




import comet  # From: unbabel-comet
import torch

import datasets


_lowerCamelCase			:	Optional[int]							 =     datasets.logging.get_logger(__name__)

_lowerCamelCase			:	Union[str, Any]							 =     """\
@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\",
}
"""

_lowerCamelCase			:	Optional[Any]							 =     """\
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.
"""

_lowerCamelCase			:	List[str]							 =     """
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        UpperCamelCase_					(				datasets.Metric  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Any:





           '''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  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[Any])		->str:





           '''simple docstring'''

           if self.config_name == "default":
                  A__   				=					comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''))
           else:
                  A__   				=					comet.load_from_checkpoint(comet.download_model(self.config_name))





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			int=False)		->Tuple:





           '''simple docstring'''

           if gpus is None:
                  A__   				=					1 if torch.cuda.is_available() else 0
           A__   				=					{'''src''': sources, '''mt''': predictions, '''ref''': references}
           A__   				=					[dict(zip(UpperCAmelCase__      ,				UpperCAmelCase__)) for t in zip(*data.values())]
           A__				,							A__   				=					self.scorer.predict(UpperCAmelCase__      ,				gpus=UpperCAmelCase__      ,				progress_bar=UpperCAmelCase__)
           return {"mean_score": mean_score, "scores": scores}








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

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


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Dict:





           '''simple docstring'''

           A__   				=					1
           A__   				=					3
           A__   				=					(32, 32)

           A__   				=					floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0)).to(UpperCAmelCase__)
           return image


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					UNetaDConditionModel(
               block_out_channels=(32, 32, 64)      ,				layers_per_block=2      ,				sample_size=32      ,				in_channels=7      ,				out_channels=4      ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')      ,				up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,				cross_attention_dim=32      ,				attention_head_dim=8      ,				use_linear_projection=UpperCAmelCase__      ,				only_cross_attention=(True, True, False)      ,				num_class_embeds=100      ,				)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					AutoencoderKL(
               block_out_channels=[32, 32, 64]      ,				in_channels=3      ,				out_channels=3      ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,				latent_channels=4      ,				)
           return model


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           torch.manual_seed(0)
           A__   				=					CLIPTextConfig(
               bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=32      ,				intermediate_size=37      ,				layer_norm_eps=1e-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1_000      ,				hidden_act='''gelu'''      ,				projection_dim=512      ,				)
           return CLIPTextModel(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Tuple:





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)

           A__   				=					output.images

           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				return_dict=UpperCAmelCase__      ,				)[0]

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

           A__   				=					low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           A__   				=					np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->int:





           '''simple docstring'''

           A__   				=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					sd_pipe(
               2 * [prompt]      ,				image=2 * [low_res_image]      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)
           A__   				=					output.images
           assert image.shape[0] == 2

           A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_images_per_prompt=2      ,				guidance_scale=6.0      ,				noise_level=20      ,				num_inference_steps=2      ,				output_type='''np'''      ,				)
           A__   				=					output.images
           assert image.shape[0] == 2





    @unittest.skipIf(torch_device != '''cuda'''      ,				'''This test requires a GPU''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           A__   				=					self.dummy_cond_unet_upscale
           A__   				=					DDPMScheduler()
           A__   				=					DDIMScheduler(prediction_type='''v_prediction''')
           A__   				=					self.dummy_vae
           A__   				=					self.dummy_text_encoder
           A__   				=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__   				=					self.dummy_image.cpu().permute(0      ,				2      ,				3      ,				1)[0]
           A__   				=					Image.fromarray(np.uinta(UpperCAmelCase__)).convert('''RGB''').resize((64, 64))

           # put models in fp16, except vae as it overflows in fp16
           A__   				=					unet.half()
           A__   				=					text_encoder.half()

           # make sure here that pndm scheduler skips prk
           A__   				=					StableDiffusionUpscalePipeline(
               unet=UpperCAmelCase__      ,				low_res_scheduler=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				vae=UpperCAmelCase__      ,				text_encoder=UpperCAmelCase__      ,				tokenizer=UpperCAmelCase__      ,				max_noise_level=350      ,				)
           A__   				=					sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__   				=					'''A painting of a squirrel eating a burger'''
           A__   				=					torch.manual_seed(0)
           A__   				=					sd_pipe(
               [prompt]      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=2      ,				output_type='''np'''      ,				).images

           A__   				=					low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)


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

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->str:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
               '''/upsampled_cat.npy''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				output_type='''np'''      ,				)
           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 1e-3


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')
           A__   				=					load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
               '''/upsampled_cat_fp16.npy''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(
               UpperCAmelCase__      ,				torch_dtype=torch.floataa      ,				)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				output_type='''np'''      ,				)
           A__   				=					output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 5e-1





    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->List[Any]:





           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           A__   				=					load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-upscale/low_res_cat.png''')

           A__   				=					'''stabilityai/stable-diffusion-x4-upscaler'''
           A__   				=					StableDiffusionUpscalePipeline.from_pretrained(
               UpperCAmelCase__      ,				torch_dtype=torch.floataa      ,				)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           A__   				=					'''a cat sitting on a park bench'''

           A__   				=					torch.manual_seed(0)
           A__   				=					pipe(
               prompt=UpperCAmelCase__      ,				image=UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				num_inference_steps=5      ,				output_type='''np'''      ,				)

           A__   				=					torch.cuda.max_memory_allocated()
           # make sure that less than 2.9 GB is allocated
           assert mem_bytes < 2.9 * 10**9


