'''simple docstring'''





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_lowerCAmelCase  :Dict  				=    logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							*lowercase__    ,							**lowercase__ )							-> int:
         super().__init__(*lowercase__    ,							**lowercase__ )
         requires_backends(self    ,							'vision' )
         self.check_model_type(
             TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING )






    def       _UpperCamelCase    (       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {}
         SCREAMING_SNAKE_CASE					:			Dict    	=       {}

         if prompt is not None:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       prompt

         if generate_kwargs is not None:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       generate_kwargs
         if max_new_tokens is not None:
              if "generate_kwargs" not in forward_kwargs:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       {}
              if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                   raise ValueError(
                       '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
                       ' please use only one' )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       max_new_tokens
         return preprocess_params, forward_kwargs, {}






    def __call__(       self    ,							lowercase__    ,							**lowercase__ )							-> Optional[Any]:
         return super().__call__(lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_image(lowercase__ )

         if prompt is not None:
              if not isinstance(lowercase__    ,							lowercase__ ):
                   raise ValueError(
                       F"""Received an invalid text input, got - {type(lowercase__ )} - but expected a single string. """
                       'Note also that one single text can be provided for conditional image to text generation.' )

              SCREAMING_SNAKE_CASE					:			int    	=       self.model.config.model_type

              if model_type == "git":
                   SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processor(images=lowercase__    ,							return_tensors=self.framework )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       self.tokenizer(text=lowercase__    ,							add_special_tokens=lowercase__ ).input_ids
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       [self.tokenizer.cls_token_id] + input_ids
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor(lowercase__ ).unsqueeze(0 )
                   model_inputs.update({'input_ids': input_ids} )

              elif model_type == "pix2struct":
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.image_processor(images=lowercase__    ,							header_text=lowercase__    ,							return_tensors=self.framework )

              elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.image_processor(images=lowercase__    ,							return_tensors=self.framework )
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.tokenizer(lowercase__    ,							return_tensors=self.framework )
                   model_inputs.update(lowercase__ )

              else:
                   raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )

         else:
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.image_processor(images=lowercase__    ,							return_tensors=self.framework )

         if self.model.config.model_type == "git" and prompt is None:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       None

         return model_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> List[str]:
         # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
         # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
         if (
             "input_ids" in model_inputs
             and isinstance(model_inputs['input_ids']    ,							lowercase__ )
             and all(x is None for x in model_inputs['input_ids'] )
         ):
              SCREAMING_SNAKE_CASE					:			Tuple    	=       None

         if generate_kwargs is None:
              SCREAMING_SNAKE_CASE					:			int    	=       {}
         # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
         #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
         #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
         #  in the `_prepare_model_inputs` method.
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_inputs.pop(self.model.main_input_name )
         SCREAMING_SNAKE_CASE					:			str    	=       self.model.generate(lowercase__    ,							**lowercase__    ,							**lowercase__ )
         return model_outputs






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			str    	=       []
         for output_ids in model_outputs:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       {
                  'generated_text': self.tokenizer.decode(
                      lowercase__    ,							skip_special_tokens=lowercase__    ,							)
              }
              records.append(lowercase__ )
         return records




'''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     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

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

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

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

          with open(a_       ,      'w'		) as p, open(a_       ,      'w'		) as t:
               # mapping function to write output
               def write_to_file(a_       ,      a_		):
                    p.write(f"""{i}""" + '\n'		)
                    p.write(batch['prediction'] + '\n'		)
                    t.write(f"""{i}""" + '\n'		)
                    t.write(batch['target'] + '\n'		)

               result.map(a_       ,      with_indices=a_		)


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       re.sub(a_       ,      ''       ,      text.lower()		)

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

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       load_dataset(args.dataset       ,      args.config       ,      split=args.split       ,      use_auth_token=a_		)

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

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

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

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

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[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.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



