


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def  SCREAMING_SNAKE_CASE_		(    __A				:    Namespace       )			->	Optional[Any]:

       """simple docstring"""

       return ConvertCommand(
           args.model_type						,					args.tf_checkpoint						,					args.pytorch_dump_output						,					args.config						,					args.finetuning_task_name       )


UpperCAmelCase_   :				List[str]						      = '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : ArgumentParser  )	->    Any:
             a_      :	Any							=						parser.add_parser(
                 'convert'  ,							help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'  ,							)
             train_parser.add_argument('--model_type'  ,							type=SCREAMING_SNAKE_CASE__  ,							required=SCREAMING_SNAKE_CASE__  ,							help='Model\'s type.'  )
             train_parser.add_argument(
                 '--tf_checkpoint'  ,							type=SCREAMING_SNAKE_CASE__  ,							required=SCREAMING_SNAKE_CASE__  ,							help='TensorFlow checkpoint path or folder.'  )
             train_parser.add_argument(
                 '--pytorch_dump_output'  ,							type=SCREAMING_SNAKE_CASE__  ,							required=SCREAMING_SNAKE_CASE__  ,							help='Path to the PyTorch saved model output.'  )
             train_parser.add_argument('--config'  ,							type=SCREAMING_SNAKE_CASE__  ,							default=''  ,							help='Configuration file path or folder.'  )
             train_parser.add_argument(
                 '--finetuning_task_name'  ,							type=SCREAMING_SNAKE_CASE__  ,							default=SCREAMING_SNAKE_CASE__  ,							help='Optional fine-tuning task name if the TF model was a finetuned model.'  ,							)
             train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__  )
      def __init__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							*SCREAMING_SNAKE_CASE__       : str  ,							)	->    Optional[Any]:
             a_      :	Any							=						logging.get_logger('transformers-cli/converting'  )

             self._logger.info(F"""Loading model {model_type}"""  )
             a_      :	Dict							=						model_type
             a_      :	Dict							=						tf_checkpoint
             a_      :	Dict							=						pytorch_dump_output
             a_      :	Dict							=						config
             a_      :	Optional[int]							=						finetuning_task_name

      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Union[str, Any]:
             if self._model_type == "albert":
                    try:
                           from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                               convert_tf_checkpoint_to_pytorch,
                           )
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             elif self._model_type == "bert":
                    try:
                           from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                               convert_tf_checkpoint_to_pytorch,
                           )
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             elif self._model_type == "funnel":
                    try:
                           from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                               convert_tf_checkpoint_to_pytorch,
                           )
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             elif self._model_type == "t5":
                    try:
                           from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    convert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             elif self._model_type == "gpt":
                    from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                        convert_openai_checkpoint_to_pytorch,
                    )

                    convert_openai_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             elif self._model_type == "transfo_xl":
                    try:
                           from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                               convert_transfo_xl_checkpoint_to_pytorch,
                           )
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    if "ckpt" in self._tf_checkpoint.lower():
                           a_      :	Optional[int]							=						self._tf_checkpoint
                           a_      :	Optional[Any]							=						''
                    else:
                           a_      :	Tuple							=						self._tf_checkpoint
                           a_      :	Union[str, Any]							=						''
                    convert_transfo_xl_checkpoint_to_pytorch(
                        SCREAMING_SNAKE_CASE__  ,							self._config  ,							self._pytorch_dump_output  ,							SCREAMING_SNAKE_CASE__  )
             elif self._model_type == "gpt2":
                    try:
                           from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                               convert_gpta_checkpoint_to_pytorch,
                           )
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             elif self._model_type == "xlnet":
                    try:
                           from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                               convert_xlnet_checkpoint_to_pytorch,
                           )
                    except ImportError:
                           raise ImportError(SCREAMING_SNAKE_CASE__  )

                    convert_xlnet_checkpoint_to_pytorch(
                        self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  ,							self._finetuning_task_name  )
             elif self._model_type == "xlm":
                    from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                        convert_xlm_checkpoint_to_pytorch,
                    )

                    convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._pytorch_dump_output  )
             elif self._model_type == "lxmert":
                    from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                        convert_lxmert_checkpoint_to_pytorch,
                    )

                    convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._pytorch_dump_output  )
             elif self._model_type == "rembert":
                    from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                        convert_rembert_tf_checkpoint_to_pytorch,
                    )

                    convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint  ,							self._config  ,							self._pytorch_dump_output  )
             else:
                    raise ValueError(
                        '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'  )






import argparse
import os
import re


UpperCAmelCase_   :				List[Any]						      = 'src/transformers'

