




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 lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



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

			lowerCamelCase__   =    [
			    """generator""",
			    """return_dict""",
			    """decoder_num_inference_steps""",
			    """super_res_num_inference_steps""",
			]
			lowerCamelCase__   =    False
			@property
			def       A_					(						self						):
							return 32
			@property
			def       A_					(						self						):
							return 32
			@property
			def       A_					(						self						):
							return self.time_input_dim
			@property
			def       A_					(						self						):
							return self.time_input_dim * 4
			@property
			def       A_					(						self						):
							return 100
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
							return tokenizer
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    int					=					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(lowercase						)
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    Union[str, Any]					=					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(lowercase						)
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)

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

							_lowerCamelCase  :    List[Any]					=					UnCLIPTextProjModel(**lowercase						)
							return model
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)

							_lowerCamelCase  :    Union[str, Any]					=					{
							    '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',
							}

							_lowerCamelCase  :    Any					=					UNetaDConditionModel(**lowercase						)
							return model
			@property
			def       A_					(						self						):
							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       A_					(						self						):
							torch.manual_seed(0						)

							_lowerCamelCase  :    int					=					UNetaDModel(**self.dummy_super_res_kwargs						)
							return model
			@property
			def       A_					(						self						):
							# seeded differently to get different unet than `self.dummy_super_res_first`
							torch.manual_seed(1						)

							_lowerCamelCase  :    Dict					=					UNetaDModel(**self.dummy_super_res_kwargs						)
							return model
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.dummy_decoder
							_lowerCamelCase  :    int					=					self.dummy_text_proj
							_lowerCamelCase  :    Any					=					self.dummy_text_encoder
							_lowerCamelCase  :    List[str]					=					self.dummy_tokenizer
							_lowerCamelCase  :    Tuple					=					self.dummy_super_res_first
							_lowerCamelCase  :    Dict					=					self.dummy_super_res_last

							_lowerCamelCase  :    Optional[int]					=					UnCLIPScheduler(
							    variance_type='learned_range'						,   prediction_type='epsilon'						,   num_train_timesteps=1000						,   )

							_lowerCamelCase  :    Dict					=					UnCLIPScheduler(
							    variance_type='fixed_small_log'						,   prediction_type='epsilon'						,   num_train_timesteps=1000						,   )

							_lowerCamelCase  :    Any					=					CLIPImageProcessor(crop_size=32						,   size=32						)

							_lowerCamelCase  :    Tuple					=					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       A_					(						self						,   lowercase						,   lowercase=0						,   lowercase=True						):
							_lowerCamelCase  :    Any					=					floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowercase						)						).to(lowercase						)
							if str(lowercase						).startswith('mps'						):
											_lowerCamelCase  :    Optional[int]					=					torch.manual_seed(lowercase						)
							else:
											_lowerCamelCase  :    List[Any]					=					torch.Generator(device=lowercase						).manual_seed(lowercase						)

							if pil_image:
											_lowerCamelCase  :    Tuple					=					input_image * 0.5 + 0.5
											_lowerCamelCase  :    Dict					=					input_image.clamp(0						,   1						)
											_lowerCamelCase  :    str					=					input_image.cpu().permute(0						,   2						,   3						,   1						).float().numpy()
											_lowerCamelCase  :    Optional[int]					=					DiffusionPipeline.numpy_to_pil(lowercase						)[0]

							return {
							    "image": input_image,
							    "generator": generator,
							    "decoder_num_inference_steps": 2,
							    "super_res_num_inference_steps": 2,
							    "output_type": "np",
							}
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					'cpu'

							_lowerCamelCase  :    Dict					=					self.get_dummy_components()

							_lowerCamelCase  :    str					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    Any					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    List[Any]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    str					=					pipe(**lowercase						)
							_lowerCamelCase  :    List[str]					=					output.images

							_lowerCamelCase  :    Union[str, Any]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    List[Any]					=					pipe(
							    **lowercase						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    int					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    str					=					image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase  :    List[Any]					=					np.array(
							    [
							        0.99_97,
							        0.00_02,
							        0.99_97,
							        0.99_97,
							        0.99_69,
							        0.00_23,
							        0.99_97,
							        0.99_69,
							        0.99_70,
							    ]						)

							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       A_					(						self						):
							_lowerCamelCase  :    str					=					'cpu'

							_lowerCamelCase  :    Tuple					=					self.get_dummy_components()

							_lowerCamelCase  :    str					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    Any					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    Any					=					pipe(**lowercase						)
							_lowerCamelCase  :    Dict					=					output.images

							_lowerCamelCase  :    str					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    Any					=					pipe(
							    **lowercase						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    Optional[Any]					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    Dict					=					image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase  :    str					=					np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]						)

							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       A_					(						self						):
							_lowerCamelCase  :    Dict					=					'cpu'

							_lowerCamelCase  :    str					=					self.get_dummy_components()

							_lowerCamelCase  :    int					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    int					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Tuple					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)
							_lowerCamelCase  :    List[str]					=					[
							    pipeline_inputs['image'],
							    pipeline_inputs['image'],
							]

							_lowerCamelCase  :    List[Any]					=					pipe(**lowercase						)
							_lowerCamelCase  :    Tuple					=					output.images

							_lowerCamelCase  :    Tuple					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)
							_lowerCamelCase  :    Any					=					[
							    tuple_pipeline_inputs['image'],
							    tuple_pipeline_inputs['image'],
							]

							_lowerCamelCase  :    Any					=					pipe(
							    **lowercase						,   return_dict=lowercase						,   )[0]

