




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =MgpstrTokenizer
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      ={}
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								super().setUp()

								# fmt: off
								SCREAMING_SNAKE_CASE_					      =							['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								SCREAMING_SNAKE_CASE_					      =							dict(zip(snake_case__					,							range(len(snake_case__  )  )  )  )

								SCREAMING_SNAKE_CASE_					      =							os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']  )
								with open(self.vocab_file					,							'w'					,							encoding='utf-8'  ) as fp:
										fp.write(json.dumps(snake_case__  ) + '\n'  )



						def 	__a					(			self	:   Optional[int]					,							**snake_case__	:   str  ):

								"""simple docstring"""

								return MgpstrTokenizer.from_pretrained(self.tmpdirname					,							**snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'tester'
								SCREAMING_SNAKE_CASE_					      =							'tester'
								return input_text, output_text



						@unittest.skip('MGP-STR always lower cases letters.'  )
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								pass



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizers(do_lower_case=snake_case__  )
								for tokenizer in tokenizers:
										with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
												SCREAMING_SNAKE_CASE_					      =							'[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token}  )
												SCREAMING_SNAKE_CASE_					      =							tokenizer.encode([special_token]					,							add_special_tokens=snake_case__  )
												self.assertEqual(len(snake_case__  )					,							1  )

												SCREAMING_SNAKE_CASE_					      =							tokenizer.decode(snake_case__					,							skip_special_tokens=snake_case__  )
												self.assertTrue(special_token not in decoded  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizers()
								for tokenizer in tokenizers:
										with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
												SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.get_input_output_texts(snake_case__  )

												SCREAMING_SNAKE_CASE_					      =							tokenizer.tokenize(snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							tokenizer.convert_tokens_to_ids(snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							tokenizer.encode(snake_case__					,							add_special_tokens=snake_case__  )
												self.assertListEqual(snake_case__					,							snake_case__  )

												SCREAMING_SNAKE_CASE_					      =							tokenizer.convert_ids_to_tokens(snake_case__  )
												self.assertNotEqual(len(snake_case__  )					,							0  )
												SCREAMING_SNAKE_CASE_					      =							tokenizer.decode(snake_case__  )
												self.assertIsInstance(snake_case__					,							snake_case__  )

												self.assertEqual(text_a.replace(' '					,							''  )					,							snake_case__  )



						@unittest.skip('MGP-STR tokenizer only handles one sequence.'  )
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								pass






						@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'  )
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								pass





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


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''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()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''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 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

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

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= 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'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)