


import collections
import importlib.util
import os
import re
from pathlib import Path


lowerCAmelCase           =		'src/transformers'


# Matches is_xxx_available()
lowerCAmelCase           =		re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase           =		re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase           =		re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowerCAmelCase           =		re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase           =		re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase           =		re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCAmelCase           =		re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCAmelCase           =		re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase           =		re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowerCAmelCase           =		re.compile(R'''^\s*try:''')
# Catches a line with else:
lowerCAmelCase           =		re.compile(R'''^\s*else:''')







def        _lowerCamelCase(	lowercase__	)						->	Optional[int]:

		'''simple docstring'''







		if _re_test_backend.search(lowercase__	) is None:
				return None
		__lowercase=			[b[0] for b in _re_backend.findall(lowercase__	)]
		backends.sort()
		return "_and_".join(lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Optional[Any]:

		'''simple docstring'''







		with open(lowercase__   , 'r'   , encoding='utf-8'   , newline='\n'	) as f:
				__lowercase=			f.readlines()

		__lowercase=			0
		while line_index < len(lowercase__	) and not lines[line_index].startswith('_import_structure = {'	):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(lowercase__	):
				return None

		# First grab the objects without a specific backend in _import_structure
		__lowercase=			[]
		while not lines[line_index].startswith('if TYPE_CHECKING'	) and find_backend(lines[line_index]	) is None:
				__lowercase=			lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(lowercase__	):
						__lowercase=			_re_one_line_import_struct.search(lowercase__	).groups()[0]
						__lowercase=			re.findall('\[([^\]]+)\]'   , lowercase__	)
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(', '	)]	)
						line_index += 1
						continue
				__lowercase=			_re_import_struct_key_value.search(lowercase__	)
				if single_line_import_search is not None:
						__lowercase=			[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '	) if len(lowercase__	) > 0]
						objects.extend(lowercase__	)
				elif line.startswith(' ' * 8 + '\"'	):
						objects.append(line[9:-3]	)
				line_index += 1

		__lowercase=			{'none': objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith('if TYPE_CHECKING'	):
				# If the line is an if not is_backend_available, we grab all objects associated.
				__lowercase=			find_backend(lines[line_index]	)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]	) is None:
						__lowercase=			None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]	) is None:
								line_index += 1

						line_index += 1

						__lowercase=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]	) <= 1 or lines[line_index].startswith(' ' * 4	):
								__lowercase=			lines[line_index]
								if _re_import_struct_add_one.search(lowercase__	) is not None:
										objects.append(_re_import_struct_add_one.search(lowercase__	).groups()[0]	)
								elif _re_import_struct_add_many.search(lowercase__	) is not None:
										__lowercase=			_re_import_struct_add_many.search(lowercase__	).groups()[0].split(', '	)
										__lowercase=			[obj[1:-1] for obj in imports if len(lowercase__	) > 0]
										objects.extend(lowercase__	)
								elif _re_between_brackets.search(lowercase__	) is not None:
										__lowercase=			_re_between_brackets.search(lowercase__	).groups()[0].split(', '	)
										__lowercase=			[obj[1:-1] for obj in imports if len(lowercase__	) > 0]
										objects.extend(lowercase__	)
								elif _re_quote_object.search(lowercase__	) is not None:
										objects.append(_re_quote_object.search(lowercase__	).groups()[0]	)
								elif line.startswith(' ' * 8 + '\"'	):
										objects.append(line[9:-3]	)
								elif line.startswith(' ' * 1_2 + '\"'	):
										objects.append(line[1_3:-3]	)
								line_index += 1

						__lowercase=			objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		__lowercase=			[]
		while (
		    line_index < len(lowercase__	)
		    and find_backend(lines[line_index]	) is None
		    and not lines[line_index].startswith('else'	)
		):
				__lowercase=			lines[line_index]
				__lowercase=			_re_import.search(lowercase__	)
				if single_line_import_search is not None:
						objects.extend(single_line_import_search.groups()[0].split(', '	)	)
				elif line.startswith(' ' * 8	):
						objects.append(line[8:-2]	)
				line_index += 1

