code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase_ : int = tuple[int, int, int]
UpperCAmelCase_ : Union[str, Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCAmelCase_ : str = "ABCD... | 120 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_ca... | 120 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 1 |
'''simple docstring'''
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, ... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Union[str, Any] = {
"configuration_blende... | 120 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 1 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( A , A = 0 , A = 0 ):
'''simple docstring'''
_a : int = end or len(A )
for i in range(A , A ):
_a : Optional[int] = i
_a : Optional[int] = array[i]
while temp_ind... | 120 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 1 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 120 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 1 |
'''simple docstring'''
UpperCAmelCase_ : dict[tuple[int, int, int], int] = {}
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a priz... | 120 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : int = {}
try:
if not is_sentencepiece_available():
ra... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2S... | 120 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 120 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = shutil.get_terminal_size()
UpperCAmelCase_ : Optional[Any] = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class a ( enum.Enum ):
'''simp... | 120 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@... | 120 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import... | 120 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 1 |
'''simple docstring'''
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
_a : Dict = size
_a : Tuple = [0] * size
_a : Optional[int] = [0] * size
@staticmethod
def __UpperCamelCase ( lowerC... | 120 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_ch... | 120 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 120 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
clas... | 120 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
UpperCAmelCase_ : Optional[Any] = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thiri... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 1 |
'''simple docstring'''
from collections import defaultdict
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : Optional[Any] = first_str.lower().strip()
_a : Optional[int] = second_str.lower().strip()
# Remove whitespace
_a : Dict = f... | 120 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 |
'''simple docstring'''
import warnings
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_ : int = logging.get_logger(__name... | 120 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
clas... | 120 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 1 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
_a : Optional[Any] =... | 120 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTI... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVis... | 120 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Tuple = []
_a : List[str] = set({'(', '[', '{'} )
_a : Any = set({')', ']', '}'} )
_a : List[str] = {'{': '}', '[': ']', '(': ')'}
for i in ra... | 120 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 120 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 1 |
'''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 transformers import glue... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
fr... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 120 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 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 transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils im... | 120 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
if not isinstance(A , A ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_a : List[str] = str(A )
_a : Dict = ''.join(sorted(A ) )
... | 120 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
if len(A ) != len(A ):
raise ValueError('String lengths must match!' )
_a : Optional[Any] = 0
for chara, chara in zip(A , A ):
if chara != chara:
co... | 120 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Optional[Any] = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_visi... | 120 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Any = credit_card_number
_a : int ... | 120 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import... | 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name='my_... | 120 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 120 | 1 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def UpperCAmelCase_ ( A , A , A = 1 , A = 1 , A = 1.0E4 , A = False , A = 1.0 , ):
'''simple docstring'''
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0... | 120 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 1 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, 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 Mod... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ): # noqa: E741
'''simple docstring'''
_a : int = len(A )
_a : str = 0
_a : Optional[int] = [0] * n
_a : int = [False] * n
_a : List[str] = [False] * n
def dfs(A ... | 120 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpos... | 120 |
'''simple docstring'''
import warnings
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_ : int = logging.get_logger(__name... | 120 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_... | 120 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name... | 120 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pr... | 120 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacit... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
if num <= 0:
raise ValueError('Input must be a positive integer' )
_a : Union[str, Any] = [True] * (num + 1)
_a : int = 2
while p * p <= num:
if primes[p]:
... | 120 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_sim... | 120 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 1 |
'''simple docstring'''
import os
def UpperCAmelCase_ ( ):
'''simple docstring'''
with open(os.path.dirname(A ) + '/p022_names.txt' ) as file:
_a : str = str(file.readlines()[0] )
_a : Union[str, Any] = names.replace('"' , '' ).split(',... | 120 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 1 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(A , x % y )
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return (x * y) // greatest_common_divisor(A , A )
de... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 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():
from .tok... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 1 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMix... | 120 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A = 1_0_0_0 ):
'''simple docstring'''
_a : Tuple = 2**power
_a : Union[str, Any] = 0
while n:
_a , _a : Dict = r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(i... | 120 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 1 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
f... | 120 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 1 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class a ( snake_case__ ):... | 120 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 1 |
'''simple docstring'''
import re
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[Any] = re.compile(r'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(A , A ):
return match.string == phone
return False
if __name__ == "__main__... | 120 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEnco... | 120 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
"snap-research/efficientformer-l1-300": (
... | 120 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 120 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 120 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
_a : Dict = len(A )
_a : Optional[Any] = max(A )
_a : Union[str, Any] = min(A... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Any = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optio... | 120 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffus... | 120 |
'''simple docstring'''
import warnings
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_ : int = logging.get_logger(__name... | 120 | 1 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 1 |
'''simple docstring'''
UpperCAmelCase_ : List[str] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n":... | 120 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 1 |
'''simple docstring'''
import warnings
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_ : int = logging.get_logger(__name... | 120 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 1 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class a... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.ut... | 120 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgume... | 120 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 120 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Optional[int] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"P... | 120 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 1 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def UpperCAmelCase_ ( A ):
... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 1 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return 1_0 - x * x
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
if equation(A ) * equation(A ) >= 0:
raise ValueError('Wrong space!' )
_a : Option... | 120 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 1 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def UpperCAmelCase_ ( A = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def UpperCAmelCase_ ( A = "" )... | 120 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_visi... | 120 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 1 |
'''simple docstring'''
import random
def UpperCAmelCase_ ( A , A , A = False ):
'''simple docstring'''
_a : dict = {i: [] for i in range(A )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete... | 120 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 120 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension... | 120 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 120 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip instal... | 120 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 120 | 1 |
'''simple docstring'''
import numpy as np
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 120 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : str = {
"configuration_squeezebert": [
"SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Squee... | 120 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 120 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {"configuration_plbart": ["PLBART_PRETRAINED_... | 120 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 120 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[Any] = str(A )
return n == n[::-1]
def UpperCAmelCase_ ( A = 1_0_0_0_0_0_0 ):
'''simple docstring'''
_a : str ... | 120 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
'''simple docstring'''
__lowerCAmelCase : int
__lowerCAmelCase : TreeNode | None = None
__lowerCAmelCase : TreeNode | ... | 120 | 1 |
'''simple docstring'''
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_... | 120 |
'''simple docstring'''
import warnings
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_ : int = logging.get_logger(__name... | 120 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
UpperCAmelCase_ : List[str] = (720, 1280) # Height, Width
UpperCAmelCase_ : Union[str, Any] = (0.4, 0.6) # if height or width lower... | 120 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None ) -> Tuple:
# Input as list
_a : Optional[int] = list(poly_a or [0] ... | 120 | 1 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@data... | 120 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCAmelCase_ ( A , A , A = 1 / sqrt(2 ) ):
'''simple docstring'''
_a : List[Any] = tau * frequency / samplerate
_a : Tuple = sin(A )
... | 120 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelC... | 120 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 120 | 1 |
'''simple docstring'''
UpperCAmelCase_ : List[str] = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : List[Any] = ["""torch""", """scipy"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ )... | 120 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""note_seq"""]
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) ... | 120 | 1 |
'''simple docstring'''
from math import isqrt
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[int] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , A , A ):
... | 120 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 120 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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 .tokeniz... | 120 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokeniz... | 120 | 1 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentPa... | 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google... | 120 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : L... | 120 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstring'''
def __init__( self , *low... | 120 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Dict = logging.g... | 120 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Dict = args.pruning_method
_a : Optional[Any] ... | 120 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def Upper... | 120 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int ... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCAmelCase_ : Union[str, Any] = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"sus... | 120 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCon... | 120 | 1 |
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