




from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_lowerCamelCase	:		Tuple							       =   """CompVis/stable-diffusion-v1-1"""
_lowerCamelCase	:		Tuple							       =   """CompVis/stable-diffusion-v1-2"""
_lowerCamelCase	:		List[Any]							       =   """CompVis/stable-diffusion-v1-3"""
_lowerCamelCase	:		Optional[Any]							       =   """CompVis/stable-diffusion-v1-4"""


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	AutoencoderKL					, UpperCAmelCase__      :	CLIPTextModel					, UpperCAmelCase__      :	CLIPTokenizer					, UpperCAmelCase__      :	UNetaDConditionModel					, UpperCAmelCase__      :	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					, UpperCAmelCase__      :	StableDiffusionSafetyChecker					, UpperCAmelCase__      :	CLIPImageProcessor					, UpperCAmelCase__      :	bool = True					, )						->List[Any]:





           '''simple docstring'''

           super()._init_()

           A__       =    StableDiffusionPipeline.from_pretrained(UpperCAmelCase__)
           A__       =    StableDiffusionPipeline.from_pretrained(UpperCAmelCase__)
           A__       =    StableDiffusionPipeline.from_pretrained(UpperCAmelCase__)
           A__       =    StableDiffusionPipeline(
               vae=UpperCAmelCase__					, text_encoder=UpperCAmelCase__					, tokenizer=UpperCAmelCase__					, unet=UpperCAmelCase__					, scheduler=UpperCAmelCase__					, safety_checker=UpperCAmelCase__					, feature_extractor=UpperCAmelCase__					, requires_safety_checker=UpperCAmelCase__					, )

           self.register_modules(pipelinea=self.pipea					, pipelinea=self.pipea					, pipelinea=self.pipea					, pipelinea=self.pipea)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Dict[str, Any]:





           '''simple docstring'''

           return {k: getattr(self					, UpperCAmelCase__) for k in self.config.keys() if not k.startswith('''_''')}


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	Optional[Union[str, int]] = "auto")						->List[Any]:





           '''simple docstring'''

           if slice_size == "auto":
                  # half the attention head size is usually a good trade-off between
                  # speed and memory
                  A__       =    self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           self.enable_attention_slicing(UpperCAmelCase__)


    @torch.no_grad()
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, UpperCAmelCase__      :	Union[str, List[str]]					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	float = 7.5					, UpperCAmelCase__      :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase__      :	Optional[int] = 1					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	Optional[torch.Generator] = None					, UpperCAmelCase__      :	Optional[torch.FloatTensor] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase__      :	int = 1					, **UpperCAmelCase__      :	str					, )						->Tuple:





           '''simple docstring'''

           return self.pipea(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )


    @torch.no_grad()
    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	Union[str, List[str]]					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	float = 7.5					, UpperCAmelCase__      :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase__      :	Optional[int] = 1					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	Optional[torch.Generator] = None					, UpperCAmelCase__      :	Optional[torch.FloatTensor] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase__      :	int = 1					, **UpperCAmelCase__      :	str					, )						->Optional[int]:





           '''simple docstring'''

           return self.pipea(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )


    @torch.no_grad()
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Union[str, List[str]]					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	float = 7.5					, UpperCAmelCase__      :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase__      :	Optional[int] = 1					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	Optional[torch.Generator] = None					, UpperCAmelCase__      :	Optional[torch.FloatTensor] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase__      :	int = 1					, **UpperCAmelCase__      :	List[str]					, )						->List[Any]:





           '''simple docstring'''

           return self.pipea(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )


    @torch.no_grad()
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Union[str, List[str]]					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	float = 7.5					, UpperCAmelCase__      :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase__      :	Optional[int] = 1					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	Optional[torch.Generator] = None					, UpperCAmelCase__      :	Optional[torch.FloatTensor] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase__      :	int = 1					, **UpperCAmelCase__      :	Union[str, Any]					, )						->Optional[Any]:





           '''simple docstring'''

           return self.pipea(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )





    @torch.no_grad()
    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	Union[str, List[str]]					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 512					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	float = 7.5					, UpperCAmelCase__      :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase__      :	Optional[int] = 1					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	Optional[torch.Generator] = None					, UpperCAmelCase__      :	Optional[torch.FloatTensor] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase__      :	int = 1					, **UpperCAmelCase__      :	Union[str, Any]					, )						->Tuple:





           '''simple docstring'''

           A__       =    '''cuda''' if torch.cuda.is_available() else '''cpu'''
           self.to(UpperCAmelCase__)

           # Checks if the height and width are divisible by 8 or not
           if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}.""")

           # Get first result from Stable Diffusion Checkpoint v1.1
           A__       =    self.textaimg_sda_a(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )

           # Get first result from Stable Diffusion Checkpoint v1.2
           A__       =    self.textaimg_sda_a(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )

           # Get first result from Stable Diffusion Checkpoint v1.3
           A__       =    self.textaimg_sda_a(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )

           # Get first result from Stable Diffusion Checkpoint v1.4
           A__       =    self.textaimg_sda_a(
               prompt=UpperCAmelCase__					, height=UpperCAmelCase__					, width=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__					, guidance_scale=UpperCAmelCase__					, negative_prompt=UpperCAmelCase__					, num_images_per_prompt=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, latents=UpperCAmelCase__					, output_type=UpperCAmelCase__					, return_dict=UpperCAmelCase__					, callback=UpperCAmelCase__					, callback_steps=UpperCAmelCase__					, **UpperCAmelCase__					, )

           # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
           return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]])








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				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       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(lowercase_						)

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

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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

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

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

       # 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=lowercase_						)						)

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

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


if __name__ == "__main__":
  _lowerCamelCase	:		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.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


