



"""simple docstring"""
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:   Optional[Any]								=		logging.getLogger(__name__)



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



@dataclass
class       SCREAMING_SNAKE_CASE__  :
   __lowerCAmelCase  :   str						=						field(
       metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
   __lowerCAmelCase  :   Optional[str]						=						field(
       default='summarization'      ,			metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,			)
   __lowerCAmelCase  :   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.'
           )
       }      ,			)
   __lowerCAmelCase  :   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.'
           )
       }      ,			)
   __lowerCAmelCase  :   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``.'
           )
       }      ,			)
   __lowerCAmelCase  :   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.'
           )
       }      ,			)
   __lowerCAmelCase  :   Optional[int]						=						field(default=-1      ,			metadata={'help': '# training examples. -1 means use all.'} )
   __lowerCAmelCase  :   Optional[int]						=						field(default=-1      ,			metadata={'help': '# validation examples. -1 means use all.'} )
   __lowerCAmelCase  :   Optional[int]						=						field(default=-1      ,			metadata={'help': '# test examples. -1 means use all.'} )
   __lowerCAmelCase  :   Optional[str]						=						field(default=UpperCAmelCase__      ,			metadata={'help': 'Source language id for translation.'} )
   __lowerCAmelCase  :   Optional[str]						=						field(default=UpperCAmelCase__      ,			metadata={'help': 'Target language id for translation.'} )
   __lowerCAmelCase  :   Optional[int]						=						field(default=UpperCAmelCase__      ,			metadata={'help': '# num_beams to use for evaluation.'} )
   __lowerCAmelCase  :   bool						=						field(
       default=UpperCAmelCase__      ,			metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,			)

def       _snake_case						(						UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		List[Any]						):
 logger.info(F"***** {split} metrics *****"						)
 for key in sorted(metrics.keys()						):
  logger.info(F"  {key} = {metrics[key]}"						)
 save_json(UpperCamelCase			,		os.path.join(UpperCamelCase			,		F"{split}_results.json"						)						)

def       _snake_case						(						):
 # 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.

 UpperCAmelCase	:					List[str]	=	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.
  UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
 else:
  UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	parser.parse_args_into_dataclasses()

 check_output_dir(UpperCamelCase						)

 # 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"""			,		UpperCamelCase						)

 # 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.

 UpperCAmelCase	:					Any	=	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			,		)

 UpperCAmelCase	:					Union[str, Any]	=	("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
 for p in extra_model_params:
  if getattr(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						):
   assert hasattr(UpperCamelCase			,		UpperCamelCase						), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
   setattr(UpperCamelCase			,		UpperCamelCase			,		getattr(UpperCamelCase			,		UpperCamelCase						)						)

 UpperCAmelCase	:					Union[str, Any]	=	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			,		)
 UpperCAmelCase	:					List[Any]	=	AutoModelForSeqaSeqLM.from_pretrained(
     model_args.model_name_or_path			,		from_tf=""".ckpt""" in model_args.model_name_or_path			,		config=UpperCamelCase			,		cache_dir=model_args.cache_dir			,		)

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

 # set num_beams for evaluation
 if data_args.eval_beams is None:
  UpperCAmelCase	:					Union[str, Any]	=	model.config.num_beams

 # set decoder_start_token_id for MBart
 if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase			,		(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(UpperCamelCase			,		UpperCamelCase						):
   UpperCAmelCase	:					List[str]	=	tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   UpperCAmelCase	:					Any	=	tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

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

 UpperCAmelCase	:					Any	=	SeqaSeqDataset

 # Get datasets
 UpperCAmelCase	:					str	=	(
     dataset_class(
         UpperCamelCase			,		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
 )
 UpperCAmelCase	:					List[Any]	=	(
     dataset_class(
         UpperCamelCase			,		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
 )
 UpperCAmelCase	:					List[Any]	=	(
     dataset_class(
         UpperCamelCase			,		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
 UpperCAmelCase	:					Dict	=	(
     build_compute_metrics_fn(data_args.task			,		UpperCamelCase						) if training_args.predict_with_generate else None
 )
 UpperCAmelCase	:					Optional[int]	=	SeqaSeqTrainer(
     model=UpperCamelCase			,		args=UpperCamelCase			,		data_args=UpperCamelCase			,		train_dataset=UpperCamelCase			,		eval_dataset=UpperCamelCase			,		data_collator=SeqaSeqDataCollator(
         UpperCamelCase			,		UpperCamelCase			,		model.config.decoder_start_token_id			,		training_args.tpu_num_cores						)			,		compute_metrics=UpperCamelCase			,		tokenizer=UpperCamelCase			,		)

