


import comet  # From: unbabel-comet
import torch

import datasets


snake_case							:       str     			=      datasets.logging.get_logger(__name__)

snake_case							:       str     			=      '''\
@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",
}
'''

snake_case							:       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.
'''

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








import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case							:       int     			=      False







@skip_mps
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			StableDiffusionAttendAndExcitePipeline
					UpperCAmelCase__					:    List[str]          =			False
					UpperCAmelCase__					:    int          =			TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__					:    List[Any]          =			TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
					UpperCAmelCase__					:    List[str]          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__					:    str          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					@classmethod
					def  lowerCamelCase__(      cls   :int      )					->    int:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    int:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    Optional[Any]:
												torch.manual_seed(0      )
												a__             =     UNetaDConditionModel(
												    block_out_channels=(32, 64)			,layers_per_block=1			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,cross_attention_dim=32			,attention_head_dim=(2, 4)			,use_linear_projection=__snake_case			,)
												a__             =     DDIMScheduler(
												    beta_start=0.0_00_85			,beta_end=0.0_12			,beta_schedule='scaled_linear'			,clip_sample=__snake_case			,set_alpha_to_one=__snake_case			,)
												torch.manual_seed(0      )
												a__             =     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			,sample_size=1_28			,)
												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=10_00			,hidden_act='gelu'			,projection_dim=5_12			,)
												a__             =     CLIPTextModel(__snake_case      )
												a__             =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

												a__             =     {
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}

												return components
					def  lowerCamelCase__(      self   :Any			,__snake_case   :Union[str, Any]			,__snake_case   :Tuple=0      )					->    Any:
												if str(__snake_case      ).startswith('mps'      ):
																			a__             =     torch.manual_seed(__snake_case      )
												else:
																			a__             =     torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )
												a__             =     a__             =     {
												    'prompt': 'a cat and a frog',
												    'token_indices': [2, 5],
												    'generator': generator,
												    'num_inference_steps': 1,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'max_iter_to_alter': 2,
												    'thresholds': {0: 0.7},
												}
												return inputs
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Any:
												a__             =     'cpu'

												a__             =     self.get_dummy_components()
												a__             =     self.pipeline_class(**__snake_case      )
												pipe.to(__snake_case      )
												pipe.set_progress_bar_config(disable=__snake_case      )

												a__             =     self.get_dummy_inputs(__snake_case      )
												a__             =     pipe(**__snake_case      ).images
												a__             =     image[0, -3:, -3:, -1]

												self.assertEqual(image.shape			,(1, 64, 64, 3)      )
												a__             =     np.array(
												    [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]      )
												a__             =     np.abs(image_slice.flatten() - expected_slice      ).max()
												self.assertLessEqual(__snake_case			,1E-3      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    List[str]:
												super().test_cpu_offload_forward_pass(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    List[str]:
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												self._test_inference_batch_single_identical(batch_size=2			,expected_max_diff=7E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    Dict:
												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Dict:
												super().test_save_load_local(expected_max_difference=5E-4      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												super().test_save_load_optional_components(expected_max_difference=4E-4      )







@require_torch_gpu
@slow
class        snake_case_   (unittest.TestCase      ):
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    Tuple:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[int]      )					->    Tuple:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Tuple:
												a__             =     torch.manual_seed(51      )

												a__             =     StableDiffusionAttendAndExcitePipeline.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'			,safety_checker=__snake_case			,torch_dtype=torch.floataa      )
												pipe.to('cuda'      )

												a__             =     'a painting of an elephant with glasses'
												a__             =     [5, 7]

												a__             =     pipe(
												    prompt=__snake_case			,token_indices=__snake_case			,guidance_scale=7.5			,generator=__snake_case			,num_inference_steps=5			,max_iter_to_alter=5			,output_type='numpy'			,).images[0]

												a__             =     load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'      )
												assert np.abs((expected_image - image).max()      ) < 5E-1