		__lowercase=			{'none': objects}
		# Let's continue with backend-specific objects
		while line_index < len(lowercase__	):
				# If the line is an if is_backend_available, we grab all objects associated.
				__lowercase=			find_backend(lines[line_index]	)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]	) is None:
						__lowercase=			None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]	) is None:
								line_index += 1

						line_index += 1

						__lowercase=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]	) <= 1 or lines[line_index].startswith(' ' * 8	):
								__lowercase=			lines[line_index]
								__lowercase=			_re_import.search(lowercase__	)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(', '	)	)
								elif line.startswith(' ' * 1_2	):
										objects.append(line[1_2:-2]	)
								line_index += 1

						__lowercase=			objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Tuple:

		'''simple docstring'''







		def find_duplicates(lowercase__	):
				return [k for k, v in collections.Counter(lowercase__	).items() if v > 1]

		if list(import_dict_objects.keys()	) != list(type_hint_objects.keys()	):
				return ["Both sides of the init do not have the same backends!"]

		__lowercase=			[]
		for key in import_dict_objects.keys():
				__lowercase=			find_duplicates(import_dict_objects[key]	)
				if duplicate_imports:
						errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'	)
				__lowercase=			find_duplicates(type_hint_objects[key]	)
				if duplicate_type_hints:
						errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'	)

				if sorted(set(import_dict_objects[key]	)	) != sorted(set(type_hint_objects[key]	)	):
						__lowercase=			'base imports' if key == 'none' else F'{key} backend'
						errors.append(F'Differences for {name}:'	)
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(F'  {a} in TYPE_HINT but not in _import_structure.'	)
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(F'  {a} in _import_structure but not in TYPE_HINT.'	)
		return errors







def        _lowerCamelCase(	)						->	Tuple:

		'''simple docstring'''







		__lowercase=			[]
		for root, _, files in os.walk(lowercase__	):
				if "__init__.py" in files:
						__lowercase=			os.path.join(lowercase__   , '__init__.py'	)
						__lowercase=			parse_init(lowercase__	)
						if objects is not None:
								__lowercase=			analyze_results(*lowercase__	)
								if len(lowercase__	) > 0:
										__lowercase=			F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
										failures.append('\n'.join(lowercase__	)	)
		if len(lowercase__	) > 0:
				raise ValueError('\n\n'.join(lowercase__	)	)







def        _lowerCamelCase(	)						->	int:

		'''simple docstring'''







		__lowercase=			[]
		for path, directories, files in os.walk(lowercase__	):
				for folder in directories:
						# Ignore private modules
						if folder.startswith('_'	):
								directories.remove(lowercase__	)
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(lowercase__	) / folder).glob('*.py'	)	)	) == 0:
								continue
						__lowercase=			str((Path(lowercase__	) / folder).relative_to(lowercase__	)	)
						__lowercase=			short_path.replace(os.path.sep   , '.'	)
						submodules.append(lowercase__	)
				for fname in files:
						if fname == "__init__.py":
								continue
						__lowercase=			str((Path(lowercase__	) / fname).relative_to(lowercase__	)	)
						__lowercase=			short_path.replace('.py'   , ''	).replace(os.path.sep   , '.'	)
						if len(submodule.split('.'	)	) == 1:
								submodules.append(lowercase__	)
		return submodules


lowerCAmelCase           =		[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]







def        _lowerCamelCase(	)						->	Dict:

		'''simple docstring'''







		__lowercase=			importlib.util.spec_from_file_location(
		    'transformers'   , os.path.join(lowercase__   , '__init__.py'	)   , submodule_search_locations=[PATH_TO_TRANSFORMERS]   , )
		__lowercase=			spec.loader.load_module()

		__lowercase=			[
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
		]
		if len(lowercase__	) > 0:
				__lowercase=			'\n'.join(F'- {module}' for module in module_not_registered	)
				raise ValueError(
				    'The following submodules are not properly registered in the main init of Transformers:\n'
				    F'{list_of_modules}\n'
				    'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'	)


if __name__ == "__main__":
	check_all_inits()
	check_submodules()













"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


