


'''simple docstring'''

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      (A  , A       )       ->       str:






       """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(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:
              lowercase__      			= f"log_{dataset_id}_predictions.txt"
              lowercase__      			= 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(A  , A       ):
                            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 _SCREAMING_SNAKE_CASE      (A       )       ->       Union[str, Any]:






       """simple docstring"""







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

       lowercase__      			= 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!
       lowercase__      			= ["\n\n", "\n", "   ", "  "]

       for t in token_sequences_to_ignore:
              lowercase__      			= " ".join(text.split(A       )       )

       return text

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= 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
       lowercase__      			= AutoFeatureExtractor.from_pretrained(args.model_id       )
       lowercase__      			= feature_extractor.sampling_rate

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

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

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


if __name__ == "__main__":
 lowerCamelCase							:   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							:   Tuple  						=	parser.parse_args()

 main(args)










from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCAmelCase		:  int	=   logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase		:  Dict	=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase=8      ):
  lowercase			:List[str]							=  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowercase			:List[Any]							=  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class       __lowerCAmelCase				(							lowerCAmelCase):






       def __init__(      self:    Tuple				,    _lowerCAmelCase:    UNetaDConditionModel				,    _lowerCAmelCase:    DDPMScheduler				,    _lowerCAmelCase:    VQModel				,    ):
         super().__init__()

         self.register_modules(
             unet=_lowerCAmelCase				,    scheduler=_lowerCAmelCase				,    movq=_lowerCAmelCase				,    )
         lowercase			:List[Any]							=  2 ** (len(self.movq.config.block_out_channels ) - 1)






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Any				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[int] ):
         if latents is None:
           lowercase			:int							=  randn_tensor(_lowerCAmelCase				,    generator=_lowerCAmelCase				,    device=_lowerCAmelCase				,    dtype=_lowerCAmelCase )
         else:
           if latents.shape != shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
           lowercase			:Optional[Any]							=  latents.to(_lowerCAmelCase )

         lowercase			:int							=  latents * scheduler.init_noise_sigma
         return latents






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str]=0 ):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("Please install accelerate via `pip install accelerate`" )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         lowercase			:List[str]							=  [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(_lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict=0 ):
         if is_accelerate_available() and is_accelerate_version(">="				,    "0.17.0.dev0" ):
           from accelerate import cpu_offload_with_hook
         else:
           raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         if self.device.type != "cpu":
           self.to("cpu"				,    silence_dtype_warnings=_lowerCAmelCase )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         lowercase			:List[Any]							=  None
         for cpu_offloaded_model in [self.unet, self.movq]:
           lowercase		,			lowercase			:Dict							=  cpu_offload_with_hook(_lowerCAmelCase				,    _lowerCAmelCase				,    prev_module_hook=_lowerCAmelCase )

         # We'll offload the last model manually.
         lowercase			:Dict							=  hook






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         if not hasattr(self.unet				,    "_hf_hook" ):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(_lowerCAmelCase				,    "_hf_hook" )
               and hasattr(module._hf_hook				,    "execution_device" )
               and module._hf_hook.execution_device is not None
           ):
             return torch.device(module._hf_hook.execution_device )
         return self.device






       @torch.no_grad()
       @replace_example_docstring(_lowerCAmelCase )
       def __call__(      self:    str				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 1_00				,    _lowerCAmelCase:    float = 4.0				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    _lowerCAmelCase:    Optional[torch.FloatTensor] = None				,    _lowerCAmelCase:    Optional[str] = "pil"				,    _lowerCAmelCase:    bool = True				,    ):
         lowercase			:str							=  self._execution_device

         lowercase			:List[str]							=  guidance_scale > 1.0

         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:Any							=  torch.cat(_lowerCAmelCase				,    dim=0 )
         lowercase			:Any							=  image_embeds.shape[0] * num_images_per_prompt
         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:List[Any]							=  torch.cat(_lowerCAmelCase				,    dim=0 )

         if do_classifier_free_guidance:
           lowercase			:int							=  image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )
           lowercase			:Any							=  negative_image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )

         lowercase			:Optional[int]							=  torch.cat([negative_image_embeds, image_embeds]				,    dim=0 ).to(dtype=self.unet.dtype				,    device=_lowerCAmelCase )

         self.scheduler.set_timesteps(_lowerCAmelCase				,    device=_lowerCAmelCase )
         lowercase			:Optional[Any]							=  self.scheduler.timesteps

         lowercase			:Tuple							=  self.unet.config.in_channels

         lowercase		,			lowercase			:Optional[int]							=  downscale_height_and_width(_lowerCAmelCase				,    _lowerCAmelCase				,    self.movq_scale_factor )

         # create initial latent
         lowercase			:List[Any]							=  self.prepare_latents(
             (batch_size, num_channels_latents, height, width)				,    image_embeds.dtype				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    self.scheduler				,    )

         for i, t in enumerate(self.progress_bar(_lowerCAmelCase ) ):
           # expand the latents if we are doing classifier free guidance
           lowercase			:Tuple							=  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

           lowercase			:List[str]							=  {"image_embeds": image_embeds}
           lowercase			:List[Any]							=  self.unet(
               sample=_lowerCAmelCase				,    timestep=_lowerCAmelCase				,    encoder_hidden_states=_lowerCAmelCase				,    added_cond_kwargs=_lowerCAmelCase				,    return_dict=_lowerCAmelCase				,    )[0]

           if do_classifier_free_guidance:
             lowercase		,			lowercase			:List[Any]							=  noise_pred.split(latents.shape[1]				,    dim=1 )
             lowercase		,			lowercase			:Any							=  noise_pred.chunk(2 )
             lowercase		,			lowercase			:Dict							=  variance_pred.chunk(2 )
             lowercase			:str							=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowercase			:Any							=  torch.cat([noise_pred, variance_pred_text]				,    dim=1 )

           if not (
               hasattr(self.scheduler.config				,    "variance_type" )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
             lowercase		,			lowercase			:str							=  noise_pred.split(latents.shape[1]				,    dim=1 )

           # compute the previous noisy sample x_t -> x_t-1
           lowercase			:Tuple							=  self.scheduler.step(
               _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    generator=_lowerCAmelCase				,    )[0]
         # post-processing
         lowercase			:Dict							=  self.movq.decode(_lowerCAmelCase				,    force_not_quantize=_lowerCAmelCase )["sample"]

         if output_type not in ["pt", "np", "pil"]:
           raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )

         if output_type in ["np", "pil"]:
           lowercase			:Any							=  image * 0.5 + 0.5
           lowercase			:Tuple							=  image.clamp(0				,    1 )
           lowercase			:List[str]							=  image.cpu().permute(0				,    2				,    3				,    1 ).float().numpy()

         if output_type == "pil":
           lowercase			:List[Any]							=  self.numpy_to_pil(_lowerCAmelCase )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase )



