


import random
import unittest

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

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class  lowerCamelCase__  (			_lowercase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     KandinskyVaaPriorEmbaEmbPipeline
     SCREAMING_SNAKE_CASE__      =     ['''prompt''', '''image''']
     SCREAMING_SNAKE_CASE__      =     ['''prompt''', '''image''']
     SCREAMING_SNAKE_CASE__      =     [
         '''num_images_per_prompt''',
         '''strength''',
         '''generator''',
         '''num_inference_steps''',
         '''latents''',
         '''negative_prompt''',
         '''guidance_scale''',
         '''output_type''',
         '''return_dict''',
     ]
     SCREAMING_SNAKE_CASE__      =     False





     @property
     def 	__A   (self			)	->  str:
      return 3_2





     @property
     def 	__A   (self			)	->  Optional[Any]:
      return 3_2





     @property
     def 	__A   (self			)	->  Optional[int]:
      return self.time_input_dim





     @property
     def 	__A   (self			)	->  List[Any]:
      return self.time_input_dim * 4





     @property
     def 	__A   (self			)	->  Tuple:
      return 1_0_0





     @property
     def 	__A   (self			)	->  List[str]:
      _lowercase				 =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
      return tokenizer





     @property
     def 	__A   (self			)	->  Optional[int]:
      torch.manual_seed(0			)
      _lowercase				 =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=3_7						,	layer_norm_eps=1e-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=1_0_0_0						,	)
      return CLIPTextModelWithProjection(__snake_case			)





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

      _lowercase				 ={
          'num_attention_heads': 2,
          'attention_head_dim': 1_2,
          'embedding_dim': self.text_embedder_hidden_size,
          'num_layers': 1,
      }

      _lowercase				 =PriorTransformer(**__snake_case			)
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _lowercase				 =nn.Parameter(torch.ones(model.clip_std.shape			)			)
      return model





     @property
     def 	__A   (self			)	->  Tuple:
      torch.manual_seed(0			)
      _lowercase				 =CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size						,	image_size=2_2_4						,	projection_dim=self.text_embedder_hidden_size						,	intermediate_size=3_7						,	num_attention_heads=4						,	num_channels=3						,	num_hidden_layers=5						,	patch_size=1_4						,	)

      _lowercase				 =CLIPVisionModelWithProjection(__snake_case			)
      return model





     @property
     def 	__A   (self			)	->  Tuple:
      _lowercase				 =CLIPImageProcessor(
          crop_size=2_2_4						,	do_center_crop=__snake_case						,	do_normalize=__snake_case						,	do_resize=__snake_case						,	image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,	image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,	resample=3						,	size=2_2_4						,	)

      return image_processor





     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =self.dummy_prior
      _lowercase				 =self.dummy_image_encoder
      _lowercase				 =self.dummy_text_encoder
      _lowercase				 =self.dummy_tokenizer
      _lowercase				 =self.dummy_image_processor

      _lowercase				 =UnCLIPScheduler(
          variance_type='''fixed_small_log'''						,	prediction_type='''sample'''						,	num_train_timesteps=1_0_0_0						,	clip_sample=__snake_case						,	clip_sample_range=1_0.0						,	)

      _lowercase				 ={
          'prior': prior,
          'image_encoder': image_encoder,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'scheduler': scheduler,
          'image_processor': image_processor,
      }

      return components





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase=0			)	->  Tuple:
      if str(__snake_case			).startswith('''mps'''			):
       _lowercase				 =torch.manual_seed(__snake_case			)
      else:
       _lowercase				 =torch.Generator(device=__snake_case			).manual_seed(__snake_case			)

      _lowercase				 =floats_tensor((1, 3, 6_4, 6_4)						,	rng=random.Random(__snake_case			)			).to(__snake_case			)
      _lowercase				 =image.cpu().permute(0						,	2						,	3						,	1			)[0]
      _lowercase				 =Image.fromarray(np.uinta(__snake_case			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)

      _lowercase				 ={
          'prompt': 'horse',
          'image': init_image,
          'strength': 0.5,
          'generator': generator,
          'guidance_scale': 4.0,
          'num_inference_steps': 2,
          'output_type': 'np',
      }
      return inputs





     def 	__A   (self			)	->  List[str]:
      _lowercase				 ='cpu'

      _lowercase				 =self.get_dummy_components()

      _lowercase				 =self.pipeline_class(**__snake_case			)
      _lowercase				 =pipe.to(__snake_case			)

      pipe.set_progress_bar_config(disable=__snake_case			)

      _lowercase				 =pipe(**self.get_dummy_inputs(__snake_case			)			)
      _lowercase				 =output.image_embeds

      _lowercase				 =pipe(
          **self.get_dummy_inputs(__snake_case			)						,	return_dict=__snake_case						,	)[0]

      _lowercase				 =image[0, -1_0:]
      _lowercase				 =image_from_tuple[0, -1_0:]

      assert image.shape == (1, 3_2)

      _lowercase				 =np.array(
          [
              0.107_1284,
              1.333_0271,
              0.6126_0223,
              -0.669_1065,
              -0.384_6852,
              -1.030_3661,
              0.2271_6111,
              0.0334_8901,
              0.3004_0675,
              -0.2480_5029,
          ]			)

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





     @skip_mps
     def 	__A   (self			)	->  Any:
      _lowercase				 =torch_device == 'cpu'
      _lowercase				 =True
      _lowercase				 =False

      self._test_inference_batch_single_identical(
          test_max_difference=__snake_case						,	relax_max_difference=__snake_case						,	test_mean_pixel_difference=__snake_case						,	)





     @skip_mps
     def 	__A   (self			)	->  int:
      _lowercase				 =torch_device == 'cpu'
      _lowercase				 =False

      self._test_attention_slicing_forward_pass(
          test_max_difference=__snake_case						,	test_mean_pixel_difference=__snake_case						,	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__             =		{"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ViTHybridForImageClassification""",
							    """ViTHybridModel""",
							    """ViTHybridPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




