


'''simple docstring'''
import argparse
import os
import re


UpperCamelCase__          = '''src/diffusers'''

# Pattern that looks at the indentation in a line.
UpperCamelCase__          = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase__          = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCamelCase__          = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCamelCase__          = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCamelCase__          = re.compile(R'''\[([^\]]+)\]''')





def    a__			(   lowerCAmelCase__		)       ->      Optional[Any]:
    UpperCAmelCase__     :						Optional[int]						=							_re_indent.search(lowerCAmelCase__		)
    return "" if search is None else search.groups()[0]





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=""    ,   lowerCAmelCase__=None    ,   lowerCAmelCase__=None		)       ->      List[Any]:
    UpperCAmelCase__     :						int						=							0
    UpperCAmelCase__     :						Union[str, Any]						=							code.split('''\n'''		)
    if start_prompt is not None:
        while not lines[index].startswith(lowerCAmelCase__		):
            index += 1
        UpperCAmelCase__     :						Any						=							['''\n'''.join(lines[:index]		)]
    else:
        UpperCAmelCase__     :						Union[str, Any]						=							[]

    # We split into blocks until we get to the `end_prompt` (or the end of the block).
    UpperCAmelCase__     :						int						=							[lines[index]]
    index += 1
    while index < len(lowerCAmelCase__		) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__		)):
        if len(lines[index]		) > 0 and get_indent(lines[index]		) == indent_level:
            if len(lowerCAmelCase__		) > 0 and get_indent(current_block[-1]		).startswith(indent_level + ''' '''		):
                current_block.append(lines[index]		)
                blocks.append('''\n'''.join(lowerCAmelCase__		)		)
                if index < len(lowerCAmelCase__		) - 1:
                    UpperCAmelCase__     :						str						=							[lines[index + 1]]
                    index += 1
                else:
                    UpperCAmelCase__     :						Optional[int]						=							[]
            else:
                blocks.append('''\n'''.join(lowerCAmelCase__		)		)
                UpperCAmelCase__     :						Dict						=							[lines[index]]
        else:
            current_block.append(lines[index]		)
        index += 1

    # Adds current block if it's nonempty.
    if len(lowerCAmelCase__		) > 0:
        blocks.append('''\n'''.join(lowerCAmelCase__		)		)

    # Add final block after end_prompt if provided.
    if end_prompt is not None and index < len(lowerCAmelCase__		):
        blocks.append('''\n'''.join(lines[index:]		)		)

    return blocks





def    a__			(   lowerCAmelCase__		)       ->      List[Any]:

    def _inner(lowerCAmelCase__		):
        return key(lowerCAmelCase__		).lower().replace('''_'''    ,   ''''''		)

    return _inner





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=None		)       ->      int:

    # If no key is provided, we use a noop.
    def noop(lowerCAmelCase__		):
        return x

    if key is None:
        UpperCAmelCase__     :						Tuple						=							noop
    # Constants are all uppercase, they go first.
    UpperCAmelCase__     :						Optional[Any]						=							[obj for obj in objects if key(lowerCAmelCase__		).isupper()]
    # Classes are not all uppercase but start with a capital, they go second.
    UpperCAmelCase__     :						int						=							[obj for obj in objects if key(lowerCAmelCase__		)[0].isupper() and not key(lowerCAmelCase__		).isupper()]
    # Functions begin with a lowercase, they go last.
    UpperCAmelCase__     :						Dict						=							[obj for obj in objects if not key(lowerCAmelCase__		)[0].isupper()]

    UpperCAmelCase__     :						Optional[Any]						=							ignore_underscore(lowerCAmelCase__		)
    return sorted(lowerCAmelCase__    ,   key=lowerCAmelCase__		) + sorted(lowerCAmelCase__    ,   key=lowerCAmelCase__		) + sorted(lowerCAmelCase__    ,   key=lowerCAmelCase__		)





def    a__			(   lowerCAmelCase__		)       ->      Any:

