






import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       _UpperCAmelCase	,			_UpperCAmelCase							=    image.size
       _UpperCAmelCase	,			_UpperCAmelCase							=    (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
       _UpperCAmelCase							=    image.resize((w, h)   ,      resample=PIL_INTERPOLATION["""lanczos"""] )
       _UpperCAmelCase							=    np.array(__snake_case ).astype(np.floataa ) / 255.0
       _UpperCAmelCase							=    image[None].transpose(0   ,      3   ,      1   ,      2 )
       _UpperCAmelCase							=    torch.from_numpy(__snake_case )
       return 2.0 * image - 1.0





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			VQModel       ,				lowerCamelCase		:			UNetaDModel       ,				lowerCamelCase		:			Union[
          DDIMScheduler,
          PNDMScheduler,
          LMSDiscreteScheduler,
          EulerDiscreteScheduler,
          EulerAncestralDiscreteScheduler,
          DPMSolverMultistepScheduler,
      ]       ,				)						->	Any:


         """simple docstring"""
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase       ,				unet=lowerCamelCase       ,				scheduler=lowerCamelCase							)





  @torch.no_grad()
  def __call__(      self		:			Tuple       ,				lowerCamelCase		:			Union[torch.Tensor, PIL.Image.Image] = None       ,				lowerCamelCase		:			Optional[int] = 1       ,				lowerCamelCase		:			Optional[int] = 100       ,				lowerCamelCase		:			Optional[float] = 0.0       ,				lowerCamelCase		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				lowerCamelCase		:			Optional[str] = "pil"       ,				lowerCamelCase		:			bool = True       ,				)						->	Union[Tuple, ImagePipelineOutput]:


         """simple docstring"""
         if isinstance(lowerCamelCase       ,				PIL.Image.Image							):
                _UpperCAmelCase							=    1
         elif isinstance(lowerCamelCase       ,				torch.Tensor							):
                _UpperCAmelCase							=    image.shape[0]
         else:
                raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase							)}"""							)

         if isinstance(lowerCamelCase       ,				PIL.Image.Image							):
                _UpperCAmelCase							=    preprocess(lowerCamelCase							)

         _UpperCAmelCase	,			_UpperCAmelCase							=    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         _UpperCAmelCase							=    (batch_size, self.unet.config.in_channels // 2, height, width)
         _UpperCAmelCase							=    next(self.unet.parameters()							).dtype

         _UpperCAmelCase							=    randn_tensor(lowerCamelCase       ,				generator=lowerCamelCase       ,				device=self.device       ,				dtype=lowerCamelCase							)

         _UpperCAmelCase							=    image.to(device=self.device       ,				dtype=lowerCamelCase							)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase       ,				device=self.device							)
         _UpperCAmelCase							=    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         _UpperCAmelCase							=    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         _UpperCAmelCase							=    """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
         _UpperCAmelCase							=    {}
         if accepts_eta:
                _UpperCAmelCase							=    eta

         for t in self.progress_bar(lowerCamelCase							):
                # concat latents and low resolution image in the channel dimension.
                _UpperCAmelCase							=    torch.cat([latents, image]       ,				dim=1							)
                _UpperCAmelCase							=    self.scheduler.scale_model_input(lowerCamelCase       ,				lowerCamelCase							)
                # predict the noise residual
                _UpperCAmelCase							=    self.unet(lowerCamelCase       ,				lowerCamelCase							).sample
                # compute the previous noisy sample x_t -> x_t-1
                _UpperCAmelCase							=    self.scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample

         # decode the image latents with the VQVAE
         _UpperCAmelCase							=    self.vqvae.decode(lowerCamelCase							).sample
         _UpperCAmelCase							=    torch.clamp(lowerCamelCase       ,				-1.0       ,				1.0							)
         _UpperCAmelCase							=    image / 2 + 0.5
         _UpperCAmelCase							=    image.cpu().permute(0       ,				2       ,				3       ,				1							).numpy()

         if output_type == "pil":
                _UpperCAmelCase							=    self.numpy_to_pil(lowerCamelCase							)

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=lowerCamelCase							)

import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_A  =	"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_A  =	"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_A  =	"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





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




				def 	__a		(   self	)							-> str:
							if version.parse(scb.__version__	) < version.parse("1.4.12"	):
										raise ImportWarning(
										    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
										    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
							return datasets.MetricInfo(
							    description=_DESCRIPTION							,							citation=_CITATION							,							homepage="http://www.cs.umd.edu/~snover/tercom/"							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"							,							id="sequence"	),
							            "references": datasets.Sequence(datasets.Value("string"							,							id="sequence"	)							,							id="references"	),
							        }	)							,							codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]							,							reference_urls=[
							        "https://github.com/jhclark/tercom",
							    ]							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							)							-> Any:
							lowerCAmelCase_         =      len(references[0]	)
							if any(len(_UpperCamelCase	) != references_per_prediction for refs in references	):
										raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
							lowerCAmelCase_         =      [[refs[i] for refs in references] for i in range(_UpperCamelCase	)]

							lowerCAmelCase_         =      TER(
							    normalized=_UpperCamelCase							,							no_punct=_UpperCamelCase							,							asian_support=_UpperCamelCase							,							case_sensitive=_UpperCamelCase							,							)
							lowerCAmelCase_         =      sb_ter.corpus_score(_UpperCamelCase							,							_UpperCamelCase	)

							return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}



