os
import sys
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = ''
try:
with open(A__, 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE_ : List[Any] = binary_file.read()
for dat in data:
... | 162 |
import argparse
import os
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_task_guides.py
lowerCAmelCase__ : Optional[Any] ='src/transformers'
lowerCAmelC... | 162 | 1 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
A : int = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _Uppe... | 57 |
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_PRETRAINED_HIFIGAN_... | 11 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : Union[str, Any] = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 250 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 250 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 1_00 ) -> int:
__lowerCamelCase : List[Any] = set()
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : List[str] = n + 1 # maximum limit
for a in range(2 ... | 185 |
'''simple docstring'''
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
impor... | 185 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _A , _A , _A ):
# Initialise PyTorch model
a : ... | 96 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase: Any = logging.get_logger(__name__)
class a__( lowerCamelCase__ ):
lowercase__ = """encoder-decoder"""
lowercase__ = True
def ... | 96 | 1 |
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configurat... | 16 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
a = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _a ):
def __init__( self : Tuple , *lowerCAmelCase : Tuple , *... | 155 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
class _snake_case ( __lowercase ):
snake_case__ = "encoder-decoder"
snake_case__ = True
def... | 350 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''... | 64 | 0 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def __snake_case(... | 35 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowercase_ (A : List[str] ):
snake_case_... | 277 | 0 |
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SN... | 351 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline ... | 61 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list[list] ) -> list[list]:
'''simple docstring'''
_UpperCAmelCase = current_set.copy()
for row_index, row in enumerate(__lowercase ):
_UpperCAmelCase = r... | 22 |
# 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
class lowe... | 5 | 0 |
'''simple docstring'''
import math
class a__ :
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : list[list[float]] , a : list[int] ):
"""simple docstring"""
__lowerCamelCase = 0.0
__lowerCamelCase = 0.0
... | 367 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def __lowerCAmelCase ( UpperCamelCase__ ) -> None:
create_state_space_tree(UpperCamelCase__ , [] , 0 )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamel... | 237 | 0 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 234 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowerCAmelCase (__lowerCAmel... | 234 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...... | 74 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ ):
if not isinstance(A_ , A_ ):
lowerCAmelCase__ : int = f'Input value of [number={number}] must be an integer'
raise TypeError(A_ )
if number < 0:
return False
lowerCAmelCase__ : List[Any] = ... | 74 | 1 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase__ = get_tests_dir("""fixtures/spiece.mod... | 212 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class A__ ( __magic_name__ ):
lowercase = field(default='audio-cl... | 212 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class _lowerCAmelCase ( unittest.TestCase ):
def _a (self ):
A_ : Dict = 0
A_ : List[str] = [0]
A_ : str = [0]
A_ : Tupl... | 365 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowerCAmelCase ( __UpperCAmelCase ):
def _a (self , lowercase=None , lowercase=None , lowercase=None , **lowercase ):
if tokenize_kwargs is None:
A_ ... | 135 | 0 |
from __future__ import annotations
import math
def a__ ( A_, A_, A_, A_, A_ ):
'''simple docstring'''
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if not scores:
raise ValueError("""Scores cannot be empty""" )
... | 88 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
f... | 194 | 0 |
import os
import pytest
from attr import dataclass
_lowerCamelCase : Union[str, Any] = "us-east-1" # defaults region
@dataclass
class __UpperCAmelCase :
UpperCamelCase = 4_2
UpperCamelCase = """arn:aws:iam::558105141721:role/sagemaker_execution_r... | 364 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from hugging... | 10 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 315 | 0 |
from __future__ import annotations
import numpy as np
def _A ( __magic_name__ ):
return np.maximum(0 , __magic_name__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 201 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, R... | 201 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : int = "bert-generation"
def __init__( self , a=5_0_3_5_8 , a=1_0_2_4 , a=2_4 , a=1_6 , a=4_0_9_6 , a="gelu" , a=0.1 , a=0.1 , a=5_1_... | 77 | """simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingSt... | 77 | 1 |
