

import unittest

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

from diffusers import KandinskyPriorPipeline, 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 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  KandinskyPriorPipeline
  _a						      =  ["""prompt"""]
  _a						      =  ["""prompt""", """negative_prompt"""]
  _a						      =  [
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  _a						      =  False




  @property
  def   A__					(    self )  ->			str:
        '''simple docstring'''






        return 32




  @property
  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        return 32




  @property
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return self.time_input_dim




  @property
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return self.time_input_dim * 4




  @property
  def   A__					(    self )  ->			str:
        '''simple docstring'''






        return 100




  @property
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






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




  @property
  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        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=37							,       layer_norm_eps=1e-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_000							,       )
        return CLIPTextModelWithProjection(lowerCAmelCase )




  @property
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        torch.manual_seed(0 )

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

        _lowercase     	=PriorTransformer(**lowerCAmelCase )
        # 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 )  ->			str:
        '''simple docstring'''






        torch.manual_seed(0 )
        _lowercase     	=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							,       )

        _lowercase     	=CLIPVisionModelWithProjection(lowerCAmelCase )
        return model




  @property
  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=CLIPImageProcessor(
            crop_size=224							,       do_center_crop=lowerCAmelCase							,       do_normalize=lowerCAmelCase							,       do_resize=lowerCAmelCase							,       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 )  ->			Any:
        '''simple docstring'''






        _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_000							,       clip_sample=lowerCAmelCase							,       clip_sample_range=10.0							,       )

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

        return components




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=0 )  ->			Optional[Any]:
        '''simple docstring'''






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




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	='cpu'

        _lowercase     	=self.get_dummy_components()

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

        pipe.set_progress_bar_config(disable=lowerCAmelCase )

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

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

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

        assert image.shape == (1, 32)

        _lowercase     	=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 )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=torch_device == 'cpu'
        _lowercase     	=True
        _lowercase     	=False

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




  @skip_mps
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=torch_device == 'cpu'
        _lowercase     	=False

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





import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


