





"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase					      =   logging.getLogger()



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[Any]     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							__lowerCamelCase   :   str       ='\n'.join(_snake_case )
							Path(_snake_case ).open('''w''' ).writelines(_snake_case )


_UpperCamelCase					      =   'patrickvonplaten/t5-tiny-random'
_UpperCamelCase					      =   'sshleifer/bart-tiny-random'
_UpperCamelCase					      =   'sshleifer/tiny-mbart'

_UpperCamelCase					      =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:Dict      ):
											__lowerCamelCase   :   Optional[Any]       =Path(self.get_auto_remove_tmp_dir()      ) / 'utest_input.source'
											__lowerCamelCase   :   Optional[Any]       =input_file_name.parent / 'utest_output.txt'
											assert not output_file_name.exists()
											__lowerCamelCase   :   Tuple       =[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
											_dump_articles(UpperCamelCase_    ,     UpperCamelCase_      )

											__lowerCamelCase   :   Union[str, Any]       =str(Path(self.get_auto_remove_tmp_dir()      ) / '''scores.json'''      )
											__lowerCamelCase   :   int       ='translation_en_to_de' if model == T5_TINY else 'summarization'
											__lowerCamelCase   :   List[str]       =f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

											with patch.object(UpperCamelCase_    ,     '''argv'''    ,     UpperCamelCase_      ):
																		run_generate()
																		assert Path(UpperCamelCase_      ).exists()
																		# os.remove(Path(output_file_name))




				def        __lowercase	(  self	:Optional[Any]      ):
											self.run_eval_tester(UpperCamelCase_      )




				@parameterized.expand([BART_TINY, MBART_TINY]      )
				@slow
				def        __lowercase	(  self	:Dict    ,     __lowercase	:List[str]      ):
											self.run_eval_tester(UpperCamelCase_      )




				@parameterized.expand([T5_TINY, MBART_TINY]      )
				@slow
				def        __lowercase	(  self	:Dict    ,     __lowercase	:Dict      ):
											__lowerCamelCase   :   str       =Path(self.get_auto_remove_tmp_dir()      ) / 'utest_input.source'
											__lowerCamelCase   :   Any       =input_file_name.parent / 'utest_output.txt'
											assert not output_file_name.exists()

											__lowerCamelCase   :   int       ={
											    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
											    'de': [
											        'Maschinelles Lernen ist großartig, oder?',
											        'Ich esse gerne Bananen',
											        'Morgen ist wieder ein toller Tag!',
											    ],
											}

											__lowerCamelCase   :   Any       =Path(self.get_auto_remove_tmp_dir()      )
											__lowerCamelCase   :   Optional[Any]       =str(tmp_dir / '''scores.json'''      )
											__lowerCamelCase   :   Tuple       =str(tmp_dir / '''val.target'''      )
											_dump_articles(UpperCamelCase_    ,     text['''en''']      )
											_dump_articles(UpperCamelCase_    ,     text['''de''']      )
											__lowerCamelCase   :   Tuple       ='translation_en_to_de' if model == T5_TINY else 'summarization'
											__lowerCamelCase   :   Optional[int]       =f'\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase_      )}\n            {str(UpperCamelCase_      )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
											testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0''']      )

											with patch.object(UpperCamelCase_    ,     '''argv'''    ,     UpperCamelCase_      ):
																		with CaptureStdout() as cs:
																									run_search()
																		__lowerCamelCase   :   Union[str, Any]       =[' num_beams | length_penalty', model, 'Best score args']
																		__lowerCamelCase   :   Dict       =['Info']
																		if "translation" in task:
																									expected_strings.append('''bleu'''      )
																		else:
																									expected_strings.extend(UpperCamelCase_      )
																		for w in expected_strings:
																									assert w in cs.out
																		for w in un_expected_strings:
																									assert w not in cs.out
																		assert Path(UpperCamelCase_      ).exists()
																		os.remove(Path(UpperCamelCase_      )      )






"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      int     							=					[
        '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(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,max_position_embeddings=1024    ,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    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

    UpperCAmelCase__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='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.')
    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

