


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   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Tuple       					=			StableDiffusionXLImgaImgPipeline
 __lowercase			:							int       					=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
 __lowercase			:							Tuple       					=			PipelineTesterMixin.required_optional_params - {'latents'}
 __lowercase			:							Optional[Any]       					=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __lowercase			:							Dict       					=			IMAGE_TO_IMAGE_IMAGE_PARAMS
 __lowercase			:							Union[str, Any]       					=			IMAGE_TO_IMAGE_IMAGE_PARAMS
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       List[str]        =       UNetaDConditionModel(
            block_out_channels=(32, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			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=80    ,			cross_attention_dim=64    ,			)
        lowercase__:       List[Any]        =       EulerDiscreteScheduler(
            beta_start=0.0_0_0_8_5    ,			beta_end=0.0_1_2    ,			steps_offset=1    ,			beta_schedule='scaled_linear'    ,			timestep_spacing='leading'    ,			)
        torch.manual_seed(0			)
        lowercase__:       Optional[int]        =       AutoencoderKL(
            block_out_channels=[32, 64]    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,			latent_channels=4    ,			sample_size=128    ,			)
        torch.manual_seed(0			)
        lowercase__:       Optional[int]        =       CLIPTextConfig(
            bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=32    ,			intermediate_size=37    ,			layer_norm_eps=1E-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=1_000    ,			hidden_act='gelu'    ,			projection_dim=32    ,			)
        lowercase__:       Union[str, Any]        =       CLIPTextModel(lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    ,			local_files_only=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       CLIPTextModelWithProjection(lowerCAmelCase__			)
        lowercase__:       int        =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    ,			local_files_only=lowerCAmelCase__			)

        lowercase__:       Union[str, Any]        =       {
            '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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       floats_tensor((1, 3, 32, 32)    ,			rng=random.Random(lowerCAmelCase__			)			).to(lowerCAmelCase__			)
        lowercase__:       str        =       image / 2 + 0.5
        if str(lowerCAmelCase__			).startswith('mps'			):
               lowercase__:       str        =       torch.manual_seed(lowerCAmelCase__			)
        else:
               lowercase__:       Optional[Any]        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
        lowercase__:       Tuple        =       {
            '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': 0.7_5,
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[str]        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       Dict        =       self.get_dummy_components()
        lowercase__:       Union[str, Any]        =       StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Any        =       sd_pipe(**lowerCAmelCase__			).images
        lowercase__:       Dict        =       image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)

        lowercase__:       Tuple        =       np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        super().test_attention_slicing_forward_pass(expected_max_diff=3E-3			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        super().test_inference_batch_single_identical(expected_max_diff=3E-3			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.get_dummy_components()
        lowercase__:       str        =       StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       sd_pipe.to(lowerCAmelCase__			)
        lowercase__:       Tuple        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        # forward without prompt embeds
        lowercase__:       Dict        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Tuple        =       3 * ['this is a negative prompt']
        lowercase__:       Dict        =       negative_prompt
        lowercase__:       List[str]        =       3 * [inputs['prompt']]

        lowercase__:       Union[str, Any]        =       sd_pipe(**lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        lowercase__:       Dict        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Dict        =       3 * ['this is a negative prompt']
        lowercase__:       Optional[int]        =       3 * [inputs.pop('prompt'			)]

        (
            (
            lowercase__
        )      ,						(
            lowercase__
        )      ,						(
            lowercase__
        )      ,						(
            lowercase__
        )      ,						
        ):       Any        =       sd_pipe.encode_prompt(lowerCAmelCase__    ,			negative_prompt=lowerCAmelCase__			)

        lowercase__:       Union[str, Any]        =       sd_pipe(
            **lowerCAmelCase__    ,			prompt_embeds=lowerCAmelCase__    ,			negative_prompt_embeds=lowerCAmelCase__    ,			pooled_prompt_embeds=lowerCAmelCase__    ,			negative_pooled_prompt_embeds=lowerCAmelCase__    ,			)
        lowercase__:       Optional[int]        =       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   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__="cpu"    ,			lowerCAmelCase__=torch.floataa    ,			lowerCAmelCase__=0			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Any        =       torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
        lowercase__:       Any        =       np.random.RandomState(lowerCAmelCase__			).standard_normal((1, 4, 64, 64)			)
        lowercase__:       Dict        =       torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__    ,			dtype=lowerCAmelCase__			)
        lowercase__:       Tuple        =       {
            '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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'			)
        pipe.to(lowerCAmelCase__			)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       self.get_inputs(lowerCAmelCase__			)
        lowercase__:       Tuple        =       pipe(**lowerCAmelCase__			).images
        lowercase__:       Dict        =       image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        lowercase__:       int        =       np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]			)
        assert np.abs(image_slice - expected_slice			).max() < 7E-3






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = False					)  ->    list[float]:
       if radian_mode:
              return [magnitude * cos(snake_case					), magnitude * sin(snake_case					)]
       return [magnitude * cos(radians(snake_case					)					), magnitude * sin(radians(snake_case					)					)]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = 10**-1					)  ->    bool:
       lowercase__:       NDArray[floataa]        =       cross(snake_case      ,							snake_case					)
       lowercase__:       float        =       sum(snake_case					)
       return abs(snake_case					) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowerCAmelCase			=							array(
     [
         polar_force(718.4, 1_80 - 30),
         polar_force(879.54, 45),
         polar_force(1_00, -90),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowerCAmelCase			=							array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(2_15, 1_80 - 45),
         polar_force(2_64, 90 - 30),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowerCAmelCase			=							array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

 __lowerCAmelCase			=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()


