'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




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




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													SCREAMING_SNAKE_CASE__     :int						    = 1
													SCREAMING_SNAKE_CASE__     :Any						    = 3
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = (32, 32)

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = floats_tensor((batch_size, num_channels) + sizes							,							rng=random.Random(0      )      ).to(UpperCamelCase_      )
													return image




						@property
						def      __lowerCamelCase       (    self :       Any      )     ->				Dict:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = UNetaDConditionModel(
													    block_out_channels=(32, 64)							,							layers_per_block=2							,							sample_size=32							,							in_channels=4							,							out_channels=4							,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,							cross_attention_dim=32							,							)
													return model




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				Optional[int]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = AutoencoderKL(
													    block_out_channels=[32, 64]							,							in_channels=3							,							out_channels=3							,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,							latent_channels=4							,							)
													return model




						@property
						def      __lowerCamelCase       (    self :       str      )     ->				List[str]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :str						    = RobertaSeriesConfig(
													    hidden_size=32							,							project_dim=32							,							intermediate_size=37							,							layer_norm_eps=1e-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=50_06							,							)
													return RobertaSeriesModelWithTransformation(UpperCamelCase_      )




						@property
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Optional[Any]:
													def extract(*UpperCamelCase_ :       Any							,							**UpperCamelCase_ :       List[str]      ):




																				class _SCREAMING_SNAKE_CASE:




																										def __init__(    self :       Union[str, Any]      )     ->				Tuple:
																																	SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.ones([0]      )




																										def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       List[Any]      )     ->				int:
																																	self.pixel_values.to(UpperCamelCase_      )
																																	return self
																				return Out()

													return extract




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Dict						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator
													SCREAMING_SNAKE_CASE__     :Dict						    = self.dummy_cond_unet
													SCREAMING_SNAKE_CASE__     :List[Any]						    = PNDMScheduler(skip_prk_steps=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = self.dummy_vae
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.dummy_text_encoder
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'      )
													SCREAMING_SNAKE_CASE__     :Dict						    = 77

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.dummy_image.to(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = init_image / 2 + 0.5

													# make sure here that pndm scheduler skips prk
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = AltDiffusionImgaImgPipeline(
													    unet=UpperCamelCase_							,							scheduler=UpperCamelCase_							,							vae=UpperCamelCase_							,							text_encoder=UpperCamelCase_							,							tokenizer=UpperCamelCase_							,							safety_checker=UpperCamelCase_							,							feature_extractor=self.dummy_extractor							,							)
													SCREAMING_SNAKE_CASE__     :Any						    = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,							do_normalize=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = alt_pipe.to(UpperCamelCase_      )
													alt_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'A painting of a squirrel eating a burger'
													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :int						    = alt_pipe(
													    [prompt]							,							generator=UpperCamelCase_							,							guidance_scale=6.0							,							num_inference_steps=2							,							output_type='np'							,							image=UpperCamelCase_							,							)

													SCREAMING_SNAKE_CASE__     :Any						    = output.images

													SCREAMING_SNAKE_CASE__     :Dict						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = alt_pipe(
													    [prompt]							,							generator=UpperCamelCase_							,							guidance_scale=6.0							,							num_inference_steps=2							,							output_type='np'							,							image=UpperCamelCase_							,							return_dict=UpperCamelCase_							,							)[0]

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :Tuple						    = image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 5e-3




						@unittest.skipIf(torch_device != 'cuda'							,							'This test requires a GPU'      )
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :str						    = self.dummy_cond_unet
													SCREAMING_SNAKE_CASE__     :List[Any]						    = PNDMScheduler(skip_prk_steps=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = self.dummy_vae
													SCREAMING_SNAKE_CASE__     :Dict						    = self.dummy_text_encoder
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = 77

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.dummy_image.to(UpperCamelCase_      )

													# put models in fp16
													SCREAMING_SNAKE_CASE__     :List[str]						    = unet.half()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = vae.half()
													SCREAMING_SNAKE_CASE__     :str						    = bert.half()

