
import sacrebleu as scb
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     '\\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'

__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     '\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n'

__SCREAMING_SNAKE_CASE			:   str         =     '\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = ["hello there general kenobi", "foo bar foobar"]\n        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = ["hello there general kenobi",\n        ...                 "on our way to ankh morpork"]\n        >>> references = [["hello there general kenobi", "hello there !"],\n        ...                 ["goodbye ankh morpork", "ankh morpork"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        39.8\n'


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





		def       _A							(							self    :	List[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='''https://github.com/mjpost/sacreBLEU'''						,  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''']						,  reference_urls=[
							        '''https://github.com/mjpost/sacreBLEU''',
							        '''https://en.wikipedia.org/wiki/BLEU''',
							        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
							    ]						,  )





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	Optional[int]						,  lowerCamelCase__    :	int						,  lowerCamelCase__    :	Tuple="exp"						,  lowerCamelCase__    :	Tuple=None						,  lowerCamelCase__    :	List[str]=False						,  lowerCamelCase__    :	int=False						,  lowerCamelCase__    :	List[Any]=None						,  lowerCamelCase__    :	str=False						,  ):
							lowerCAmelCase		:					List[str]  	=     len(references[0]				)
							if any(len(lowerCamelCase__				) != references_per_prediction for refs in references				):
												raise ValueError('''Sacrebleu requires the same number of references for each prediction'''				)
							lowerCAmelCase		:					List[Any]  	=     [[refs[i] for refs in references] for i in range(lowerCamelCase__				)]
							lowerCAmelCase		:					Dict  	=     scb.corpus_bleu(
							    lowerCamelCase__						,  lowerCamelCase__						,  smooth_method=lowerCamelCase__						,  smooth_value=lowerCamelCase__						,  force=lowerCamelCase__						,  lowercase=lowerCamelCase__						,  use_effective_order=lowerCamelCase__						,  **({'''tokenize''': tokenize} if tokenize else {})						,  )
							lowerCAmelCase		:					Optional[int]  	=     {
							    '''score''': output.score,
							    '''counts''': output.counts,
							    '''totals''': output.totals,
							    '''precisions''': output.precisions,
							    '''bp''': output.bp,
							    '''sys_len''': output.sys_len,
							    '''ref_len''': output.ref_len,
							}
							return output_dict


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	PaintByExamplePipeline
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase		=	frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





		def       _A							(							self    :	Dict				):
							torch.manual_seed(0				)
							lowerCAmelCase		:					Optional[int]  	=     UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)						,  layers_per_block=2						,  sample_size=3_2						,  in_channels=9						,  out_channels=4						,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,  cross_attention_dim=3_2						,  )
							lowerCAmelCase		:					List[str]  	=     PNDMScheduler(skip_prk_steps=lowerCamelCase__				)
							torch.manual_seed(0				)
							lowerCAmelCase		:					List[str]  	=     AutoencoderKL(
							    block_out_channels=[3_2, 6_4]						,  in_channels=3						,  out_channels=3						,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,  latent_channels=4						,  )
							torch.manual_seed(0				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPVisionConfig(
							    hidden_size=3_2						,  projection_dim=3_2						,  intermediate_size=3_7						,  layer_norm_eps=1E-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  image_size=3_2						,  patch_size=4						,  )
							lowerCAmelCase		:					List[Any]  	=     PaintByExampleImageEncoder(lowerCamelCase__						,  proj_size=3_2				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPImageProcessor(crop_size=3_2						,  size=3_2				)

							lowerCAmelCase		:					str  	=     {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''image_encoder''': image_encoder,
							    '''safety_checker''': None,
							    '''feature_extractor''': feature_extractor,
							}
							return components





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					List[Any]  	=     np.array(image.convert('''RGB'''				)				)
							lowerCAmelCase		:					int  	=     image[None].transpose(0						,  3						,  1						,  2				)
							lowerCAmelCase		:					Any  	=     torch.from_numpy(lowerCamelCase__				).to(dtype=torch.floataa				) / 1_2_7.5 - 1.0
							return image





		def       _A							(							self    :	str						,  lowerCamelCase__    :	Union[str, Any]="cpu"						,  lowerCamelCase__    :	Any=0				):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							lowerCAmelCase		:					str  	=     floats_tensor((1, 3, 3_2, 3_2)						,  rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
							lowerCAmelCase		:					Any  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					Dict  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
							lowerCAmelCase		:					Optional[int]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				).resize((3_2, 3_2)				)

							if str(lowerCamelCase__				).startswith('''mps'''				):
												lowerCAmelCase		:					Dict  	=     torch.manual_seed(lowerCamelCase__				)
							else:
												lowerCAmelCase		:					str  	=     torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     {
							    '''example_image''': example_image,
							    '''image''': init_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs





		def       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Tuple  	=     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline(**lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     pipe.to('''cpu'''				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Tuple  	=     self.get_dummy_inputs()
							lowerCAmelCase		:					List[str]  	=     pipe(**lowerCamelCase__				)
							lowerCAmelCase		:					Optional[Any]  	=     output.images

							lowerCAmelCase		:					Optional[int]  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							lowerCAmelCase		:					List[Any]  	=     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2





		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					List[str]  	=     '''cpu'''
							lowerCAmelCase		:					Union[str, Any]  	=     self.get_dummy_inputs()
							inputs.pop('''mask_image'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     self.convert_to_pt(inputs.pop('''image'''				)				)
							lowerCAmelCase		:					Dict  	=     image.clamp(0						,  1				) / 2

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					str  	=     PaintByExamplePipeline(**self.get_dummy_components()				)
							lowerCAmelCase		:					Tuple  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Optional[int]  	=     pipe(image=lowerCamelCase__						,  mask_image=mask_image[:, 0]						,  **lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     output.images

							lowerCAmelCase		:					int  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					int  	=     mask_image.cpu().permute(0						,  2						,  3						,  1				)[0]

							lowerCAmelCase		:					List[str]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)

							lowerCAmelCase		:					List[Any]  	=     pipe(**self.get_dummy_inputs()				)
							lowerCAmelCase		:					Any  	=     output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()				).max() < 5E-2





		def       _A							(							self    :	Optional[Any]				):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



@slow
@require_torch_gpu
class 		__magic_name__   (						unittest.TestCase    ):





		def       _A							(							self    :	Tuple				):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def       _A							(							self    :	Optional[int]				):
							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/dog_in_bucket.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/mask.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/panda.jpg'''				)

							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''				)
							lowerCAmelCase		:					str  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					int  	=     torch.manual_seed(3_2_1				)
							lowerCAmelCase		:					List[str]  	=     pipe(
							    image=lowerCamelCase__						,  mask_image=lowerCamelCase__						,  example_image=lowerCamelCase__						,  generator=lowerCamelCase__						,  guidance_scale=5.0						,  num_inference_steps=5_0						,  output_type='''np'''						,  )

							lowerCAmelCase		:					Any  	=     output.images

							lowerCAmelCase		:					Tuple  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase		:					Dict  	=     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
