




"""simple docstring"""
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a_				:    Optional[Any]										=     2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a_				:    Optional[int]										=     {
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.1_5},
    '''wmt19-en-de''': {'''length_penalty''': 1.0},
    '''wmt19-de-en''': {'''length_penalty''': 1.1},
    # allenai:
    '''wmt16-en-de-dist-12-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-dist-6-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-12-1''': {'''length_penalty''': 0.8},
    '''wmt19-de-en-6-6-base''': {'''length_penalty''': 0.6},
    '''wmt19-de-en-6-6-big''': {'''length_penalty''': 0.6},
}

# this remaps the different models to their organization names
a_				:    Optional[int]										=     {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
					a_				:    Dict										=     '''facebook'''
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
					a_				:    Tuple										=     '''allenai'''





def 						UpperCAmelCase				(			A__:    List[str]     )  ->     int:

			'''simple docstring'''





			__lowerCamelCase					:	Any	       =				dict((re.sub(r'@@$'							,				''							,				A__     ), v) if k.endswith('@@'     ) else (re.sub(r'$'							,				'</w>'							,				A__     ), v) for k, v in d.items()     )
			__lowerCamelCase					:	Any	       =				'<s> <pad> </s> <unk>'.split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'''{k}</w>''']
						__lowerCamelCase					:	Tuple	       =				d[k]  # restore
			return da





def 						UpperCAmelCase				(			A__:    str							,				A__:    Any     )  ->     Tuple:

			'''simple docstring'''





			assert os.path.exists(A__     )
			os.makedirs(A__							,				exist_ok=A__     )
			print(f'''Writing results to {pytorch_dump_folder_path}'''     )

			# handle various types of models

			__lowerCamelCase					:	str	       =				basename(A__     )
			__lowerCamelCase					:	Tuple	       =				dirname(A__     )

			__lowerCamelCase					:	Optional[int]	       =				fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			__lowerCamelCase					:	str	       =				cls.hub_models()
			__lowerCamelCase					:	Union[str, Any]	       =				{'bpe': 'fastbpe', 'tokenizer': 'moses'}
			__lowerCamelCase					:	List[Any]	       =				'.'
			# note: since the model dump is old, fairseq has upgraded its model some
			# time later, and it does a whole lot of rewrites and splits on the saved
			# weights, therefore we can't use torch.load() directly on the model file.
			# see: upgrade_state_dict(state_dict) in fairseq_model.py
			print(f'''using checkpoint {checkpoint_file}'''     )
			__lowerCamelCase					:	Any	       =				hub_utils.from_pretrained(
			    A__							,				A__							,				A__							,				archive_map=A__							,				**A__     )

			__lowerCamelCase					:	Tuple	       =				vars(chkpt['args']['model']     )

			__lowerCamelCase					:	Union[str, Any]	       =				args['source_lang']
			__lowerCamelCase					:	Optional[Any]	       =				args['target_lang']

			__lowerCamelCase					:	List[Any]	       =				dirname(A__     )
			__lowerCamelCase					:	List[str]	       =				basename(A__     )

			# dicts
			__lowerCamelCase					:	Union[str, Any]	       =				os.path.join(A__							,				f'''dict.{src_lang}.txt'''     )
			__lowerCamelCase					:	List[str]	       =				os.path.join(A__							,				f'''dict.{tgt_lang}.txt'''     )

			__lowerCamelCase					:	Optional[int]	       =				Dictionary.load(A__     )
			__lowerCamelCase					:	int	       =				rewrite_dict_keys(src_dict.indices     )
			__lowerCamelCase					:	Optional[int]	       =				len(A__     )
			__lowerCamelCase					:	List[Any]	       =				os.path.join(A__							,				'vocab-src.json'     )
			print(f'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''     )
			with open(A__							,				'w'							,				encoding='utf-8'     ) as f:
						f.write(json.dumps(A__							,				ensure_ascii=A__							,				indent=A__     )     )

