



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								torch.manual_seed(0					)
								A__       =    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     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.dummy_uncond_unet
								A__       =    ScoreSdeVeScheduler()

								A__       =    ScoreSdeVePipeline(unet=_snake_case				,					scheduler=_snake_case					)
								sde_ve.to(_snake_case					)
								sde_ve.set_progress_bar_config(disable=_snake_case					)

								A__       =    torch.manual_seed(0					)
								A__       =    sde_ve(num_inference_steps=2				,					output_type='numpy'				,					generator=_snake_case					).images

								A__       =    torch.manual_seed(0					)
								A__       =    sde_ve(num_inference_steps=2				,					output_type='numpy'				,					generator=_snake_case				,					return_dict=_snake_case					)[
								    0
								]

								A__       =    image[0, -3:, -3:, -1]
								A__       =    image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								A__       =    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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    'google/ncsnpp-church-256'
								A__       =    UNetaDModel.from_pretrained(_snake_case					)

								A__       =    ScoreSdeVeScheduler.from_pretrained(_snake_case					)

								A__       =    ScoreSdeVePipeline(unet=_snake_case				,					scheduler=_snake_case					)
								sde_ve.to(_snake_case					)
								sde_ve.set_progress_bar_config(disable=_snake_case					)

								A__       =    torch.manual_seed(0					)
								A__       =    sde_ve(num_inference_steps=10				,					output_type='numpy'				,					generator=_snake_case					).images

								A__       =    image[0, -3:, -3:, -1]

								assert image.shape == (1, 2_56, 2_56, 3)

								A__       =    np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

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




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 A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			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(__UpperCamelCase					)

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

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

									result.map(__UpperCamelCase ,	with_indices=__UpperCamelCase					)


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

			A__       =    re.sub(__UpperCamelCase ,	'' ,	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(__UpperCamelCase					)					)

			return text


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# load dataset
			A__       =    load_dataset(args.dataset ,	args.config ,	split=args.split ,	use_auth_token=__UpperCamelCase					)

			# 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=__UpperCamelCase					)					)

			# 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(__UpperCamelCase					):
						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(__UpperCamelCase ,	remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      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.''',
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

							main(args)




