

"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__A						       =      False





class    snake_case     (    unittest.TestCase  ):
				pass





@nightly
@require_torch_gpu
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    Optional[Any])->       Tuple:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"					,  torch_dtype=torch.floataa)
						pipe.to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")

						__lowerCAmelCase:	Optional[int]   				=      torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]   				=      pipe.dual_guided(
						    prompt="first prompt"					,  image=UpperCamelCase__					,  text_to_image_strength=0.75					,  generator=UpperCamelCase__					,  guidance_scale=7.5					,  num_inference_steps=2					,  output_type="numpy"					,  ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
								pipe.save_pretrained(UpperCamelCase__)
								__lowerCAmelCase:	List[Any]   				=      VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,  torch_dtype=torch.floataa)
						pipe.to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      generator.manual_seed(0)
						__lowerCAmelCase:	Dict   				=      pipe.dual_guided(
						    prompt="first prompt"					,  image=UpperCamelCase__					,  text_to_image_strength=0.75					,  generator=UpperCamelCase__					,  guidance_scale=7.5					,  num_inference_steps=2					,  output_type="numpy"					,  ).images

						assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"




				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"					,  torch_dtype=torch.floataa)
						pipe.to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      "cyberpunk 2077"
						__lowerCAmelCase:	Any   				=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")
						__lowerCAmelCase:	List[str]   				=      torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]   				=      pipe.dual_guided(
						    prompt=UpperCamelCase__					,  image=UpperCamelCase__					,  text_to_image_strength=0.75					,  generator=UpperCamelCase__					,  guidance_scale=7.5					,  num_inference_steps=5_0					,  output_type="numpy"					,  ).images

						__lowerCAmelCase:	Union[str, Any]   				=      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:	Any   				=      np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])

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

						__lowerCAmelCase:	Optional[Any]   				=      "A painting of a squirrel eating a burger "
						__lowerCAmelCase:	Optional[Any]   				=      torch.manual_seed(0)
						__lowerCAmelCase:	Tuple   				=      pipe.text_to_image(
						    prompt=UpperCamelCase__					,  generator=UpperCamelCase__					,  guidance_scale=7.5					,  num_inference_steps=5_0					,  output_type="numpy").images

						__lowerCAmelCase:	Tuple   				=      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:	str   				=      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])

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

						__lowerCAmelCase:	Union[str, Any]   				=      pipe.image_variation(UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type="numpy").images

						__lowerCAmelCase:	Optional[Any]   				=      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:	Optional[Any]   				=      np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])

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





"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


__A						       =      datasets.logging.get_logger(__name__)

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

__A						       =      "\\nCrosslingual 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).\nWith 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.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

__A						       =      "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"





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

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						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 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Any)->       Dict:



						'''simple docstring'''


						if self.config_name == "default":
								__lowerCAmelCase:	Union[str, Any]   				=      comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
						else:
								__lowerCAmelCase:	Tuple   				=      comet.load_from_checkpoint(comet.download_model(self.config_name))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    List[str]=False)->       str:



						'''simple docstring'''


						if gpus is None:
								__lowerCAmelCase:	Union[str, Any]   				=      1 if torch.cuda.is_available() else 0
						__lowerCAmelCase:	Dict   				=      {"src": sources, "mt": predictions, "ref": references}
						__lowerCAmelCase:	Union[str, Any]   				=      [dict(zip(UpperCamelCase__					,  UpperCamelCase__)) for t in zip(*data.values())]
						__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self.scorer.predict(UpperCamelCase__					,  gpus=UpperCamelCase__					,  progress_bar=UpperCamelCase__)
						return {"mean_score": mean_score, "scores": scores}


