




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Dict=0.999	,					__UpperCamelCase      :						List[Any]="cosine"	,					)  ->							Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCamelCase      :						List[str]				):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCamelCase      :						List[str]				):
            return math.exp(t * -12.0				)

    else:
        raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

    UpperCAmelCase_										=					[]
    for i in range(lowerCamelCase_				):
        UpperCAmelCase_										=					i / num_diffusion_timesteps
        UpperCAmelCase_										=					(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowerCamelCase_				) / alpha_bar_fn(lowerCamelCase_				)	,					lowerCamelCase_				)				)
    return torch.tensor(lowerCamelCase_	,					dtype=torch.floataa				)



class       a					(       _a     ,       _a      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  [e.name for e in KarrasDiffusionSchedulers]
     lowerCAmelCase    :	List[Any]       		=  2
     @register_to_config
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int] = 10_00 ,   __snake_case       :      int = 0.00_085 ,   __snake_case       :      Any = 0.012 ,   __snake_case       :      Union[str, Any] = "linear" ,   __snake_case       :      Tuple = None ,   __snake_case       :      List[Any] = "epsilon" ,   __snake_case       :      int = "linspace" ,   __snake_case       :      List[Any] = 0 ,   ):

         if trained_betas is not None:
             UpperCAmelCase_										=					torch.tensor(__snake_case ,   dtype=torch.floataa    )
         elif beta_schedule == "linear":
             UpperCAmelCase_										=					torch.linspace(__snake_case ,   __snake_case ,   __snake_case ,   dtype=torch.floataa    )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCAmelCase_										=					(
                 torch.linspace(beta_start**0.5 ,   beta_end**0.5 ,   __snake_case ,   dtype=torch.floataa    ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCAmelCase_										=					betas_for_alpha_bar(__snake_case    )
         else:
             raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'    )

         UpperCAmelCase_										=					1.0 - self.betas
         UpperCAmelCase_										=					torch.cumprod(self.alphas ,   dim=0    )

         #  set all values
         self.set_timesteps(__snake_case ,   __snake_case ,   __snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Tuple ,   __snake_case       :      int=None    ):

         if schedule_timesteps is None:
             UpperCAmelCase_										=					self.timesteps

         UpperCAmelCase_										=					(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter    ) == 0:
             UpperCAmelCase_										=					1 if len(__snake_case    ) > 1 else 0
         else:
             UpperCAmelCase_										=					timestep.cpu().item() if torch.is_tensor(__snake_case    ) else timestep
             UpperCAmelCase_										=					self._index_counter[timestep_int]

         return indices[pos].item()
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):

         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[int] ,   ):
         UpperCAmelCase_										=					self.index_for_timestep(__snake_case    )

         if self.state_in_first_order:
             UpperCAmelCase_										=					self.sigmas[step_index]
         else:
             UpperCAmelCase_										=					self.sigmas_interpol[step_index]

         UpperCAmelCase_										=					sample / ((sigma**2 + 1) ** 0.5)
         return sample
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      Optional[int] ,   __snake_case       :      Any = None ,   __snake_case       :      Optional[int] = None ,   ):
         UpperCAmelCase_										=					num_inference_steps

         UpperCAmelCase_										=					num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             UpperCAmelCase_										=					np.linspace(0 ,   num_train_timesteps - 1 ,   __snake_case ,   dtype=__snake_case    )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             UpperCAmelCase_										=					num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             UpperCAmelCase_										=					(np.arange(0 ,   __snake_case    ) * step_ratio).round()[::-1].copy().astype(__snake_case    )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             UpperCAmelCase_										=					num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             UpperCAmelCase_										=					(np.arange(__snake_case ,   0 ,   -step_ratio    )).round().copy().astype(__snake_case    )
             timesteps -= 1
         else:
             raise ValueError(
                 F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'    )

         UpperCAmelCase_										=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
         UpperCAmelCase_										=					torch.from_numpy(np.log(__snake_case    )    ).to(__snake_case    )

         UpperCAmelCase_										=					np.interp(__snake_case ,   np.arange(0 ,   len(__snake_case    )    ) ,   __snake_case    )
         UpperCAmelCase_										=					np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
         UpperCAmelCase_										=					torch.from_numpy(__snake_case    ).to(device=__snake_case    )

         # interpolate sigmas
         UpperCAmelCase_										=					sigmas.log().lerp(sigmas.roll(1    ).log() ,   0.5    ).exp()

         UpperCAmelCase_										=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
         UpperCAmelCase_										=					torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

         if str(__snake_case    ).startswith('''mps'''    ):
             # mps does not support float64
             UpperCAmelCase_										=					torch.from_numpy(__snake_case    ).to(__snake_case ,   dtype=torch.floataa    )
         else:
             UpperCAmelCase_										=					torch.from_numpy(__snake_case    ).to(__snake_case    )

         # interpolate timesteps
         UpperCAmelCase_										=					self.sigma_to_t(__snake_case    ).to(__snake_case ,   dtype=timesteps.dtype    )
         UpperCAmelCase_										=					torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,   dim=-1    ).flatten()