 UpperCAmelCase	:					List[str]	=	{}
 # Training
 if training_args.do_train:
  logger.info("""*** Train ***"""						)

  UpperCAmelCase	:					Dict	=	trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
  UpperCAmelCase	:					Any	=	train_result.metrics
  UpperCAmelCase	:					Union[str, Any]	=	data_args.n_train

  trainer.save_model()  # this also saves the tokenizer

  if trainer.is_world_process_zero():
   handle_metrics("""train"""			,		UpperCamelCase			,		training_args.output_dir						)
   all_metrics.update(UpperCamelCase						)

   # 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 ***"""						)

  UpperCAmelCase	:					Optional[int]	=	trainer.evaluate(metric_key_prefix="""val"""						)
  UpperCAmelCase	:					Any	=	data_args.n_val
  UpperCAmelCase	:					Union[str, Any]	=	round(metrics["""val_loss"""]			,		4						)

  if trainer.is_world_process_zero():
   handle_metrics("""val"""			,		UpperCamelCase			,		training_args.output_dir						)
   all_metrics.update(UpperCamelCase						)

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

  UpperCAmelCase	:					Dict	=	trainer.predict(test_dataset=UpperCamelCase			,		metric_key_prefix="""test"""						)
  UpperCAmelCase	:					str	=	test_output.metrics
  UpperCAmelCase	:					Optional[Any]	=	data_args.n_test

  if trainer.is_world_process_zero():
   UpperCAmelCase	:					Optional[Any]	=	round(metrics["""test_loss"""]			,		4						)
   handle_metrics("""test"""			,		UpperCamelCase			,		training_args.output_dir						)
   all_metrics.update(UpperCamelCase						)

   if training_args.predict_with_generate:
    UpperCAmelCase	:					Optional[Any]	=	tokenizer.batch_decode(
        test_output.predictions			,		skip_special_tokens=UpperCamelCase			,		clean_up_tokenization_spaces=UpperCamelCase						)
    UpperCAmelCase	:					Union[str, Any]	=	lmap(str.strip			,		UpperCamelCase						)
    write_txt_file(UpperCamelCase			,		os.path.join(training_args.output_dir			,		"""test_generations.txt"""						)						)

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

 return all_metrics

def       _snake_case						(						UpperCamelCase						:		List[Any]						):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()





"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

 with open(F"{dataset_id}_eval_results.txt"			,		"""w"""						) as f:
  f.write(UpperCamelCase						)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  UpperCAmelCase	:					str	=	F"log_{dataset_id}_predictions.txt"
  UpperCAmelCase	:					Tuple	=	F"log_{dataset_id}_targets.txt"

  with open(UpperCamelCase			,		"""w"""						) as p, open(UpperCamelCase			,		"""w"""						) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]						):
    p.write(F"{i}" + """\n"""						)
    p.write(batch["""prediction"""] + """\n"""						)
    t.write(F"{i}" + """\n"""						)
    t.write(batch["""target"""] + """\n"""						)

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 UpperCAmelCase	:					List[str]	=	["""\n\n""", """\n""", """   """, """  """]

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

 # load eval pipeline
 if args.device is None:
  UpperCAmelCase	:					Optional[int]	=	0 if torch.cuda.is_available() else -1
 UpperCAmelCase	:					Tuple	=	pipeline("""automatic-speech-recognition"""			,		model=args.model_id			,		device=args.device						)

 # map function to decode audio
 def map_to_pred(UpperCamelCase						:		Any						):
  UpperCAmelCase	:					Any	=	asr(
      batch["""audio"""]["""array"""]			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s						)

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

 # compute and log_results
 # do not change function below
 log_results(UpperCamelCase			,		UpperCamelCase						)


if __name__ == "__main__":
      A:   List[Any]								=		argparse.ArgumentParser()

      parser.add_argument(
          "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
      )
      parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
      )
      parser.add_argument(
          "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
      )
      parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
      parser.add_argument(
          "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
      )
      parser.add_argument(
          "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
      )
      parser.add_argument(
          "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
      )
      parser.add_argument(
          "--device",
          type=int,
          default=None,
          help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
      )
      A:   Union[str, Any]								=		parser.parse_args()

      main(args)
