



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      KandinskyVaaControlnetPipeline
    __lowerCamelCase			: Optional[int]					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: Dict					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: List[str]					=      [
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase			: Dict					=      False






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim * 4






    @property
    def 			_lowerCAmelCase					(       self		):
        return 100






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)

        A :       Any          =   {
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """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,
        }

        A :       List[str]          =   UNetaDConditionModel(**lowerCamelCase__		)
        return model






    @property
    def 			_lowerCAmelCase					(       self		):
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)
        A :       List[Any]          =   VQModel(**self.dummy_movq_kwargs		)
        return model






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.dummy_unet
        A :       Tuple          =   self.dummy_movq

        A :       List[Any]          =   DDIMScheduler(
            num_train_timesteps=1000,		beta_schedule="""linear""",		beta_start=0.0_0085,		beta_end=0.012,		clip_sample=lowerCamelCase__,		set_alpha_to_one=lowerCamelCase__,		steps_offset=1,		prediction_type="""epsilon""",		thresholding=lowerCamelCase__,		)

        A :       int          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       Optional[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)
        A :       List[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
            lowerCamelCase__		)

        # create hint
        A :       int          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)

        if str(lowerCamelCase__		).startswith("""mps"""		):
            A :       Optional[Any]          =   torch.manual_seed(lowerCamelCase__		)
        else:
            A :       str          =   torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
        A :       List[str]          =   {
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   """cpu"""

        A :       List[str]          =   self.get_dummy_components()

        A :       Dict          =   self.pipeline_class(**lowerCamelCase__		)
        A :       Optional[Any]          =   pipe.to(lowerCamelCase__		)

        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       int          =   pipe(**self.get_dummy_inputs(lowerCamelCase__		)		)
        A :       Union[str, Any]          =   output.images

        A :       str          =   pipe(
            **self.get_dummy_inputs(lowerCamelCase__		),		return_dict=lowerCamelCase__,		)[0]

        A :       Optional[int]          =   image[0, -3:, -3:, -1]
        A :       Any          =   image_from_tuple[0, -3:, -3:, -1]

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

        A :       Dict          =   np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]		)

        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()}'''






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




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""		)

        A :       Any          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png"""		)
        A :       Optional[Any]          =   torch.from_numpy(np.array(lowerCamelCase__		)		).float() / 255.0
        A :       List[str]          =   hint.permute(2,		0,		1		).unsqueeze(0		)

        A :       Union[str, Any]          =   KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior""",		torch_dtype=torch.floataa		)
        pipe_prior.to(lowerCamelCase__		)

        A :       Tuple          =   KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth""",		torch_dtype=torch.floataa		)
        A :       Union[str, Any]          =   pipeline.to(lowerCamelCase__		)
        pipeline.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Optional[Any]          =   """A robot, 4k photo"""

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A     ,							A :       int          =   pipe_prior(
            lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=5,		negative_prompt="""""",		).to_tuple()

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A :       int          =   pipeline(
            image_embeds=lowerCamelCase__,		negative_image_embeds=lowerCamelCase__,		hint=lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=100,		output_type="""np""",		)

        A :       Optional[int]          =   output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(lowerCamelCase__,		lowerCamelCase__		)







from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_:Any             =						[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_:Optional[int]             =						[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2"""]		)
    assert (
        pd.DataFrame(no_aggregation["""rouge2"""]		).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]		).fmeasure.mean()
    )




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
    ]
    A :       Optional[Any]          =   [
        """Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
        """ the final seconds on board Flight 9525.""",
    ]
    assert calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		) == calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		)




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
    ]
    A :       Union[str, Any]          =   [
        """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
    ]

    A :       int          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]      ,     newline_sep=_lowerCAmelCase		)["""rougeLsum"""]
    A :       Optional[Any]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]		)["""rougeLsum"""]
    assert new_score > prev_score




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


