
"""simple docstring"""


import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase							   =				'''src/diffusers'''
_lowercase							   =				'''.'''


# This is to make sure the diffusers module imported is the one in the repo.
_lowercase							   =				importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_lowercase							   =				spec.loader.load_module()






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     List[str]     ):
	return line.startswith(lowerCAmelCase__     ) or len(lowerCAmelCase__     ) <= 1 or re.search(r'^\s*\)(\s*->.*:|:)\s*$'	,      lowerCAmelCase__     ) is not None






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	A						   =   object_name.split('.'     )
	A						   =   0

	# First let's find the module where our object lives.
	A						   =   parts[i]
	while i < len(lowerCAmelCase__     ) and not os.path.isfile(os.path.join(lowerCAmelCase__	,      F'{module}.py'     )     ):
		i += 1
		if i < len(lowerCAmelCase__     ):
			A						   =   os.path.join(lowerCAmelCase__	,      parts[i]     )
	if i >= len(lowerCAmelCase__     ):
		raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'     )

	with open(os.path.join(lowerCAmelCase__	,      F'{module}.py'     )	,      'r'	,      encoding='utf-8'	,      newline='\n'     ) as f:
		A						   =   f.readlines()

	# Now let's find the class / func in the code!
	A						   =   ''
	A						   =   0
	for name in parts[i + 1 :]:
		while (
		    line_index < len(lowerCAmelCase__     ) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)'	,      lines[line_index]     ) is None
		):
			line_index += 1
		indent += "    "
		line_index += 1

	if line_index >= len(lowerCAmelCase__     ):
		raise ValueError(F' {object_name} does not match any function or class in {module}.'     )

	# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
	A						   =   line_index
	while line_index < len(lowerCAmelCase__     ) and _should_continue(lines[line_index]	,      lowerCAmelCase__     ):
		line_index += 1
	# Clean up empty lines at the end (if any).
	while len(lines[line_index - 1]     ) <= 1:
		line_index -= 1

	A						   =   lines[start_index:line_index]
	return "".join(lowerCAmelCase__     )


_lowercase							   =				re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
_lowercase							   =				re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
_lowercase							   =				re.compile(r"""<FILL\s+[^>]*>""")






def       _snake_case				(		snake_case__   :     Any     ):
	A						   =   code.split('\n'     )
	A						   =   0
	while idx < len(lowerCAmelCase__     ) and len(lines[idx]     ) == 0:
		idx += 1
	if idx < len(lowerCAmelCase__     ):
		return re.search(r'^(\s*)\S'	,      lines[idx]     ).groups()[0]
	return ""






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	A						   =   len(get_indent(lowerCAmelCase__     )     ) > 0
	if has_indent:
		A						   =   F'class Bla:\n{code}'
	A						   =   black.Mode(target_versions={black.TargetVersion.PYaa}	,      line_length=119	,      preview=lowerCAmelCase__     )
	A						   =   black.format_str(lowerCAmelCase__	,      mode=lowerCAmelCase__     )
	A   ,      A						   =   style_docstrings_in_code(lowerCAmelCase__     )
	return result[len('class Bla:\n'     ) :] if has_indent else result






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str=False     ):
	with open(lowerCAmelCase__	,      'r'	,      encoding='utf-8'	,      newline='\n'     ) as f:
		A						   =   f.readlines()
	A						   =   []
	A						   =   0
	# Not a for loop cause `lines` is going to change (if `overwrite=True`).
	while line_index < len(lowerCAmelCase__     ):
		A						   =   _re_copy_warning.search(lines[line_index]     )
		if search is None:
			line_index += 1
			continue

		# There is some copied code here, let's retrieve the original.
		A   ,      A   ,      A						   =   search.groups()
		A						   =   find_code_in_diffusers(lowerCAmelCase__     )
		A						   =   get_indent(lowerCAmelCase__     )

		A						   =   line_index + 1 if indent == theoretical_indent else line_index + 2
		A						   =   theoretical_indent
		A						   =   start_index

		# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
		A						   =   True
		while line_index < len(lowerCAmelCase__     ) and should_continue:
			line_index += 1
			if line_index >= len(lowerCAmelCase__     ):
				break
			A						   =   lines[line_index]
			A						   =   _should_continue(lowerCAmelCase__	,      lowerCAmelCase__     ) and re.search(F'^{indent}# End copy'	,      lowerCAmelCase__     ) is None
		# Clean up empty lines at the end (if any).
		while len(lines[line_index - 1]     ) <= 1:
			line_index -= 1

