'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Tuple     =    KandinskyVaaControlnetImgaImgPipeline
   A							:       Dict     =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   A							:       Union[str, Any]     =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   A							:       Optional[Any]     =    [
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   A							:       Dict     =    False
   @property
   def 		_lowerCamelCase (self				)    ->     Any:
        return 32
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return 32
   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        return self.time_input_dim
   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        return self.time_input_dim * 4
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return 100
   @property
   def 		_lowerCamelCase (self				)    ->     str:
        torch.manual_seed(0				)

        lowercase_					:       str     				=					{
            '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,
        }

        lowercase_					:       Any     				=					UNetaDConditionModel(**_a				)
        return model
   @property
   def 		_lowerCamelCase (self				)    ->     int:
        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				)    ->     List[Any]:
        torch.manual_seed(0				)
        lowercase_					:       int     				=					VQModel(**self.dummy_movq_kwargs				)
        return model
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					self.dummy_unet
        lowercase_					:       Any     				=					self.dummy_movq

        lowercase_					:       str     				=					{
            'num_train_timesteps': 1_000,
            'beta_schedule': 'linear',
            'beta_start': 0.0_00_85,
            'beta_end': 0.0_12,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        lowercase_					:       int     				=					DDIMScheduler(**_a				)

        lowercase_					:       int     				=					{
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components
   def 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     Tuple:
        lowercase_					:       Union[str, Any]     				=					floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       Tuple     				=					floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(seed + 1				)				).to(
            _a				)
        # create init_image
        lowercase_					:       Tuple     				=					floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(_a				)				).to(_a				)
        lowercase_					:       Tuple     				=					image.cpu().permute(0       ,				2       ,				3       ,				1				)[0]
        lowercase_					:       Any     				=					Image.fromarray(np.uinta(_a				)				).convert('RGB'				).resize((256, 256)				)
        # create hint
        lowercase_					:       Optional[int]     				=					floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(_a				)				).to(_a				)

        if str(_a				).startswith('mps'				):
             lowercase_					:       Any     				=					torch.manual_seed(_a				)
        else:
             lowercase_					:       Dict     				=					torch.Generator(device=_a				).manual_seed(_a				)
        lowercase_					:       Optional[int]     				=					{
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'hint': hint,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 10,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs





   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       str     				=					'cpu'

        lowercase_					:       Tuple     				=					self.get_dummy_components()

        lowercase_					:       str     				=					self.pipeline_class(**_a				)
        lowercase_					:       int     				=					pipe.to(_a				)

        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       str     				=					pipe(**self.get_dummy_inputs(_a				)				)
        lowercase_					:       int     				=					output.images

        lowercase_					:       str     				=					pipe(
            **self.get_dummy_inputs(_a				)       ,				return_dict=_a       ,				)[0]

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

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

        lowercase_					:       Tuple     				=					np.array(
            [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]				)
        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       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     Dict:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       List[str]     				=					load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'				)

        lowercase_					:       List[str]     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
        lowercase_					:       List[str]     				=					init_image.resize((512, 512)				)

        lowercase_					:       List[str]     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/hint_image_cat.png'				)
        lowercase_					:       Any     				=					torch.from_numpy(np.array(_a				)				).float() / 2_55.0
        lowercase_					:       Optional[Any]     				=					hint.permute(2       ,				0       ,				1				).unsqueeze(0				)

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

        lowercase_					:       Dict     				=					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior'       ,				torch_dtype=torch.floataa				)
        pipe_prior.to(_a				)

        lowercase_					:       Optional[Any]     				=					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-controlnet-depth'       ,				torch_dtype=torch.floataa				)
        lowercase_					:       Optional[Any]     				=					pipeline.to(_a				)

        pipeline.set_progress_bar_config(disable=_a				)

        lowercase_					:       Union[str, Any]     				=					torch.Generator(device='cpu'				).manual_seed(0				)

        lowercase_				,lowercase_					:       int     				=					pipe_prior(
            _a       ,				image=_a       ,				strength=0.85       ,				generator=_a       ,				negative_prompt=''       ,				).to_tuple()

        lowercase_					:       str     				=					pipeline(
            image=_a       ,				image_embeds=_a       ,				negative_image_embeds=_a       ,				hint=_a       ,				generator=_a       ,				num_inference_steps=100       ,				height=512       ,				width=512       ,				strength=0.5       ,				output_type='np'       ,				)

        lowercase_					:       str     				=					output.images[0]

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

        assert_mean_pixel_difference(_a       ,				_a				)





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=12       ,				_a=7       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=99       ,				_a=32       ,				_a=32       ,				_a=2       ,				_a=4       ,				_a=37       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=0.02       ,				_a=0       ,				_a=None       ,				)    ->     Optional[Any]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       List[Any]     				=					batch_size
        lowercase_					:       Optional[Any]     				=					seq_length
        lowercase_					:       Optional[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_input_mask
        lowercase_					:       Any     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					projection_dim
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					dropout
        lowercase_					:       Tuple     				=					attention_dropout
        lowercase_					:       Optional[Any]     				=					max_position_embeddings
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					scope
        lowercase_					:       Optional[Any]     				=					bos_token_id
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_input_mask:
             lowercase_					:       Dict     				=					random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
             lowercase_					:       Dict     				=					input_mask.numpy()
             lowercase_				,lowercase_					:       int     				=					input_mask.shape
             lowercase_					:       int     				=					np.random.randint(1       ,				seq_length - 1       ,				size=(batch_size,)				)
             for batch_idx, start_index in enumerate(_a				):
                  lowercase_					:       Tuple     				=					1
                  lowercase_					:       List[Any]     				=					0

        lowercase_					:       List[Any]     				=					self.get_config()

        return config, input_ids, tf.convert_to_tensor(_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        return BlipTextConfig(
            vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				projection_dim=self.projection_dim       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				dropout=self.dropout       ,				attention_dropout=self.attention_dropout       ,				max_position_embeddings=self.max_position_embeddings       ,				initializer_range=self.initializer_range       ,				bos_token_id=self.bos_token_id       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFBlipTextModel(config=_a				)
        lowercase_					:       List[str]     				=					model(_a       ,				attention_mask=_a       ,				training=_a				)
        lowercase_					:       Union[str, Any]     				=					model(_a       ,				training=_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
        self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)				)





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Any     =    (TFBlipTextModel,) if is_tf_available() else ()
   A							:       Union[str, Any]     =    False
   A							:       int     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					BlipTextModelTester(self				)
        lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        pass
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='Blip does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[str]     				=					TFBlipTextModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)





   def 		_lowerCamelCase (self       ,				_a=True				)    ->     Optional[int]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=_a				)




