
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__lowerCamelCase							=       logging.get_logger(__name__)


__lowerCamelCase							=       R"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""






class   UpperCAmelCase		(						A_ ):



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Union[str, Any]    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					Optional[int] )		->	bool:


          '''simple docstring'''


          raise NotImplementedError("StoppingCriteria needs to be subclassed" )






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int] = None )		->	Any:


          '''simple docstring'''


          snake_case       :   List[str]							=      max_length
          snake_case       :   Dict							=      max_position_embeddings



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					str    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					int )		->	bool:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      input_ids.shape[-1]
          snake_case       :   Union[str, Any]							=      cur_len >= self.max_length
          if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
                 logger.warning_once(
                     "This is a friendly reminder - the current text generation call will exceed the model's predefined "
                     f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
                     "exceptions, performance degradation, or nothing at all." )
          return is_done






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					int )		->	Union[str, Any]:


          '''simple docstring'''


          warnings.warn(
              "The class `MaxNewTokensCriteria` is deprecated. "
              f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
              "with `max_length = start_length + max_new_tokens` instead."    ,						snake_case__    ,						)
          snake_case       :   Union[str, Any]							=      start_length
          snake_case       :   Union[str, Any]							=      max_new_tokens
          snake_case       :   Union[str, Any]							=      start_length + max_new_tokens



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Optional[Any]    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					Any )		->	bool:


          '''simple docstring'''


          return input_ids.shape[-1] >= self.max_length






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					List[str]    ,						snake_case__		:					float    ,						snake_case__		:					Optional[float] = None )		->	Any:


          '''simple docstring'''


          snake_case       :   Tuple							=      max_time
          snake_case       :   List[str]							=      time.time() if initial_timestamp is None else initial_timestamp



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Dict    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					str )		->	bool:


          '''simple docstring'''


          return time.time() - self.initial_timestamp > self.max_time






class   UpperCAmelCase		(						A_ ):



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Any    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					List[str] )		->	bool:


          '''simple docstring'''


          return any(criteria(snake_case__    ,						snake_case__ ) for criteria in self )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          for stopping_criterium in self:
                 if isinstance(snake_case__    ,						snake_case__ ):
                        return stopping_criterium.max_length
                 elif isinstance(snake_case__    ,						snake_case__ ):
                        return stopping_criterium.max_length
          return None



def 			UpperCamelCase					(    __lowerCamelCase     : StoppingCriteriaList				,					__lowerCamelCase     : int    ):
       snake_case       :   Any							=      stopping_criteria.max_length
       snake_case       :   Tuple							=      deepcopy(__lowerCamelCase    )
       if stopping_max_length is not None and stopping_max_length != max_length:
              warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"				,					__lowerCamelCase    )
       elif stopping_max_length is None:
              new_stopping_criteria.append(MaxLengthCriteria(max_length=__lowerCamelCase    )    )
       return new_stopping_criteria







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 			UpperCamelCase					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

       # load metric
       snake_case       :   List[str]							=      load_metric("wer"    )
       snake_case       :   Tuple							=      load_metric("cer"    )

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

       # print & log results
       snake_case       :   int							=      f"""WER: {wer_result}\nCER: {cer_result}"""
       print(__lowerCamelCase    )

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

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              snake_case       :   int							=      f"""log_{dataset_id}_predictions.txt"""
              snake_case       :   List[Any]							=      f"""log_{dataset_id}_targets.txt"""

              with open(__lowerCamelCase				,					"w"    ) as p, open(__lowerCamelCase				,					"w"    ) as t:
                     # mapping function to write output
                     def write_to_file(__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[int]    ):
                            p.write(f"""{i}""" + "\n"    )
                            p.write(batch["prediction"] + "\n"    )
                            t.write(f"""{i}""" + "\n"    )
                            t.write(batch["target"] + "\n"    )

                     result.map(__lowerCamelCase				,					with_indices=__lowerCamelCase    )


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

       snake_case       :   List[Any]							=      re.sub(__lowerCamelCase				,					""				,					text.lower()    )

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

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       snake_case       :   str							=      load_dataset(args.dataset				,					args.config				,					split=args.split				,					use_auth_token=__lowerCamelCase    )

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

       # load processor
       snake_case       :   List[Any]							=      AutoFeatureExtractor.from_pretrained(args.model_id    )
       snake_case       :   Union[str, Any]							=      feature_extractor.sampling_rate

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

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

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      asr(
                  batch["audio"]["array"]				,					chunk_length_s=args.chunk_length_s				,					stride_length_s=args.stride_length_s    )

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

       # run inference on all examples
       snake_case       :   Dict							=      dataset.map(__lowerCamelCase				,					remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
   __lowerCamelCase							=       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.""",
   )
   __lowerCamelCase							=       parser.parse_args()

   main(args)





