from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


SCREAMING_SNAKE_CASE :	List[Any]							     =		logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE :	int							     =		"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""



def   __A		(			_A							,				_A							,				_A=8					):



 """simple docstring"""


 __a       =  height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __a       =  width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor



def   __A		(			_A							,				_A=512							,				_A=512					):



 """simple docstring"""


 __a       =  pil_image.resize((w, h)							,				resample=Image.BICUBIC							,				reducing_gap=1					)
 __a       =  np.array(pil_image.convert("RGB"					)					)
 __a       =  arr.astype(np.floataa					) / 127.5 - 1
 __a       =  np.transpose(_A							,				[2, 0, 1]					)
 __a       =  torch.from_numpy(_A					).unsqueeze(0					)
 return image






class    A_ (       a_ ):

  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      UNetaDConditionModel     ,							__SCREAMING_SNAKE_CASE :      DDPMScheduler     ,							__SCREAMING_SNAKE_CASE :      VQModel     ,							):
   super().__init__()

   self.register_modules(
       unet=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE     ,							movq=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  2 ** (len(self.movq.config.block_out_channels       ) - 1)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple       ):
   # get the original timestep using init_timestep
   __a       =  min(int(num_inference_steps * strength       )     ,							__SCREAMING_SNAKE_CASE       )

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

   return timesteps, num_inference_steps - t_start

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None       ):
   if not isinstance(__SCREAMING_SNAKE_CASE     ,							(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(__SCREAMING_SNAKE_CASE       )}"""       )

   __a       =  image.to(device=__SCREAMING_SNAKE_CASE     ,							dtype=__SCREAMING_SNAKE_CASE       )

   __a       =  batch_size * num_images_per_prompt

   if image.shape[1] == 4:
    __a       =  image

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

    elif isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
     __a       =  [
         self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(__SCREAMING_SNAKE_CASE       )
     ]
     __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=0       )
    else:
     __a       =  self.movq.encode(__SCREAMING_SNAKE_CASE       ).latent_dist.sample(__SCREAMING_SNAKE_CASE       )

    __a       =  self.movq.config.scaling_factor * init_latents

   __a       =  torch.cat([init_latents]     ,							dim=0       )

   __a       =  init_latents.shape
   __a       =  randn_tensor(__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							device=__SCREAMING_SNAKE_CASE     ,							dtype=__SCREAMING_SNAKE_CASE       )

   # get latents
   __a       =  self.scheduler.add_noise(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  init_latents

   return latents

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

   __a       =  torch.device(f"""cuda:{gpu_id}"""       )

   __a       =  [
       self.unet,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=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."       )

   __a       =  torch.device(f"""cuda:{gpu_id}"""       )

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

   __a       =  None
   for cpu_offloaded_model in [self.unet, self.movq]:
    __a       ,     __a       =  cpu_offload_with_hook(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							prev_module_hook=__SCREAMING_SNAKE_CASE       )

   # We'll offload the last model manually.
   __a       =  hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_UpperCAmelCase   (		self :      int       ):
   if not hasattr(self.unet     ,							"_hf_hook"       ):
    return self.device
   for module in self.unet.modules():
    if (
        hasattr(__SCREAMING_SNAKE_CASE     ,							"_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(__SCREAMING_SNAKE_CASE       )
  def __call__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							__SCREAMING_SNAKE_CASE :      int = 5_12     ,							__SCREAMING_SNAKE_CASE :      int = 5_12     ,							__SCREAMING_SNAKE_CASE :      int = 1_00     ,							__SCREAMING_SNAKE_CASE :      float = 4.0     ,							__SCREAMING_SNAKE_CASE :      float = 0.3     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = "pil"     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							):
   __a       =  self._execution_device

   __a       =  guidance_scale > 1.0

   if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=0       )
   __a       =  image_embeds.shape[0]
   if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=0       )

   if do_classifier_free_guidance:
    __a       =  image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,							dim=0       )
    __a       =  negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,							dim=0       )

   __a       =  torch.cat([negative_image_embeds, image_embeds]     ,							dim=0       ).to(dtype=self.unet.dtype     ,							device=__SCREAMING_SNAKE_CASE       )

   if not isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  [image]
   if not all(isinstance(__SCREAMING_SNAKE_CASE     ,							(PIL.Image.Image, torch.Tensor)       ) for i in image       ):
    raise ValueError(
        f"""Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""       )

   __a       =  torch.cat([prepare_image(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for i in image]     ,							dim=0       )
   __a       =  image.to(dtype=image_embeds.dtype     ,							device=__SCREAMING_SNAKE_CASE       )

   __a       =  self.movq.encode(__SCREAMING_SNAKE_CASE       )["latents"]
   __a       =  latents.repeat_interleave(__SCREAMING_SNAKE_CASE     ,							dim=0       )
   self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,							device=__SCREAMING_SNAKE_CASE       )
   __a       ,     __a       =  self.get_timesteps(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  timesteps[:1].repeat(batch_size * num_images_per_prompt       )
   __a       ,     __a       =  downscale_height_and_width(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							self.movq_scale_factor       )
   __a       =  self.prepare_latents(
       __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							image_embeds.dtype     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE       )       ):
    # expand the latents if we are doing classifier free guidance
    __a       =  torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

    __a       =  {"image_embeds": image_embeds}
    __a       =  self.unet(
        sample=__SCREAMING_SNAKE_CASE     ,							timestep=__SCREAMING_SNAKE_CASE     ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,							added_cond_kwargs=__SCREAMING_SNAKE_CASE     ,							return_dict=__SCREAMING_SNAKE_CASE     ,							)[0]

    if do_classifier_free_guidance:
     __a       ,     __a       =  noise_pred.split(latents.shape[1]     ,							dim=1       )
     __a       ,     __a       =  noise_pred.chunk(2       )
     __a       ,     __a       =  variance_pred.chunk(2       )
     __a       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     __a       =  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"]
    ):
     __a       ,     __a       =  noise_pred.split(latents.shape[1]     ,							dim=1       )

    # compute the previous noisy sample x_t -> x_t-1
    __a       =  self.scheduler.step(
        __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							)[0]

   # post-processing
   __a       =  self.movq.decode(__SCREAMING_SNAKE_CASE     ,							force_not_quantize=__SCREAMING_SNAKE_CASE       )["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"]:
    __a       =  image * 0.5 + 0.5
    __a       =  image.clamp(0     ,							1       )
    __a       =  image.cpu().permute(0     ,							2     ,							3     ,							1       ).float().numpy()

   if output_type == "pil":
    __a       =  self.numpy_to_pil(__SCREAMING_SNAKE_CASE       )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE       )



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		(			_A							,				_A					):



 """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(_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:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  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   __A		(			_A					):



 """simple docstring"""


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

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

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


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

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # 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(_A					):
  __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(_A							,				remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	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.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


