






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 			_UpperCamelCase(       __lowerCamelCase		,				unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       List[str]							=							KandinskyVaaPriorPipeline
		__SCREAMING_SNAKE_CASE  :       int							=							['''prompt''']
		__SCREAMING_SNAKE_CASE  :       Tuple							=							['''prompt''', '''negative_prompt''']
		__SCREAMING_SNAKE_CASE  :       Any							=							[
		    '''num_images_per_prompt''',
		    '''generator''',
		    '''num_inference_steps''',
		    '''latents''',
		    '''negative_prompt''',
		    '''guidance_scale''',
		    '''output_type''',
		    '''return_dict''',
		]
		__SCREAMING_SNAKE_CASE  :       Tuple							=							False

		@property
		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					return 3_2

		@property
		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					return 3_2

		@property
		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					return self.time_input_dim

		@property
		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					return self.time_input_dim * 4

		@property
		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					return 1_0_0

		@property
		def    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



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

		@property
		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					torch.manual_seed(0    )
					__a						:  Tuple									=     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    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					torch.manual_seed(0    )

					__a						:  Union[str, Any]									=     {
					    'num_attention_heads': 2,
					    'attention_head_dim': 1_2,
					    'embedding_dim': self.text_embedder_hidden_size,
					    'num_layers': 1,
					}

					__a						:  Optional[Any]									=     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						:  Tuple									=     nn.Parameter(torch.ones(model.clip_std.shape    )    )
					return model

		@property
		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					torch.manual_seed(0    )
					__a						:  Dict									=     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						:  int									=     CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE__    )
					return model

		@property
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     CLIPImageProcessor(
					    crop_size=2_2_4    ,       do_center_crop=SCREAMING_SNAKE_CASE__    ,       do_normalize=SCREAMING_SNAKE_CASE__    ,       do_resize=SCREAMING_SNAKE_CASE__    ,       image_mean=[0.48_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=2_2_4    ,       )

					return image_processor

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.dummy_prior
					__a						:  int									=     self.dummy_image_encoder
					__a						:  Optional[Any]									=     self.dummy_text_encoder
					__a						:  Tuple									=     self.dummy_tokenizer
					__a						:  Dict									=     self.dummy_image_processor

					__a						:  Tuple									=     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						:  Any									=     {
					    'prior': prior,
					    'image_encoder': image_encoder,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'scheduler': scheduler,
					    'image_processor': image_processor,
					}

					return components

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Any=0    ):



					'''simple docstring'''



					if str(SCREAMING_SNAKE_CASE__    ).startswith('mps'    ):
								__a						:  List[str]									=     torch.manual_seed(SCREAMING_SNAKE_CASE__    )
					else:
								__a						:  str									=     torch.Generator(device=SCREAMING_SNAKE_CASE__    ).manual_seed(SCREAMING_SNAKE_CASE__    )
					__a						:  Tuple									=     {
					    'prompt': 'horse',
					    'generator': generator,
					    'guidance_scale': 4.0,
					    'num_inference_steps': 2,
					    'output_type': 'np',
					}
					return inputs

		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     'cpu'

					__a						:  str									=     self.get_dummy_components()

					__a						:  Optional[Any]									=     self.pipeline_class(**SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[Any]									=     pipe.to(SCREAMING_SNAKE_CASE__    )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

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

					__a						:  Optional[int]									=     pipe(
					    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__    )    ,       return_dict=SCREAMING_SNAKE_CASE__    ,       )[0]

					__a						:  List[Any]									=     image[0, -1_0:]
					__a						:  Union[str, Any]									=     image_from_tuple[0, -1_0:]

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

					__a						:  Optional[int]									=     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    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					__a						:  str									=     torch_device == 'cpu'
					__a						:  Tuple									=     True
					__a						:  Optional[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    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  Optional[int]									=     torch_device == 'cpu'
					__a						:  Any									=     False

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













from __future__ import annotations






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		int    )       ->None:




       """simple docstring"""


       a						    = order

       # a_{0} ... a_{k}
       a						    = [1.0] + [0.0] * order
       # b_{0} ... b_{k}
       a						    = [1.0] + [0.0] * order

       # x[n-1] ... x[n-k]
       a						    = [0.0] * self.order
       # y[n-1] ... y[n-k]
       a						    = [0.0] * self.order







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		list[float]					,   __UpperCAmelCase :		list[float]    )       ->None:




       """simple docstring"""


       if len(__UpperCAmelCase    ) < self.order:
        a						    = [1.0, *a_coeffs]

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected a_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected b_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       a						    = a_coeffs
       a						    = b_coeffs







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		float    )       ->float:




       """simple docstring"""


       a						    = 0.0

       # Start at index 1 and do index 0 at the end.
       for i in range(1					,   self.order + 1    ):
        result += (
            self.b_coeffs[i] * self.input_history[i - 1]
            - self.a_coeffs[i] * self.output_history[i - 1]
        )

       a						    = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

       a						    = self.input_history[:-1]
       a						    = self.output_history[:-1]

       a						    = sample
       a						    = result

       return result