    # This inner function sort imports between [ ].
    def _replace(lowerCAmelCase__		):
        UpperCAmelCase__     :						Optional[int]						=							match.groups()[0]
        if "," not in imports:
            return F"""[{imports}]"""
        UpperCAmelCase__     :						Optional[int]						=							[part.strip().replace('''"'''    ,   ''''''		) for part in imports.split(''','''		)]
        # We will have a final empty element if the line finished with a comma.
        if len(keys[-1]		) == 0:
            UpperCAmelCase__     :						Tuple						=							keys[:-1]
        return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(lowerCAmelCase__		)]		) + "]"

    UpperCAmelCase__     :						Tuple						=							import_statement.split('''\n'''		)
    if len(lowerCAmelCase__		) > 3:
        # Here we have to sort internal imports that are on several lines (one per name):
        # key: [
        #     "object1",
        #     "object2",
        #     ...
        # ]

        # We may have to ignore one or two lines on each side.
        UpperCAmelCase__     :						Dict						=							2 if lines[1].strip() == '''[''' else 1
        UpperCAmelCase__     :						int						=							[(i, _re_strip_line.search(lowerCAmelCase__		).groups()[0]) for i, line in enumerate(lines[idx:-idx]		)]
        UpperCAmelCase__     :						List[str]						=							sort_objects(lowerCAmelCase__    ,   key=lambda lowerCAmelCase__		: x[1]		)
        UpperCAmelCase__     :						Optional[Any]						=							[lines[x[0] + idx] for x in sorted_indices]
        return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]		)
    elif len(lowerCAmelCase__		) == 3:
        # Here we have to sort internal imports that are on one separate line:
        # key: [
        #     "object1", "object2", ...
        # ]
        if _re_bracket_content.search(lines[1]		) is not None:
            UpperCAmelCase__     :						Optional[Any]						=							_re_bracket_content.sub(_replace    ,   lines[1]		)
        else:
            UpperCAmelCase__     :						Union[str, Any]						=							[part.strip().replace('''"'''    ,   ''''''		) for part in lines[1].split(''','''		)]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]		) == 0:
                UpperCAmelCase__     :						List[str]						=							keys[:-1]
            UpperCAmelCase__     :						str						=							get_indent(lines[1]		) + ''', '''.join([F"""\"{k}\"""" for k in sort_objects(lowerCAmelCase__		)]		)
        return "\n".join(lowerCAmelCase__		)
    else:
        # Finally we have to deal with imports fitting on one line
        UpperCAmelCase__     :						Any						=							_re_bracket_content.sub(_replace    ,   lowerCAmelCase__		)
        return import_statement





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=True		)       ->      Tuple:
    with open(lowerCAmelCase__    ,   '''r'''		) as f:
        UpperCAmelCase__     :						Dict						=							f.read()

    if "_import_structure" not in code:
        return

    # Blocks of indent level 0
    UpperCAmelCase__     :						Tuple						=							split_code_in_indented_blocks(
        lowerCAmelCase__    ,   start_prompt='''_import_structure = {'''    ,   end_prompt='''if TYPE_CHECKING:'''		)

    # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
    for block_idx in range(1    ,   len(lowerCAmelCase__		) - 1		):
        # Check if the block contains some `_import_structure`s thingy to sort.
        UpperCAmelCase__     :						List[str]						=							main_blocks[block_idx]
        UpperCAmelCase__     :						Tuple						=							block.split('''\n'''		)

        # Get to the start of the imports.
        UpperCAmelCase__     :						List[str]						=							0
        while line_idx < len(lowerCAmelCase__		) and "_import_structure" not in block_lines[line_idx]:
            # Skip dummy import blocks
            if "import dummy" in block_lines[line_idx]:
                UpperCAmelCase__     :						Dict						=							len(lowerCAmelCase__		)
            else:
                line_idx += 1
        if line_idx >= len(lowerCAmelCase__		):
            continue

