



"""simple docstring"""
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,
)


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

A:     List[str]										=	"""
    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    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	List[str]=8		)     ->   Optional[int]:
 UpperCAmelCase			:      List[str]			=			height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCAmelCase			:      Union[str, Any]			=			width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str=512    ,	UpperCamelCase		:	int=512		)     ->   Tuple:
 UpperCAmelCase			:      Any			=			pil_image.resize((w, h)    ,	resample=Image.BICUBIC    ,	reducing_gap=1		)
 UpperCAmelCase			:      List[str]			=			np.array(pil_image.convert("""RGB"""		)		)
 UpperCAmelCase			:      Tuple			=			arr.astype(np.floataa		) / 127.5 - 1
 UpperCAmelCase			:      str			=			np.transpose(UpperCamelCase    ,	[2, 0, 1]		)
 UpperCAmelCase			:      Any			=			torch.from_numpy(UpperCamelCase		).unsqueeze(0		)
 return image



class    SCREAMING_SNAKE_CASE__						(      _a				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    super().__init__()

    self.register_modules(
        unet=snake_case_   ,  scheduler=snake_case_   ,  movq=snake_case_   ,  )
    UpperCAmelCase			:      Any			=			2 ** (len(self.movq.config.block_out_channels		) - 1)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			min(int(num_inference_steps * strength		)   ,  snake_case_		)

    UpperCAmelCase			:      Union[str, Any]			=			max(num_inference_steps - init_timestep   ,  0		)
    UpperCAmelCase			:      List[str]			=			self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   str:





    '''simple docstring'''




    if not isinstance(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(snake_case_		)}"		)

    UpperCAmelCase			:      Optional[int]			=			image.to(device=snake_case_   ,  dtype=snake_case_		)

    UpperCAmelCase			:      Tuple			=			batch_size * num_images_per_prompt

    if image.shape[1] == 4:
     UpperCAmelCase			:      Optional[Any]			=			image

    else:
     if isinstance(snake_case_   ,  snake_case_		) and len(snake_case_		) != batch_size:
      raise ValueError(
          F"You have passed a list of generators of length {len(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(snake_case_   ,  snake_case_		):
      UpperCAmelCase			:      Any			=			[
          self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(snake_case_		)
      ]
      UpperCAmelCase			:      List[str]			=			torch.cat(snake_case_   ,  dim=0		)
     else:
      UpperCAmelCase			:      Tuple			=			self.movq.encode(snake_case_		).latent_dist.sample(snake_case_		)

     UpperCAmelCase			:      Dict			=			self.movq.config.scaling_factor * init_latents

    UpperCAmelCase			:      Optional[int]			=			torch.cat([init_latents]   ,  dim=0		)

    UpperCAmelCase			:      Dict			=			init_latents.shape
    UpperCAmelCase			:      str			=			randn_tensor(snake_case_   ,  generator=snake_case_   ,  device=snake_case_   ,  dtype=snake_case_		)

    # get latents
    UpperCAmelCase			:      List[Any]			=			self.scheduler.add_noise(snake_case_   ,  snake_case_   ,  snake_case_		)

    UpperCAmelCase			:      Tuple			=			init_latents

    return latents

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=0		)						->   Tuple:





    '''simple docstring'''




    if is_accelerate_available():
     from accelerate import cpu_offload
    else:
     raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

    UpperCAmelCase			:      str			=			torch.device(F"cuda:{gpu_id}"		)

    UpperCAmelCase			:      List[Any]			=			[
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
      cpu_offload(snake_case_   ,  snake_case_		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=0		)						->   List[str]:





    '''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."""		)

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

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

    UpperCAmelCase			:      Dict			=			None
    for cpu_offloaded_model in [self.unet, self.movq]:
     UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			cpu_offload_with_hook(snake_case_   ,  snake_case_   ,  prev_module_hook=snake_case_		)

    # We'll offload the last model manually.
    UpperCAmelCase			:      Optional[int]			=			hook

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




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





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self._execution_device

    UpperCAmelCase			:      str			=			guidance_scale > 1.0

    if isinstance(snake_case_   ,  snake_case_		):
     UpperCAmelCase			:      Optional[int]			=			torch.cat(snake_case_   ,  dim=0		)
    UpperCAmelCase			:      Tuple			=			image_embeds.shape[0]
    if isinstance(snake_case_   ,  snake_case_		):
     UpperCAmelCase			:      Tuple			=			torch.cat(snake_case_   ,  dim=0		)

    if do_classifier_free_guidance:
     UpperCAmelCase			:      Any			=			image_embeds.repeat_interleave(snake_case_   ,  dim=0		)
     UpperCAmelCase			:      Dict			=			negative_image_embeds.repeat_interleave(snake_case_   ,  dim=0		)

    UpperCAmelCase			:      List[Any]			=			torch.cat([negative_image_embeds, image_embeds]   ,  dim=0		).to(dtype=self.unet.dtype   ,  device=snake_case_		)

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

    UpperCAmelCase			:      int			=			torch.cat([prepare_image(snake_case_   ,  snake_case_   ,  snake_case_		) for i in image]   ,  dim=0		)
    UpperCAmelCase			:      List[Any]			=			image.to(dtype=image_embeds.dtype   ,  device=snake_case_		)

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

     UpperCAmelCase			:      Union[str, Any]			=			{"""image_embeds""": image_embeds}
     UpperCAmelCase			:      Any			=			self.unet(
         sample=snake_case_   ,  timestep=snake_case_   ,  encoder_hidden_states=snake_case_   ,  added_cond_kwargs=snake_case_   ,  return_dict=snake_case_   ,  )[0]

     if do_classifier_free_guidance:
      UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			noise_pred.split(latents.shape[1]   ,  dim=1		)
      UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			noise_pred.chunk(2		)
      UpperCAmelCase			,	UpperCAmelCase			:      List[str]			=			variance_pred.chunk(2		)
      UpperCAmelCase			:      Any			=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
      UpperCAmelCase			:      Optional[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"]
     ):
      UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			noise_pred.split(latents.shape[1]   ,  dim=1		)

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase			:      List[str]			=			self.scheduler.step(
         snake_case_   ,  snake_case_   ,  snake_case_   ,  generator=snake_case_   ,  )[0]

    # post-processing
    UpperCAmelCase			:      int			=			self.movq.decode(snake_case_   ,  force_not_quantize=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"]:
     UpperCAmelCase			:      Dict			=			image * 0.5 + 0.5
     UpperCAmelCase			:      Any			=			image.clamp(0   ,  1		)
     UpperCAmelCase			:      Any			=			image.cpu().permute(0   ,  2   ,  3   ,  1		).float().numpy()

    if output_type == "pil":
     UpperCAmelCase			:      List[Any]			=			self.numpy_to_pil(snake_case_		)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=snake_case_		)





"""simple docstring"""
import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


A:     Optional[Any]										=	"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

A:     Optional[int]										=	"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

A:     Optional[int]										=	"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION   ,  citation=_CITATION   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,  id="""token"""		)   ,  id="""sequence"""		),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,  id="""token"""		)   ,  id="""sequence"""		)   ,  id="""references"""		),
            }		)   ,  codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,  reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,  )




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=False		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			compute_bleu(
        reference_corpus=_SCREAMING_SNAKE_CASE   ,  translation_corpus=_SCREAMING_SNAKE_CASE   ,  max_order=_SCREAMING_SNAKE_CASE   ,  smooth=_SCREAMING_SNAKE_CASE		)
    ((UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase))			:      Dict			=			score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
