import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     DiTPipeline
						lowercase					 =     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
						lowercase					 =     PipelineTesterMixin.required_optional_params - {
						    'latents',
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						lowercase					 =     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
						lowercase					 =     False

						def        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									torch.manual_seed(0   )
									lowercase							=		TransformeraDModel(
									    sample_size=16							,							num_layers=2							,							patch_size=4							,							attention_head_dim=8							,							num_attention_heads=2							,							in_channels=4							,							out_channels=8							,							attention_bias=__lowerCamelCase							,							activation_fn='''gelu-approximate'''							,							num_embeds_ada_norm=10_00							,							norm_type='''ada_norm_zero'''							,							norm_elementwise_affine=__lowerCamelCase							,							)
									lowercase							=		AutoencoderKL()
									lowercase							=		DDIMScheduler()
									lowercase							=		{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
									return components

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int=0   )							->		Union[str, Any]:


									'''simple docstring'''

									if str(__lowerCamelCase   ).startswith('''mps'''   ):
												lowercase							=		torch.manual_seed(__lowerCamelCase   )
									else:
												lowercase							=		torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
									lowercase							=		{
									    '''class_labels''': [1],
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}
									return inputs

						def        __a		(	self				:      Optional[Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		'''cpu'''

									lowercase							=		self.get_dummy_components()
									lowercase							=		self.pipeline_class(**__lowerCamelCase   )
									pipe.to(__lowerCamelCase   )
									pipe.set_progress_bar_config(disable=__lowerCamelCase   )

									lowercase							=		self.get_dummy_inputs(__lowerCamelCase   )
									lowercase							=		pipe(**__lowerCamelCase   ).images
									lowercase							=		image[0, -3:, -3:, -1]

									self.assertEqual(image.shape							,							(1, 16, 16, 3)   )
									lowercase							=		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]   )
									lowercase							=		np.abs(image_slice.flatten() - expected_slice   ).max()
									self.assertLessEqual(__lowerCamelCase							,							1E-3   )

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

									self._test_inference_batch_single_identical(relax_max_difference=__lowerCamelCase							,							expected_max_diff=1E-3   )



						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()							,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,							)
						def        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )






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

						def        __a		(	self				:      List[Any]   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									lowercase							=		torch.manual_seed(0   )

									lowercase							=		DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''   )
									pipe.to('''cuda'''   )

									lowercase							=		['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
									lowercase							=		pipe.get_label_ids(__lowerCamelCase   )

									lowercase							=		pipe(__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=40							,							output_type='''np'''   ).images

									for word, image in zip(__lowerCamelCase							,							__lowerCamelCase   ):
												lowercase							=		load_numpy(
												    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'   )
												assert np.abs((expected_image - image).max()   ) < 1E-2



						def        __a		(	self				:      Any   )							->		Dict:


									'''simple docstring'''

									lowercase							=		DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''   )
									lowercase							=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
									pipe.to('''cuda'''   )

									lowercase							=		['''vase''', '''umbrella''']
									lowercase							=		pipe.get_label_ids(__lowerCamelCase   )

									lowercase							=		torch.manual_seed(0   )
									lowercase							=		pipe(__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=25							,							output_type='''np'''   ).images

									for word, image in zip(__lowerCamelCase							,							__lowerCamelCase   ):
												lowercase							=		load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    f'/dit/{word}_512.npy'   )

												assert np.abs((expected_image - image).max()   ) < 1E-1






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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase							=		random.split(__lowerCamelCase							,							num=1   )
									lowercase							=		self.config.s_noise * random.normal(key=__lowerCamelCase							,							shape=sample.shape   )
									lowercase							=		sigma + gamma * sigma
									lowercase							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





