
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__a         =   logging.getLogger(__name__)
@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,		)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether tp freeze the encoder.'}				)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether  to freeze the embeddings.'}				)

@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
	a     :Optional[str]       	=      field(
	    default='summarization'					,		metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}					,		)
	a     :Optional[int]       	=      field(
	    default=1_024					,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=128					,		metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded. '
	            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
	            'during ``evaluate`` and ``predict``.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for test target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# training examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# validation examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# test examples. -1 means use all.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Source language id for translation.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Target language id for translation.'}				)
	a     :Optional[int]       	=      field(default=UpperCAmelCase					,		metadata={'help': '# num_beams to use for evaluation.'}				)
	a     :bool       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}					,		)




def 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				logger.info(F'''***** {split} metrics *****'''   )
				for key in sorted(metrics.keys()   ):
								logger.info(F'''  {key} = {metrics[key]}'''   )
				save_json(snake_case__		,  os.path.join(snake_case__		,  F'''{split}_results.json'''   )   )




def 							a		(				):


				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowercase_	    =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

				if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_args_into_dataclasses()

				check_output_dir(snake_case__   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,  datefmt='''%m/%d/%Y %H:%M:%S'''		,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,  training_args.local_rank		,  training_args.device		,  training_args.n_gpu		,  bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )		,  training_args.fpaa		,  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank   ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''		,  snake_case__   )

				# Set seed
				set_seed(training_args.seed   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase_	    =   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )

				lowercase_	    =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
				for p in extra_model_params:
								if getattr(snake_case__		,  snake_case__		,  snake_case__   ):
												assert hasattr(snake_case__		,  snake_case__   ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
												setattr(snake_case__		,  snake_case__		,  getattr(snake_case__		,  snake_case__   )   )

				lowercase_	    =   AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )
				lowercase_	    =   AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path		,  from_tf='''.ckpt''' in model_args.model_name_or_path		,  config=snake_case__		,  cache_dir=model_args.cache_dir		,  )

				# use task specific params
				use_task_specific_params(snake_case__		,  data_args.task   )

				# set num_beams for evaluation
				if data_args.eval_beams is None:
								lowercase_	    =   model.config.num_beams

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(snake_case__		,  (MBartTokenizer, MBartTokenizerFast)   ):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(snake_case__		,  snake_case__   ):
												lowercase_	    =   tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												lowercase_	    =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

				if model_args.freeze_embeds:
								freeze_embeds(snake_case__   )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()   )
								assert_all_frozen(model.get_encoder()   )

				lowercase_	    =   SeqaSeqDataset

				# Get datasets
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''train'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_train		,  max_target_length=data_args.max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_train
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''val'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_val		,  max_target_length=data_args.val_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''test'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_test		,  max_target_length=data_args.test_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				lowercase_	    =   (
				    build_compute_metrics_fn(data_args.task		,  snake_case__   ) if training_args.predict_with_generate else None
				)
				lowercase_	    =   SeqaSeqTrainer(
				    model=snake_case__		,  args=snake_case__		,  data_args=snake_case__		,  train_dataset=snake_case__		,  eval_dataset=snake_case__		,  data_collator=SeqaSeqDataCollator(
				        snake_case__		,  snake_case__		,  model.config.decoder_start_token_id		,  training_args.tpu_num_cores   )		,  compute_metrics=snake_case__		,  tokenizer=snake_case__		,  )

				lowercase_	    =   {}
				# Training
				if training_args.do_train:
								logger.info('''*** Train ***'''   )

								lowercase_	    =   trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
								lowercase_	    =   train_result.metrics
								lowercase_	    =   data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

								if trainer.is_world_process_zero():
												handle_metrics('''train'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir		,  '''trainer_state.json'''   )   )

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''   )

								lowercase_	    =   trainer.evaluate(metric_key_prefix='''val'''   )
								lowercase_	    =   data_args.n_val
								lowercase_	    =   round(metrics['''val_loss''']		,  4   )

								if trainer.is_world_process_zero():
												handle_metrics('''val'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

				if training_args.do_predict:
								logger.info('''*** Predict ***'''   )

								lowercase_	    =   trainer.predict(test_dataset=snake_case__		,  metric_key_prefix='''test'''   )
								lowercase_	    =   test_output.metrics
								lowercase_	    =   data_args.n_test

								if trainer.is_world_process_zero():
												lowercase_	    =   round(metrics['''test_loss''']		,  4   )
												handle_metrics('''test'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												if training_args.predict_with_generate:
																lowercase_	    =   tokenizer.batch_decode(
																    test_output.predictions		,  skip_special_tokens=snake_case__		,  clean_up_tokenization_spaces=snake_case__   )
																lowercase_	    =   lmap(str.strip		,  snake_case__   )
																write_txt_file(snake_case__		,  os.path.join(training_args.output_dir		,  '''test_generations.txt'''   )   )

				if trainer.is_world_process_zero():
								save_json(snake_case__		,  os.path.join(training_args.output_dir		,  '''all_results.json'''   )   )

				return all_metrics




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()






import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a         =   logging.get_logger(__name__)


__a         =   {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__a         =   {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

__a         =   '</w>'
__a         =   '@@ '




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   set()
				lowercase_	    =   word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)   )
								lowercase_	    =   char
				return pairs


# Speech2Text2 has no max input length
__a         =   {'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Optional[int]       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :List[str]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Tuple       	=      ['input_ids', 'attention_mask']
	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Tuple="<pad>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="<unk>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				**SCREAMING_SNAKE_CASE_    :     str			,				)			->	Optional[int]:
					super().__init__(
					    unk_token=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				do_lower_case=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   do_lower_case

					with open(SCREAMING_SNAKE_CASE_			,				encoding='''utf-8'''			) as vocab_handle:
									lowercase_	    =   json.load(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {v: k for k, v in self.encoder.items()}

					if merges_file is None:
									logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''			)

									lowercase_	    =   None
									lowercase_	    =   None
					else:
									with open(SCREAMING_SNAKE_CASE_			,				encoding='''utf-8'''			) as merges_handle:
													lowercase_	    =   merges_handle.read().split('''\n'''			)[:-1]

									lowercase_	    =   [tuple(merge.split()[:2]			) for merge in merges]
									lowercase_	    =   dict(zip(SCREAMING_SNAKE_CASE_			,				range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
									lowercase_	    =   {}
	@property
	def 		_lowercase    (			self    :     Any			)			->	int:
					return len(self.decoder			)
	def 		_lowercase    (			self    :     List[str]			)			->	Dict:
					return dict(self.encoder			,				**self.added_tokens_encoder			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	int:
					lowercase_	    =   tuple(token[:-1]			) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
									return self.cache[token]
					lowercase_	    =   get_pairs(SCREAMING_SNAKE_CASE_			)

					if not pairs:
									return token

					while True:
									lowercase_	    =   min(SCREAMING_SNAKE_CASE_			,				key=lambda SCREAMING_SNAKE_CASE_			: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_			,				float('''inf'''			)			)			)
									if bigram not in self.bpe_ranks:
													break
									lowercase_    ,      lowercase_	    =   bigram
									lowercase_	    =   []
									lowercase_	    =   0
									while i < len(SCREAMING_SNAKE_CASE_			):
													try:
																	lowercase_	    =   word.index(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													except ValueError:
																	new_word.extend(word[i:]			)
																	break
													else:
																	new_word.extend(word[i:j]			)
																	lowercase_	    =   j

													if word[i] == first and i < len(SCREAMING_SNAKE_CASE_			) - 1 and word[i + 1] == second:
																	new_word.append(first + second			)
																	i += 2
													else:
																	new_word.append(word[i]			)
																	i += 1
									lowercase_	    =   tuple(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   new_word
									if len(SCREAMING_SNAKE_CASE_			) == 1:
													break
									else:
													lowercase_	    =   get_pairs(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   ''' '''.join(SCREAMING_SNAKE_CASE_			)
					if word == "\n  " + BPE_TOKEN_MERGES:
									lowercase_	    =   '''\n''' + BPE_TOKEN_MERGES

					if word.endswith(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   word.replace(SCREAMING_SNAKE_CASE_			,				''''''			)

					lowercase_	    =   word.replace(''' '''			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   word
					return word
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Tuple:

					if self.bpe_ranks is None:
									raise ValueError(
									    '''This tokenizer was instantiated without a `merges.txt` file, so'''
									    ''' that it can only be used for decoding, not for encoding.'''
									    '''Make sure to provide `merges.txt` file at instantiation to enable '''
									    '''encoding.'''			)

					if self.do_lower_case:
									lowercase_	    =   text.lower()

					lowercase_	    =   text.split()

					lowercase_	    =   []
					for token in text:
									if token:
													split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_			).split(''' '''			)			)			)

					return split_tokens
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			)			->	int:
					return self.encoder.get(SCREAMING_SNAKE_CASE_			,				self.encoder.get(self.unk_token			)			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	str:
					lowercase_	    =   self.decoder.get(SCREAMING_SNAKE_CASE_			,				self.unk_token			)
					return result
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	str:
					lowercase_	    =   ''' '''.join(SCREAMING_SNAKE_CASE_			)

					# make sure @@ tokens are concatenated
					lowercase_	    =   ''''''.join(string.split(SCREAMING_SNAKE_CASE_			)			)

					return string




	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as f:
									f.write(json.dumps(self.encoder			,				indent=2			,				sort_keys=SCREAMING_SNAKE_CASE_			,				ensure_ascii=SCREAMING_SNAKE_CASE_			) + '''\n'''			)

					lowercase_	    =   0
					if self.bpe_ranks is None:
									return (vocab_file,)

					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda SCREAMING_SNAKE_CASE_			: kv[1]			):
													if index != token_index:
																	logger.warning(
																	    f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
																	    ''' Please check that the tokenizer is not corrupted!'''			)
																	lowercase_	    =   token_index
													writer.write(''' '''.join(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
													index += 1

					return (vocab_file, merges_file)