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]       )			->	Optional[Any]:

       """simple docstring"""

       a_      :	List[Any]							=						_re_indent.search(__A       )
       return "" if search is None else search.groups()[0]







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]=""						,					__A				:    Dict=None						,					__A				:    Dict=None       )			->	int:

       """simple docstring"""

       a_      :	Tuple							=						0
       a_      :	Dict							=						code.split('\n'       )
       if start_prompt is not None:
              while not lines[index].startswith(__A       ):
                     index += 1
              a_      :	List[Any]							=						['\n'.join(lines[:index]       )]
       else:
              a_      :	List[str]							=						[]

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

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

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

       return blocks







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict       )			->	Any:

       """simple docstring"""

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

       return _inner







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    List[Any]=None       )			->	List[str]:

       """simple docstring"""

       def noop(__A				:    Tuple       ):
              return x

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

       a_      :	Optional[Any]							=						ignore_underscore(__A       )
       return sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Optional[int]:

       """simple docstring"""

       def _replace(__A				:    List[Any]       ):
              a_      :	Tuple							=						match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              a_      :	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:
                     a_      :	str							=						keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__A       )]       ) + "]"

       a_      :	Optional[int]							=						import_statement.split('\n'       )
       if len(__A       ) > 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.
              a_      :	int							=						2 if lines[1].strip() == '[' else 1
              a_      :	int							=						[(i, _re_strip_line.search(__A       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
              a_      :	str							=						sort_objects(__A						,					key=lambda __A       : x[1]       )
              a_      :	int							=						[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
       elif len(__A       ) == 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:
                     a_      :	str							=						_re_bracket_content.sub(_replace						,					lines[1]       )
              else:
                     a_      :	List[str]							=						[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:
                            a_      :	Optional[int]							=						keys[:-1]
                     a_      :	Any							=						get_indent(lines[1]       ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(__A       )]       )
              return "\n".join(__A       )
       else:
              # Finally we have to deal with imports fitting on one line
              a_      :	Union[str, Any]							=						_re_bracket_content.sub(_replace						,					__A       )
              return import_statement







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any=True       )			->	Union[str, Any]:

       """simple docstring"""

       with open(__A						,					encoding='utf-8'       ) as f:
              a_      :	Union[str, Any]							=						f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_      :	Optional[Any]							=						split_code_in_indented_blocks(
           __A						,					start_prompt='_import_structure = {'						,					end_prompt='if TYPE_CHECKING:'       )

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

              # Get to the start of the imports.
              a_      :	Any							=						0
              while line_idx < len(__A       ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_      :	Tuple							=						len(__A       )
                     else:
                            line_idx += 1
              if line_idx >= len(__A       ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_      :	List[str]							=						'\n'.join(block_lines[line_idx:-1]       )
              a_      :	List[Any]							=						get_indent(block_lines[1]       )
              # Slit the internal block into blocks of indent level 1.
              a_      :	Optional[int]							=						split_code_in_indented_blocks(__A						,					indent_level=__A       )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_      :	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.
              a_      :	Union[str, Any]							=						[(pattern.search(__A       ).groups()[0] if pattern.search(__A       ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_      :	str							=						[(i, key) for i, key in enumerate(__A       ) if key is not None]
              a_      :	Optional[int]							=						[x[0] for x in sorted(__A						,					key=lambda __A       : x[1]       )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_      :	int							=						0
              a_      :	int							=						[]
              for i in range(len(__A       )       ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]       )
                     else:
                            a_      :	List[str]							=						sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
                            reorderded_blocks.append(__A       )
                            count += 1

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

       if code != "\n".join(__A       ):
              if check_only:
                     return True
              else:
                     print(F"""Overwriting {file}."""       )
                     with open(__A						,					'w'						,					encoding='utf-8'       ) as f:
                            f.write('\n'.join(__A       )       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]=True       )			->	List[Any]:

       """simple docstring"""

       a_      :	Dict							=						[]
       for root, _, files in os.walk(__A       ):
              if "__init__.py" in files:
                     a_      :	Dict							=						sort_imports(os.path.join(__A						,					'__init__.py'       )						,					check_only=__A       )
                     if result:
                            a_      :	Tuple							=						[os.path.join(__A						,					'__init__.py'       )]
       if len(__A       ) > 0:
              raise ValueError(F"""Would overwrite {len(__A       )} files, run `make style`."""       )


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

   sort_imports_in_all_inits(check_only=args.check_only)


