
"""simple docstring"""


import argparse
import os
import re


_lowercase							      = '''src/diffusers'''

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






def 					_snake_case (						snake_case__      :   Any   ):
	A        =      _re_indent.search(snake_case__   )
	return "" if search is None else search.groups()[0]






def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   Optional[int]=""		,					snake_case__      :   Dict=None		,					snake_case__      :   int=None   ):
	A        =      0
	A        =      code.split('\n'   )
	if start_prompt is not None:
		while not lines[index].startswith(snake_case__   ):
			index += 1
		A        =      ['\n'.join(lines[:index]   )]
	else:
		A        =      []

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

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

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

	return blocks






def 					_snake_case (						snake_case__      :   Optional[Any]   ):

	def _inner(snake_case__      :   Union[str, Any]   ):
		return key(snake_case__   ).lower().replace('_'		,					''   )

	return _inner






def 					_snake_case (						snake_case__      :   int		,					snake_case__      :   int=None   ):

	# If no key is provided, we use a noop.
	def noop(snake_case__      :   Optional[int]   ):
		return x

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

	A        =      ignore_underscore(snake_case__   )
	return sorted(snake_case__		,					key=snake_case__   ) + sorted(snake_case__		,					key=snake_case__   ) + sorted(snake_case__		,					key=snake_case__   )






def 					_snake_case (						snake_case__      :   int   ):

	# This inner function sort imports between [ ].
	def _replace(snake_case__      :   Dict   ):
		A        =      match.groups()[0]
		if "," not in imports:
			return F'[{imports}]'
		A        =      [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        =      keys[:-1]
		return "[" + ", ".join([F'"{k}"' for k in sort_objects(snake_case__   )]   ) + "]"

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






def 					_snake_case (						snake_case__      :   Tuple		,					snake_case__      :   Tuple=True   ):
	with open(snake_case__		,					'r'   ) as f:
		A        =      f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	A        =      split_code_in_indented_blocks(
	    snake_case__		,					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(snake_case__   ) - 1   ):
		# Check if the block contains some `_import_structure`s thingy to sort.
		A        =      main_blocks[block_idx]
		A        =      block.split('\n'   )

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

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

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

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

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






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


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

		sort_imports_in_all_inits(check_only=args.check_only)




'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_UpperCamelCase											=						data_utils.TransfoXLTokenizer
_UpperCamelCase											=						data_utils.TransfoXLCorpus
_UpperCamelCase											=						data_utils
_UpperCamelCase											=						data_utils

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(_lowerCAmelCase       ,'rb'       ) as fp:
      __lowerCamelCase       :       Optional[Any]           = pickle.load(_lowerCAmelCase       ,encoding='latin1'       )
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    __lowerCamelCase       :       Tuple           = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
    print(F'Save vocabulary to {pytorch_vocab_dump_path}'       )
    __lowerCamelCase       :       str           = corpus.vocab.__dict__
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

    __lowerCamelCase       :       Optional[Any]           = corpus.__dict__
    corpus_dict_no_vocab.pop('vocab'       ,_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = pytorch_dump_folder_path + '/' + CORPUS_NAME
    print(F'Save dataset to {pytorch_dataset_dump_path}'       )
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    __lowerCamelCase       :       int           = os.path.abspath(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = os.path.abspath(_lowerCAmelCase       )

    print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'       )
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig()
    else:
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig.from_json_file(_lowerCAmelCase       )
    print(F'Building PyTorch model from configuration: {config}'       )
    __lowerCamelCase       :       List[str]           = TransfoXLLMHeadModel(_lowerCAmelCase       )

    __lowerCamelCase       :       Dict           = load_tf_weights_in_transfo_xl(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    # Save pytorch-model
    __lowerCamelCase       :       List[str]           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    print(F'Save PyTorch model to {os.path.abspath(_lowerCAmelCase       )}'       )
    torch.save(model.state_dict()       ,_lowerCAmelCase       )
    print(F'Save configuration file to {os.path.abspath(_lowerCAmelCase       )}'       )
    with open(_lowerCAmelCase       ,'w'       ,encoding='utf-8'       ) as f:
      f.write(config.to_json_string()       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       required=True,
       help='Path to the folder to store the PyTorch model or dataset/vocab.',
   )
   parser.add_argument(
       '--tf_checkpoint_path',
       default='',
       type=str,
       help='An optional path to a TensorFlow checkpoint path to be converted.',
   )
   parser.add_argument(
       '--transfo_xl_config_file',
       default='',
       type=str,
       help=(
           'An optional config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--transfo_xl_dataset_file',
       default='',
       type=str,
       help='An optional dataset file to be converted in a vocabulary.',
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )






