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

import black


lowerCamelCase_    :		Optional[Any]							=	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.
lowerCamelCase_    :		List[Any]							=	"    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 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,						'models/bert/'   )   )
								__a		 =       self.transformer_dir
								shutil.copy(
								    os.path.join(lowerCamelCase__				,						'src/transformers/models/bert/modeling_bert.py'   )				,						os.path.join(self.transformer_dir				,						'models/bert/modeling_bert.py'   )				,						)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       '''src/transformers'''
								shutil.rmtree(self.transformer_dir   )



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
								self.assertEqual(lowerCamelCase__				,						lowerCamelCase__   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								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'				,						lowerCamelCase__				,						)

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

								# Copy consistency with a really long name
								__a		 =       '''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'				,						lowerCamelCase__				,						lowerCamelCase__   )				,						)

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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

								__a		 =       (
								    '''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.'''
								)
								__a		 =       (
								    '''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'''
								)
								__a		 =       (
								    '''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'''
								)

								__a		 =       check_copies.convert_to_localized_md(
								    lowerCamelCase__				,						lowerCamelCase__				,						localized_readme['format_model_list']   )

								self.assertFalse(lowerCamelCase__   )
								self.assertEqual(lowerCamelCase__				,						lowerCamelCase__   )

								__a		 =       check_copies.convert_to_localized_md(
								    lowerCamelCase__				,						lowerCamelCase__				,						localized_readme['format_model_list']   )

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

								__a		 =       (
								    '''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.'''
								)
								__a		 =       (
								    '''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'''
								)
								__a		 =       (
								    '''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'''
								)

								__a		 =       check_copies.convert_to_localized_md(
								    lowerCamelCase__				,						lowerCamelCase__				,						localized_readme['format_model_list']   )

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





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
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




