





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	(      a	,				unittest.TestCase      ):
      UpperCAmelCase_  : Any				      =KandinskyPriorPipeline
      UpperCAmelCase_  : Dict				      =["prompt"]
      UpperCAmelCase_  : List[str]				      =["prompt", "negative_prompt"]
      UpperCAmelCase_  : Optional[Any]				      =[
          "num_images_per_prompt",
          "generator",
          "num_inference_steps",
          "latents",
          "negative_prompt",
          "guidance_scale",
          "output_type",
          "return_dict",
      ]
      UpperCAmelCase_  : List[str]				      =False







      @property
      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        return 32







      @property
      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        return 32







      @property
      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        return self.time_input_dim







      @property
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        return self.time_input_dim * 4







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






        '''simple docstring'''






        return 100







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






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
        return tokenizer







      @property
      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        torch.manual_seed(0				)
        __snake_case				:			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=37			,  layer_norm_eps=1E-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )
        return CLIPTextModelWithProjection(UpperCAmelCase				)







      @property
      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        torch.manual_seed(0				)

        __snake_case				:			int            =    {
            "num_attention_heads": 2,
            "attention_head_dim": 12,
            "embedding_dim": self.text_embedder_hidden_size,
            "num_layers": 1,
        }

        __snake_case				:			Optional[Any]            =    PriorTransformer(**UpperCAmelCase				)
        # 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
        __snake_case				:			Tuple            =    nn.Parameter(torch.ones(model.clip_std.shape				)				)
        return model







      @property
      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        torch.manual_seed(0				)
        __snake_case				:			List[Any]            =    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			,  )

        __snake_case				:			Tuple            =    CLIPVisionModelWithProjection(UpperCAmelCase				)
        return model







      @property
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        __snake_case				:			Dict            =    CLIPImageProcessor(
            crop_size=224			,  do_center_crop=UpperCAmelCase			,  do_normalize=UpperCAmelCase			,  do_resize=UpperCAmelCase			,  image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]			,  image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]			,  resample=3			,  size=224			,  )

        return image_processor







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






        '''simple docstring'''






        __snake_case				:			Any            =    self.dummy_prior
        __snake_case				:			List[str]            =    self.dummy_image_encoder
        __snake_case				:			int            =    self.dummy_text_encoder
        __snake_case				:			List[Any]            =    self.dummy_tokenizer
        __snake_case				:			Optional[int]            =    self.dummy_image_processor

        __snake_case				:			List[Any]            =    UnCLIPScheduler(
            variance_type="fixed_small_log"			,  prediction_type="sample"			,  num_train_timesteps=1000			,  clip_sample=UpperCAmelCase			,  clip_sample_range=10.0			,  )

        __snake_case				:			Dict            =    {
            "prior": prior,
            "image_encoder": image_encoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "scheduler": scheduler,
            "image_processor": image_processor,
        }

        return components







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






        '''simple docstring'''






        if str(UpperCAmelCase				).startswith("mps"				):
          __snake_case				:			List[str]            =    torch.manual_seed(UpperCAmelCase				)
        else:
          __snake_case				:			Dict            =    torch.Generator(device=UpperCAmelCase				).manual_seed(UpperCAmelCase				)
        __snake_case				:			str            =    {
            "prompt": "horse",
            "generator": generator,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







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






        '''simple docstring'''






        __snake_case				:			Any            =    "cpu"

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

        __snake_case				:			Optional[int]            =    self.pipeline_class(**UpperCAmelCase				)
        __snake_case				:			Tuple            =    pipe.to(UpperCAmelCase				)

        pipe.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Any            =    pipe(**self.get_dummy_inputs(UpperCAmelCase				)				)
        __snake_case				:			List[Any]            =    output.image_embeds

        __snake_case				:			Union[str, Any]            =    pipe(
            **self.get_dummy_inputs(UpperCAmelCase				)			,  return_dict=UpperCAmelCase			,  )[0]

        __snake_case				:			List[Any]            =    image[0, -10:]
        __snake_case				:			List[Any]            =    image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        __snake_case				:			List[str]            =    np.array(
            [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]				)

        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   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    torch_device == "cpu"
        __snake_case				:			Tuple            =    True
        __snake_case				:			List[Any]            =    False

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







      @skip_mps
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			Dict            =    torch_device == "cpu"
        __snake_case				:			Optional[int]            =    False

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








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


_UpperCamelCase    =	logging.get_logger(__name__)




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  *UpperCAmelCase			,  **UpperCAmelCase				)    ->				None:






        '''simple docstring'''






        warnings.warn(
            "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use MaskFormerImageProcessor instead."			,  UpperCAmelCase			,  )
        super().__init__(*UpperCAmelCase			,  **UpperCAmelCase				)

