
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def 				a						(							snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					[
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''_float_tensor''',
				    '''decoder.output_projection.weight''',
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Tuple					):


				'''simple docstring'''
				lowercase_  ,	lowercase_       =					emb.weight.shape
				lowercase_       =					nn.Linear(snake_case__						,   snake_case__						,   bias=snake_case__					)
				lowercase_       =					emb.weight.data
				return lin_layer




def 				a						(							snake_case__:					str						,   snake_case__:					Any="facebook/mbart-large-en-ro"						,   snake_case__:					Optional[Any]=False						,   snake_case__:					Optional[Any]=False					):


				'''simple docstring'''
				lowercase_       =					torch.load(snake_case__						,   map_location='''cpu'''					)['''model''']
				remove_ignore_keys_(snake_case__					)
				lowercase_       =					state_dict['''encoder.embed_tokens.weight'''].shape[0]

				lowercase_       =					MBartConfig.from_pretrained(snake_case__						,   vocab_size=snake_case__					)
				if mbart_aa and finetuned:
								lowercase_       =					'''relu'''

				lowercase_       =					state_dict['''decoder.embed_tokens.weight''']
				lowercase_       =					MBartForConditionalGeneration(snake_case__					)
				model.model.load_state_dict(snake_case__					)

				if finetuned:
								lowercase_       =					make_linear_from_emb(model.model.shared					)

				return model


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
						)
						parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--hf_config',
						    default='facebook/mbart-large-cc25',
						    type=str,
						    help='Which huggingface architecture to use: mbart-large',
						)
						parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
						parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
						__a  			=					parser.parse_args()
						__a  			=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)






import argparse
import os
import re


__a  			=					'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a  			=					re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a  			=					re.compile(r'\s*\(\s*"(\S[^"]+)"')




def 				a						(							snake_case__:					str						,   snake_case__:					bool = False					):


				'''simple docstring'''
				with open(snake_case__						,   '''r'''						,   encoding='''utf-8'''					) as f:
								lowercase_       =					f.read()

				lowercase_       =					content.split('''\n'''					)
				lowercase_       =					[]
				lowercase_       =					0
				while line_idx < len(snake_case__					):
								if _re_intro_mapping.search(lines[line_idx]					) is not None:
												lowercase_       =					len(re.search(r'''^(\s*)\S'''						,   lines[line_idx]					).groups()[0]					) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(''' ''' * indent + '''('''					):
																new_lines.append(lines[line_idx]					)
																line_idx += 1

												lowercase_       =					[]
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				lowercase_       =					line_idx
																				while not lines[line_idx].startswith(''' ''' * indent + ''')'''					):
																								line_idx += 1
																				blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]					)					)
																else:
																				blocks.append(lines[line_idx]					)
																line_idx += 1

												# Sort blocks by their identifiers
												lowercase_       =					sorted(snake_case__						,   key=lambda snake_case__					: _re_identifier.search(snake_case__					).groups()[0]					)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]					)
												line_idx += 1

				if overwrite:
								with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
												f.write('''\n'''.join(snake_case__					)					)
				elif "\n".join(snake_case__					) != content:
								return True




def 				a						(							snake_case__:					bool = False					):


				'''simple docstring'''
				lowercase_       =					[os.path.join(snake_case__						,   snake_case__					) for f in os.listdir(snake_case__					) if f.endswith('''.py'''					)]
				lowercase_       =					[sort_auto_mapping(snake_case__						,   overwrite=snake_case__					) for fname in fnames]

				if not overwrite and any(snake_case__					):
								lowercase_       =					[f for f, d in zip(snake_case__						,   snake_case__					) if d]
								raise ValueError(
								    F'''The following files have auto mappings that need sorting: {', '.join(snake_case__					)}. Run `make style` to fix'''
								    ''' this.'''					)


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

						sort_all_auto_mappings(not args.check_only)