							_lowerCamelCase  :    int					=					image[0, -3:, -3:, -1]
							_lowerCamelCase  :    Tuple					=					image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase  :    Tuple					=					np.array(
							    [
							        0.99_97,
							        0.99_89,
							        0.00_08,
							        0.00_21,
							        0.99_60,
							        0.00_18,
							        0.00_14,
							        0.00_02,
							        0.99_33,
							    ]						)

							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       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					torch.device('cpu'						)






							class lowerCAmelCase__			:






										'''simple docstring'''



										lowerCamelCase__   =    1

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

							_lowerCamelCase  :    List[str]					=					self.pipeline_class(**lowercase						)
							_lowerCamelCase  :    List[str]					=					pipe.to(lowercase						)

							pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    List[str]					=					torch.Generator(device=lowercase						).manual_seed(0						)
							_lowerCamelCase  :    List[Any]					=					pipe.decoder.dtype
							_lowerCamelCase  :    List[str]					=					1

							_lowerCamelCase  :    Tuple					=					(
							    batch_size,
							    pipe.decoder.config.in_channels,
							    pipe.decoder.config.sample_size,
							    pipe.decoder.config.sample_size,
							)
							_lowerCamelCase  :    List[Any]					=					pipe.prepare_latents(
							    lowercase						,   dtype=lowercase						,   device=lowercase						,   generator=lowercase						,   latents=lowercase						,   scheduler=DummyScheduler()						)

							_lowerCamelCase  :    List[Any]					=					(
							    batch_size,
							    pipe.super_res_first.config.in_channels // 2,
							    pipe.super_res_first.config.sample_size,
							    pipe.super_res_first.config.sample_size,
							)
							_lowerCamelCase  :    Union[str, Any]					=					pipe.prepare_latents(
							    lowercase						,   dtype=lowercase						,   device=lowercase						,   generator=lowercase						,   latents=lowercase						,   scheduler=DummyScheduler()						)

							_lowerCamelCase  :    int					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)

							_lowerCamelCase  :    Any					=					pipe(
							    **lowercase						,   decoder_latents=lowercase						,   super_res_latents=lowercase						).images

							_lowerCamelCase  :    str					=					self.get_dummy_inputs(lowercase						,   pil_image=lowercase						)
							# Don't pass image, instead pass embedding
							_lowerCamelCase  :    str					=					pipeline_inputs.pop('image'						)
							_lowerCamelCase  :    int					=					pipe.image_encoder(lowercase						).image_embeds

							_lowerCamelCase  :    Union[str, Any]					=					pipe(
							    **lowercase						,   decoder_latents=lowercase						,   super_res_latents=lowercase						,   image_embeddings=lowercase						,   ).images

							# make sure passing text embeddings manually is identical
							assert np.abs(img_out_a - img_out_a						).max() < 1E-4
			@skip_mps
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					torch_device == 'cpu'

							# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
							_lowerCamelCase  :    Union[str, Any]					=					1E-2

							self._test_attention_slicing_forward_pass(
							    test_max_difference=lowercase						,   expected_max_diff=lowercase						)
			@skip_mps
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					torch_device == 'cpu'
							_lowerCamelCase  :    Optional[int]					=					True
							_lowerCamelCase  :    Optional[Any]					=					[
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]

							self._test_inference_batch_single_identical(
							    test_max_difference=lowercase						,   relax_max_difference=lowercase						,   additional_params_copy_to_batched_inputs=lowercase						,   )
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					[
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]

							if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											_lowerCamelCase  :    Union[str, Any]					=					[2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=lowercase						,   additional_params_copy_to_batched_inputs=lowercase						,   )
							else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=lowercase						)
			@skip_mps
			def       A_					(						self						):
							return super().test_dict_tuple_outputs_equivalent()
			@skip_mps
			def       A_					(						self						):
							return super().test_save_load_local()






			@skip_mps
			def       A_					(						self						):
							return super().test_save_load_optional_components()






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






			'''simple docstring'''



			def       A_					(						self						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






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

							_lowerCamelCase  :    Optional[Any]					=					UnCLIPImageVariationPipeline.from_pretrained(
							    'kakaobrain/karlo-v1-alpha-image-variations'						,   torch_dtype=torch.floataa						)
							_lowerCamelCase  :    Any					=					pipeline.to(lowercase						)
							pipeline.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Optional[int]					=					torch.Generator(device='cpu'						).manual_seed(0						)
							_lowerCamelCase  :    str					=					pipeline(
							    lowercase						,   generator=lowercase						,   output_type='np'						,   )

							_lowerCamelCase  :    Tuple					=					output.images[0]

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

							assert_mean_pixel_difference(lowercase						,   lowercase						,   15						)





"""simple docstring"""






import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  		=		datasets.logging.get_logger(__name__)

lowercase__  		=		"""\
@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\",
}
"""

lowercase__  		=		"""\
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.
"""

lowercase__  		=		"""
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 lowerCAmelCase__			(  datasets.Metric		):






			'''simple docstring'''



			def       A_					(						self						):
							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       A_					(						self						,   lowercase						):
							if self.config_name == "default":
											_lowerCamelCase  :    List[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       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=False						):
							if gpus is None:
											_lowerCamelCase  :    Tuple					=					1 if torch.cuda.is_available() else 0
							_lowerCamelCase  :    List[Any]					=					{'src': sources, 'mt': predictions, 'ref': references}
							_lowerCamelCase  :    int					=					[dict(zip(lowercase						,   lowercase						)						) for t in zip(*data.values()						)]
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self.scorer.predict(lowercase						,   gpus=lowercase						,   progress_bar=lowercase						)
							return {"mean_score": mean_score, "scores": scores}