
"""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 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 A_      (lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						KandinskyPriorPipeline
  SCREAMING_SNAKE_CASE__     :		Any    	=						["""prompt"""]
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						["""prompt""", """negative_prompt"""]
  SCREAMING_SNAKE_CASE__     :		int    	=						[
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						False







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 32







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 32







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.time_input_dim







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.time_input_dim * 4







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 100







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
     return tokenizer







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )
     UpperCAmelCase_   :   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-0_5							,  num_attention_heads=4							,  num_hidden_layers=5							,  pad_token_id=1							,  vocab_size=1000							,  )
     return CLIPTextModelWithProjection(lowercase_    )







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )

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

     UpperCAmelCase_   :   Dict     	=       PriorTransformer(**lowercase_    )
     # 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
     UpperCAmelCase_   :   List[Any]     	=       nn.Parameter(torch.ones(model.clip_std.shape    )    )
     return model







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )
     UpperCAmelCase_   :   Tuple     	=       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							,  )

     UpperCAmelCase_   :   List[Any]     	=       CLIPVisionModelWithProjection(lowercase_    )
     return model







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       CLIPImageProcessor(
         crop_size=224							,  do_center_crop=lowercase_							,  do_normalize=lowercase_							,  do_resize=lowercase_							,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,  resample=3							,  size=224							,  )

     return image_processor







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.dummy_prior
     UpperCAmelCase_   :   int     	=       self.dummy_image_encoder
     UpperCAmelCase_   :   Union[str, Any]     	=       self.dummy_text_encoder
     UpperCAmelCase_   :   List[Any]     	=       self.dummy_tokenizer
     UpperCAmelCase_   :   List[Any]     	=       self.dummy_image_processor

     UpperCAmelCase_   :   Dict     	=       UnCLIPScheduler(
         variance_type="fixed_small_log"							,  prediction_type="sample"							,  num_train_timesteps=1000							,  clip_sample=lowercase_							,  clip_sample_range=10.0							,  )

     UpperCAmelCase_   :   Optional[int]     	=       {
         "prior": prior,
         "image_encoder": image_encoder,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "scheduler": scheduler,
         "image_processor": image_processor,
     }

     return components







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_=0    ):

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       "cpu"

     UpperCAmelCase_   :   str     	=       self.get_dummy_components()

     UpperCAmelCase_   :   Optional[int]     	=       self.pipeline_class(**lowercase_    )
     UpperCAmelCase_   :   Union[str, Any]     	=       pipe.to(lowercase_    )

     pipe.set_progress_bar_config(disable=lowercase_    )

     UpperCAmelCase_   :   Optional[int]     	=       pipe(**self.get_dummy_inputs(lowercase_    )    )
     UpperCAmelCase_   :   int     	=       output.image_embeds

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

     UpperCAmelCase_   :   Optional[Any]     	=       image[0, -10:]
     UpperCAmelCase_   :   List[str]     	=       image_from_tuple[0, -10:]

     assert image.shape == (1, 32)

     UpperCAmelCase_   :   Optional[int]     	=       np.array(
         [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]    )

     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    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       torch_device == "cpu"
     UpperCAmelCase_   :   Union[str, Any]     	=       True
     UpperCAmelCase_   :   Optional[Any]     	=       False

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







  @skip_mps
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       torch_device == "cpu"
     UpperCAmelCase_   :   str     	=       False

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








'''simple docstring'''







def 		snake_case_				(_a      :				float       ,  _a      :				float       ,  _a      :				float	):
    if hue < 0 or hue > 3_6_0:
        raise Exception('''hue should be between 0 and 360'''	)

    if saturation < 0 or saturation > 1:
        raise Exception('''saturation should be between 0 and 1'''	)

    if value < 0 or value > 1:
        raise Exception('''value should be between 0 and 1'''	)

    UpperCAmelCase     			=						value * saturation
    UpperCAmelCase     			=						hue / 6_0
    UpperCAmelCase     			=						chroma * (1 - abs(hue_section % 2 - 1	))
    UpperCAmelCase     			=						value - chroma

    if hue_section >= 0 and hue_section <= 1:
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
    elif hue_section > 1 and hue_section <= 2:
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
    elif hue_section > 2 and hue_section <= 3:
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
    elif hue_section > 3 and hue_section <= 4:
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
    elif hue_section > 4 and hue_section <= 5:
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
    else:
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)

    return [red, green, blue]





def 		snake_case_				(_a      :				int       ,  _a      :				int       ,  _a      :				int	):
    if red < 0 or red > 2_5_5:
        raise Exception('''red should be between 0 and 255'''	)

    if green < 0 or green > 2_5_5:
        raise Exception('''green should be between 0 and 255'''	)

    if blue < 0 or blue > 2_5_5:
        raise Exception('''blue should be between 0 and 255'''	)

    UpperCAmelCase     			=						red / 2_5_5
    UpperCAmelCase     			=						green / 2_5_5
    UpperCAmelCase     			=						blue / 2_5_5
    UpperCAmelCase     			=						max(_a       ,  _a       ,  _a	)
    UpperCAmelCase     			=						value - min(_a       ,  _a       ,  _a	)
    UpperCAmelCase     			=						0 if value == 0 else chroma / value

    if chroma == 0:
        UpperCAmelCase     			=						0.0
    elif value == float_red:
        UpperCAmelCase     			=						6_0 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        UpperCAmelCase     			=						6_0 * (2 + (float_blue - float_red) / chroma)
    else:
        UpperCAmelCase     			=						6_0 * (4 + (float_red - float_green) / chroma)

    UpperCAmelCase     			=						(hue + 3_6_0) % 3_6_0

    return [hue, saturation, value]







def 		snake_case_				(_a      :				list[float]       ,  _a      :				list[float]	):
    UpperCAmelCase     			=						abs(hsv_a[0] - hsv_a[0]	) < 0.2
    UpperCAmelCase     			=						abs(hsv_a[1] - hsv_a[1]	) < 0.002
    UpperCAmelCase     			=						abs(hsv_a[2] - hsv_a[2]	) < 0.002

    return check_hue and check_saturation and check_value




