

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	UpperCAmelCase__		(	A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= UnCLIPImageVariationPipeline
  a									= IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
  a									= IMAGE_VARIATION_BATCH_PARAMS

  a									= [
      "generator",
      "return_dict",
      "decoder_num_inference_steps",
      "super_res_num_inference_steps",
  ]
  a									= False






  @property
  def 				lowercase_		(							self						:		str )		->							Union[str, Any]:
       return 32






  @property
  def 				lowercase_		(							self						:		int )		->							Any:
       return 32






  @property
  def 				lowercase_		(							self						:		Tuple )		->							Dict:
       return self.time_input_dim






  @property
  def 				lowercase_		(							self						:		Tuple )		->							Any:
       return self.time_input_dim * 4






  @property
  def 				lowercase_		(							self						:		Optional[Any] )		->							Dict:
       return 100






  @property
  def 				lowercase_		(							self						:		int )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
       return tokenizer






  @property
  def 				lowercase_		(							self						:		Tuple )		->							Optional[int]:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__   =   CLIPTextConfig(
           bos_token_id=0     ,       eos_token_id=2     ,       hidden_size=self.text_embedder_hidden_size     ,       projection_dim=self.text_embedder_hidden_size     ,       intermediate_size=37     ,       layer_norm_eps=1e-05     ,       num_attention_heads=4     ,       num_hidden_layers=5     ,       pad_token_id=1     ,       vocab_size=1000     ,       )
       return CLIPTextModelWithProjection(__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		Any )		->							Union[str, Any]:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__   =   CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size     ,       projection_dim=self.text_embedder_hidden_size     ,       num_hidden_layers=5     ,       num_attention_heads=4     ,       image_size=32     ,       intermediate_size=37     ,       patch_size=1     ,       )
       return CLIPVisionModelWithProjection(__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		Tuple )		->							str:
       torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   {
           '''clip_embeddings_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''cross_attention_dim''': self.cross_attention_dim,
       }

       SCREAMING_SNAKE_CASE__   =   UnCLIPTextProjModel(**__lowerCamelCase )
       return model






  @property
  def 				lowercase_		(							self						:		Any )		->							List[str]:
       torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   {
           '''sample_size''': 32,
           # RGB in channels
           '''in_channels''': 3,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 6,
           '''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,
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': '''identity''',
       }

       SCREAMING_SNAKE_CASE__   =   UNetaDConditionModel(**__lowerCamelCase )
       return model






  @property
  def 				lowercase_		(							self						:		Union[str, Any] )		->							Tuple:
       return {
           "sample_size": 64,
           "layers_per_block": 1,
           "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
           "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "in_channels": 6,
           "out_channels": 3,
       }






  @property
  def 				lowercase_		(							self						:		str )		->							Union[str, Any]:
       torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   UNetaDModel(**self.dummy_super_res_kwargs )
       return model






  @property
  def 				lowercase_		(							self						:		int )		->							List[Any]:
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1 )

       SCREAMING_SNAKE_CASE__   =   UNetaDModel(**self.dummy_super_res_kwargs )
       return model






  def 				lowercase_		(							self						:		Optional[int] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.dummy_decoder
       SCREAMING_SNAKE_CASE__   =   self.dummy_text_proj
       SCREAMING_SNAKE_CASE__   =   self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__   =   self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__   =   self.dummy_super_res_first
       SCREAMING_SNAKE_CASE__   =   self.dummy_super_res_last

       SCREAMING_SNAKE_CASE__   =   UnCLIPScheduler(
           variance_type='''learned_range'''     ,       prediction_type='''epsilon'''     ,       num_train_timesteps=1000     ,       )

       SCREAMING_SNAKE_CASE__   =   UnCLIPScheduler(
           variance_type='''fixed_small_log'''     ,       prediction_type='''epsilon'''     ,       num_train_timesteps=1000     ,       )

       SCREAMING_SNAKE_CASE__   =   CLIPImageProcessor(crop_size=32     ,       size=32 )

       SCREAMING_SNAKE_CASE__   =   self.dummy_image_encoder

       return {
           "decoder": decoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "text_proj": text_proj,
           "feature_extractor": feature_extractor,
           "image_encoder": image_encoder,
           "super_res_first": super_res_first,
           "super_res_last": super_res_last,
           "decoder_scheduler": decoder_scheduler,
           "super_res_scheduler": super_res_scheduler,
       }