        # Ignore beginning and last line: they don't contain anything.
        UpperCAmelCase__     :						List[Any]						=							'''\n'''.join(block_lines[line_idx:-1]		)
        UpperCAmelCase__     :						Tuple						=							get_indent(block_lines[1]		)
        # Slit the internal block into blocks of indent level 1.
        UpperCAmelCase__     :						List[Any]						=							split_code_in_indented_blocks(lowerCAmelCase__    ,   indent_level=lowerCAmelCase__		)
        # We have two categories of import key: list or _import_structure[key].append/extend
        UpperCAmelCase__     :						Tuple						=							_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
        # Grab the keys, but there is a trap: some lines are empty or just comments.
        UpperCAmelCase__     :						Tuple						=							[(pattern.search(lowerCAmelCase__		).groups()[0] if pattern.search(lowerCAmelCase__		) is not None else None) for b in internal_blocks]
        # We only sort the lines with a key.
        UpperCAmelCase__     :						Any						=							[(i, key) for i, key in enumerate(lowerCAmelCase__		) if key is not None]
        UpperCAmelCase__     :						Dict						=							[x[0] for x in sorted(lowerCAmelCase__    ,   key=lambda lowerCAmelCase__		: x[1]		)]

        # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
        UpperCAmelCase__     :						Tuple						=							0
        UpperCAmelCase__     :						Tuple						=							[]
        for i in range(len(lowerCAmelCase__		)		):
            if keys[i] is None:
                reordered_blocks.append(internal_blocks[i]		)
            else:
                UpperCAmelCase__     :						Any						=							sort_objects_in_import(internal_blocks[sorted_indices[count]]		)
                reordered_blocks.append(lowerCAmelCase__		)
                count += 1

        # And we put our main block back together with its first and last line.
        UpperCAmelCase__     :						Union[str, Any]						=							'''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]		)

    if code != "\n".join(lowerCAmelCase__		):
        if check_only:
            return True
        else:
            print(F"""Overwriting {file}."""		)
            with open(lowerCAmelCase__    ,   '''w'''		) as f:
                f.write('''\n'''.join(lowerCAmelCase__		)		)





def    a__			(   lowerCAmelCase__=True		)       ->      Optional[Any]:
    UpperCAmelCase__     :						str						=							[]
    for root, _, files in os.walk(lowerCAmelCase__		):
        if "__init__.py" in files:
            UpperCAmelCase__     :						Optional[int]						=							sort_imports(os.path.join(lowerCAmelCase__    ,   '''__init__.py'''		)    ,   check_only=lowerCAmelCase__		)
            if result:
                UpperCAmelCase__     :						int						=							[os.path.join(lowerCAmelCase__    ,   '''__init__.py'''		)]
    if len(lowerCAmelCase__		) > 0:
        raise ValueError(F"""Would overwrite {len(lowerCAmelCase__		)} files, run `make style`."""		)


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()
  parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
  UpperCamelCase__          = parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)



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

import black


lowercase_   							=  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_   							=  '    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				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir				,  'models/bert/'   )   )
        _lowercase      			=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   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			='src/transformers'
        shutil.rmtree(self.transformer_dir   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=None   )    ->					str:
        '''simple docstring'''






        _lowercase      			=comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
              _lowercase      			=comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        _lowercase      			=black.Mode(target_versions={black.TargetVersion.PYaa}				,  line_length=119   )
        _lowercase      			=black.format_str(lowerCAmelCase				,  mode=lowerCAmelCase   )
        _lowercase      			=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   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        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
        _lowercase      			='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   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=check_copies.LOCALIZED_READMES['README_zh-hans.md']

        _lowercase      			=(
            '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.'
        )
        _lowercase      			=(
            '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'
        )
        _lowercase      			=(
            '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'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

        self.assertFalse(lowerCAmelCase   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase			,			_lowercase      			=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   )

        _lowercase      			=(
            '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.'
        )
        _lowercase      			=(
            '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'
        )
        _lowercase      			=(
            '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'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

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