			# detect whether this is a do_lower_case situation, which can be derived by checking whether we
			# have at least one uppercase letter in the source vocab
			__lowerCamelCase					:	Dict	       =				True
			for k in src_vocab.keys():
						if not k.islower():
									__lowerCamelCase					:	Tuple	       =				False
									break

			__lowerCamelCase					:	List[str]	       =				Dictionary.load(A__     )
			__lowerCamelCase					:	Tuple	       =				rewrite_dict_keys(tgt_dict.indices     )
			__lowerCamelCase					:	Tuple	       =				len(A__     )
			__lowerCamelCase					:	Union[str, Any]	       =				os.path.join(A__							,				'vocab-tgt.json'     )
			print(f'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''     )
			with open(A__							,				'w'							,				encoding='utf-8'     ) as f:
						f.write(json.dumps(A__							,				ensure_ascii=A__							,				indent=A__     )     )

			# merges_file (bpecodes)
			__lowerCamelCase					:	Optional[int]	       =				os.path.join(A__							,				VOCAB_FILES_NAMES['merges_file']     )
			for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
						__lowerCamelCase					:	List[Any]	       =				os.path.join(A__							,				A__     )
						if os.path.exists(A__     ):
									break
			with open(A__							,				encoding='utf-8'     ) as fin:
						__lowerCamelCase					:	int	       =				fin.read()
			__lowerCamelCase					:	int	       =				re.sub(r' \d+$'							,				''							,				A__							,				0							,				re.M     )  # remove frequency number
			print(f'''Generating {merges_file}'''     )
			with open(A__							,				'w'							,				encoding='utf-8'     ) as fout:
						fout.write(A__     )

			# model config
			__lowerCamelCase					:	List[str]	       =				os.path.join(A__							,				'config.json'     )

			# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
			# may have to modify the tokenizer if a different type is used by a future model
			assert args["bpe"] == "fastbpe", f'''need to extend tokenizer to support bpe={args["bpe"]}'''
			assert args["tokenizer"] == "moses", f'''need to extend tokenizer to support bpe={args["tokenizer"]}'''

			__lowerCamelCase					:	Union[str, Any]	       =				{
			    'architectures': ['FSMTForConditionalGeneration'],
			    'model_type': 'fsmt',
			    'activation_dropout': args['activation_dropout'],
			    'activation_function': 'relu',
			    'attention_dropout': args['attention_dropout'],
			    'd_model': args['decoder_embed_dim'],
			    'dropout': args['dropout'],
			    'init_std': 0.02,
			    'max_position_embeddings': args['max_source_positions'],
			    'num_hidden_layers': args['encoder_layers'],
			    'src_vocab_size': src_vocab_size,
			    'tgt_vocab_size': tgt_vocab_size,
			    'langs': [src_lang, tgt_lang],
			    'encoder_attention_heads': args['encoder_attention_heads'],
			    'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
			    'encoder_layerdrop': args['encoder_layerdrop'],
			    'encoder_layers': args['encoder_layers'],
			    'decoder_attention_heads': args['decoder_attention_heads'],
			    'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
			    'decoder_layerdrop': args['decoder_layerdrop'],
			    'decoder_layers': args['decoder_layers'],
			    'bos_token_id': 0,
			    'pad_token_id': 1,
			    'eos_token_id': 2,
			    'is_encoder_decoder': True,
			    'scale_embedding': not args['no_scale_embedding'],
			    'tie_word_embeddings': args['share_all_embeddings'],
			}

			# good hparam defaults to start with
			__lowerCamelCase					:	List[Any]	       =				5
			__lowerCamelCase					:	int	       =				False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						__lowerCamelCase					:	Optional[Any]	       =				best_score_hparams[model_dir]['length_penalty']
			else:
						__lowerCamelCase					:	Optional[int]	       =				1.0

			print(f'''Generating {fsmt_model_config_file}'''     )
			with open(A__							,				'w'							,				encoding='utf-8'     ) as f:
						f.write(json.dumps(A__							,				ensure_ascii=A__							,				indent=A__     )     )

