

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Optional[Any]  					=    StableDiffusionXLImgaImgPipeline
   _lowercase     :       Optional[int]  					=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
   _lowercase     :       Union[str, Any]  					=    PipelineTesterMixin.required_optional_params - {'latents'}
   _lowercase     :       int  					=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _lowercase     :       Any  					=    IMAGE_TO_IMAGE_IMAGE_PARAMS
   _lowercase     :       List[str]  					=    IMAGE_TO_IMAGE_IMAGE_PARAMS

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			str:
         """simple docstring"""





         torch.manual_seed(0       )
         A_							       =		UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)			,						layers_per_block=2			,						sample_size=3_2			,						in_channels=4			,						out_channels=4			,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,						attention_head_dim=(2, 4)			,						use_linear_projection=lowerCamelCase__			,						addition_embed_type='''text_time'''			,						addition_time_embed_dim=8			,						transformer_layers_per_block=(1, 2)			,						projection_class_embeddings_input_dim=8_0			,						cross_attention_dim=6_4			,						)
         A_							       =		EulerDiscreteScheduler(
             beta_start=0.00085			,						beta_end=0.012			,						steps_offset=1			,						beta_schedule='''scaled_linear'''			,						timestep_spacing='''leading'''			,						)
         torch.manual_seed(0       )
         A_							       =		AutoencoderKL(
             block_out_channels=[3_2, 6_4]			,						in_channels=3			,						out_channels=3			,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,						latent_channels=4			,						sample_size=1_2_8			,						)
         torch.manual_seed(0       )
         A_							       =		CLIPTextConfig(
             bos_token_id=0			,						eos_token_id=2			,						hidden_size=3_2			,						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			,						hidden_act='''gelu'''			,						projection_dim=3_2			,						)
         A_							       =		CLIPTextModel(lowerCamelCase__       )
         A_							       =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			,						local_files_only=lowerCamelCase__       )

         A_							       =		CLIPTextModelWithProjection(lowerCamelCase__       )
         A_							       =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			,						local_files_only=lowerCamelCase__       )

         A_							       =		{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''text_encoder_2''': text_encoder_a,
             '''tokenizer_2''': tokenizer_a,
             # "safety_checker": None,
             # "feature_extractor": None,
         }
         return components

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Dict=0       ) ->			List[str]:
         """simple docstring"""





         A_							       =		floats_tensor((1, 3, 3_2, 3_2)			,						rng=random.Random(lowerCamelCase__       )       ).to(lowerCamelCase__       )
         A_							       =		image / 2 + 0.5
         if str(lowerCamelCase__       ).startswith('''mps'''       ):
               A_							       =		torch.manual_seed(lowerCamelCase__       )
         else:
               A_							       =		torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
         A_							       =		{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 5.0,
             '''output_type''': '''numpy''',
             '''strength''': 0.75,
         }
         return inputs

   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





         A_							       =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         A_							       =		self.get_dummy_components()
         A_							       =		StableDiffusionXLImgaImgPipeline(**lowerCamelCase__       )
         A_							       =		sd_pipe.to(lowerCamelCase__       )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		self.get_dummy_inputs(lowerCamelCase__       )
         A_							       =		sd_pipe(**lowerCamelCase__       ).images
         A_							       =		image[0, -3:, -3:, -1]

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

         A_							       =		np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Union[str, Any]:
         """simple docstring"""





         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )

   def     UpperCamelCase					(				self	:    Any       ) ->			str:
         """simple docstring"""





         super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         pass





   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.get_dummy_components()
         A_							       =		StableDiffusionXLImgaImgPipeline(**lowerCamelCase__       )
         A_							       =		sd_pipe.to(lowerCamelCase__       )
         A_							       =		sd_pipe.to(lowerCamelCase__       )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

         # forward without prompt embeds
         A_							       =		self.get_dummy_inputs(lowerCamelCase__       )
         A_							       =		3 * ['''this is a negative prompt''']
         A_							       =		negative_prompt
         A_							       =		3 * [inputs['''prompt''']]

         A_							       =		sd_pipe(**lowerCamelCase__       )
         A_							       =		output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         A_							       =		self.get_dummy_inputs(lowerCamelCase__       )
         A_							       =		3 * ['''this is a negative prompt''']
         A_							       =		3 * [inputs.pop('''prompt'''       )]

         (
             (
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,
         )							       =		sd_pipe.encode_prompt(lowerCamelCase__			,						negative_prompt=lowerCamelCase__       )

         A_							       =		sd_pipe(
             **lowerCamelCase__			,						prompt_embeds=lowerCamelCase__			,						negative_prompt_embeds=lowerCamelCase__			,						pooled_prompt_embeds=lowerCamelCase__			,						negative_pooled_prompt_embeds=lowerCamelCase__			,						)
         A_							       =		output.images[0, -3:, -3:, -1]

         # make sure that it's equal
         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1e-4



@slow
@require_torch_gpu
class    _lowercase				(					unittest.TestCase			):

   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Optional[int]="cpu"			,						lowerCamelCase__	:    Dict=torch.floataa			,						lowerCamelCase__	:    List[Any]=0       ) ->			str:
         """simple docstring"""





         A_							       =		torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
         A_							       =		np.random.RandomState(lowerCamelCase__       ).standard_normal((1, 4, 6_4, 6_4)       )
         A_							       =		torch.from_numpy(lowerCamelCase__       ).to(device=lowerCamelCase__			,						dtype=lowerCamelCase__       )
         A_							       =		{
             '''prompt''': '''a photograph of an astronaut riding a horse''',
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''       )
         pipe.to(lowerCamelCase__       )
         pipe.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		self.get_inputs(lowerCamelCase__       )
         A_							       =		pipe(**lowerCamelCase__       ).images
         A_							       =		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         A_							       =		np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]       )
         assert np.abs(image_slice - expected_slice       ).max() < 7e-3








from math import factorial, radians







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 18		,				SCREAMING_SNAKE_CASE = 10   ):
      '''simple docstring'''







      A_							       =		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

      # Converting from degrees to radians
      A_							       =		radians(SCREAMING_SNAKE_CASE   )

      A_							       =		angle_in_radians
      A_							       =		3
      A_							       =		-1

      for _ in range(SCREAMING_SNAKE_CASE   ):
            result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE   )

            A_							       =		-b  # One positive term and the next will be negative and so on...
            a += 2  # Increased by 2 for every term.

      return round(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      __import__("""doctest""").testmod()





