code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logg... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a( A : Union[str, Any] ) -> List[str]:
"""simple docstring"""
for param in module.parameters():
a = False
def a( ) -> int:
"""simple doc... | 227 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 1 |
def a( A : int ) -> int:
"""simple docstring"""
if not isinstance(A , A ):
a = f'''Input value of [number={number}] must be an integer'''
raise TypeError(A )
if number < 1:
a = f'''Input value of ... | 227 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class _lowercase :
"""simple docstring"""
__A = field(
default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be trained."} )
__A = field(
default="./"... | 227 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 |
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 ThreadedIterator
from tqdm imp... | 227 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (PNDMScheduler,)
__A = (("num_inference_steps", 50),)
def UpperCamelCase_ ... | 227 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 1 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import ... | 227 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 227 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 227 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
# `task` is not a ClassVar since... | 227 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 227 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 1 |
from __future__ import annotations
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
a = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and... | 227 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 1 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEI... | 227 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowercase: Union[str, Any] = get_t... | 227 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (KDPMaDiscreteScheduler,)
__A = 10
def UpperCa... | 227 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 1 |
# limitations under the License.
# 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 .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprec... | 227 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 1 |
import unittest
from transformers import DonutProcessor
_lowercase: int = "naver-clova-ix/donut-base"
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
a = DonutProcessor.from_pret... | 227 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 1 |
def a( A : dict ) -> bool:
"""simple docstring"""
a = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
a = set()
return any(
node not in visited and depth_first_search(A , A , A ... | 227 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowercase: Dict = re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _lowercase :
... | 227 |
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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A :... | 227 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 227 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a( A : Dict , A : Union[str, Any] , A : List[str]=1024 , A : str=1024 , A : ... | 227 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 1 |
from itertools import product
def a( A : int , A : int ) -> list[int]:
"""simple docstring"""
a = sides_number
a = max_face_number * dice_number
a = [0] * (max_total + 1)
a = 1
a ... | 227 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Dict = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Mod... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_lowercase: Any = TypeVar("T")
def a( A : int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def a( A : int ) -> ... | 227 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowercase: Union[str, Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang... | 227 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 1 |
import datasets
from .evaluate import evaluate
_lowercase: Optional[int] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={... | 227 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 1 |
def a( A : str , A : str ) -> bool:
"""simple docstring"""
a = len(A )
a = len(A )
a = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
a = True
for i in range(A ... | 227 |
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 ThreadedIterator
from tqdm imp... | 227 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableU... | 227 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 1 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
... | 227 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 1 |
from __future__ import annotations
def a( A : float , A : float , A : float ) -> dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must b... | 227 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 227 | 1 |
from math import factorial
_lowercase: int = {str(d): factorial(d) for d in range(10)}
def a( A : int ) -> int:
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(A ) )
def a( ) -> int:
"""simple docs... | 227 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 1 |
def a( A : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
a = []
for data in source_data:
for i, el in enumerate(A ):
if len(A ) < i + 1:
data_lists.append([] )
... | 227 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 227 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase: Optional[int] = {
"iou_pred... | 227 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowercase: Union[str, Any] = get_t... | 227 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_sh... | 227 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 1 |
def a( A : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
"""simple docstring"""
a = set()
# Replace all the whitespace in our sentence
a = input_str.replace(" " , "" )
for alpha in input_str:
if ... | 227 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 1 |
def a( A : list , A : list , A : int ) -> list:
"""simple docstring"""
a = len(A )
a = [[0] * n for i in range(A )]
for i in range(A ):
a = y_points[i]
for i in range(... | 227 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import... | 227 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 1 |
_lowercase: List[Any] = 256
# Modulus to hash a string
_lowercase: List[Any] = 1000003
def a( A : str , A : str ) -> bool:
"""simple docstring"""
a = len(A )
a = len(A )
if p_len > t_len:
... | 227 |
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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase: Optional[int] = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig... | 227 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransform... | 227 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 1 |
_lowercase: List[Any] = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowercase: List[Any] = ["a", "b", "c", "d", "e"]
def a( A : Any , A : Optional[Any] , A : Optional[int] ) -> Dict:
"""simple docstring"""
... | 227 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 1 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_lowercase: Any = "<<<<<<< This should probably be modified because it mentions: "
_lowercase: List[Any] = "======... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
from __future__ import annotations
def a( A : list[int | str] ) -> None:
"""simple docstring"""
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def a( A : list[int | str] , A : ... | 227 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def a( ) -> List[Any]:
"""simple docstring"""
a = HfArgumentParser(A )
a = parser.parse_args_into_dataclasses()[0]
a = TensorFlowBenchmar... | 227 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 1 |
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 import logging
logging.set... | 227 |
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 ThreadedIterator
from tqdm imp... | 227 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availab... | 227 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 227 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 1 |
def a( A : int ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
a = 1
a = 1
while repunit:
a = (10 * repunit + 1) % divisor
repunit_index += 1
return rep... | 227 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
... | 227 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 227 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def a( A : List[str] ) -> Optional[int]:
"""simple docstring"""... | 227 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 1 |
import doctest
from collections import deque
import numpy as np
class _lowercase :
"""simple docstring"""
def __init__(self ):
"""simple docstring"""
a = [2, 1, 2, -1]
a = [1, 2, 3, 4]
def UpperCamelCase_ (self ):
"""simple docst... | 227 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 1 |
import requests
_lowercase: Optional[int] = "" # <-- Put your OpenWeatherMap appid here!
