





'''simple docstring'''



import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_SCREAMING_SNAKE_CASE					=    '''scheduler_config.json'''






class 							__lowercase       (      __UpperCAmelCase						):







	'''simple docstring'''

	a    :				Dict											=       1
	a    :				int											=       2
	a    :				List[Any]											=       3
	a    :				Optional[int]											=       4
	a    :				Any											=       5






@dataclass
class 							__lowercase       (      __UpperCAmelCase						):







	'''simple docstring'''

	a    :				jnp.ndarray






class 							__lowercase       :







	'''simple docstring'''

	a    :				str											=       SCHEDULER_CONFIG_NAME
	a    :				str											=       ["dtype"]
	a    :				List[str]											=       []
	a    :				Dict											=       True


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							cls.load_config(
				    pretrained_model_name_or_path=_lowerCamelCase     ,subfolder=_lowerCamelCase     ,return_unused_kwargs=_lowerCamelCase     ,**_lowerCamelCase     ,)
				__lowercase						,		__lowercase										=							cls.from_config(_lowerCamelCase     ,return_unused_kwargs=_lowerCamelCase     ,**_lowerCamelCase )

				if hasattr(_lowerCamelCase     ,'''create_state''' ) and getattr(_lowerCamelCase     ,'''has_state'''     ,_lowerCamelCase ):
							__lowercase										=							scheduler.create_state()

				if return_unused_kwargs:
							return scheduler, state, unused_kwargs

				return scheduler, state


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = False     ,**_lowerCamelCase )							->							str:







				'''simple docstring'''





				self.save_config(save_directory=_lowerCamelCase     ,push_to_hub=_lowerCamelCase     ,**_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return self._get_compatibles()


	@classmethod
	def 						_UpperCAmelCase     (cls )							->							int:







				'''simple docstring'''





				__lowercase										=							list(set([cls.__name__] + cls._compatibles ) )
				__lowercase										=							importlib.import_module(__name__.split('''.''' )[0] )
				__lowercase										=							[
				    getattr(_lowerCamelCase     ,_lowerCamelCase ) for c in compatible_classes_str if hasattr(_lowerCamelCase     ,_lowerCamelCase )
				]
				return compatible_classes





def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Dict				):
			assert len(_lowerCAmelCase				) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowerCAmelCase				) - x.ndim)				)		,       _lowerCAmelCase				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Optional[int]=0.9_99		,       lowerCamelCase_			:  List[Any]=jnp.floataa				):

			def alpha_bar(lowerCamelCase_			:  Optional[int]				):
						return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

			__lowercase										=							[]
			for i in range(_lowerCAmelCase				):
						__lowercase										=							i / num_diffusion_timesteps
						__lowercase										=							(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar(_lowerCAmelCase				) / alpha_bar(_lowerCAmelCase				)		,       _lowerCAmelCase				)				)
			return jnp.array(_lowerCAmelCase		,       dtype=_lowerCAmelCase				)






@flax.struct.dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				jnp.ndarray
	a    :				jnp.ndarray
	a    :				jnp.ndarray


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							scheduler.config

				if config.trained_betas is not None:
							__lowercase										=							jnp.asarray(config.trained_betas     ,dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
							__lowercase										=							jnp.linspace(config.beta_start     ,config.beta_end     ,config.num_train_timesteps     ,dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							__lowercase										=							(
							    jnp.linspace(
							        config.beta_start**0.5     ,config.beta_end**0.5     ,config.num_train_timesteps     ,dtype=scheduler.dtype )
							    ** 2
							)
				elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							__lowercase										=							betas_for_alpha_bar(config.num_train_timesteps     ,dtype=scheduler.dtype )
				else:
							raise NotImplementedError(
							    f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}" )

				__lowercase										=							1.0 - betas

				__lowercase										=							jnp.cumprod(_lowerCamelCase     ,axis=0 )

				return cls(
				    alphas=_lowerCamelCase     ,betas=_lowerCamelCase     ,alphas_cumprod=_lowerCamelCase     ,)





def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Any		,       lowerCamelCase_			:  str				):
			__lowercase										=							state.alphas_cumprod

			__lowercase										=							alphas_cumprod[timesteps] ** 0.5
			__lowercase										=							sqrt_alpha_prod.flatten()
			__lowercase										=							broadcast_to_shape_from_left(_lowerCAmelCase		,       original_samples.shape				)

			__lowercase										=							(1 - alphas_cumprod[timesteps]) ** 0.5
			__lowercase										=							sqrt_one_minus_alpha_prod.flatten()
			__lowercase										=							broadcast_to_shape_from_left(_lowerCAmelCase		,       original_samples.shape				)

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Any				):
			__lowercase						,		__lowercase										=							get_sqrt_alpha_prod(_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase				)
			__lowercase										=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples




def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase						,		__lowercase										=							get_sqrt_alpha_prod(_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase				)
			__lowercase										=							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity












'''simple docstring'''



import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_SCREAMING_SNAKE_CASE					=    False






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





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


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return 12


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return 12


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return 32


	@property
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							VQModel(
				    block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,latent_channels=3     ,num_vq_embeddings=self.num_embed     ,vq_embed_dim=3     ,)
				return model


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
				return tokenizer


	@property
	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							CLIPTextConfig(
				    bos_token_id=0     ,eos_token_id=2     ,hidden_size=self.text_embedder_hidden_size     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,)
				return CLIPTextModel(_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0 )

				__lowercase										=							12
				__lowercase										=							12

				__lowercase										=							{
				    '''attention_bias''': True,
				    '''cross_attention_dim''': 32,
				    '''attention_head_dim''': height * width,
				    '''num_attention_heads''': 1,
				    '''num_vector_embeds''': self.num_embed,
				    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
				    '''norm_num_groups''': 32,
				    '''sample_size''': width,
				    '''activation_fn''': '''geglu-approximate''',
				}

				__lowercase										=							TransformeraDModel(**_lowerCamelCase )
				return model


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

				__lowercase										=							image[0, -3:, -3:, -1]
				__lowercase										=							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )

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


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(
				    learnable=_lowerCamelCase     ,hidden_size=self.text_embedder_hidden_size     ,length=tokenizer.model_max_length )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

				__lowercase										=							image[0, -3:, -3:, -1]
				__lowercase										=							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )

				assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






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







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

				__lowercase										=							VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
				__lowercase										=							pipeline.to(_lowerCamelCase )
				pipeline.set_progress_bar_config(disable=_lowerCamelCase )

				# requires GPU generator for gumbel softmax
				# don't use GPU generator in tests though
				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipeline(
				    '''teddy bear playing in the pool'''     ,num_images_per_prompt=1     ,generator=_lowerCamelCase     ,output_type='''np'''     ,)

				__lowercase										=							output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image ).max() < 2.0





