
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      (	_lowerCamelCase				:    Dataset		,						_lowerCamelCase				:    Dict[str, str])      ->					str:






			'''simple docstring'''





			__UpperCamelCase			:     int         =      args.log_outputs
			__UpperCamelCase			:     Optional[Any]         =      "_".join(args.dataset.split("/") + [args.config, args.split])

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

			# compute metrics
			__UpperCamelCase			:     List[str]         =      wer.compute(references=result["target"]		,						predictions=result["prediction"])
			__UpperCamelCase			:     Optional[Any]         =      cer.compute(references=result["target"]		,						predictions=result["prediction"])

			# print & log results
			__UpperCamelCase			:     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:
						__UpperCamelCase			:     Optional[Any]         =      F'log_{dataset_id}_predictions.txt'
						__UpperCamelCase			:     Tuple         =      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(_lowerCamelCase				:    int		,						_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(_A		,						with_indices=_A)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					str:






			'''simple docstring'''





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

			__UpperCamelCase			:     int         =      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!
			__UpperCamelCase			:     str         =      ["\n\n", "\n", "   ", "  "]

			for t in token_sequences_to_ignore:
						__UpperCamelCase			:     Tuple         =      " ".join(text.split(_A))

			return text




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Tuple:






			'''simple docstring'''





			__UpperCamelCase			:     Optional[int]         =      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
			__UpperCamelCase			:     Optional[Any]         =      AutoFeatureExtractor.from_pretrained(args.model_id)
			__UpperCamelCase			:     Any         =      feature_extractor.sampling_rate

			# resample audio
			__UpperCamelCase			:     Optional[int]         =      dataset.cast_column("audio"		,						Audio(sampling_rate=_A))

			# load eval pipeline
			if args.device is None:
						__UpperCamelCase			:     str         =      0 if torch.cuda.is_available() else -1
			__UpperCamelCase			:     Union[str, Any]         =      pipeline("automatic-speech-recognition"		,						model=args.model_id		,						device=args.device)

			# map function to decode audio
			def map_to_pred(_lowerCamelCase				:    Dict):
						__UpperCamelCase			:     List[str]         =      asr(
						    batch["audio"]["array"]		,						chunk_length_s=args.chunk_length_s		,						stride_length_s=args.stride_length_s)

						__UpperCamelCase			:     Optional[int]         =      prediction["text"]
						__UpperCamelCase			:     Optional[int]         =      normalize_text(batch["sentence"])
						return batch

			# run inference on all examples
			__UpperCamelCase			:     Union[str, 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__":
		lowercase						:      Union[str, 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.',
		)
		lowercase						:      Dict									= parser.parse_args()

		main(args)

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'facebook/bart-large-mnli'
	_A			=  (
	    'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
	    'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
	    'It returns the most likely label in the list of provided `labels` for the input text.'
	)
	_A			=  'text_classifier'
	_A			=  AutoTokenizer
	_A			=  AutoModelForSequenceClassification

	_A			=  ['text', ['text']]
	_A			=  ['text']







	def      _lowerCamelCase       (      self	:str		)	->				Tuple:
								super().setup()
								__UpperCamelCase			:     Any         =      self.model.config
								__UpperCamelCase			:     List[Any]         =      -1
								for idx, label in config.idalabel.items():
															if label.lower().startswith("entail"		):
																						__UpperCamelCase			:     Optional[Any]         =      int(a		)
								if self.entailment_id == -1:
															raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Dict  ,     a	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      labels
								return self.pre_processor(
								    [text] * len(a		)  ,     [f'This example is {label}' for label in labels]  ,     return_tensors="pt"  ,     padding="max_length"  ,     )







	def      _lowerCamelCase       (      self	:Any  ,     a	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[int]         =      outputs.logits
								__UpperCamelCase			:     Dict         =      torch.argmax(logits[:, 2]		).item()
								return self._labels[label_id]