



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 'pegasus'
 __lowerCamelCase					: Tuple    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self				,     A=50_265				,     A=1_024				,     A=12				,     A=4_096				,     A=16				,     A=12				,     A=4_096				,     A=16				,     A=0.0				,     A=0.0				,     A=True				,     A=True				,     A="gelu"				,     A=1_024				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=0				,     A=False				,     A=0				,     A=1				,     A=1				,     **A				,     )					->  str:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						max_position_embeddings
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						use_cache
      _a											=						encoder_layers
      _a											=						scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=A				,     eos_token_id=A				,     is_encoder_decoder=A				,     decoder_start_token_id=A				,     forced_eos_token_id=A				,     **A				,     )



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model








'''simple docstring'''





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 __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = KandinskyVaaPriorPipeline
 __lowerCamelCase					: List[str]    = ['prompt']
 __lowerCamelCase					: str    = ['prompt', 'negative_prompt']
 __lowerCamelCase					: Union[str, Any]    = [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 __lowerCamelCase					: Union[str, Any]    = False



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.time_input_dim



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      return self.time_input_dim * 4



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


      """simple docstring"""



      return 100



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
      return tokenizer



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						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=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
      return CLIPTextModelWithProjection(A )



 @property
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      torch.manual_seed(0 )

      _a											=						{
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      _a											=						PriorTransformer(**A )
      # 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											=						nn.Parameter(torch.ones(model.clip_std.shape ) )
      return model



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

      _a											=						CLIPVisionModelWithProjection(A )
      return model



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


      """simple docstring"""



      _a											=						CLIPImageProcessor(
          crop_size=224				,     do_center_crop=A				,     do_normalize=A				,     do_resize=A				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

      return image_processor



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


      """simple docstring"""



      _a											=						self.dummy_prior
      _a											=						self.dummy_image_encoder
      _a											=						self.dummy_text_encoder
      _a											=						self.dummy_tokenizer
      _a											=						self.dummy_image_processor

      _a											=						UnCLIPScheduler(
          variance_type='''fixed_small_log'''				,     prediction_type='''sample'''				,     num_train_timesteps=1_000				,     clip_sample=A				,     clip_sample_range=10.0				,     )

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

      return components



 def       a__						(self				,     A				,     A=0 )					->  Optional[int]:


      """simple docstring"""



      if str(A ).startswith('''mps''' ):
           _a											=						torch.manual_seed(A )
      else:
           _a											=						torch.Generator(device=A ).manual_seed(A )
      _a											=						{
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



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


      """simple docstring"""



      _a											=						'''cpu'''

      _a											=						self.get_dummy_components()

      _a											=						self.pipeline_class(**A )
      _a											=						pipe.to(A )

      pipe.set_progress_bar_config(disable=A )

      _a											=						pipe(**self.get_dummy_inputs(A ) )
      _a											=						output.image_embeds

      _a											=						pipe(
          **self.get_dummy_inputs(A )				,     return_dict=A				,     )[0]

      _a											=						image[0, -10:]
      _a											=						image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _a											=						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       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						True
      _a											=						False

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



 @skip_mps
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						False

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



