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 |
|---|---|---|---|---|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelC... | 320 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''':... | 361 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata... | 133 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE__:List[Any] = """Input must be a string of 8 numbers plus letter"""
SCREAMING_SNAKE_CASE__:Any = """TRWAGMYFPDXBNJZSQVHLCKE"""
def _lowerCamelCase( a ):
if not isinstance(a , a ):
__a = F... | 261 | """simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase( a ... | 261 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe... | 360 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase__ ( lowerCamelCase_ : ndarray):
'''simple docstring'''
return np.dot(lowerCamelCase_ ,lowerCamelCase_)
class lowerCamelCase__ :
'''simple docstri... | 94 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__A =2_0_4_8
__A =4_0_9_6
__A =4_2
__A =os.environ.pop('''PROCESS_TRAIN''', '''false''')
__A ={'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def lowerCamelCase_ ( lowerCamelCase__ ):
def c... | 19 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE_( lowercase ) -> int:
raise NotImplementedError()
@abstractmethod
def SCREAMING_SNAKE_CAS... | 19 | 1 |
'''simple docstring'''
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... | 43 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCam... | 225 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( ) -> str:
... | 225 | 1 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Tuple = len(UpperCAmelCase_ )
_UpperCamelCase : Optional[int] = len(matrix[0] )
_UpperCamelCase : List[Any] = min(UpperCAmelCase_ , UpperCAmelCase_ )
... | 364 |
'''simple docstring'''
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 im... | 236 | 0 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"""vocab_file""": """vocab.txt""",
"""merges_file""": ""... | 315 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbi... | 50 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 357 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSched... | 195 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = ... | 306 |
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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 76 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 360 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase__ : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __ini... | 287 | 0 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE... | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A: Optional[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenize... | 109 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 84 |
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE : Tuple = "docs/source/en/_toctree.yml"
def UpperCamelCase_( lowerCamelCase_ ) -> Tuple:
_lowercase : Tuple = defaultdict(lowerCamelCase_ )
_lowercase : int = []
... | 84 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
lowercase_ = "examples/"
lowercase_ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+... | 211 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
lowercase_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two clas... | 211 | 1 |
import datasets
UpperCamelCase = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n ... | 221 |
from maths.prime_factors import prime_factors
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
lowerCAmelCase__ = F'Input value of [number={number}] must be an int... | 221 | 1 |
def a ( A__ : list[list] ) -> list[list]:
"""simple docstring"""
_lowercase =current_set.copy()
for row_index, row in enumerate(A__ ):
_lowercase =row[0]
for column_index, column in enumerate(A__ ):
... | 205 |
from __future__ import annotations
def a ( A__ : list[int] ) -> int:
"""simple docstring"""
if not nums:
return 0
_lowercase =nums[0]
_lowercase =0
for num in nums[1:]:
_lowercase , _low... | 205 | 1 |
"""simple docstring"""
UpperCAmelCase__ = 256
# Modulus to hash a string
UpperCAmelCase__ = 100_0003
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = len(lowerC... | 358 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 290 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy ... | 75 |
def A ( lowercase ) -> list:
'''simple docstring'''
UpperCamelCase = len(lowercase )
for i in range(1 , lowercase ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
while low <= high:
UpperCamelCase = (low + hig... | 222 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_dd... | 350 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case_ = logging.get_logger(__name__)
snake_case_ = OrderedDict(
[
# Base mod... | 216 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( snake_case_ : float ) ->float:
return 1_0 - x * x
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float ) ->float:
# Bolzano theory in order to find if there is a root between a and b
if equation... | 126 |
"""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... | 126 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 81 | 1 |
"""simple docstring"""
from math import ceil
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase : Any = list(range(0 , _UpperCAmelCase ) )
lowercase : str = [it... | 255 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 1 |
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase_ = re.compile(r'([A-Z]+)([A-Z][a-z])')
UpperCAmelCase_ = re.compile(r'([a-z\d])([A-Z])')
UpperCAmelCase_ = re.compile(r'(?<!_)_(?!_)')
UpperCAmelCase_ = re.compile(r'(_{2,})')
UpperCAmelCase_ = r'^\w+... | 29 |
def lowerCamelCase__ ( A__ : int ):
'''simple docstring'''
__lowerCamelCase = [[0 for _ in range(A__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowerCamelCase = 1
for n in range(m + 1 ):
