'''simple docstring'''





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

import black


SCREAMING_SNAKE_CASE_:					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.
SCREAMING_SNAKE_CASE_:					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 	__A				(			unittest.TestCase     ):



    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir       ,      "models/bert/"			)			)
         UpperCAmelCase_	   =       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  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       "src/transformers"
         shutil.rmtree(self.transformer_dir			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Union[str, Any]       ,      __a   :   str       ,      __a   :   Tuple       ,      __a   :   Union[str, Any]=None			):
         UpperCAmelCase_	   =       comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
         if overwrite_result is not None:
              UpperCAmelCase_	   =       comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
         UpperCAmelCase_	   =       black.Mode(target_versions={black.TargetVersion.PYaa}       ,      line_length=119			)
         UpperCAmelCase_	   =       black.format_str(__a       ,      mode=__a			)
         UpperCAmelCase_	   =       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  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"			)
         self.assertEqual(__a       ,      __a			)



    def  _lowercase						(self   :   List[Any]			):
         # 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_	   =       "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  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       check_copies.LOCALIZED_READMES["README_zh-hans.md"]

         UpperCAmelCase_	   =       (
             "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_	   =       (
             "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_	   =       (
             "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_   ,			UpperCAmelCase_	   =       check_copies.convert_to_localized_md(
             __a       ,      __a       ,      localized_readme["format_model_list"]			)

         self.assertFalse(__a			)
         self.assertEqual(__a       ,      __a			)

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       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_	   =       (
             "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_	   =       (
             "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_	   =       (
             "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_   ,			UpperCAmelCase_	   =       check_copies.convert_to_localized_md(
             __a       ,      __a       ,      localized_readme["format_model_list"]			)

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





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

         UpperCAmelCase_	   =       (image / 2 + 0.5).clamp(0       ,      1			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




