





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase       :     Union[str, Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Any   =     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Dict						    =   'bart'
      _UpperCAmelCase	:str						    =   ['past_key_values']
      _UpperCAmelCase	:Any						    =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__( self       ,							A_=5_0265       ,							A_=1024       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=0.0       ,							A_=0.0       ,							A_="gelu"       ,							A_=1024       ,							A_=0.1       ,							A_=0.0       ,							A_=0.0       ,							A_=0.02       ,							A_=0.0       ,							A_=False       ,							A_=True       ,							A_=3       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_=True       ,							A_=2       ,							A_=2       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   vocab_size
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   d_model
       UpperCamelCase	:					Optional[Any]       	=   encoder_ffn_dim
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   encoder_attention_heads
       UpperCamelCase	:					Optional[int]       	=   decoder_ffn_dim
       UpperCamelCase	:					List[str]       	=   decoder_layers
       UpperCamelCase	:					Optional[int]       	=   decoder_attention_heads
       UpperCamelCase	:					int       	=   dropout
       UpperCamelCase	:					int       	=   attention_dropout
       UpperCamelCase	:					Tuple       	=   activation_dropout
       UpperCamelCase	:					Tuple       	=   activation_function
       UpperCamelCase	:					int       	=   init_std
       UpperCamelCase	:					List[Any]       	=   encoder_layerdrop
       UpperCamelCase	:					List[str]       	=   decoder_layerdrop
       UpperCamelCase	:					Dict       	=   classifier_dropout
       UpperCamelCase	:					Optional[int]       	=   use_cache
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           num_labels=A_       ,							pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							is_encoder_decoder=A_       ,							decoder_start_token_id=A_       ,							forced_eos_token_id=A_       ,							**A_       ,							)

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,							A_   ):
        UpperCamelCase	:					int       	=   self.bos_token_id
        warnings.warn(
            F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
            "The config can simply be saved and uploaded again to be fixed."   )



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )

        if self.use_past:
         UpperCamelCase	:					List[str]       	=   {0: "batch"}
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "decoder_sequence"}
         UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
         self.fill_with_past_key_values_(A_       ,							direction="inputs"   )
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        UpperCamelCase	:					Any       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					Optional[Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
       else:
        UpperCamelCase	:					Optional[Any]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]   )

       return common_inputs





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Tuple       	=   super().outputs
       else:
        UpperCamelCase	:					Dict       	=   super(A_       ,							self   ).outputs
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					int       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					int       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Tuple       	=   {0: "batch", 2: "past_sequence + sequence"}
       return common_outputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       # Generate decoder inputs
       UpperCamelCase	:					List[Any]       	=   seq_length if not self.use_past else 1
       UpperCamelCase	:					Tuple       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )
       UpperCamelCase	:					Optional[int]       	=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase	:					List[Any]       	=   dict(**A_       ,							**A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   common_inputs["input_ids"].shape
        UpperCamelCase	:					List[Any]       	=   common_inputs["decoder_input_ids"].shape[1]
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   self.num_attention_heads
        UpperCamelCase	:					int       	=   (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        UpperCamelCase	:					List[Any]       	=   decoder_seq_length + 3
        UpperCamelCase	:					str       	=   (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(A_       ,							A_   )]       ,							dim=1   )

        UpperCamelCase	:					int       	=   []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   self.num_layers
        UpperCamelCase	:					Any       	=   min(A_       ,							A_   )
        UpperCamelCase	:					List[str]       	=   max(A_       ,							A_   ) - min_num_layers
        UpperCamelCase	:					Dict       	=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(A_   ):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
             )   )
        # TODO: test this.
        UpperCamelCase	:					Optional[Any]       	=   encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(A_       ,							A_   ):
         common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        UpperCamelCase	:					Optional[Any]       	=   seqlen + 2
        UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.num_layers
        UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_attention_heads
        UpperCamelCase	:					str       	=   (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        UpperCamelCase	:					Optional[Any]       	=   common_inputs["attention_mask"].dtype
        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["attention_mask"], torch.ones(A_       ,							A_       ,							dtype=A_   )]       ,							dim=1   )
        UpperCamelCase	:					Optional[Any]       	=   [
            (torch.zeros(A_   ), torch.zeros(A_   )) for _ in range(A_   )
        ]
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0   )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase	:					Union[str, Any]       	=   tokenizer.num_special_tokens_to_add(A_   )
       UpperCamelCase	:					int       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=A_   )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase	:					int       	=   [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
       UpperCamelCase	:					Dict       	=   dict(tokenizer(A_       ,							return_tensors=A_   )   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       elif self.task == "causal-lm":
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_causal_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )
       else:
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[Any]       	=   super()._flatten_past_key_values_(A_       ,							A_       ,							A_       ,							A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   super(A_       ,							self   )._flatten_past_key_values_(
            A_       ,							A_       ,							A_       ,							A_   )






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

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

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

						# print & log results
						lowerCAmelCase_      			=  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:
												lowerCAmelCase_      			=  f"log_{dataset_id}_predictions.txt"
												lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

						lowerCAmelCase_      			=  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!
						lowerCAmelCase_      			=  ['''\n\n''', '''\n''', '''   ''', '''  ''']

						for t in token_sequences_to_ignore:
												lowerCAmelCase_      			=  ''' '''.join(text.split(_A  )  )

						return text





def __UpperCamelCase			(       _A  ):
						# load dataset
						lowerCAmelCase_      			=  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
						lowerCAmelCase_      			=  AutoFeatureExtractor.from_pretrained(args.model_id  )
						lowerCAmelCase_      			=  feature_extractor.sampling_rate

						# resample audio
						lowerCAmelCase_      			=  dataset.cast_column('''audio''' ,  Audio(sampling_rate=_A  )  )

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

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

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

						# run inference on all examples
						lowerCAmelCase_      			=  dataset.map(_A ,  remove_columns=dataset.column_names  )

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


if __name__ == "__main__":
						_A     =			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     =			parser.parse_args()

						main(args)