  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		List[str]=True )		->							Any:
       SCREAMING_SNAKE_CASE__   =   floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
       if str(__lowerCamelCase ).startswith('''mps''' ):
            SCREAMING_SNAKE_CASE__   =   torch.manual_seed(__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )

       if pil_image:
            SCREAMING_SNAKE_CASE__   =   input_image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__   =   input_image.clamp(0     ,       1 )
            SCREAMING_SNAKE_CASE__   =   input_image.cpu().permute(0     ,       2     ,       3     ,       1 ).float().numpy()
            SCREAMING_SNAKE_CASE__   =   DiffusionPipeline.numpy_to_pil(__lowerCamelCase )[0]

       return {
           "image": input_image,
           "generator": generator,
           "decoder_num_inference_steps": 2,
           "super_res_num_inference_steps": 2,
           "output_type": "np",
       }






  def 				lowercase_		(							self						:		Union[str, Any] )		->							int:
       SCREAMING_SNAKE_CASE__   =   '''cpu'''

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_components()

       SCREAMING_SNAKE_CASE__   =   self.pipeline_class(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   pipe.to(__lowerCamelCase )

       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   pipe(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   output.images

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   pipe(
           **__lowerCamelCase     ,       return_dict=__lowerCamelCase     ,       )[0]

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

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

       SCREAMING_SNAKE_CASE__   =   np.array(
           [
               0.9997,
               0.0002,
               0.9997,
               0.9997,
               0.9969,
               0.0023,
               0.9997,
               0.9969,
               0.9970,
           ] )

       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 				lowercase_		(							self						:		Optional[int] )		->							int:
       SCREAMING_SNAKE_CASE__   =   '''cpu'''

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_components()

       SCREAMING_SNAKE_CASE__   =   self.pipeline_class(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   pipe.to(__lowerCamelCase )

       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   pipe(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   output.images

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   pipe(
           **__lowerCamelCase     ,       return_dict=__lowerCamelCase     ,       )[0]

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

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

       SCREAMING_SNAKE_CASE__   =   np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )

       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 				lowercase_		(							self						:		Optional[int] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   '''cpu'''

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_components()

       SCREAMING_SNAKE_CASE__   =   self.pipeline_class(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   pipe.to(__lowerCamelCase )

       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           pipeline_inputs['''image'''],
           pipeline_inputs['''image'''],
       ]

       SCREAMING_SNAKE_CASE__   =   pipe(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   output.images

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           tuple_pipeline_inputs['''image'''],
           tuple_pipeline_inputs['''image'''],
       ]

       SCREAMING_SNAKE_CASE__   =   pipe(
           **__lowerCamelCase     ,       return_dict=__lowerCamelCase     ,       )[0]

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

       assert image.shape == (2, 64, 64, 3)

       SCREAMING_SNAKE_CASE__   =   np.array(
           [
               0.9997,
               0.9989,
               0.0008,
               0.0021,
               0.9960,
               0.0018,
               0.0014,
               0.0002,
               0.9933,
           ] )

       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 				lowercase_		(							self						:		Union[str, Any] )		->							int:
       SCREAMING_SNAKE_CASE__   =   torch.device('''cpu''' )






       class 	UpperCAmelCase__		:






         """simple docstring"""


         a									= 1



       SCREAMING_SNAKE_CASE__   =   self.get_dummy_components()

       SCREAMING_SNAKE_CASE__   =   self.pipeline_class(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   pipe.to(__lowerCamelCase )

       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
       SCREAMING_SNAKE_CASE__   =   pipe.decoder.dtype
       SCREAMING_SNAKE_CASE__   =   1

       SCREAMING_SNAKE_CASE__   =   (
           batch_size,
           pipe.decoder.config.in_channels,
           pipe.decoder.config.sample_size,
           pipe.decoder.config.sample_size,
       )
       SCREAMING_SNAKE_CASE__   =   pipe.prepare_latents(
           __lowerCamelCase     ,       dtype=__lowerCamelCase     ,       device=__lowerCamelCase     ,       generator=__lowerCamelCase     ,       latents=__lowerCamelCase     ,       scheduler=DummyScheduler() )

