






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A__			:    Optional[Any]         =				transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if isinstance(lowerCamelCase_	,     torch.Tensor       ):
								return image
				elif isinstance(lowerCamelCase_	,     PIL.Image.Image       ):
								lowercase__     				=    [image]

				lowercase__     				=    [trans(img.convert('''RGB'''       )       ) for img in image]
				lowercase__     				=    torch.stack(lowerCamelCase_       )
				return image




class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								lowercase__     				=    DDIMScheduler.from_config(scheduler.config						)

								self.register_modules(unet=lowerCamelCase,						scheduler=lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					str						):

								'''simple docstring'''
								if strength < 0 or strength > 1:
												raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								# get the original timestep using init_timestep
								lowercase__     				=    min(int(num_inference_steps * strength						),						lowerCamelCase						)

								lowercase__     				=    max(num_inference_steps - init_timestep,						0						)
								lowercase__     				=    self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Dict,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					List[Any]=None						):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,						(torch.Tensor, PIL.Image.Image, list)						):
												raise ValueError(
												    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase						)}"""						)

								lowercase__     				=    image.to(device=lowerCamelCase,						dtype=lowerCamelCase						)

								if isinstance(lowerCamelCase,						lowerCamelCase						) and len(lowerCamelCase						) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(lowerCamelCase						)}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

								lowercase__     				=    init_latents.shape
								lowercase__     				=    randn_tensor(lowerCamelCase,						generator=lowerCamelCase,						device=lowerCamelCase,						dtype=lowerCamelCase						)

								# get latents
								print('''add noise to latents at timestep''',						lowerCamelCase						)
								lowercase__     				=    self.scheduler.add_noise(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
								lowercase__     				=    init_latents

								return latents




				@torch.no_grad()
				def __call__(  self		:					List[str],						lowerCamelCase		:					Union[torch.FloatTensor, PIL.Image.Image] = None,						lowerCamelCase		:					float = 0.8,						lowerCamelCase		:					int = 1,						lowerCamelCase		:					Optional[Union[torch.Generator, List[torch.Generator]]] = None,						lowerCamelCase		:					float = 0.0,						lowerCamelCase		:					int = 50,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Optional[str] = "pil",						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								self.check_inputs(lowerCamelCase						)

								# 2. Preprocess image
								lowercase__     				=    preprocess(lowerCamelCase						)

								# 3. set timesteps
								self.scheduler.set_timesteps(lowerCamelCase,						device=self.device						)
								lowercase__				,		lowercase__     				=    self.get_timesteps(lowerCamelCase,						lowerCamelCase,						self.device						)
								lowercase__     				=    timesteps[:1].repeat(lowerCamelCase						)

								# 4. Prepare latent variables
								lowercase__     				=    self.prepare_latents(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						self.unet.dtype,						self.device,						lowerCamelCase						)
								lowercase__     				=    latents

								# 5. Denoising loop
								for t in self.progress_bar(lowerCamelCase						):
												# 1. predict noise model_output
												lowercase__     				=    self.unet(lowerCamelCase,						lowerCamelCase						).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												lowercase__     				=    self.scheduler.step(
												    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						eta=lowerCamelCase,						use_clipped_model_output=lowerCamelCase,						generator=lowerCamelCase,						).prev_sample

								lowercase__     				=    (image / 2 + 0.5).clamp(0,						1						)
								lowercase__     				=    image.cpu().permute(0,						2,						3,						1						).numpy()
								if output_type == "pil":
												lowercase__     				=    self.numpy_to_pil(lowerCamelCase						)

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowerCamelCase						)












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    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    args.log_outputs
				lowercase__     				=    '''_'''.join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

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

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

				# print & log results
				lowercase__     				=    F"""WER: {wer_result}\nCER: {cer_result}"""
				print(lowerCamelCase_       )

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

				# log all results in text file. Possibly interesting for analysis
				if log_outputs is not None:
								lowercase__     				=    F"""log_{dataset_id}_predictions.txt"""
								lowercase__     				=    F"""log_{dataset_id}_targets.txt"""

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

												result.map(lowerCamelCase_	,     with_indices=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






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

				lowercase__     				=    re.sub(lowerCamelCase_	,     ''''''	,     text.lower()       )

				# In addition, we can normalize the target text, e.g. removing new lines characters etc...
				# note that order is important here!
				lowercase__     				=    ['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								lowercase__     				=    ''' '''.join(text.split(lowerCamelCase_       )       )

				return text






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# load dataset
				lowercase__     				=    load_dataset(args.dataset	,     args.config	,     split=args.split	,     use_auth_token=lowerCamelCase_       )

				# for testing: only process the first two examples as a test
				# dataset = dataset.select(range(10))

				# load processor
				lowercase__     				=    AutoFeatureExtractor.from_pretrained(args.model_id       )
				lowercase__     				=    feature_extractor.sampling_rate

				# resample audio
				lowercase__     				=    dataset.cast_column('''audio'''	,     Audio(sampling_rate=lowerCamelCase_       )       )

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

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

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

				# run inference on all examples
				lowercase__     				=    dataset.map(lowerCamelCase_	,     remove_columns=dataset.column_names       )

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


if __name__ == "__main__":
							A__			:    int         =				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__			:    Union[str, Any]         =				parser.parse_args()

							main(args)




