






from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					TransformeraDModel,						lowerCamelCase		:					AutoencoderKL,						lowerCamelCase		:					KarrasDiffusionSchedulers,						lowerCamelCase		:					Optional[Dict[int, str]] = None,						):

								'''simple docstring'''
								super().__init__()
								self.register_modules(transformer=lowerCamelCase,						vae=lowerCamelCase,						scheduler=lowerCamelCase						)

								# create a imagenet -> id dictionary for easier use
								lowercase__     				=    {}
								if idalabel is not None:
												for key, value in idalabel.items():
																for label in value.split(''','''						):
																				lowercase__     				=    int(lowerCamelCase						)
												lowercase__     				=    dict(sorted(self.labels.items()						)						)




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

								'''simple docstring'''

								if not isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    list(lowerCamelCase						)

								for l in label:
												if l not in self.labels:
																raise ValueError(
																    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""						)

								return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(  self		:					Optional[Any],						lowerCamelCase		:					List[int],						lowerCamelCase		:					float = 4.0,						lowerCamelCase		:					Optional[Union[torch.Generator, List[torch.Generator]]] = None,						lowerCamelCase		:					int = 50,						lowerCamelCase		:					Optional[str] = "pil",						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								lowercase__     				=    len(lowerCamelCase						)
								lowercase__     				=    self.transformer.config.sample_size
								lowercase__     				=    self.transformer.config.in_channels

								lowercase__     				=    randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size),						generator=lowerCamelCase,						device=self.device,						dtype=self.transformer.dtype,						)
								lowercase__     				=    torch.cat([latents] * 2						) if guidance_scale > 1 else latents

								lowercase__     				=    torch.tensor(lowerCamelCase,						device=self.device						).reshape(-1						)
								lowercase__     				=    torch.tensor([1_000] * batch_size,						device=self.device						)
								lowercase__     				=    torch.cat([class_labels, class_null],						0						) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase						)

								for t in self.progress_bar(self.scheduler.timesteps						):
												if guidance_scale > 1:
																lowercase__     				=    latent_model_input[: len(lowerCamelCase						) // 2]
																lowercase__     				=    torch.cat([half, half],						dim=0						)
												lowercase__     				=    self.scheduler.scale_model_input(lowerCamelCase,						lowerCamelCase						)

												lowercase__     				=    t
												if not torch.is_tensor(lowerCamelCase						):
																# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																# This would be a good case for the `match` statement (Python 3.10+)
																lowercase__     				=    latent_model_input.device.type == '''mps'''
																if isinstance(lowerCamelCase,						lowerCamelCase						):
																				lowercase__     				=    torch.floataa if is_mps else torch.floataa
																else:
																				lowercase__     				=    torch.intaa if is_mps else torch.intaa
																lowercase__     				=    torch.tensor([timesteps],						dtype=lowerCamelCase,						device=latent_model_input.device						)
												elif len(timesteps.shape						) == 0:
																lowercase__     				=    timesteps[None].to(latent_model_input.device						)
												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												lowercase__     				=    timesteps.expand(latent_model_input.shape[0]						)
												# predict noise model_output
												lowercase__     				=    self.transformer(
												    lowerCamelCase,						timestep=lowerCamelCase,						class_labels=lowerCamelCase						).sample

												# perform guidance
												if guidance_scale > 1:
																lowercase__				,		lowercase__     				=    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																lowercase__				,		lowercase__     				=    torch.split(lowerCamelCase,						len(lowerCamelCase						) // 2,						dim=0						)

																lowercase__     				=    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																lowercase__     				=    torch.cat([half_eps, half_eps],						dim=0						)

																lowercase__     				=    torch.cat([eps, rest],						dim=1						)

												# learned sigma
												if self.transformer.config.out_channels // 2 == latent_channels:
																lowercase__				,		lowercase__     				=    torch.split(lowerCamelCase,						lowerCamelCase,						dim=1						)
												else:
																lowercase__     				=    noise_pred

												# compute previous image: x_t -> x_t-1
												lowercase__     				=    self.scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								if guidance_scale > 1:
												lowercase__				,		lowercase__     				=    latent_model_input.chunk(2,						dim=0						)
								else:
												lowercase__     				=    latent_model_input

								lowercase__     				=    1 / self.vae.config.scaling_factor * latents
								lowercase__     				=    self.vae.decode(lowerCamelCase						).sample

								lowercase__     				=    (samples / 2 + 0.5).clamp(0,						1						)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								lowercase__     				=    samples.cpu().permute(0,						2,						3,						1						).float().numpy()

								if output_type == "pil":
												lowercase__     				=    self.numpy_to_pil(lowerCamelCase						)

								if not return_dict:
												return (samples,)

								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)




