




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

_lowerCamelCase	:		int							       =   {
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''blip_2_vision_model'''


    def __init__(			self      :	Optional[Any]					, UpperCAmelCase__      :	Any=1_408					, UpperCAmelCase__      :	Optional[int]=6_144					, UpperCAmelCase__      :	List[str]=39					, UpperCAmelCase__      :	str=16					, UpperCAmelCase__      :	int=224					, UpperCAmelCase__      :	List[str]=14					, UpperCAmelCase__      :	Any="gelu"					, UpperCAmelCase__      :	Dict=0.00001					, UpperCAmelCase__      :	str=0.0					, UpperCAmelCase__      :	Dict=1e-10					, UpperCAmelCase__      :	Optional[int]=True					, **UpperCAmelCase__      :	Union[str, Any]					, )						->Tuple:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    hidden_size
           A__       =    intermediate_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    patch_size
           A__       =    image_size
           A__       =    initializer_range
           A__       =    attention_dropout
           A__       =    layer_norm_eps
           A__       =    hidden_act
           A__       =    qkv_bias





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Optional[int]					, UpperCAmelCase__      :	Union[str, os.PathLike]					, **UpperCAmelCase__      :	Tuple)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__ ,							A__       =    cls.get_config_dict(UpperCAmelCase__					, **UpperCAmelCase__)

           # get the vision config dict if we are loading from Blip2Config
           if config_dict.get('''model_type''') == "blip-2":
                  A__       =    config_dict['''vision_config''']

           if "model_type" in config_dict and hasattr(cls					, '''model_type''') and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                      f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

           return cls.from_dict(UpperCAmelCase__					, **UpperCAmelCase__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''blip_2_qformer'''


    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	List[str]=30_522					, UpperCAmelCase__      :	Union[str, Any]=768					, UpperCAmelCase__      :	List[Any]=12					, UpperCAmelCase__      :	str=12					, UpperCAmelCase__      :	Any=3_072					, UpperCAmelCase__      :	int="gelu"					, UpperCAmelCase__      :	Optional[int]=0.1					, UpperCAmelCase__      :	Optional[Any]=0.1					, UpperCAmelCase__      :	Tuple=512					, UpperCAmelCase__      :	Optional[Any]=0.02					, UpperCAmelCase__      :	Tuple=1e-12					, UpperCAmelCase__      :	Optional[int]=0					, UpperCAmelCase__      :	Union[str, Any]="absolute"					, UpperCAmelCase__      :	int=2					, UpperCAmelCase__      :	str=1_408					, **UpperCAmelCase__      :	Optional[int]					, )						->Union[str, Any]:





           '''simple docstring'''

           super().__init__(pad_token_id=UpperCAmelCase__					, **UpperCAmelCase__)

           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    hidden_act
           A__       =    intermediate_size
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    initializer_range
           A__       =    layer_norm_eps
           A__       =    position_embedding_type
           A__       =    cross_attention_frequency
           A__       =    encoder_hidden_size





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Union[str, Any]					, UpperCAmelCase__      :	Union[str, os.PathLike]					, **UpperCAmelCase__      :	str)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__ ,							A__       =    cls.get_config_dict(UpperCAmelCase__					, **UpperCAmelCase__)

           # get the qformer config dict if we are loading from Blip2Config
           if config_dict.get('''model_type''') == "blip-2":
                  A__       =    config_dict['''qformer_config''']

           if "model_type" in config_dict and hasattr(cls					, '''model_type''') and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                      f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

           return cls.from_dict(UpperCAmelCase__					, **UpperCAmelCase__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''blip-2'''
    UpperCAmelCase__							  =      True


    def __init__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	str=None					, UpperCAmelCase__      :	List[Any]=None					, UpperCAmelCase__      :	Optional[Any]=None					, UpperCAmelCase__      :	str=32					, **UpperCAmelCase__      :	Tuple)						->List[str]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           if vision_config is None:
                  A__       =    {}
                  logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''')

           if qformer_config is None:
                  A__       =    {}
                  logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''')

           if text_config is None:
                  A__       =    {}
                  logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''')

           A__       =    BlipaVisionConfig(**UpperCAmelCase__)
           A__       =    BlipaQFormerConfig(**UpperCAmelCase__)
           A__       =    text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
           A__       =    CONFIG_MAPPING[text_model_type](**UpperCAmelCase__)

           A__       =    self.text_config.tie_word_embeddings
           A__       =    self.text_config.is_encoder_decoder

           A__       =    num_query_tokens
           A__       =    self.vision_config.hidden_size
           A__       =    self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           A__       =    1.0
           A__       =    0.02


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	int					, UpperCAmelCase__      :	BlipaVisionConfig					, UpperCAmelCase__      :	BlipaQFormerConfig					, UpperCAmelCase__      :	PretrainedConfig					, **UpperCAmelCase__      :	Dict					, )						->Union[str, Any]:





           '''simple docstring'''

           return cls(
               vision_config=vision_config.to_dict()					, qformer_config=qformer_config.to_dict()					, text_config=text_config.to_dict()					, **UpperCAmelCase__					, )





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.vision_config.to_dict()
           A__       =    self.qformer_config.to_dict()
           A__       =    self.text_config.to_dict()
           A__       =    self.__class__.model_type
           return output








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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    args.log_outputs
       A__       =    '''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

       # load metric
       A__       =    load_metric('''wer'''						)
       A__       =    load_metric('''cer'''						)

       # compute metrics
       A__       =    wer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)
       A__       =    cer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)

       # print & log results
       A__       =    f"""WER: {wer_result}\nCER: {cer_result}"""
       print(lowercase_						)

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

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

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

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

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

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

       # load processor
       A__       =    AutoFeatureExtractor.from_pretrained(args.model_id						)
       A__       =    feature_extractor.sampling_rate

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

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

       # map function to decode audio
       def map_to_pred(lowercase_						):
              A__       =    asr(
                  batch['''audio''']['''array'''] ,				chunk_length_s=args.chunk_length_s ,				stride_length_s=args.stride_length_s						)

              A__       =    prediction['''text''']
              A__       =    normalize_text(batch['''sentence''']						)
              return batch

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

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


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

  main(args)


