


'''simple docstring'''







import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     StableDiffusionXLImgaImgPipeline
     __magic_name__           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     __magic_name__           =     PipelineTesterMixin.required_optional_params - {"latents"}
     __magic_name__           =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __magic_name__           =     IMAGE_TO_IMAGE_IMAGE_PARAMS
     __magic_name__           =     IMAGE_TO_IMAGE_IMAGE_PARAMS







     def a						(			self ):

            '''simple docstring'''

            torch.manual_seed(0 )
            _lowerCAmelCase     :				Tuple       =							UNetaDConditionModel(
                block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=4   ,						out_channels=4   ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,						attention_head_dim=(2, 4)   ,						use_linear_projection=snake_case__   ,						addition_embed_type='text_time'   ,						addition_time_embed_dim=8   ,						transformer_layers_per_block=(1, 2)   ,						projection_class_embeddings_input_dim=80   ,						cross_attention_dim=64   ,						)
            _lowerCAmelCase     :				List[Any]       =							EulerDiscreteScheduler(
                beta_start=0.0_0085   ,						beta_end=0.012   ,						steps_offset=1   ,						beta_schedule='scaled_linear'   ,						timestep_spacing='leading'   ,						)
            torch.manual_seed(0 )
            _lowerCAmelCase     :				Optional[int]       =							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=128   ,						)
            torch.manual_seed(0 )
            _lowerCAmelCase     :				List[Any]       =							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=1000   ,						hidden_act='gelu'   ,						projection_dim=32   ,						)
            _lowerCAmelCase     :				Optional[Any]       =							CLIPTextModel(snake_case__ )
            _lowerCAmelCase     :				str       =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   ,						local_files_only=snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							CLIPTextModelWithProjection(snake_case__ )
            _lowerCAmelCase     :				List[str]       =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   ,						local_files_only=snake_case__ )

            _lowerCAmelCase     :				Optional[Any]       =							{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'text_encoder_2': text_encoder_a,
                'tokenizer_2': tokenizer_a,
                # "safety_checker": None,
                # "feature_extractor": None,
            }
            return components







     def a						(			self   ,						snake_case__   ,						snake_case__=0 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							floats_tensor((1, 3, 32, 32)   ,						rng=random.Random(snake_case__ ) ).to(snake_case__ )
            _lowerCAmelCase     :				Tuple       =							image / 2 + 0.5
            if str(snake_case__ ).startswith('mps' ):
                   _lowerCAmelCase     :				Dict       =							torch.manual_seed(snake_case__ )
            else:
                   _lowerCAmelCase     :				Any       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
            _lowerCAmelCase     :				Tuple       =							{
                'prompt': 'A painting of a squirrel eating a burger',
                'image': image,
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 5.0,
                'output_type': 'numpy',
                'strength': 0.75,
            }
            return inputs







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							'cpu'  # ensure determinism for the device-dependent torch.Generator
            _lowerCAmelCase     :				str       =							self.get_dummy_components()
            _lowerCAmelCase     :				List[str]       =							StableDiffusionXLImgaImgPipeline(**snake_case__ )
            _lowerCAmelCase     :				Tuple       =							sd_pipe.to(snake_case__ )
            sd_pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							self.get_dummy_inputs(snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							sd_pipe(**snake_case__ ).images
            _lowerCAmelCase     :				str       =							image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)

            _lowerCAmelCase     :				List[Any]       =							np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )

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







     def a						(			self ):

            '''simple docstring'''

            super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )







     def a						(			self ):

            '''simple docstring'''

            super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.get_dummy_components()
            _lowerCAmelCase     :				str       =							StableDiffusionXLImgaImgPipeline(**snake_case__ )
            _lowerCAmelCase     :				str       =							sd_pipe.to(snake_case__ )
            _lowerCAmelCase     :				Any       =							sd_pipe.to(snake_case__ )
            sd_pipe.set_progress_bar_config(disable=snake_case__ )

            # forward without prompt embeds
            _lowerCAmelCase     :				Any       =							self.get_dummy_inputs(snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							3 * ['this is a negative prompt']
            _lowerCAmelCase     :				Any       =							negative_prompt
            _lowerCAmelCase     :				int       =							3 * [inputs['prompt']]

            _lowerCAmelCase     :				Optional[Any]       =							sd_pipe(**snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							output.images[0, -3:, -3:, -1]

            # forward with prompt embeds
            _lowerCAmelCase     :				List[str]       =							self.get_dummy_inputs(snake_case__ )
            _lowerCAmelCase     :				int       =							3 * ['this is a negative prompt']
            _lowerCAmelCase     :				Optional[Any]       =							3 * [inputs.pop('prompt' )]

            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Tuple       =							sd_pipe.encode_prompt(snake_case__   ,						negative_prompt=snake_case__ )

            _lowerCAmelCase     :				int       =							sd_pipe(
                **snake_case__   ,						prompt_embeds=snake_case__   ,						negative_prompt_embeds=snake_case__   ,						pooled_prompt_embeds=snake_case__   ,						negative_pooled_prompt_embeds=snake_case__   ,						)
            _lowerCAmelCase     :				List[str]       =							output.images[0, -3:, -3:, -1]

            # make sure that it's equal
            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__   ,						snake_case__="cpu"   ,						snake_case__=torch.floataa   ,						snake_case__=0 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
            _lowerCAmelCase     :				List[str]       =							np.random.RandomState(snake_case__ ).standard_normal((1, 4, 64, 64) )
            _lowerCAmelCase     :				List[Any]       =							torch.from_numpy(snake_case__ ).to(device=snake_case__   ,						dtype=snake_case__ )
            _lowerCAmelCase     :				int       =							{
                'prompt': 'a photograph of an astronaut riding a horse',
                'latents': latents,
                'generator': generator,
                'num_inference_steps': 3,
                'guidance_scale': 7.5,
                'output_type': 'numpy',
            }
            return inputs







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Any       =							self.get_inputs(snake_case__ )
            _lowerCAmelCase     :				List[str]       =							pipe(**snake_case__ ).images
            _lowerCAmelCase     :				Any       =							image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase     :				str       =							np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506] )
            assert np.abs(image_slice - expected_slice ).max() < 7E-3





'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


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


lowerCAmelCase     :  List[str]											=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase     :  List[Any]											=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase     :  str											=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase     :  Optional[Any]											=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION						)
class 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='https://github.com/google-research/bleurt'   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'   ,						id='sequence' ),
                        'references': datasets.Value('string'   ,						id='sequence' ),
                    } )   ,						codebase_urls=['https://github.com/google-research/bleurt']   ,						reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,						)







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.config_name == "default":
                   logger.warning(
                       'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
                       'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
                   _lowerCAmelCase     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							self.config_name.upper()

            else:
                   raise KeyError(
                       F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )

            # download the model checkpoint specified by self.config_name and set up the scorer
            _lowerCAmelCase     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