         UpperCAmelCase_										=					torch.cat([timesteps[:1], interleaved_timesteps]    )

         UpperCAmelCase_										=					None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         UpperCAmelCase_										=					defaultdict(__snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      List[str]    ):
         UpperCAmelCase_										=					sigma.log()

         # get distribution
         UpperCAmelCase_										=					log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         UpperCAmelCase_										=					dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
         UpperCAmelCase_										=					low_idx + 1

         UpperCAmelCase_										=					self.log_sigmas[low_idx]
         UpperCAmelCase_										=					self.log_sigmas[high_idx]

         # interpolate sigmas
         UpperCAmelCase_										=					(low - log_sigma) / (low - high)
         UpperCAmelCase_										=					w.clamp(0 ,   1    )

         # transform interpolation to time range
         UpperCAmelCase_										=					(1 - w) * low_idx + w * high_idx
         UpperCAmelCase_										=					t.view(sigma.shape    )
         return t
     @property
     def   lowerCamelCase_       (     self       :      str    ):
         return self.sample is None
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      List[str] ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[Any] = True ,   ):
         UpperCAmelCase_										=					self.index_for_timestep(__snake_case    )

         # advance index counter by 1
         UpperCAmelCase_										=					timestep.cpu().item() if torch.is_tensor(__snake_case    ) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             UpperCAmelCase_										=					self.sigmas[step_index]
             UpperCAmelCase_										=					self.sigmas_interpol[step_index + 1]
             UpperCAmelCase_										=					self.sigmas[step_index + 1]
         else:
             # 2nd order / KDPM2's method
             UpperCAmelCase_										=					self.sigmas[step_index - 1]
             UpperCAmelCase_										=					self.sigmas_interpol[step_index]
             UpperCAmelCase_										=					self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         UpperCAmelCase_										=					0
         UpperCAmelCase_										=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             UpperCAmelCase_										=					sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCAmelCase_										=					sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             UpperCAmelCase_										=					sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCAmelCase_										=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
         else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'    )

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             UpperCAmelCase_										=					(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCAmelCase_										=					sigma_interpol - sigma_hat

             # store for 2nd order step
             UpperCAmelCase_										=					sample
         else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCAmelCase_										=					(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCAmelCase_										=					sigma_next - sigma_hat

             UpperCAmelCase_										=					self.sample
             UpperCAmelCase_										=					None

         UpperCAmelCase_										=					sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=__snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Tuple ,   ):
         UpperCAmelCase_										=					self.sigmas.to(device=original_samples.device ,   dtype=original_samples.dtype    )
         if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case    ):
             # mps does not support float64
             UpperCAmelCase_										=					self.timesteps.to(original_samples.device ,   dtype=torch.floataa    )
             UpperCAmelCase_										=					timesteps.to(original_samples.device ,   dtype=torch.floataa    )
         else:
             UpperCAmelCase_										=					self.timesteps.to(original_samples.device    )
             UpperCAmelCase_										=					timesteps.to(original_samples.device    )

         UpperCAmelCase_										=					[self.index_for_timestep(__snake_case ,   __snake_case    ) for t in timesteps]

         UpperCAmelCase_										=					sigmas[step_indices].flatten()
         while len(sigma.shape    ) < len(original_samples.shape    ):
             UpperCAmelCase_										=					sigma.unsqueeze(-1    )

         UpperCAmelCase_										=					original_samples + noise * sigma
         return noisy_samples




     def __len__(     self       :      Optional[int]    ):
         return self.config.num_train_timesteps











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


_lowerCamelCase      			=						False



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      str    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     @property
     def   lowerCamelCase_       (     self       :      Any    ):
         return 12
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return 12
     @property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         return 32
     @property
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
         return tokenizer
     @property
     def   lowerCamelCase_       (     self       :      int    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					CLIPTextConfig(
             bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=self.text_embedder_hidden_size ,   intermediate_size=37 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=10_00 ,   )
         return CLIPTextModel(__snake_case    )
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         torch.manual_seed(0    )

         UpperCAmelCase_										=					12
         UpperCAmelCase_										=					12

         UpperCAmelCase_										=					{
             '''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''',
         }

         UpperCAmelCase_										=					TransformeraDModel(**__snake_case    )
         return model
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					'''cpu'''

         UpperCAmelCase_										=					self.dummy_vqvae
         UpperCAmelCase_										=					self.dummy_text_encoder
         UpperCAmelCase_										=					self.dummy_tokenizer
         UpperCAmelCase_										=					self.dummy_transformer
         UpperCAmelCase_										=					VQDiffusionScheduler(self.num_embed    )
         UpperCAmelCase_										=					LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case    )

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

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

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

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

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

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

         UpperCAmelCase_										=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]    )

         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   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					'''cpu'''

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

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

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

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

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

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

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

         UpperCAmelCase_										=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]    )

         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       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''    )

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

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

         UpperCAmelCase_										=					output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image    ).max() < 2.0