       SCREAMING_SNAKE_CASE__   =   (
           batch_size,
           pipe.super_res_first.config.in_channels // 2,
           pipe.super_res_first.config.sample_size,
           pipe.super_res_first.config.sample_size,
       )
       SCREAMING_SNAKE_CASE__   =   pipe.prepare_latents(
           __lowerCamelCase     ,       dtype=__lowerCamelCase     ,       device=__lowerCamelCase     ,       generator=__lowerCamelCase     ,       latents=__lowerCamelCase     ,       scheduler=DummyScheduler() )

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   pipe(
           **__lowerCamelCase     ,       decoder_latents=__lowerCamelCase     ,       super_res_latents=__lowerCamelCase ).images

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_inputs(__lowerCamelCase     ,       pil_image=__lowerCamelCase )
       # Don't pass image, instead pass embedding
       SCREAMING_SNAKE_CASE__   =   pipeline_inputs.pop('''image''' )
       SCREAMING_SNAKE_CASE__   =   pipe.image_encoder(__lowerCamelCase ).image_embeds

       SCREAMING_SNAKE_CASE__   =   pipe(
           **__lowerCamelCase     ,       decoder_latents=__lowerCamelCase     ,       super_res_latents=__lowerCamelCase     ,       image_embeddings=__lowerCamelCase     ,       ).images

       # make sure passing text embeddings manually is identical
       assert np.abs(img_out_a - img_out_a ).max() < 1e-4






  @skip_mps
  def 				lowercase_		(							self						:		Dict )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   torch_device == '''cpu'''

       # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
       SCREAMING_SNAKE_CASE__   =   1e-2

       self._test_attention_slicing_forward_pass(
           test_max_difference=__lowerCamelCase     ,       expected_max_diff=__lowerCamelCase )






  @skip_mps
  def 				lowercase_		(							self						:		Optional[int] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__   =   True
       SCREAMING_SNAKE_CASE__   =   [
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

       self._test_inference_batch_single_identical(
           test_max_difference=__lowerCamelCase     ,       relax_max_difference=__lowerCamelCase     ,       additional_params_copy_to_batched_inputs=__lowerCamelCase     ,       )






  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   [
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

       if torch_device == "mps":
            # TODO: MPS errors with larger batch sizes
            SCREAMING_SNAKE_CASE__   =   [2, 3]
            self._test_inference_batch_consistent(
                batch_sizes=__lowerCamelCase     ,       additional_params_copy_to_batched_inputs=__lowerCamelCase     ,       )
       else:
            self._test_inference_batch_consistent(
                additional_params_copy_to_batched_inputs=__lowerCamelCase )






  @skip_mps
  def 				lowercase_		(							self						:		Tuple )		->							str:
       return super().test_dict_tuple_outputs_equivalent()






  @skip_mps
  def 				lowercase_		(							self						:		int )		->							Dict:
       return super().test_save_load_local()







  @skip_mps
  def 				lowercase_		(							self						:		int )		->							Any:
       return super().test_save_load_optional_components()






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






  """simple docstring"""


  def 				lowercase_		(							self						:		Optional[int] )		->							Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def 				lowercase_		(							self						:		Optional[Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''' )
       SCREAMING_SNAKE_CASE__   =   load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''' )

       SCREAMING_SNAKE_CASE__   =   UnCLIPImageVariationPipeline.from_pretrained(
           '''kakaobrain/karlo-v1-alpha-image-variations'''     ,       torch_dtype=torch.floataa )
       SCREAMING_SNAKE_CASE__   =   pipeline.to(__lowerCamelCase )
       pipeline.set_progress_bar_config(disable=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   torch.Generator(device='''cpu''' ).manual_seed(0 )
       SCREAMING_SNAKE_CASE__   =   pipeline(
           __lowerCamelCase     ,       generator=__lowerCamelCase     ,       output_type='''np'''     ,       )

       SCREAMING_SNAKE_CASE__   =   output.images[0]

       assert image.shape == (256, 256, 3)

       assert_mean_pixel_difference(__lowerCamelCase     ,       __lowerCamelCase     ,       15 )





import comet  # From: unbabel-comet
import torch

import datasets


_SCREAMING_SNAKE_CASE					:  List[str]             =      datasets.logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  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",
}
'''

_SCREAMING_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.
'''

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







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


