

import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCAmelCase_       	=	['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase_       	=	{'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
					raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	''' Hello world! cécé herlolip'''

lowerCAmelCase_       	=	[
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''encoder.version''',
						    '''decoder.version''',
						    '''model.encoder.version''',
						    '''model.decoder.version''',
						    '''_float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	dct.pop(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	val


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	torch.load(SCREAMING_SNAKE_CASE__  ,		map_location='''cpu'''       )
						snake_case_   =	torch.hub.load('''pytorch/fairseq'''  ,		'''bart.large.cnn'''       ).eval()
						hub_interface.model.load_state_dict(sd['''model''']       )
						return hub_interface


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_,    snake_case_   =	emb.weight.shape
						snake_case_   =	nn.Linear(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		bias=SCREAMING_SNAKE_CASE__       )
						snake_case_   =	emb.weight.data
						return lin_layer


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None       ):
						if not os.path.exists(SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	torch.hub.load('''pytorch/fairseq'''  ,		SCREAMING_SNAKE_CASE__       ).eval()
						else:
												snake_case_   =	load_xsum_checkpoint(SCREAMING_SNAKE_CASE__       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												snake_case_   =	checkpoint_path.replace('''.'''  ,		'''-'''       )
						snake_case_   =	BartConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	bart.encode(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )
						snake_case_   =	BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       ).encode(SCREAMING_SNAKE_CASE__  ,		return_tensors='''pt'''       ).unsqueeze(0       )
						if not torch.eq(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ).all():
												raise ValueError(
												    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''       )

						if checkpoint_path == "bart.large.mnli":
												snake_case_   =	bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE__       )
												snake_case_   =	state_dict['''model.decoder.embed_tokens.weight''']
												for src, dest in mnli_rename_keys:
																		rename_key(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												snake_case_   =	BartForSequenceClassification(SCREAMING_SNAKE_CASE__       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE__       )
												snake_case_   =	bart.predict('''mnli'''  ,		SCREAMING_SNAKE_CASE__  ,		return_logits=SCREAMING_SNAKE_CASE__       )
												snake_case_   =	model(SCREAMING_SNAKE_CASE__       )[0]  # logits
						else:  # no classification heads to worry about
												snake_case_   =	bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE__       )
												snake_case_   =	state_dict['''decoder.embed_tokens.weight''']
												snake_case_   =	bart.extract_features(SCREAMING_SNAKE_CASE__       )
												if hf_checkpoint_name == "facebook/bart-large":
																		snake_case_   =	BartModel(SCREAMING_SNAKE_CASE__       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE__       )
																		snake_case_   =	model(SCREAMING_SNAKE_CASE__       ).model[0]
												else:
																		snake_case_   =	BartForConditionalGeneration(SCREAMING_SNAKE_CASE__       ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(SCREAMING_SNAKE_CASE__       )
																		if hasattr(SCREAMING_SNAKE_CASE__  ,		'''lm_head'''       ):
																								snake_case_   =	make_linear_from_emb(model.model.shared       )
																		snake_case_   =	model.model(SCREAMING_SNAKE_CASE__       )[0]

    # Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''       )
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''       )
						Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
						model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
					lowerCAmelCase_       	=	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=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
					)
					lowerCAmelCase_       	=	parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)


from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()