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'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return number | (1 << position)
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return number & ~(1 << position)
def UpperCAmelCase_ ( A , A ):
'''simple... | 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 argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
def UpperCAmelCase... | 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'''
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__)
UpperCAmelC... | 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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Optional[int] = """ClapFeatureExtractor"""
__lowerCAmelCase : str = (... | 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_torch_available, is_vision_available
UpperCAmelCase_ : Optional[Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 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'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict ... | 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 collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCon... | 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 copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torc... | 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 math import pi
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(90, 10))
| 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 itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : Optional[Any] = k_size // 2... | 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 math
def UpperCAmelCase_ ( A ):
'''simple docstring'''
if not isinstance(A , A ):
_a : int = f'''Input value of [number={number}] must be an integer'''
raise TypeError(A )
if number < 1:
_a : List[str] ... | 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 unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : Dict = get_tests_dir("fixtures/t... | 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 multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thre... | 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 shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transf... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Any = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
... | 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 unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_F... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : Tuple = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_tor... | 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
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a ( snake_ca... | 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 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 |
'''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'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
"microsoft/xprophetnet-large-wiki... | 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 re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCAmelCase_ : Union[str, Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translati... | 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 argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from data... | 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 ):
'''simple docstring'''
if not isinstance(A , A ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
_a : List[str] = 0
while number:
# This way we arrive at next set bit (nex... | 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'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all fi... | 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 argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {name: ge... | 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 argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
UpperCAmelCase_ : Optional[Any]... | 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 math import factorial
def UpperCAmelCase_ ( A = 2_0 ):
'''simple docstring'''
_a : Any = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_a : int = n // 2
return int(factorial(A ... | 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 shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCAmelCase_ ( A , A , A , A=1_0_2_4 ):
'''simple docstring'''
_a , _a : List[str] = [], []
_a : List[str] ... | 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 __future__ import annotations
import typing
from collections import Counter
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : typing.Counter[int] = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in ran... | 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 transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class a ( snake_case__ ):
'''simple docstring'''
... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/... | 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 itertools import product
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : Optional[Any] = sides_number
_a : Tuple = max_face_number * dice_number
_a : Optional[int] = [0] * (max_total + 1)
_a : List... | 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'''
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_ : List[str] = l... | 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
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
Uppe... | 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 fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCAmelCase_ ( A , A , **A ):
'''simple docstring'''
_a : Tuple = AutoConfig.from_pretrained(A , **A )
_a : Dict = AutoModelForSeqaSeqL... | 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'''
from __future__ import annotations
from math import gcd
def UpperCAmelCase_ ( A , A = 2 , A = 1 , A = 3 , ):
'''simple docstring'''
if num < 2:
raise ValueError('The input value cannot be less than 2' )
# Because of the relationship between ``f(f(... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Dict = {"conf... | 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 sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
UpperCAmelCase_ : Optional[int] = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Pop... | 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 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
... | 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 __future__ import annotations
import numpy as np
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a , _a : List[str] = np.shape(A )
if rows != columns:
_a : int = (
'\'table\' has to be of square shaped... | 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 ...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 |
'''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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc... | 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 __future__ import annotations
from fractions import Fraction
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def UpperCAmelCase_ ( A ... | 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 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 |
'''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 dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
... | 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 argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[Any] = {}
_a : Any = job['started_at']
_a : List[str] = job['c... | 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 json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingSt... | 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 copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
class a ( snake_case__ ):
'''simple docstri... | 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 __future__ import annotations
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : int = len(A ) // 2
# choose the middle 3 elements
_a : str = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] ... | 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 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.d... | 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 json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class a :
'''simple docstring'''
__lowerCAmelCase : str = None
def __UpperCamelCase ( self ) -> str:
_a : Uni... | 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_tf_available, is_torch_available
UpperCAmelCase_ : List[str] = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl":... | 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 os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 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 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... | 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'''
class a :
'''simple docstring'''
def __init__( self ) -> List[Any]:
_a : Optional[int] = 0
_a : Tuple = 0
_a : str = {}
def __UpperCamelCase ( self , lowerCamelCase_ ) -> Optional[int]:... | 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 warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__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 numpy import inf
from scipy.integrate import quad
def UpperCAmelCase_ ( A ):
'''simple docstring'''
if num <= 0:
raise ValueError('math domain error' )
return quad(A , 0 , A , args=(A) )[0]
def UpperCAmelCase_ ( ... | 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'''
from functools import reduce
UpperCAmelCase_ : Optional[int] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290... | 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 . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 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 itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
fr... | 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'''
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> Any:
_a : int = n
_a : Tuple = [None] * self.n
_a : Optional[Any] = 0 # index of the first element
_a : int = 0
_a ... | 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
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 |
'''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
UpperCAmelCase_ : List[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebox... | 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'''
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 |
'''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 dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class a ( snake_case__ ):
'''s... | 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 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.pipel... | 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 os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import ... | 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 collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPR... | 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'''
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 |
'''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
UpperCAmelCase_ : int = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDep... | 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'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
_a : Tuple = OmegaConf.load(A )
_a : Optional[Any] = ... | 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 shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProces... | 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'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Union[str, Any] = [0] * len(A )
_a : Optional[Any] = []
_a : Any = []
_a : List[str] = 0
for values in graph.values():
for i in values:
... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/c... | 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 __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from .... | 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'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCAmelCase_ : Any = None
try:
import msvcrt
except ImportError:
UpperCAmelCase_ : Any = None
try:
import fcntl
except ImportError:
UpperCAmelCase_ ... | 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'''
# 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: F401
from ..controln... | 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
from .keymap import KEYMAP, get_character
def UpperCAmelCase_ ( A ):
'''simple docstring'''
def decorator(A ):
_a : str = getattr(A , 'handle_key' , [] )
handle += [key]
setattr(A , 'handle_key' ... | 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
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
fro... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__na... | 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'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[Any] = False
while is_sorted is False: # Until all the indices are traversed keep looping
_a : str = True
for i in range(0 , len(A ) - 1 , 2 ): # ite... | 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 __future__ import annotations
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a , _a : int = position
_a : Union[str, Any] = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),... | 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 typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Tuple = """EncodecFeatureExtractor"""
__lowerCAmelC... | 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 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 |
'''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 ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/ma... | 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 numpy as np
from transformers import Pipeline
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : str = np.max(A , axis=-1 , keepdims=A )
_a : List[str] = np.exp(outputs - maxes )
return shifted_exp / shifted_ex... | 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
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : int = os.path.join(args.tf_model_dir , 'parameters.json' )
... | 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'''
_a , _a : Optional[int] = [], []
while len(A ) > 1:
_a , _a : List[Any] = min(A ), max(A )
start.append(A )
end.append(A )
colle... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Tuple = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"]... | 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 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 |
'''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 = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
_a : Tuple = set()
# Replace all the whitespace in our sentence
_a : int = input_str.replace(' ' , '' )
for alpha in input_str:
... | 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 numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a ( snake_case__ ):
'''simple docstring'''
d... | 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 Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..mode... | 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'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return str(A ) == str(A )[::-1]
def UpperCAmelCase_ ( A ):
'''simple docstring'''
return int(A ) + int(str(A )[::-1] )
def UpperCAmelCase_ ( A = 1_0_... | 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'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Any = len(A )
for i in range(A ):
for j in range(i + 1 , A ):
if numbers[j] < numbers[i]:
_a , _a : Union[str, Any] = numbers[j], numbe... | 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'''
def UpperCAmelCase_ ( A ):
'''simple docstring'''
assert (
isinstance(A , A ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
_a , _a ... | 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
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 (
... | 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'''
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 |
'''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'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Tuple = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenizat... | 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 importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_avail... | 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 |
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