for k... | 29 | 1 |
"""simple docstring"""
import operator
def lowerCamelCase ( _UpperCamelCase : list , _UpperCamelCase : bool = False , _UpperCamelCase : list | None = None ) -> list:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] ... | 115 |
"""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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils ... | 115 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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 ... | 356 |
import comet # From: unbabel-comet
import torch
import datasets
_A = datasets.logging.get_logger(__name__)
_A = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Me... | 261 | 0 |
from random import randint, random
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ = False , lowercase_ = False , lowercase_ = 5 , ) -> list:
"""simple docstring"""
A__ = [[-1] * number_of_cells] # Create a highway w... | 14 |
'''simple docstring'''
import heapq
def __UpperCamelCase ( lowercase__ : dict ):
'''simple docstring'''
__lowercase =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Prio... | 141 | 0 |
def lowerCamelCase__ (_UpperCAmelCase = 100_0000):
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = {1: 1}
for inputa in range(2 , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = inputa
... | 327 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ... | 327 | 1 |
'''simple docstring'''
lowercase_ = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowercase_ = ["a", "b", "c", "d", "e"]
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = start
# add current to visited
visited.append(__A)
... | 211 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 211 | 1 |
import copy
import re
class UpperCAmelCase_ :
'''simple docstring'''
__UpperCamelCase : Optional[int] = "hp"
__UpperCamelCase : Any = {}
__UpperCamelCase : Tuple = None
@classmethod
def _lowercase ... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
snake_case_ : Dict = logging.get_logger(__name__)
snake_case_ : Any = {"vocab_file": "voca... | 51 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __UpperCAmelCase ( unittest.TestCase ):
@prope... | 365 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 0 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_snake_case = 'src/diffusers'
# Matches is_xxx_available()
_snake_case = re.compile(r'is\_([a-z_]*)_availabl... | 250 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class a__ ( unittest.TestCase ):
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : ... | 250 | 1 |
def _UpperCamelCase ( snake_case__ ) -> int:
assert isinstance(snake_case__, snake_case__ ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
__UpperCAmelCase : Any ... | 342 | import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name... | 342 | 1 |
'''simple docstring'''
def a__ ( a__ ): # noqa: E741
"""simple docstring"""
__SCREAMING_SNAKE_CASE = len(a__ )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [0] * n
__SCREAMING_SNAKE_CASE = [False] * n
__SCREAMING_SN... | 267 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCAmelCase__ ( ... | 267 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__snake_case : List[Any] = [False] * len(__lowerCamelCase )
__snake_cas... | 367 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) -> None:
"""simple docstring"""
__snake_case : int ... | 13 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : List[str] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAIN... | 146 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase ... | 315 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__lowerCamelCase = HfArgumentParser(InitializationArguments)
__lowerCamelCase = parser.parse_args()
# Load codeparrot tokenizer trained for ... | 358 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCamelCase = get_tests_... | 10 | 0 |
"""simple docstring"""
from manim import *
class _a ( SCREAMING_SNAKE_CASE_):
"""simple docstring"""
def lowercase__ ( self : List[str] )->int:
_UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
_UpperCAmelCase = Rec... | 260 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 243 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch... | 365 |
import socket
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Dict = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE_ : Any = socket.gethostname()
SCREAMING_SNAKE_CASE_ : List[str] = 1_2_3_1_2
sock.... | 162 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
f... | 56 |
def lowerCAmelCase__( lowercase : list[int] , lowercase : int ) -> bool:
__snake_case : List[str] = len(lowercase )
__snake_case : int = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed ... | 326 | 0 |
class lowercase : # Public class to implement a graph
'''simple docstring'''
def __init__(self , __a , __a , __a ) -> None:
"""simple docstring"""
UpperCAmelCase__ = row
UpperCAmelCase__ = col
UpperCAmelCase__ = graph
... | 335 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfo... | 335 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_a = TypeVar('KEY')
_a = TypeVar('VAL')
@dataclass(frozen=lowercase__ ,slots=lowercase__ )
class A_ (Generic[KEY, VAL] ):
... | 61 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
... | 321 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( UpperCAmelCase__ : ArgumentParser ) -> Optional[Any... | 195 |
"""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_pyt... | 195 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()... | 227 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __a ( unittest.TestCase ):
_a : List[str] = JukeboxTokenizer
_a : List[Any] = {
'artist': 'Zac Brown Band',
'genres': 'Country',
