


import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 					SCREAMING_SNAKE_CASE__    (							lowercase__		,       unittest.TestCase     ):
      snake_case__							:	Any	  =		KandinskyVaaPriorPipeline
      snake_case__							:	Optional[Any]	  =		['prompt']
      snake_case__							:	Tuple	  =		['prompt', 'negative_prompt']
      snake_case__							:	Union[str, Any]	  =		[
          'num_images_per_prompt',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      snake_case__							:	int	  =		False
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			str	)						->     Union[str, Any]:
             return 3_2
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     Optional[Any]:
             return 3_2
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	)						->     Optional[int]:
             return self.time_input_dim
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     Tuple:
             return self.time_input_dim * 4
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			List[str]	)						->     Any:
             return 1_0_0
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[Any]	)						->     str:
             a_   :						Optional[int]				=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
             return tokenizer
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			str	)						->     Tuple:
             torch.manual_seed(0	)
             a_   :						Optional[Any]				=    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(SCREAMING_SNAKE_CASE__	)
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			List[str]	)						->     str:
             torch.manual_seed(0	)

             a_   :						Optional[Any]				=    {
                 'num_attention_heads': 2,
                 'attention_head_dim': 1_2,
                 'embedding_dim': self.text_embedder_hidden_size,
                 'num_layers': 1,
             }

             a_   :						Optional[int]				=    PriorTransformer(**SCREAMING_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
             a_   :						int				=    nn.Parameter(torch.ones(model.clip_std.shape	)	)
             return model
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     Tuple:
             torch.manual_seed(0	)
             a_   :						List[str]				=    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	,				)

             a_   :						Optional[Any]				=    CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE__	)
             return model
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     Optional[Any]:
             a_   :						int				=    CLIPImageProcessor(
                 crop_size=2_2_4	,				do_center_crop=SCREAMING_SNAKE_CASE__	,				do_normalize=SCREAMING_SNAKE_CASE__	,				do_resize=SCREAMING_SNAKE_CASE__	,				image_mean=[0.48145466, 0.4578275, 0.40821073]	,				image_std=[0.26862954, 0.26130258, 0.27577711]	,				resample=3	,				size=2_2_4	,				)

             return image_processor
      def 			SCREAMING_SNAKE_CASE				(			self							:			str	)						->     Optional[Any]:
             a_   :						List[str]				=    self.dummy_prior
             a_   :						Dict				=    self.dummy_image_encoder
             a_   :						str				=    self.dummy_text_encoder
             a_   :						Any				=    self.dummy_tokenizer
             a_   :						Optional[Any]				=    self.dummy_image_processor

             a_   :						List[str]				=    UnCLIPScheduler(
                 variance_type='fixed_small_log'	,				prediction_type='sample'	,				num_train_timesteps=1_0_0_0	,				clip_sample=SCREAMING_SNAKE_CASE__	,				clip_sample_range=10.0	,				)

             a_   :						Union[str, Any]				=    {
                 'prior': prior,
                 'image_encoder': image_encoder,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'scheduler': scheduler,
                 'image_processor': image_processor,
             }

             return components
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[int]	,				SCREAMING_SNAKE_CASE__							:			int	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=0	)						->     Union[str, Any]:



             if str(SCREAMING_SNAKE_CASE__	).startswith('mps'	):
                    a_   :						Tuple				=    torch.manual_seed(SCREAMING_SNAKE_CASE__	)
             else:
                    a_   :						List[Any]				=    torch.Generator(device=SCREAMING_SNAKE_CASE__	).manual_seed(SCREAMING_SNAKE_CASE__	)
             a_   :						Any				=    {
                 'prompt': 'horse',
                 'generator': generator,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     int:
             a_   :						Union[str, Any]				=    'cpu'

             a_   :						Optional[Any]				=    self.get_dummy_components()

             a_   :						Tuple				=    self.pipeline_class(**SCREAMING_SNAKE_CASE__	)
             a_   :						Any				=    pipe.to(SCREAMING_SNAKE_CASE__	)

             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__	)

             a_   :						Optional[int]				=    pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__	)	)
             a_   :						List[Any]				=    output.image_embeds

             a_   :						List[Any]				=    pipe(
                 **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__	)	,				return_dict=SCREAMING_SNAKE_CASE__	,				)[0]

             a_   :						Dict				=    image[0, -1_0:]
             a_   :						int				=    image_from_tuple[0, -1_0:]

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

             a_   :						Optional[Any]				=    np.array(
                 [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]	)

             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 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     List[str]:
             a_   :						Union[str, Any]				=    torch_device == 'cpu'
             a_   :						Any				=    True
             a_   :						Union[str, Any]				=    False

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

      @skip_mps
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	)						->     Optional[int]:
             a_   :						Tuple				=    torch_device == 'cpu'
             a_   :						Dict				=    False

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







'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



