"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 			UpperCamelCase__(	__A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  List[str]  							=						KandinskyImgaImgPipeline
						lowerCAmelCase__     :  int  							=						['prompt', 'image_embeds', 'negative_image_embeds', 'image']
						lowerCAmelCase__     :  int  							=						[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						]
						lowerCAmelCase__     :  Optional[Any]  							=						[
						    'generator',
						    'height',
						    'width',
						    'strength',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__     :  Tuple  							=						False





						@property
						def  snake_case__		(				self   )    -> int:
													return 32





						@property
						def  snake_case__		(				self   )    -> Dict:
													return 32





						@property
						def  snake_case__		(				self   )    -> Any:
													return self.time_input_dim





						@property
						def  snake_case__		(				self   )    -> Any:
													return self.time_input_dim * 4





						@property
						def  snake_case__		(				self   )    -> Dict:
													return 1_00





						@property
						def  snake_case__		(				self   )    -> str:
													A__		   =							XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
													return tokenizer





						@property
						def  snake_case__		(				self   )    -> int:
													torch.manual_seed(0   )
													A__		   =							MCLIPConfig(
													    numDims=self.cross_attention_dim						,transformerDimensions=self.text_embedder_hidden_size						,hidden_size=self.text_embedder_hidden_size						,intermediate_size=37						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=10_05						,)

													A__		   =							MultilingualCLIP(__UpperCAmelCase   )
													A__		   =							text_encoder.eval()

													return text_encoder





						@property
						def  snake_case__		(				self   )    -> List[str]:
													torch.manual_seed(0   )

													A__		   =							{
													    'in_channels': 4,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 8,
													    'addition_embed_type': 'text_image',
													    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
													    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
													    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
													    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    'layers_per_block': 1,
													    'encoder_hid_dim': self.text_embedder_hidden_size,
													    'encoder_hid_dim_type': 'text_image_proj',
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': None,
													}

													A__		   =							UNetaDConditionModel(**__UpperCAmelCase   )
													return model





						@property
						def  snake_case__		(				self   )    -> int:
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}





						@property
						def  snake_case__		(				self   )    -> Union[str, Any]:
													torch.manual_seed(0   )
													A__		   =							VQModel(**self.dummy_movq_kwargs   )
													return model





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							self.dummy_text_encoder
													A__		   =							self.dummy_tokenizer
													A__		   =							self.dummy_unet
													A__		   =							self.dummy_movq

													A__		   =							{
													    'num_train_timesteps': 10_00,
													    'beta_schedule': 'linear',
													    'beta_start': 0.0_0_0_8_5,
													    'beta_end': 0.0_1_2,
													    'clip_sample': False,
													    'set_alpha_to_one': False,
													    'steps_offset': 0,
													    'prediction_type': 'epsilon',
													    'thresholding': False,
													}

													A__		   =							DDIMScheduler(**__UpperCAmelCase   )

													A__		   =							{
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}

													return components





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=0   )    -> Dict:
													A__		   =							floats_tensor((1, self.cross_attention_dim)						,rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
													A__		   =							floats_tensor((1, self.cross_attention_dim)						,rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
													# create init_image
													A__		   =							floats_tensor((1, 3, 64, 64)						,rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
													A__		   =							image.cpu().permute(0						,2						,3						,1   )[0]
													A__		   =							Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((2_56, 2_56)   )

													if str(__UpperCAmelCase   ).startswith('mps'   ):
																				A__		   =							torch.manual_seed(__UpperCAmelCase   )
													else:
																				A__		   =							torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
													A__		   =							{
													    'prompt': 'horse',
													    'image': init_image,
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'num_inference_steps': 10,
													    'guidance_scale': 7.0,
													    'strength': 0.2,
													    'output_type': 'np',
													}
													return inputs





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							'cpu'

													A__		   =							self.get_dummy_components()

													A__		   =							self.pipeline_class(**__UpperCAmelCase   )
													A__		   =							pipe.to(__UpperCAmelCase   )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

													A__		   =							pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
													A__		   =							output.images

													A__		   =							pipe(
													    **self.get_dummy_inputs(__UpperCAmelCase   )						,return_dict=__UpperCAmelCase						,)[0]

													A__		   =							image[0, -3:, -3:, -1]
													A__		   =							image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													A__		   =							np.array(
													    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]   )
													assert (
													    np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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





						def  snake_case__		(				self   )    -> Optional[int]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def  snake_case__		(				self   )    -> int:
													A__		   =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_img2img_frog.npy'   )

													A__		   =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
													A__		   =							'A red cartoon frog, 4k'

													A__		   =							KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'						,torch_dtype=torch.floataa   )
													pipe_prior.to(__UpperCAmelCase   )

													A__		   =							KandinskyImgaImgPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1'						,torch_dtype=torch.floataa   )
													A__		   =							pipeline.to(__UpperCAmelCase   )

													pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

													A__		   =							torch.Generator(device='cpu'   ).manual_seed(0   )
													A__   ,     A__		   =							pipe_prior(
													    __UpperCAmelCase						,generator=__UpperCAmelCase						,num_inference_steps=5						,negative_prompt=''						,).to_tuple()

													A__		   =							pipeline(
													    __UpperCAmelCase						,image=__UpperCAmelCase						,image_embeds=__UpperCAmelCase						,negative_image_embeds=__UpperCAmelCase						,generator=__UpperCAmelCase						,num_inference_steps=1_00						,height=7_68						,width=7_68						,strength=0.2						,output_type='np'						,)

													A__		   =							output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(__UpperCAmelCase						,__UpperCAmelCase   )






"""simple docstring"""




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCamelCase							 =     "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\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"

__lowerCamelCase							 =     "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\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#chrf--chrf for more information.\n"

__lowerCamelCase							 =     "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION       )
class 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Tuple:
													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#chrf--chrf'						,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#chrf--chrf']						,reference_urls=[
													        'https://github.com/m-popovic/chrF',
													    ]						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = CHRF.CHAR_ORDER						,__UpperCAmelCase = CHRF.WORD_ORDER						,__UpperCAmelCase = CHRF.BETA						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,)    -> Union[str, Any]:
													A__		   =							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'   )
													A__		   =							[[refs[i] for refs in references] for i in range(__UpperCAmelCase   )]

													A__		   =							CHRF(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							sb_chrf.corpus_score(__UpperCAmelCase						,__UpperCAmelCase   )

													return {
													    "score": output.score,
													    "char_order": output.char_order,
													    "word_order": output.word_order,
													    "beta": output.beta,
													}





