

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

	# load metric
	SCREAMING_SNAKE_CASE :   List[Any]					  =							load_metric("wer")
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							load_metric("cer")

	# compute metrics
	SCREAMING_SNAKE_CASE :   Dict					  =							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 :   Optional[Any]					  =							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 :   str					  =							f"log_{dataset_id}_predictions.txt"
		SCREAMING_SNAKE_CASE :   str					  =							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):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	SCREAMING_SNAKE_CASE :   Tuple					  =							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 :   Tuple					  =							["\n\n", "\n", "   ", "  "]

	for t in token_sequences_to_ignore:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							" ".join(text.split(_a))

	return text

def 			lowerCamelCase__			(       _a):
	# load dataset
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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 :   Dict					  =							AutoFeatureExtractor.from_pretrained(args.model_id)
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							feature_extractor.sampling_rate

	# resample audio
	SCREAMING_SNAKE_CASE :   List[str]					  =							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 :   Optional[int]					  =							asr(
		    batch["audio"]["array"]				,      chunk_length_s=args.chunk_length_s				,      stride_length_s=args.stride_length_s)

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

	# run inference on all examples
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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)



'''simple docstring'''





from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

    # Transformer architecture configuration
    UpperCAmelCase__	:Optional[Any]      =      input_size * len(self.lags_sequence ) + self._number_of_features
    UpperCAmelCase__	:Optional[Any]      =      d_model
    UpperCAmelCase__	:Any      =      encoder_attention_heads
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:int      =      encoder_ffn_dim
    UpperCAmelCase__	:Union[str, Any]      =      decoder_ffn_dim
    UpperCAmelCase__	:Optional[Any]      =      encoder_layers
    UpperCAmelCase__	:str      =      decoder_layers

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

    super().__init__(is_encoder_decoder=__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return (
        sum(self.embedding_dimension )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )



