




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						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     Optional[int]  )       ->  List[str]:







			"""simple docstring"""





			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(lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     Dict  ):
												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						(							lowerCamelCase_ :     Dict  )       ->  List[Any]:







			"""simple docstring"""





			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        __UpperCAmelCase						(							lowerCamelCase_ :     int  )       ->  Optional[int]:







			"""simple docstring"""





			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(lowerCamelCase_ :     Optional[int]  ):
						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__":
						UpperCamelCase__ :   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.''',
						)
						UpperCamelCase__ :   Dict						=							parser.parse_args()

						main(args)






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							UNetaDModel(
				    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,   )
				return model






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2






@slow
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2