

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
       _UpperCAmelCase   :  Optional[int]										=				ShapEImgaImgPipeline
       _UpperCAmelCase   :  List[str]										=				['''image''']
       _UpperCAmelCase   :  int										=				['''image''']
       _UpperCAmelCase   :  Tuple										=				[
           '''num_images_per_prompt''',
           '''num_inference_steps''',
           '''generator''',
           '''latents''',
           '''guidance_scale''',
           '''frame_size''',
           '''output_type''',
           '''return_dict''',
       ]
       _UpperCAmelCase   :  Union[str, Any]										=				False
       @property
       def    lowerCAmelCase					(		self		:      List[Any]):
        return 3_2
       @property
       def    lowerCAmelCase					(		self		:      Any):
        return 3_2
       @property
       def    lowerCAmelCase					(		self		:      Any):
        return self.time_input_dim * 4
       @property
       def    lowerCAmelCase					(		self		:      Any):
        return 8
       @property
       def    lowerCAmelCase					(		self		:      Optional[int]):
        torch.manual_seed(0)
        __lowerCamelCase    :    List[str] 	=			CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size       ,image_size=6_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       ,)

        __lowerCamelCase    :    Optional[int] 	=			CLIPVisionModel(SCREAMING_SNAKE_CASE__)
        return model
       @property
       def    lowerCAmelCase					(		self		:      Any):
        __lowerCamelCase    :    Dict 	=			CLIPImageProcessor(
            crop_size=2_2_4       ,do_center_crop=SCREAMING_SNAKE_CASE__       ,do_normalize=SCREAMING_SNAKE_CASE__       ,do_resize=SCREAMING_SNAKE_CASE__       ,image_mean=[0.48145466, 0.4578275, 0.40821073]       ,image_std=[0.26862954, 0.26130258, 0.27577711]       ,resample=3       ,size=2_2_4       ,)

        return image_processor
       @property
       def    lowerCAmelCase					(		self		:      Optional[int]):
        torch.manual_seed(0)

        __lowerCamelCase    :    Any 	=			{
            'num_attention_heads': 2,
            'attention_head_dim': 1_6,
            'embedding_dim': self.time_input_dim,
            'num_embeddings': 3_2,
            'embedding_proj_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'num_layers': 1,
            'clip_embed_dim': self.time_input_dim * 2,
            'additional_embeddings': 0,
            'time_embed_act_fn': 'gelu',
            'norm_in_type': 'layer',
            'embedding_proj_norm_type': 'layer',
            'encoder_hid_proj_type': None,
            'added_emb_type': None,
        }

        __lowerCamelCase    :    List[str] 	=			PriorTransformer(**SCREAMING_SNAKE_CASE__)
        return model
       @property
       def    lowerCAmelCase					(		self		:      Optional[int]):
        torch.manual_seed(0)

        __lowerCamelCase    :    Optional[int] 	=			{
            'param_shapes': (
                (self.renderer_dim, 9_3),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
            ),
            'd_latent': self.time_input_dim,
            'd_hidden': self.renderer_dim,
            'n_output': 1_2,
            'background': (
                0.1,
                0.1,
                0.1,
            ),
        }
        __lowerCamelCase    :    int 	=			ShapERenderer(**SCREAMING_SNAKE_CASE__)
        return model
       def    lowerCAmelCase					(		self		:      Union[str, Any]):
        __lowerCamelCase    :    Optional[int] 	=			self.dummy_prior
        __lowerCamelCase    :    Optional[int] 	=			self.dummy_image_encoder
        __lowerCamelCase    :    Optional[int] 	=			self.dummy_image_processor
        __lowerCamelCase    :    Optional[Any] 	=			self.dummy_renderer

        __lowerCamelCase    :    int 	=			HeunDiscreteScheduler(
            beta_schedule='exp'       ,num_train_timesteps=1_0_2_4       ,prediction_type='sample'       ,use_karras_sigmas=SCREAMING_SNAKE_CASE__       ,clip_sample=SCREAMING_SNAKE_CASE__       ,clip_sample_range=1.0       ,)
        __lowerCamelCase    :    Tuple 	=			{
            'prior': prior,
            'image_encoder': image_encoder,
            'image_processor': image_processor,
            'renderer': renderer,
            'scheduler': scheduler,
        }

        return components
       def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str=0):
        __lowerCamelCase    :    List[str] 	=			floats_tensor((1, 3, 6_4, 6_4)       ,rng=random.Random(SCREAMING_SNAKE_CASE__)).to(SCREAMING_SNAKE_CASE__)

        if str(SCREAMING_SNAKE_CASE__).startswith('mps'):
         __lowerCamelCase    :    List[str] 	=			torch.manual_seed(SCREAMING_SNAKE_CASE__)
        else:
         __lowerCamelCase    :    Tuple 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    str 	=			{
            'image': input_image,
            'generator': generator,
            'num_inference_steps': 1,
            'frame_size': 3_2,
            'output_type': 'np',
        }
        return inputs
       def    lowerCAmelCase					(		self		:      List[str]):
        __lowerCamelCase    :    Dict 	=			'cpu'

        __lowerCamelCase    :    str 	=			self.get_dummy_components()

        __lowerCamelCase    :    Tuple 	=			self.pipeline_class(**SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    Union[str, Any] 	=			pipe.to(SCREAMING_SNAKE_CASE__)

        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    Union[str, Any] 	=			pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__))
        __lowerCamelCase    :    Optional[Any] 	=			output.images[0]
        __lowerCamelCase    :    str 	=			image[0, -3:, -3:, -1]

        assert image.shape == (2_0, 3_2, 3_2, 3)

        __lowerCamelCase    :    Optional[Any] 	=			np.array(
            [
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
            ])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
       def    lowerCAmelCase					(		self		:      Optional[Any]):
        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
        self._test_inference_batch_consistent(batch_sizes=[1, 2])
       def    lowerCAmelCase					(		self		:      int):
        __lowerCamelCase    :    int 	=			torch_device == 'cpu'
        __lowerCamelCase    :    Dict 	=			True
        self._test_inference_batch_single_identical(
            batch_size=2       ,test_max_difference=SCREAMING_SNAKE_CASE__       ,relax_max_difference=SCREAMING_SNAKE_CASE__       ,)


       def    lowerCAmelCase					(		self		:      Tuple):
        __lowerCamelCase    :    List[Any] 	=			self.get_dummy_components()
        __lowerCamelCase    :    Optional[Any] 	=			self.pipeline_class(**SCREAMING_SNAKE_CASE__)
        __lowerCamelCase    :    Tuple 	=			pipe.to(SCREAMING_SNAKE_CASE__)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    Optional[int] 	=			1
        __lowerCamelCase    :    str 	=			2

        __lowerCamelCase    :    Optional[int] 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)

        for key in inputs.keys():
         if key in self.batch_params:
          __lowerCamelCase    :    List[Any] 	=			batch_size * [inputs[key]]

        __lowerCamelCase    :    int 	=			pipe(**SCREAMING_SNAKE_CASE__       ,num_images_per_prompt=SCREAMING_SNAKE_CASE__)[0]

        assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
       def    lowerCAmelCase					(		self		:      int):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


       def    lowerCAmelCase					(		self		:      Tuple):
        __lowerCamelCase    :    List[Any] 	=			load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
        __lowerCamelCase    :    Optional[int] 	=			load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/shap_e/test_shap_e_img2img_out.npy')
        __lowerCamelCase    :    Optional[int] 	=			ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
        __lowerCamelCase    :    Any 	=			pipe.to(SCREAMING_SNAKE_CASE__)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

        __lowerCamelCase    :    Any 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(0)

        __lowerCamelCase    :    Tuple 	=			pipe(
            SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,guidance_scale=3.0       ,num_inference_steps=6_4       ,frame_size=6_4       ,output_type='np'       ,).images[0]

        assert images.shape == (2_0, 6_4, 6_4, 3)

        assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:

	# A local function to see if a dot lands in the circle.
	def is_in_circle(lowerCamelCase__     ,			lowerCamelCase__      ) -> bool:
		__lowerCamelCase    :    Dict 	=			sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__lowerCamelCase    :    Optional[int] 	=			mean(
	    int(is_in_circle(uniform(-1.0     ,			1.0      )     ,			uniform(-1.0     ,			1.0      )      )      )
	    for _ in range(lowerCamelCase__      )      )
	# The ratio of the area for circle to square is pi/4.
	__lowerCamelCase    :    Any 	=			proportion * 4
	print(F"The estimated value of pi is {pi_estimate}"      )
	print(F"The numpy value of pi is {pi}"      )
	print(F"The total error is {abs(pi - pi_estimate      )}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0     ,			)  ->					float:
	return mean(
	    function_to_integrate(uniform(lowerCamelCase__     ,			lowerCamelCase__      )      ) for _ in range(lowerCamelCase__      )      ) * (max_value - min_value)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0      )  ->					None:

	def identity_function(lowerCamelCase__      ) -> float:
		return x

	__lowerCamelCase    :    str 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			(max_value * max_value - min_value * min_value) / 2

	print('******************'      )
	print(F"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {expected_value}"      )
	print(F"Total error is {abs(estimated_value - expected_value      )}"      )
	print('******************'      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:

	def function_to_integrate(lowerCamelCase__      ) -> float:
		return sqrt(4.0 - x * x      )

	__lowerCamelCase    :    Any 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			0.0     ,			2.0      )

	print('******************'      )
	print('Estimating pi using area_under_curve_estimator'      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {pi}"      )
	print(F"Total error is {abs(estimated_value - pi      )}"      )
	print('******************'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




