



from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


__snake_case						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

           if self.framework != "pt":
               raise ValueError(F'The {self.__class__} is only available in PyTorch.')
           # No specific FOR_XXX available yet



       def __call__(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[np.ndarray, bytes, str]     ,		**__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				{}
           if "candidate_labels" in kwargs:
               __a						       =				kwargs['''candidate_labels''']
           if "hypothesis_template" in kwargs:
               __a						       =				kwargs['''hypothesis_template''']

           return preprocess_params, {}, {}



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple="This is a sound of {}."):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               if audio.startswith('''http://''') or audio.startswith('''https://'''):
                   # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                   # like http_huggingface_co.png
                   __a						       =				requests.get(__SCREAMING_SNAKE_CASE).content
               else:
                   with open(__SCREAMING_SNAKE_CASE     ,		'''rb''') as f:
                       __a						       =				f.read()

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				ffmpeg_read(__SCREAMING_SNAKE_CASE     ,		self.feature_extractor.sampling_rate)

           if not isinstance(__SCREAMING_SNAKE_CASE     ,		np.ndarray):
               raise ValueError('''We expect a numpy ndarray as input''')
           if len(audio.shape) != 1:
               raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''')

           __a						       =				self.feature_extractor(
               [audio]     ,		sampling_rate=self.feature_extractor.sampling_rate     ,		return_tensors='''pt''')
           __a						       =				candidate_labels
           __a						       =				[hypothesis_template.format(__SCREAMING_SNAKE_CASE) for x in candidate_labels]
           __a						       =				self.tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors=self.framework     ,		padding=__SCREAMING_SNAKE_CASE)
           __a						       =				[text_inputs]
           return inputs



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a						       =				model_inputs.pop('''candidate_labels''')
           __a						       =				model_inputs.pop('''text_inputs''')
           if isinstance(text_inputs[0]     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				text_inputs[0]
           else:
               # Batching case.
               __a						       =				text_inputs[0][0]

           __a						       =				self.model(**__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				{
               '''candidate_labels''': candidate_labels,
               '''logits''': outputs.logits_per_audio,
           }
           return model_outputs





       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a						       =				model_outputs.pop('''candidate_labels''')
           __a						       =				model_outputs['''logits'''][0]

           if self.framework == "pt":
               __a						       =				logits.softmax(dim=0)
               __a						       =				probs.tolist()
           else:
               raise ValueError('''`tf` framework not supported.''')

           __a						       =				[
               {'''score''': score, '''label''': candidate_label}
               for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)     ,		key=lambda __SCREAMING_SNAKE_CASE: -x[0])
           ]
           return result







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)