													# make sure here that pndm scheduler skips prk
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = AltDiffusionImgaImgPipeline(
													    unet=UpperCamelCase_							,							scheduler=UpperCamelCase_							,							vae=UpperCamelCase_							,							text_encoder=UpperCamelCase_							,							tokenizer=UpperCamelCase_							,							safety_checker=UpperCamelCase_							,							feature_extractor=self.dummy_extractor							,							)
													SCREAMING_SNAKE_CASE__     :Any						    = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,							do_normalize=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = alt_pipe.to(UpperCamelCase_      )
													alt_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = 'A painting of a squirrel eating a burger'
													SCREAMING_SNAKE_CASE__     :Any						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = alt_pipe(
													    [prompt]							,							generator=UpperCamelCase_							,							num_inference_steps=2							,							output_type='np'							,							image=UpperCamelCase_							,							).images

													assert image.shape == (1, 32, 32, 3)




						@unittest.skipIf(torch_device != 'cuda'							,							'This test requires a GPU'      )
						def      __lowerCamelCase       (    self :       str      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/img2img/sketch-mountains-input.jpg'      )
													# resize to resolution that is divisible by 8 but not 16 or 32
													SCREAMING_SNAKE_CASE__     :str						    = init_image.resize((7_60, 5_04)      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = 'BAAI/AltDiffusion'
													SCREAMING_SNAKE_CASE__     :str						    = AltDiffusionImgaImgPipeline.from_pretrained(
													    UpperCamelCase_							,							safety_checker=UpperCamelCase_							,							)
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :int						    = 'A fantasy landscape, trending on artstation'

													SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipe(
													    prompt=UpperCamelCase_							,							image=UpperCamelCase_							,							strength=0.75							,							guidance_scale=7.5							,							generator=UpperCamelCase_							,							output_type='np'							,							)
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = output.images[0]

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = image[2_55:2_58, 3_83:3_86, -1]

													assert image.shape == (5_04, 7_60, 3)
													SCREAMING_SNAKE_CASE__     :str						    = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]      )

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




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




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/img2img/sketch-mountains-input.jpg'      )
													SCREAMING_SNAKE_CASE__     :str						    = init_image.resize((7_68, 5_12)      )
													SCREAMING_SNAKE_CASE__     :Any						    = load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'BAAI/AltDiffusion'
													SCREAMING_SNAKE_CASE__     :List[str]						    = AltDiffusionImgaImgPipeline.from_pretrained(
													    UpperCamelCase_							,							safety_checker=UpperCamelCase_							,							)
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )
													pipe.enable_attention_slicing()

													SCREAMING_SNAKE_CASE__     :Dict						    = 'A fantasy landscape, trending on artstation'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe(
													    prompt=UpperCamelCase_							,							image=UpperCamelCase_							,							strength=0.75							,							guidance_scale=7.5							,							generator=UpperCamelCase_							,							output_type='np'							,							)
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = output.images[0]

													assert image.shape == (5_12, 7_68, 3)
													# img2img is flaky across GPUs even in fp32, so using MAE here
													assert np.abs(expected_image - image      ).max() < 1e-2







'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

UpperCamelCase_      	=	'''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

UpperCamelCase_      	=	'''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCamelCase_      	=	'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,							citation=_CITATION							,							homepage='https://unbabel.github.io/COMET/html/index.html'							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
													        {
													            'sources': datasets.Value('string'							,							id='sequence'      ),
													            'predictions': datasets.Value('string'							,							id='sequence'      ),
													            'references': datasets.Value('string'							,							id='sequence'      ),
													        }      )							,							codebase_urls=['https://github.com/Unbabel/COMET']							,							reference_urls=[
													        'https://github.com/Unbabel/COMET',
													        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
													        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
													    ]							,							)




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       List[str]      )     ->				Dict:
													if self.config_name == "default":
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model(self.config_name      )      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Optional[Any]=False      )     ->				Any:
													if gpus is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = 1 if torch.cuda.is_available() else 0
													SCREAMING_SNAKE_CASE__     :Dict						    = {'src': sources, 'mt': predictions, 'ref': references}
													SCREAMING_SNAKE_CASE__     :List[str]						    = [dict(zip(UpperCamelCase_							,							UpperCamelCase_      )      ) for t in zip(*data.values()      )]
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.scorer.predict(UpperCamelCase_							,							gpus=UpperCamelCase_							,							progress_bar=UpperCamelCase_      )
													return {"mean_score": mean_score, "scores": scores}






