

"""simple docstring"""


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

import black


UpperCAmelCase													=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.
UpperCAmelCase													="    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 UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir ,"""models/bert/"""    )    )
          A				  =		self.transformer_dir
          shutil.copy(
              os.path.join(__snake_case ,"""src/transformers/models/bert/modeling_bert.py"""    ) ,os.path.join(self.transformer_dir ,"""models/bert/modeling_bert.py"""    ) ,)




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		"""src/transformers"""
          shutil.rmtree(self.transformer_dir    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_=None    )    ->      Union[str, 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(__snake_case ,mode=__snake_case    )
          A				  =		os.path.join(self.transformer_dir ,"""new_code.py"""    )
          with open(__snake_case ,"""w""" ,newline="""\n"""    ) as f:
                f.write(__snake_case    )
          if overwrite_result is None:
                self.assertTrue(len(check_copies.is_copy_consistent(__snake_case    )    ) == 0    )
          else:
                check_copies.is_copy_consistent(f.name ,overwrite=__snake_case    )
                with open(__snake_case ,"""r"""    ) as f:
                      self.assertTrue(f.read() ,__snake_case    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
          self.assertEqual(__snake_case ,__snake_case    )




    def UpperCamelCase__		( self    )    ->      List[str]:
          # 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""" ,__snake_case ,)

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

          # 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""" ,__snake_case ,__snake_case    ) ,)

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




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          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  ,			A				  =		check_copies.convert_to_localized_md(
              __snake_case ,__snake_case ,localized_readme["""format_model_list"""]    )

          self.assertFalse(__snake_case    )
          self.assertEqual(__snake_case ,__snake_case    )

          A  ,			A				  =		check_copies.convert_to_localized_md(
              __snake_case ,__snake_case ,localized_readme["""format_model_list"""]    )

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

          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  ,			A				  =		check_copies.convert_to_localized_md(
              __snake_case ,__snake_case ,localized_readme["""format_model_list"""]    )

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











'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   42




class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     snake_case_						      =   1



     @register_to_config
     def __init__(   self							:			str   ,       __snake_case							:			int = 20_00   ,       __snake_case							:			float = 0.15   ,       __snake_case							:			float = 0.01   ,       __snake_case							:			float = 13_48.0   ,       __snake_case							:			float = 1e-5   ,       __snake_case							:			int = 1   ,       )->	str:
        # standard deviation of the initial noise distribution
        snake_case 						=   sigma_max

        # setable values
        snake_case 						=   None

        self.set_sigmas(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[int] = None						)->	torch.FloatTensor:
        return sample



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			Union[str, torch.device] = None						)->	Optional[Any]:
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        snake_case 						=   torch.linspace(1   ,       __snake_case   ,       __snake_case   ,       device=__snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None						)->	str:
        snake_case 						=   sigma_min if sigma_min is not None else self.config.sigma_min
        snake_case 						=   sigma_max if sigma_max is not None else self.config.sigma_max
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(__snake_case   ,       __snake_case						)

        snake_case 						=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        snake_case 						=   torch.exp(torch.linspace(math.log(__snake_case						)   ,       math.log(__snake_case						)   ,       __snake_case						)						)
        snake_case 						=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			List[str]   ,       __snake_case							:			str						)->	Optional[int]:
        return torch.where(
            timesteps == 0   ,       torch.zeros_like(t.to(timesteps.device						)						)   ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device						)   ,       )



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SdeVeOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        snake_case 						=   timestep * torch.ones(
            sample.shape[0]   ,       device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
        snake_case 						=   (timestep * (len(self.timesteps						) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        snake_case 						=   timesteps.to(self.discrete_sigmas.device						)

        snake_case 						=   self.discrete_sigmas[timesteps].to(sample.device						)
        snake_case 						=   self.get_adjacent_sigma(__snake_case   ,       __snake_case						).to(sample.device						)
        snake_case 						=   torch.zeros_like(__snake_case						)
        snake_case 						=   (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        snake_case 						=   diffusion.flatten()
        while len(diffusion.shape						) < len(sample.shape						):
           snake_case 						=   diffusion.unsqueeze(-1						)
        snake_case 						=   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        snake_case 						=   randn_tensor(
            sample.shape   ,       layout=sample.layout   ,       generator=__snake_case   ,       device=sample.device   ,       dtype=sample.dtype						)
        snake_case 						=   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        snake_case 						=   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
           return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=__snake_case   ,       prev_sample_mean=__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SchedulerOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        snake_case 						=   randn_tensor(sample.shape   ,       layout=sample.layout   ,       generator=__snake_case						).to(sample.device						)

        # compute step size from the model_output, the noise, and the snr
        snake_case 						=   torch.norm(model_output.reshape(model_output.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   torch.norm(noise.reshape(noise.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        snake_case 						=   step_size * torch.ones(sample.shape[0]						).to(sample.device						)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        snake_case 						=   step_size.flatten()
        while len(step_size.shape						) < len(sample.shape						):
           snake_case 						=   step_size.unsqueeze(-1						)
        snake_case 						=   sample + step_size * model_output
        snake_case 						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=__snake_case						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       )->	torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        snake_case 						=   timesteps.to(original_samples.device						)
        snake_case 						=   self.discrete_sigmas.to(original_samples.device						)[timesteps]
        snake_case 						=   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__snake_case						) * sigmas[:, None, None, None]
        )
        snake_case 						=   noise + original_samples
        return noisy_samples



     def __len__(   self							:			str						)->	Any:
        return self.config.num_train_timesteps

