
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowercase						:      int									= os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowercase						:      str									= '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'




class 				lowerCamelCase__							(       unittest.TestCase):




 '''simple docstring'''




 def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
        __UpperCamelCase			:     int         =      tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir  ,     "models/bert/"		)		)
        __UpperCamelCase			:     List[Any]         =      self.transformer_dir
        shutil.copy(
            os.path.join(a  ,     "src/transformers/models/bert/modeling_bert.py"		)  ,     os.path.join(self.transformer_dir  ,     "models/bert/modeling_bert.py"		)  ,     )







 def      _lowerCamelCase       (      self	:List[str]		)	->				Any:
        __UpperCamelCase			:     int         =      "src/transformers"
        shutil.rmtree(self.transformer_dir		)







 def      _lowerCamelCase       (      self	:str  ,     a	:Tuple  ,     a	:Union[str, Any]  ,     a	:int  ,     a	:List[str]=None		)	->				List[Any]:
        __UpperCamelCase			:     List[Any]         =      comment + f'\nclass {class_name}(nn.Module):\n' + class_code
        if overwrite_result is not None:
               __UpperCamelCase			:     int         =      comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
        __UpperCamelCase			:     int         =      black.Mode(target_versions={black.TargetVersion.PYaa}  ,     line_length=1_1_9		)
        __UpperCamelCase			:     List[str]         =      black.format_str(a  ,     mode=a		)
        __UpperCamelCase			:     List[Any]         =      os.path.join(self.transformer_dir  ,     "new_code.py"		)
        with open(a  ,     "w"  ,     newline="\n"		) as f:
               f.write(a		)
        if overwrite_result is None:
               self.assertTrue(len(check_copies.is_copy_consistent(a		)		) == 0		)
        else:
               check_copies.is_copy_consistent(f.name  ,     overwrite=a		)
               with open(a  ,     "r"		) as f:
                      self.assertTrue(f.read()  ,     a		)







 def      _lowerCamelCase       (      self	:Optional[int]		)	->				str:
        __UpperCamelCase			:     Optional[int]         =      check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"		)
        self.assertEqual(a  ,     a		)







 def      _lowerCamelCase       (      self	:Any		)	->				int:
        # Base copy consistency
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"  ,     "BertLMPredictionHead"  ,     REFERENCE_CODE + "\n"  ,     )

        # With no empty line at the end
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"  ,     "BertLMPredictionHead"  ,     a  ,     )

        # Copy consistency with rename
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"  ,     "TestModelLMPredictionHead"  ,     re.sub("Bert"  ,     "TestModel"  ,     a		)  ,     )

        # Copy consistency with a really long name
        __UpperCamelCase			:     Any         =      "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
        self.check_copy_consistency(
            f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'  ,     f'{long_class_name}LMPredictionHead'  ,     re.sub("Bert"  ,     a  ,     a		)  ,     )

        # Copy consistency with overwrite
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"  ,     "TestModelLMPredictionHead"  ,     a  ,     overwrite_result=re.sub("Bert"  ,     "TestModel"  ,     a		)  ,     )







 def      _lowerCamelCase       (      self	:Any		)	->				Tuple:
        __UpperCamelCase			:     Any         =      check_copies.LOCALIZED_READMES["README_zh-hans.md"]

        __UpperCamelCase			:     Optional[Any]         =      (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
            " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
            " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
            " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
            " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
            " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
            " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
            " method has been applied to compress GPT2 into"
            " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
            " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
            " Multilingual BERT into"
            " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
            " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
            " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
            " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
            " Luong, Quoc V. Le, Christopher D. Manning."
        )
        __UpperCamelCase			:     Optional[int]         =      (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )
        __UpperCamelCase			:     Optional[int]         =      (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
            " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
            " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
            " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
            " method has been applied to compress GPT2 into"
            " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
            " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
            " Multilingual BERT into"
            " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
            " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
            " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
            " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
            " Christopher D. Manning 发布。\n"
        )

        __UpperCamelCase			:     Tuple         =      check_copies.convert_to_localized_md(
            a  ,     a  ,     localized_readme["format_model_list"]		)

        self.assertFalse(a		)
        self.assertEqual(a  ,     a		)

        __UpperCamelCase			:     Tuple         =      check_copies.convert_to_localized_md(
            a  ,     a  ,     localized_readme["format_model_list"]		)

        # Check whether the number of models is equal to README.md after conversion.
        self.assertTrue(a		)

        __UpperCamelCase			:     Any         =      (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
            " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
            " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
            " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
        )
        __UpperCamelCase			:     str         =      (
            "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
            " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )
        __UpperCamelCase			:     Tuple         =      (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )

        __UpperCamelCase			:     Any         =      check_copies.convert_to_localized_md(
            a  ,     a  ,     localized_readme["format_model_list"]		)

        # Check if the model link is synchronized.
        self.assertEqual(a  ,     a		)

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int | None, int | None, float]:






							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							__UpperCamelCase			:     Tuple         =      (low + high) // 2
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_subarray(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      max_subarray(_lowerCamelCase		,						mid + 1		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_cross_sum(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int, int, float]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      float("-inf"), -1
							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      float("-inf"), -1

							__UpperCamelCase			:     int | float         =      0
							for i in range(_lowerCamelCase		,						low - 1		,						-1):
														summ += arr[i]
														if summ > left_sum:
																					__UpperCamelCase			:     Tuple         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							__UpperCamelCase			:     Union[str, Any]         =      0
							for i in range(mid + 1		,						high + 1):
														summ += arr[i]
														if summ > right_sum:
																					__UpperCamelCase			:     Union[str, Any]         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							return max_left, max_right, (left_sum + right_sum)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					float:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      [randint(1		,						_lowerCamelCase) for _ in range(_lowerCamelCase)]
							__UpperCamelCase			:     Optional[Any]         =      time.time()
							max_subarray(_lowerCamelCase		,						0		,						input_size - 1)
							__UpperCamelCase			:     Any         =      time.time()
							return end - start




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
							__UpperCamelCase			:     Union[str, Any]         =      [time_max_subarray(_lowerCamelCase) for input_size in input_sizes]
							print("No of Inputs\t\tTime Taken")
							for input_size, runtime in zip(_lowerCamelCase		,						_lowerCamelCase):
														print(_lowerCamelCase		,						"\t\t"		,						_lowerCamelCase)
							plt.plot(_lowerCamelCase		,						_lowerCamelCase)
							plt.xlabel("Number of Inputs")
							plt.ylabel("Time taken in seconds")
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()