

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,
)


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

lowerCamelCase     =      '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def     UpperCAmelCase__       (    _A   :				Any			,   _A   :				List[Any]			,   _A   :				List[Any]=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     UpperCAmelCase__       (    _A   :				str			,   _A   :				Dict=5_12			,   _A   :				Optional[int]=5_12							):





  '''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 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_,					lowercase_,					)							->					List[Any]:






     """simple docstring"""






     super().__init__()

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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_ )							->					Tuple:






     """simple docstring"""






     a__						=min(int(num_inference_steps * strength ),					lowercase_ )

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

     return timesteps, num_inference_steps - t_start



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_=None )							->					Dict:






     """simple docstring"""






     if not isinstance(lowercase_,					(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(lowercase_ )}""" )

     a__						=image.to(device=lowercase_,					dtype=lowercase_ )

     a__						=batch_size * num_images_per_prompt

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

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

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

       a__						=self.movq.config.scaling_factor * init_latents

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

     a__						=init_latents.shape
     a__						=randn_tensor(lowercase_,					generator=lowercase_,					device=lowercase_,					dtype=lowercase_ )

     # get latents
     a__						=self.scheduler.add_noise(lowercase_,					lowercase_,					lowercase_ )

     a__						=init_latents

     return latents



   def 				_UpperCAmelCase				(       self,					lowercase_=0 )							->					Any:






     """simple docstring"""






     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(lowercase_,					lowercase_ )



   def 				_UpperCAmelCase				(       self,					lowercase_=0 )							->					List[Any]:






     """simple docstring"""






     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=lowercase_ )
       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(lowercase_,					lowercase_,					prev_module_hook=lowercase_ )

     # 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 )							->					List[str]:






     """simple docstring"""






     if not hasattr(self.unet,					'''_hf_hook''' ):
       return self.device
     for module in self.unet.modules():
       if (
           hasattr(lowercase_,					'''_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(lowercase_ )
   def __call__(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = 512,					lowercase_ = 512,					lowercase_ = 100,					lowercase_ = 4.0,					lowercase_ = 0.3,					lowercase_ = 1,					lowercase_ = None,					lowercase_ = "pil",					lowercase_ = True,					)							->					List[str]:






     """simple docstring"""






     a__						=self._execution_device

     a__						=guidance_scale > 1.0

     if isinstance(lowercase_,					lowercase_ ):
       a__						=torch.cat(lowercase_,					dim=0 )
     if isinstance(lowercase_,					lowercase_ ):
       a__						=torch.cat(lowercase_,					dim=0 )
     if isinstance(lowercase_,					lowercase_ ):
       a__						=torch.cat(lowercase_,					dim=0 )

     a__						=image_embeds.shape[0]

     if do_classifier_free_guidance:
       a__						=image_embeds.repeat_interleave(lowercase_,					dim=0 )
       a__						=negative_image_embeds.repeat_interleave(lowercase_,					dim=0 )
       a__						=hint.repeat_interleave(lowercase_,					dim=0 )

     a__						=torch.cat([negative_image_embeds, image_embeds],					dim=0 ).to(dtype=self.unet.dtype,					device=lowercase_ )
     a__						=torch.cat([hint, hint],					dim=0 ).to(dtype=self.unet.dtype,					device=lowercase_ )

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

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

     a__						=self.movq.encode(lowercase_ )['''latents''']
     a__						=latents.repeat_interleave(lowercase_,					dim=0 )
     self.scheduler.set_timesteps(lowercase_,					device=lowercase_ )
     a__,   a__						=self.get_timesteps(lowercase_,					lowercase_,					lowercase_ )
     a__						=timesteps[:1].repeat(batch_size * num_images_per_prompt )
     a__,   a__						=downscale_height_and_width(lowercase_,					lowercase_,					self.movq_scale_factor )
     a__						=self.prepare_latents(
         lowercase_,					lowercase_,					lowercase_,					lowercase_,					image_embeds.dtype,					lowercase_,					lowercase_ )
     for i, t in enumerate(self.progress_bar(lowercase_ ) ):
       # 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, '''hint''': hint}
       a__						=self.unet(
           sample=lowercase_,					timestep=lowercase_,					encoder_hidden_states=lowercase_,					added_cond_kwargs=lowercase_,					return_dict=lowercase_,					)[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(
           lowercase_,					lowercase_,					lowercase_,					generator=lowercase_,					)[0]

     # post-processing
     a__						=self.movq.decode(lowercase_,					force_not_quantize=lowercase_ )['''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(lowercase_ )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=lowercase_ )



import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCamelCase     =      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

lowerCamelCase     =      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


lowerCamelCase     =      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	, _KWARGS_DESCRIPTION							)
class 		__magic_name__    (							datasets.Metric							):

   '''simple docstring'''




   def 				_UpperCAmelCase				(       self )							->					Optional[int]:






     """simple docstring"""






     return datasets.MetricInfo(
         description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string''' ),
                 '''references''': datasets.Value('''string''' ),
             } ),					homepage='''https://github.com/moussaKam/FrugalScore''',					)



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Dict:






     """simple docstring"""






     if self.config_name == "default":
       a__						='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
     else:
       a__						=self.config_name
     a__						=AutoModelForSequenceClassification.from_pretrained(lowercase_ )
     a__						=AutoTokenizer.from_pretrained(lowercase_ )



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_=32,					lowercase_=128,					lowercase_=None,					)							->					Union[str, Any]:






     """simple docstring"""






     assert len(lowercase_ ) == len(
         lowercase_ ), "predictions and references should have the same number of sentences."
     if device is not None:
       assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
     else:
       a__						='''gpu''' if torch.cuda.is_available() else '''cpu'''
     a__						=TrainingArguments(
         '''trainer''',					fpaa=(device == '''gpu'''),					per_device_eval_batch_size=lowercase_,					report_to='''all''',					no_cuda=(device == '''cpu'''),					log_level='''warning''',					)
     a__						={'''sentence1''': predictions, '''sentence2''': references}
     a__						=datasets.Dataset.from_dict(lowercase_ )

     def tokenize_function(lowercase_ ):
       return self.tokenizer(
           data['''sentence1'''],					data['''sentence2'''],					max_length=lowercase_,					truncation=lowercase_,					padding=lowercase_ )

     a__						=raw_datasets.map(lowercase_,					batched=lowercase_ )
     tokenized_datasets.remove_columns(['''sentence1''', '''sentence2'''] )
     a__						=Trainer(self.model,					lowercase_,					tokenizer=self.tokenizer )
     a__						=trainer.predict(lowercase_ )
     return {"scores": list(predictions.predictions.squeeze(-1 ) )}