_lowercase: Any = "https://api.openweathermap.org/data/2.5/"
def a( A : str = "Chicago" , A : str = APPID ) -> dict:
"""simple docstring"""
re... | 227 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 1 |
class _lowercase :
"""simple docstring"""
def __init__(self ):
"""simple docstring"""
a = {}
def UpperCamelCase_ (self ):
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(lowerCamelCase_ , " -> " ... | 227 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowercase: Union[str, Any] = get_t... | 227 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, sl... | 227 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_lowercase: Optional[int] = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at t... | 227 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.model... | 227 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _lowercase :
"""simple docstring"""
pass
| 227 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCamelCase_ (lowerCamelCase_ ):
"""simple docstring"""
raise NotImplementedError()
@abstrac... | 227 |
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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 1 |
def a( A : int = 10 ) -> str:
"""simple docstring"""
if not isinstance(A , A ) or n < 0:
raise ValueError("Invalid input" )
a = 10**n
a = 2_8433 * (pow(2 , 783_0457 , A )) + 1
return str(number... | 227 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: int = logging.get_logger(__name__)
_lowercase: List[Any] = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggin... | 227 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowercase: str = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "... | 227 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 1 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_lowercase: ... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase: Optional[Any] = logging.get_logger(__name__)
_lowercase: int = "▁"
_lowercase:... | 227 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 1 |
from math import pow, sqrt
def a( *A : float ) -> bool:
"""simple docstring"""
a = len(A ) > 0 and all(value > 0.0 for value in values )
return result
def a( A : float , A : float ) -> f... | 227 |
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 ThreadedIterator
from tqdm imp... | 227 | 1 |
def a( A : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 227 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 1 |
from collections import deque
def a( A : List[Any] ) -> Tuple:
"""simple docstring"""
a = len(A )
a = deque()
a = [False for _ in range(A )]
a = [-1 for _ in range(A )]
a = ... | 227 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 1 |
from ... import PretrainedConfig
_lowercase: str = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__A = ... | 227 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 227 | 1 |
from __future__ import annotations
def a( A : int ) -> list[int]:
"""simple docstring"""
a = [True] * limit
a = False
a = False
a = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ... | 227 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 227 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase: int = "src/di... | 227 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase: int = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
],
"proce... | 227 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowercase: Union[str, Any] = get_t... | 227 | 1 |
def a( A : str ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def a( A : str ) -> bool:
"""simple docstring"""
a = credit_card_number
a ... | 227 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 227 | 1 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 227 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 227 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
a = list_of_points
# Degree determines the flexibility of the curve.
... | 227 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: List[str] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 227 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Dict = logging.get_logger(__name__)
_lowercase: int = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class _lowercase ( ... | 227 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def a( A : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = SwinConfig(image_size=192 ... | 227 |
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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 227 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
... | 227 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 227 | 1 |
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
_lowercase: Dict = logging.get_logger(__name__)
_lowercase: Optional[int] = {
"hustv... | 227 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 227 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
_lowercase: List[Any] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account tr... | 227 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def a( A : int , A : int , A : int , A : int , A : int , A : int ) -> np.ndarray:
"""simple docstring"""
... | 227 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase: Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig",
"BridgeT... | 227 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
fr... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
def a( ) -> int:
"""simple docstring"""
a = 0
for i in range(1 , 1001 ):
total += i**i
return str(A )[-10:]
if __name__ == "__main__":
print(solution())
| 227 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 | 1 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowercase: Optional[int] = logging.getLogger(__name__)
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def ... | 227 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 227 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING... | 227 |
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 ThreadedIterator
from tqdm imp... | 227 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.... | 227 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 227 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_lowercase: Any = get_logger(__name__)
_lowercase: Tuple = r"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_l... | 227 |
import cmath
import math
def a( A : float , A : float , A : float , A : float ) -> complex:
"""simple docstring"""
a = math.radians(A )
a = math.radians(A )
# Convert voltage and c... | 227 | 1 |
import math
import sys
import cva
import numpy as np
def a( A : np.ndarray , A : float ) -> np.ndarray:
"""simple docstring"""
a = math.sqrt(A )
a = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.ex... | 227 |
def a( A : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
a = len(bin(A )[3:] )
a = bin(abs(A ) - (1 << binary_number_length) )[3:]
a ... | 227 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a( A : np.ndarray , A : np.ndarray , A : np.ndarray , A : int , A : int ) -> np.ndarray:
"""simple docstring"""
a ... | 227 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 227 | 1 |
import math
def a( A : int ) -> bool:
"""simple docstring"""
a = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(A )
def a( A : float = 1 / 1_2345 ) -> int:... | 227 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 1 |
_lowercase: Any = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is... | 227 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = (DDPMScheduler,)
def UpperCamelCase_ (self , **lowerCamelCase_ ):
"""simple docstring"""
... | 227 | 1 |
from bisect import bisect
from itertools import accumulate
def a( A : List[str] , A : int , A : Tuple , A : Any ) -> str:
"""simple docstring"""
a = sorted(zip(A , A ) , key=lambda A : x[0] / x[... | 227 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a( A : dict ) -> tuple:
"""simple doc... | 227 | 1 |
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