			# tokenizer config
			__lowerCamelCase					:	Dict	       =				os.path.join(A__							,				A__     )

			__lowerCamelCase					:	int	       =				{
			    'langs': [src_lang, tgt_lang],
			    'model_max_length': 1024,
			    'do_lower_case': do_lower_case,
			}

			print(f'''Generating {fsmt_tokenizer_config_file}'''     )
			with open(A__							,				'w'							,				encoding='utf-8'     ) as f:
						f.write(json.dumps(A__							,				ensure_ascii=A__							,				indent=A__     )     )

			# model
			__lowerCamelCase					:	Optional[Any]	       =				chkpt['models'][0]
			__lowerCamelCase					:	Any	       =				model.state_dict()

			# rename keys to start with 'model.'
			__lowerCamelCase					:	Dict	       =				OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()     )

			# remove unneeded keys
			__lowerCamelCase					:	Dict	       =				[
			    'model.model',
			    'model.encoder.version',
			    'model.decoder.version',
			    'model.encoder_embed_tokens.weight',
			    'model.decoder_embed_tokens.weight',
			    'model.encoder.embed_positions._float_tensor',
			    'model.decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						model_state_dict.pop(A__							,				A__     )

			__lowerCamelCase					:	Any	       =				FSMTConfig.from_pretrained(A__     )
			__lowerCamelCase					:	Optional[int]	       =				FSMTForConditionalGeneration(A__     )

			# check that it loads ok
			model_new.load_state_dict(A__							,				strict=A__     )

			# save
			__lowerCamelCase					:	Optional[int]	       =				os.path.join(A__							,				A__     )
			print(f'''Generating {pytorch_weights_dump_path}'''     )
			torch.save(A__							,				A__     )

			print('Conversion is done!'     )
			print('\nLast step is to upload the files to s3'     )
			print(f'''cd {data_root}'''     )
			print(f'''transformers-cli upload {model_dir}'''     )


if __name__ == "__main__":
					a_				:    Dict										=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--fsmt_checkpoint_path''',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
					        ''' bpecodes, etc.'''
					    ),
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
					)
					a_				:    Union[str, Any]										=     parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= 'Speech2TextFeatureExtractor'
					__a						:						Optional[int]     		= 'Speech2TextTokenizer'
					def __init__(    self						,	__a						,	__a     ):
								super().__init__(__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False
					def __call__(    self						,	*__a						,	**__a     ):
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*__a						,	**__a     )

								if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
											__lowerCamelCase					:	str	       =				kwargs.pop('raw_speech'     )
								else:
											__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('audio'						,	__a     )
								__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('sampling_rate'						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				kwargs.pop('text'						,	__a     )
								if len(__a     ) > 0:
											__lowerCamelCase					:	Optional[int]	       =				args[0]
											__lowerCamelCase					:	str	       =				args[1:]

								if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

								if audio is not None:
											__lowerCamelCase					:	Dict	       =				self.feature_extractor(__a						,	*__a						,	sampling_rate=__a						,	**__a     )
								if text is not None:
											__lowerCamelCase					:	Optional[int]	       =				self.tokenizer(__a						,	**__a     )

								if text is None:
											return inputs
								elif audio is None:
											return encodings
								else:
											__lowerCamelCase					:	Any	       =				encodings['input_ids']
											return inputs
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*__a						,	**__a     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*__a						,	**__a     )





					@contextmanager
					def     snake_case_				(    self     ):
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'     )
								__lowerCamelCase					:	int	       =				True
								__lowerCamelCase					:	Union[str, Any]	       =				self.tokenizer
								yield
								__lowerCamelCase					:	Tuple	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False



