
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline





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

import black


__magic_name__            =  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.
__magic_name__            =  '''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir		,	"models/bert/" ) )
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				"src/transformers"
          shutil.rmtree(self.transformer_dir )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None ):
          snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
              snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          snake_case__						=				black.Mode(target_versions={black.TargetVersion.PYaa}		,	line_length=1_19 )
          snake_case__						=				black.format_str(lowerCamelCase		,	mode=lowerCamelCase )
          snake_case__						=				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 A_			(			self ):
          snake_case__						=				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          # 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"		,	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
          snake_case__						=				"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 A_			(			self ):
          snake_case__						=				check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          snake_case__						=				(
              "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."
          )
          snake_case__						=				(
              "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"
          )
          snake_case__						=				(
              "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"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          self.assertFalse(lowerCamelCase )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )

          snake_case__						,					snake_case__						=				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 )

          snake_case__						=				(
              "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."
          )
          snake_case__						=				(
              "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"
          )
          snake_case__						=				(
              "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"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

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