"""simple docstring"""
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
_A = logging.get_logger(__name__)
_A = {"""vocab_file... | 366 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational... | 212 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __SCREAMING_SNAKE_CASE( UpperCamelCase__ ):
def __init__( self: ... | 307 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, reca... | 110 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
__lowerCAmelCase : Any =TypeVar("""T""")
class _A ( Generic[T] ):
def __init__( self , __lowerCAmelCase ):
"""simple docstring"""
... | 363 | """simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase : Tuple ={
"""facebook/mask2former-swin-small-coco-instance""": (
""... | 32 | 0 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def snake_case_ (_a : Optional[int] , _a : Optional[Any]=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split... | 34 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : int | float | str ):
try:
A__ = float(_lowerCamelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
A__ = decimal - int(_lowerCamelCase )
... | 237 | 0 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = len(_UpperCAmelCase )
for i in range(length - 1 ):
SCREAMING_SNAKE_CASE_: List[Any] = i
for k in range(i + 1 , _UpperCAmelCase ):
if collection[k] < collection... | 127 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase : Tuple = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value... | 127 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .token... | 123 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.s... | 123 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n... | 43 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 1 |
"""simple docstring"""
import requests
a__ : Dict = '''''' # <-- Put your OpenWeatherMap appid here!
a__ : Union[str, Any] = '''https://api.openweathermap.org/data/2.5/'''
def UpperCAmelCase__ (lowerCAmelCase_ = "Chicago" , lowerCAmelCase_ = APPID ):
... | 54 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
'''simple docstring'''
if start is None:
__SCREAMING_SNAKE_CASE = 0
if end is None:
... | 54 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab... | 368 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetCo... | 81 | 0 |
import math
__A =1_0
__A =7
__A =BALLS_PER_COLOUR * NUM_COLOURS
def lowerCamelCase_ ( lowerCamelCase__ = 2_0 ):
lowerCamelCase_ = math.comb(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR , lowerCamelCase__ )
lowerC... | 19 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, S... | 78 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : int = 1000) -> Tuple:
'''simple docstring'''
__lowercase = 1, 1
__lowercase = []
for i in range(1, n + 1):
__lowercase = prev_numerator + 2 * prev_denominator
__lowercase =... | 370 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers im... | 144 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE( a_ ):
_Upper... | 307 |
import os
def a_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = os.path.join(os.path.dirname(_A ) , 'num.txt' )
with open(_A ) as file_hand:
return str(sum(int(_A ) for line in file_hand ) )[:10... | 307 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffuser... | 86 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ... | 86 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 273 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
'featur... | 355 | # Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase_ ( _lowerCamelCase : List[str]):
return 1 / (1 + np.exp(-z))
def lowercase_ ... | 333 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
f... | 235 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 0 |
import baseaa
def _a ( SCREAMING_SNAKE_CASE_ : str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def _a ( SCREAMING_SNAKE_CASE_ : bytes ):
return baseaa.baadecode(SCREAMING_SNAKE_CASE_ ).decode("utf-8" )
if __name_... | 102 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_t... | 102 | 1 |
"""simple docstring"""
import math
import unittest
def _SCREAMING_SNAKE_CASE ( __snake_case : Union[str, Any] ):
'''simple docstring'''
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < num... | 220 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( A__ ,unittest.TestCase ):
"""simple docstring"""
lowercase__ ... | 207 | 0 |
"""simple docstring"""
from collections.abc import Callable
def lowercase ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) ->float:
"""simple docstring"""
__snake_case : float = a
__sn... | 353 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArg... | 24 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 253 |
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
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Any = ... | 253 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class __a:
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> None:
if len(_SCREAMING_SNAKE_CASE ) != degree + 1:
raise ValueErr... | 235 |
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, BlipaProcessor, BlipImagePro... | 235 | 1 |
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