





'''simple docstring'''
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      snake_case     (    lowerCamelCase_			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=ShapEImgaImgPipeline
 SCREAMING_SNAKE_CASE_	:					Tuple					=["image"]
 SCREAMING_SNAKE_CASE_	:					str					=["image"]
 SCREAMING_SNAKE_CASE_	:					List[str]					=[
     "num_images_per_prompt",
     "num_inference_steps",
     "generator",
     "latents",
     "guidance_scale",
     "frame_size",
     "output_type",
     "return_dict",
 ]
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=False






 @property
 def        _lowerCamelCase					(       self       :							str	):
        return 3_2






 @property
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return 3_2






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return self.time_input_dim * 4






 @property
 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return 8






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        torch.manual_seed(0	)
        __UpperCamelCase       =  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							,	)

        __UpperCamelCase       =  CLIPVisionModel(__SCREAMING_SNAKE_CASE	)
        return model






 @property
 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  CLIPImageProcessor(
            crop_size=2_2_4							,	do_center_crop=__SCREAMING_SNAKE_CASE							,	do_normalize=__SCREAMING_SNAKE_CASE							,	do_resize=__SCREAMING_SNAKE_CASE							,	image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]							,	image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,	resample=3							,	size=2_2_4							,	)

        return image_processor






 @property
 def        _lowerCamelCase					(       self       :							Any	):
        torch.manual_seed(0	)

        __UpperCamelCase       =  {
            '''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,
        }

        __UpperCamelCase       =  PriorTransformer(**__SCREAMING_SNAKE_CASE	)
        return model






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        torch.manual_seed(0	)

        __UpperCamelCase       =  {
            '''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,
            ),
        }
        __UpperCamelCase       =  ShapERenderer(**__SCREAMING_SNAKE_CASE	)
        return model






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.dummy_prior
        __UpperCamelCase       =  self.dummy_image_encoder
        __UpperCamelCase       =  self.dummy_image_processor
        __UpperCamelCase       =  self.dummy_renderer

        __UpperCamelCase       =  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							,	)
        __UpperCamelCase       =  {
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''image_processor''': image_processor,
            '''renderer''': renderer,
            '''scheduler''': scheduler,
        }

        return components






 def        _lowerCamelCase					(       self       :							str							,	__A       :							str							,	__A       :							Optional[int]=0	):
        __UpperCamelCase       =  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'	):
               __UpperCamelCase       =  torch.manual_seed(__SCREAMING_SNAKE_CASE	)
        else:
               __UpperCamelCase       =  torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
        __UpperCamelCase       =  {
            '''image''': input_image,
            '''generator''': generator,
            '''num_inference_steps''': 1,
            '''frame_size''': 3_2,
            '''output_type''': '''np''',
        }
        return inputs






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  '''cpu'''

        __UpperCamelCase       =  self.get_dummy_components()

        __UpperCamelCase       =  self.pipeline_class(**__SCREAMING_SNAKE_CASE	)
        __UpperCamelCase       =  pipe.to(__SCREAMING_SNAKE_CASE	)

        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

        __UpperCamelCase       =  pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)	)
        __UpperCamelCase       =  output.images[0]
        __UpperCamelCase       =  image[0, -3:, -3:, -1]

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

        __UpperCamelCase       =  np.array(
            [
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
            ]	)

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






 def        _lowerCamelCase					(       self       :							str	):
        self._test_inference_batch_consistent(batch_sizes=[1, 2]	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  torch_device == '''cpu'''
        __UpperCamelCase       =  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       :							str	):
        __UpperCamelCase       =  self.get_dummy_components()
        __UpperCamelCase       =  self.pipeline_class(**__SCREAMING_SNAKE_CASE	)
        __UpperCamelCase       =  pipe.to(__SCREAMING_SNAKE_CASE	)
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

        __UpperCamelCase       =  1
        __UpperCamelCase       =  2

        __UpperCamelCase       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)

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

        __UpperCamelCase       =  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      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'	)
        __UpperCamelCase       =  load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/shap_e/test_shap_e_img2img_out.npy'	)
        __UpperCamelCase       =  ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'	)
        __UpperCamelCase       =  pipe.to(__SCREAMING_SNAKE_CASE	)
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

        __UpperCamelCase       =  torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(0	)

        __UpperCamelCase       =  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	)


'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Tuple													=       CommonSchedulerState.create(self		)

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, Any]													=       jnp.array(1.0				,							dtype=self.dtype		)

											lowercase_	:     List[Any]													=       jnp.arange(0				,							self.config.num_train_timesteps		).round()[::-1]

											return DDPMSchedulerState.create(
											    common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self.config.num_train_timesteps // num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# rounding to avoid issues when num_inference_step is power of 3
											lowercase_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

											return state.replace(
											    num_inference_steps=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     str													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											lowercase_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowercase_						, lowercase_	:     Optional[Any]													=       jnp.split(__SCREAMING_SNAKE_CASE				,							sample.shape[1]				,							axis=1		)
											else:
																	lowercase_	:     int													=       None

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[int]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     int													=       1 - alpha_prod_t
											lowercase_	:     str													=       1 - alpha_prod_t_prev

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																	lowercase_	:     Tuple													=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											else:
																	raise ValueError(
																	    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																	    ''' for the FlaxDDPMScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	lowercase_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-1				,							1		)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[Any]													=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     Optional[Any]													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																	lowercase_	:     str													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							num=1		)
																	lowercase_	:     List[Any]													=       jax.random.normal(__SCREAMING_SNAKE_CASE				,							shape=model_output.shape				,							dtype=self.dtype		)
																	return (self._get_variance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							predicted_variance=__SCREAMING_SNAKE_CASE		) ** 0.5) * noise

											lowercase_	:     Optional[Any]													=       jnp.where(t > 0				,							random_variance()				,							jnp.zeros(model_output.shape				,							dtype=self.dtype		)		)

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return add_noise_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return get_velocity_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



