

'''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     __UpperCamelCase						( self			:      Optional[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     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
     if self.config_name == "default":
       SCREAMING_SNAKE_CASE:Any						      =					comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"							)							)
     else:
       SCREAMING_SNAKE_CASE:Any						      =					comet.load_from_checkpoint(comet.download_model(self.config_name							)							)







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









'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     str       =					KandinskyVaaInpaintPipeline
   _A       :     Tuple       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   _A       :     List[str]       =					[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   _A       :     Optional[Any]       =					[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _A       :     int       =					False
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Tuple							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     return self.time_input_dim
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return self.time_input_dim * 4
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 100
   @property
   def     __UpperCamelCase						( self			:      Any							):
     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
         "in_channels": 9,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "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": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     SCREAMING_SNAKE_CASE:Dict						      =					UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
     return model
   @property
   def     __UpperCamelCase						( self			:      Dict							):
     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     __UpperCamelCase						( self			:      int							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					VQModel(**self.dummy_movq_kwargs							)
     return model
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_unet
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.dummy_movq

     SCREAMING_SNAKE_CASE:Any						      =					DDIMScheduler(
         num_train_timesteps=1_000     ,beta_schedule="linear"     ,beta_start=0.00_085     ,beta_end=0.012     ,clip_sample=SCREAMING_SNAKE_CASE__     ,set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,steps_offset=1     ,prediction_type="epsilon"     ,thresholding=SCREAMING_SNAKE_CASE__     ,)

     SCREAMING_SNAKE_CASE:Any						      =					{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=0							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					floats_tensor((1, self.text_embedder_hidden_size)     ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					floats_tensor((1, self.text_embedder_hidden_size)     ,rng=random.Random(seed + 1							)							).to(
         SCREAMING_SNAKE_CASE__							)
     # create init_image
     SCREAMING_SNAKE_CASE:Any						      =					floats_tensor((1, 3, 64, 64)     ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image.cpu().permute(0     ,2     ,3     ,1							)[0]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							).resize((256, 256)							)
     # create mask
     SCREAMING_SNAKE_CASE:int						      =					np.ones((64, 64)     ,dtype=np.floataa							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					0

     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Tuple						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					{
         "image": init_image,
         "mask_image": mask,
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "num_inference_steps": 2,
         "guidance_scale": 4.0,
         "output_type": "np",
     }
     return inputs
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					"cpu"

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_dummy_components()

     SCREAMING_SNAKE_CASE:List[Any]						      =					self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					pipe.to(SCREAMING_SNAKE_CASE__							)

     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images

     SCREAMING_SNAKE_CASE:int						      =					pipe(
         **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)     ,return_dict=SCREAMING_SNAKE_CASE__     ,)[0]

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_from_tuple[0, -3:, -3:, -1]

     print(F'''image.shape {image.shape}'''							)

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

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.array(
         [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]							)

     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()}'''







   def     __UpperCamelCase						( self			:      Any							):
     super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





@slow
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      Any							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"							)

     SCREAMING_SNAKE_CASE:str						      =					load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					np.ones((768, 768)     ,dtype=np.floataa							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0

     SCREAMING_SNAKE_CASE:Optional[int]						      =					"a hat"

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"     ,torch_dtype=torch.floataa							)
     pipe_prior.to(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaInpaintPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder-inpaint"     ,torch_dtype=torch.floataa							)
     SCREAMING_SNAKE_CASE:List[str]						      =					pipeline.to(SCREAMING_SNAKE_CASE__							)
     pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device="cpu"							).manual_seed(0							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					pipe_prior(
         SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=5     ,negative_prompt=""     ,).to_tuple()

     SCREAMING_SNAKE_CASE:Dict						      =					pipeline(
         image=SCREAMING_SNAKE_CASE__     ,mask_image=SCREAMING_SNAKE_CASE__     ,image_embeds=SCREAMING_SNAKE_CASE__     ,negative_image_embeds=SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=100     ,height=768     ,width=768     ,output_type="np"     ,)

     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)