		A						   =   lines[start_index:line_index]
		A						   =   ''.join(lowerCAmelCase__     )

		# Remove any nested `Copied from` comments to avoid circular copies
		A						   =   [line for line in theoretical_code.split('\n'     ) if _re_copy_warning.search(lowerCAmelCase__     ) is None]
		A						   =   '\n'.join(lowerCAmelCase__     )

		# Before comparing, use the `replace_pattern` on the original code.
		if len(lowerCAmelCase__     ) > 0:
			A						   =   replace_pattern.replace('with'	,      ''     ).split(','     )
			A						   =   [_re_replace_pattern.search(lowerCAmelCase__     ) for p in patterns]
			for pattern in patterns:
				if pattern is None:
					continue
				A   ,      A   ,      A						   =   pattern.groups()
				A						   =   re.sub(lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__     )
				if option.strip() == "all-casing":
					A						   =   re.sub(obja.lower()	,      obja.lower()	,      lowerCAmelCase__     )
					A						   =   re.sub(obja.upper()	,      obja.upper()	,      lowerCAmelCase__     )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
			A						   =   blackify(lines[start_index - 1] + theoretical_code     )
			A						   =   theoretical_code[len(lines[start_index - 1]     ) :]

		# Test for a diff and act accordingly.
		if observed_code != theoretical_code:
			diffs.append([object_name, start_index]     )
			if overwrite:
				A						   =   lines[:start_index] + [theoretical_code] + lines[line_index:]
				A						   =   start_index + 1

	if overwrite and len(lowerCAmelCase__     ) > 0:
		# Warn the user a file has been modified.
		print(F'Detected changes, rewriting {filename}.'     )
		with open(lowerCAmelCase__	,      'w'	,      encoding='utf-8'	,      newline='\n'     ) as f:
			f.writelines(lowerCAmelCase__     )
	return diffs






def       _snake_case				(		snake_case__   :     Optional[int] = False     ):
	A						   =   glob.glob(os.path.join(lowerCAmelCase__	,      '**/*.py'     )	,      recursive=lowerCAmelCase__     )
	A						   =   []
	for filename in all_files:
		A						   =   is_copy_consistent(lowerCAmelCase__	,      lowerCAmelCase__     )
		diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
	if not overwrite and len(lowerCAmelCase__     ) > 0:
		A						   =   '\n'.join(lowerCAmelCase__     )
		raise Exception(
		    'Found the following copy inconsistencies:\n'
		    + diff
		    + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
		_lowercase							   =				parser.parse_args()

		check_copies(args.fix_and_overwrite)

"""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
_lowercase							   =				data_utils.TransfoXLTokenizer
_lowercase							   =				data_utils.TransfoXLCorpus
_lowercase							   =				data_utils
_lowercase							   =				data_utils






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

		A						   =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab'	,      snake_case__     )
		A						   =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F'Save dataset to {pytorch_dataset_dump_path}'     )
		torch.save(snake_case__	,      snake_case__     )

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		A						   =   os.path.abspath(snake_case__     )
		A						   =   os.path.abspath(snake_case__     )

		print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'     )
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			A						   =   TransfoXLConfig()
		else:
			A						   =   TransfoXLConfig.from_json_file(snake_case__     )
		print(F'Building PyTorch model from configuration: {config}'     )
		A						   =   TransfoXLLMHeadModel(snake_case__     )

		A						   =   load_tf_weights_in_transfo_xl(snake_case__	,      snake_case__	,      snake_case__     )
		# Save pytorch-model
		A						   =   os.path.join(snake_case__	,      snake_case__     )
		A						   =   os.path.join(snake_case__	,      snake_case__     )
		print(F'Save PyTorch model to {os.path.abspath(snake_case__     )}'     )
		torch.save(model.state_dict()	,      snake_case__     )
		print(F'Save configuration file to {os.path.abspath(snake_case__     )}'     )
		with open(snake_case__	,      'w'	,      encoding='utf-8'     ) as f:
			f.write(config.to_json_string()     )


if __name__ == "__main__":
		_lowercase							   =				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.''',
		)
		_lowercase							   =				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,
		)