'lyrics': 'I met a travelle... | 329 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
... | 370 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if not (isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )):
ra... | 220 | 0 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Dict = {
# 1536-bit
5: {
'prime': int(
... | 107 |
def lowerCAmelCase__(__snake_case ,__snake_case ) -> float:
'''simple docstring'''
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(__snake_case ) * abs(__snake_case )
if __name__ == "__main__":
import... | 209 | 0 |
'''simple docstring'''
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,
EfficientFormerForImageClassificationWithTea... | 368 |
'''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_pytorc... | 37 | 0 |
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_lowerCamelCase , n - 1 , _lowerCamelCase ) * a) % mod
else:
... | 36 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE... | 37 | 0 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
def __init__( self : List[str]... | 218 |
from functools import reduce
_SCREAMING_SNAKE_CASE : Any = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443... | 218 | 1 |
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowerCAmelCase_ = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
i... | 16 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( s... | 16 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
a : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
class a ( ... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 72 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 94 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testi... | 237 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}... | 351 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def __lowerCamelCase ( __magi... | 42 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
... | 194 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_a = logging.get_logger(__name__)
... | 194 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
f... | 366 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( lowerCamelCase__ ):
... | 340 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ['image_processor', 'tokenizer']
... | 104 | 0 |
from string import ascii_uppercase
lowerCAmelCase__ : int ={char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase__ : int =dict(enumerate(ascii_uppercase))
def __lowercase ( a__ , a__ ) -> str:
__SCREAMING_SNAKE_CASE = len(a... | 118 |
from __future__ import annotations
from collections.abc import Generator
def __lowercase ( ) -> Generator[int, None, None]:
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = 2
while True:
__SCREAMING_SNAKE_CASE = factor_map.pop(a... | 118 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def UpperCAmelCase_ (__a : list[int] , __a : list[int] , __a : int ):
"""simple docstring"""
_a : str = [0] * no_of_processes
_a : Optional[int] = [0] * no_of_p... | 271 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transf... | 271 | 1 |
'''simple docstring'''
from __future__ import annotations
class lowerCamelCase_ :
def __init__( self : Optional[int] , _A : int=None ):
'''simple docstring'''
UpperCAmelCase__ : Any = data
... | 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase ( enum.Enum ):
... | 15 |
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 torch
... | 15 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperCAmelCase ... | 360 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 28 | 0 |
"""simple docstring"""
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,
... | 40 |
"""simple docstring"""
class UpperCAmelCase_ :
def __init__( self , UpperCamelCase_ ) -> Tuple:
__lowercase : Any = n
__lowercase : Any = [None] * self.n
__lowercase : Optional[int] = 0 # index of the first... | 249 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.num... | 367 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 323 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConf... | 151 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) < k or k < 0:
raise ValueError('Invalid Input' )
UpperCAmelCase : Tuple = sum(array[:k] )
for i in range(len(UpperCAmelCas... | 151 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_snake_case = get_logger(__name__)
_snake_case = r'''\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_le... | 361 | 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 import Conversatio... | 342 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_cas... | 283 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowerCamelCase : List[Any] = 1
lowerCamelCase : Union[str, Any] = 1
while repunit:
lowerCamelCase : Union[str, Any] = (10 * repunit + 1)... | 283 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = 'T5Config'
class a_ ( SCREAMING_SNAKE_CASE ):
... | 119 | import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distr... | 119 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 211 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase_ = datasets.utils.logging.get_logger(__name__)
class __A ( folder_based_builder.FolderBasedBuilderConfig ):
... | 211 | 1 |
A : List[str] = "Alexander Joslin"
import operator as op
from .stack import Stack
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
SCREAMING_SNAKE_CASE_ = Stack()
SC... | 305 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCAmelCase : Tuple = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAIN... | 136 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transforme... | 136 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A ( lowercase , lowercase ):
"""simple docstring"""
a , a =set(lowercase ), [start]
while stack:
a =stack.pop()
explored.add(lowercase )
# Differences... | 215 |
"""simple docstring"""
from __future__ import annotations
def _A ( lowercase , lowercase , lowercase , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2... | 215 | 1 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCas... | 4 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = 0
if start < end:
UpperCamelCase = randint(lowercase , lowercase )
Upper... | 222 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = len(lowerCamelCase__ )
UpperCAmelCase_ = len(lowerCamelCase__ )
UpperCAmelCase_ = [[False for _ in range(m + 1 )]... | 353 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = 1 # (0 is vertical, 1 is horizontal)
de... | 241 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase__( lowercase : Union[str, Any] , lowercase : str , lowercase : str , lowercase : Path ... | 326 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.m... | 326 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> float:
lowercase_ : List[Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of seri... | 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__... | 21 | 1 |
'''simple docstring'''
class A__ : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : list[list[bool]] ) -> int:
... | 145 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms imp... | 91 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( snake_case__ : str = "AAPL" ) -> str:
UpperCamelCase : Union[str, Any] = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Dict = BeautifulSoup(requests.get(... | 350 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 103 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipe... | 276 |
'''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.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : List[Any] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 265 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion impo... | 265 | 1 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes ... | 206 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _lowerCAmelCase ( unittest.TestCase ):
def _a (self ):
A_ : Optional[Any] = 10
def _... | 206 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : ... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'google/canine-s': 'https://huggingface.co/google/canine-s/... | 233 | 0 |
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE__ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
SCR... | 314 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a__: List[Any] = logging.getLogger()... | 193 | 0 |
"""simple docstring"""
_lowerCAmelCase : int = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git... | 361 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 202 | 0 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path'... | 106 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acc... | 106 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 |
def lowerCamelCase_ ( _a : int = 50 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = [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 ... | 59 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 333 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A__ : Any =logging.g... | 70 | 0 |
def lowerCamelCase_ (UpperCamelCase__ : Optional[int]=2_8123 ):
_UpperCAmelCase : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 368 |
"""simple docstring"""
from itertools import count
def lowerCamelCase_ (UpperCamelCase__ : int = 50 ):
_UpperCAmelCase : Tuple = [1] * min_block_length
for n in count(UpperCamelCase__ ):
fill_count_functions.append(1 )
for block_length in... | 68 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def ... | 19 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLA... | 59 | 0 |
from __future__ import annotations
from fractions import Fraction
def __UpperCamelCase ( _A : int , _A : int ) ->bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 49 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsPr... | 49 | 1 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` in... | 105 | 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 __snake_case ( lowerCamelCase_ ... | 219 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Au... | 362 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_ber... | 37 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise... | 71 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase ... | 355 |
"""simple docstring"""
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... | 27 | 0 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowerCAmelCase__ ... | 108 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import v... | 271 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingfa... | 84 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCamelCase( _a, unittest.TestCase ):
lowercase_ : List[str] = CTRLTokenizer... | 84 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case_ = TypeVar('''T''')
snake_case_ = TypeVar('''U''')
class SCREAMING_SNAKE_CASE__ (Generic[T, U] ):
def __init__( self , a , a):
lowercase... | 214 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case_ = logging.getLogger(__name__)
snake_case_ = 50 # max width of layer names
snake_cas... | 214 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return lst
_snake_case : List[Any] = 1
while i < len(snake_case__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_sna... | 132 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme impor... | 132 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 77 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfor... | 103 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resiz... | 259 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : List[str] = {
"dist... | 259 | 1 |
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