





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    _A,			_A     =							image.size
    _A,			_A     =							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    _A     =							image.resize((w, h)							,     resample=PIL_INTERPOLATION['lanczos']				)
    _A     =							np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 255.0
    _A     =							image[None].transpose(0							,     3							,     1							,     2				)
    _A     =							torch.from_numpy(_SCREAMING_SNAKE_CASE				)
    return 2.0 * image - 1.0







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         super().__init__()
         self.register_modules(vqvae=snake_case_				,     unet=snake_case_				,     scheduler=snake_case_							)




     @torch.no_grad()
     def __call__(    self				,     snake_case_ = None				,     snake_case_ = 1				,     snake_case_ = 100				,     snake_case_ = 0.0				,     snake_case_ = None				,     snake_case_ = "pil"				,     snake_case_ = True				,     ):
         if isinstance(snake_case_				,     PIL.Image.Image							):
             _A     =							1
         elif isinstance(snake_case_				,     torch.Tensor							):
             _A     =							image.shape[0]
         else:
             raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_							)}"							)

         if isinstance(snake_case_				,     PIL.Image.Image							):
             _A     =							preprocess(snake_case_							)

         _A,			_A     =							image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         _A     =							(batch_size, self.unet.config.in_channels // 2, height, width)
         _A     =							next(self.unet.parameters()							).dtype

         _A     =							randn_tensor(snake_case_				,     generator=snake_case_				,     device=self.device				,     dtype=snake_case_							)

         _A     =							image.to(device=self.device				,     dtype=snake_case_							)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(snake_case_				,     device=self.device							)
         _A     =							self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         _A     =							latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         _A     =							'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
         _A     =							{}
         if accepts_eta:
             _A     =							eta

         for t in self.progress_bar(snake_case_							):
             # concat latents and low resolution image in the channel dimension.
             _A     =							torch.cat([latents, image]				,     dim=1							)
             _A     =							self.scheduler.scale_model_input(snake_case_				,     snake_case_							)
             # predict the noise residual
             _A     =							self.unet(snake_case_				,     snake_case_							).sample
             # compute the previous noisy sample x_t -> x_t-1
             _A     =							self.scheduler.step(snake_case_				,     snake_case_				,     snake_case_				,     **snake_case_							).prev_sample

         # decode the image latents with the VQVAE
         _A     =							self.vqvae.decode(snake_case_							).sample
         _A     =							torch.clamp(snake_case_				,     -1.0				,     1.0							)
         _A     =							image / 2 + 0.5
         _A     =							image.cpu().permute(0				,     2				,     3				,     1							).numpy()

         if output_type == "pil":
             _A     =							self.numpy_to_pil(snake_case_							)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=snake_case_							)













import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "sew"







		def __init__(					self       ,A__=32       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__=2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=0.1       ,A__=0.0       ,A__=0.1       ,A__=0.1       ,A__=0.02       ,A__=1E-5       ,A__="group"       ,A__="gelu"       ,A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,A__=False       ,A__=128       ,A__=16       ,A__=True       ,A__=0.05       ,A__=10       ,A__=2       ,A__=0.0       ,A__=10       ,A__=0       ,A__="mean"       ,A__=False       ,A__=False       ,A__=256       ,A__=0       ,A__=1       ,A__=2       ,**A__       ,):
						super().__init__(**A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__		)
						_A	:				str   =      hidden_size
						_A	:				Optional[Any]   =      feat_extract_norm
						_A	:				Any   =      feat_extract_activation
						_A	:				Optional[int]   =      list(A__		)
						_A	:				str   =      list(A__		)
						_A	:				Optional[Any]   =      list(A__		)
						_A	:				List[Any]   =      conv_bias
						_A	:				Optional[int]   =      num_conv_pos_embeddings
						_A	:				Any   =      num_conv_pos_embedding_groups
						_A	:				Union[str, Any]   =      len(self.conv_dim		)
						_A	:				List[Any]   =      num_hidden_layers
						_A	:				str   =      intermediate_size
						_A	:				List[Any]   =      squeeze_factor
						_A	:				Tuple   =      hidden_act
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				Optional[int]   =      hidden_dropout
						_A	:				Optional[int]   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				Tuple   =      feat_proj_dropout
						_A	:				Optional[Any]   =      final_dropout
						_A	:				str   =      layerdrop
						_A	:				int   =      layer_norm_eps
						_A	:				List[str]   =      initializer_range
						_A	:				List[Any]   =      vocab_size

						if (
						    (len(self.conv_stride		) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
						    or (len(self.conv_dim		) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    '''Configuration for convolutional layers is incorrect.'''
										    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A	:				Union[str, Any]   =      apply_spec_augment
						_A	:				str   =      mask_time_prob
						_A	:				Any   =      mask_time_length
						_A	:				int   =      mask_time_min_masks
						_A	:				str   =      mask_feature_prob
						_A	:				Optional[Any]   =      mask_feature_length
						_A	:				Dict   =      mask_feature_min_masks

						# ctc loss
						_A	:				Optional[int]   =      ctc_loss_reduction
						_A	:				List[str]   =      ctc_zero_infinity

						# sequence classification
						_A	:				List[Any]   =      use_weighted_layer_sum
						_A	:				Optional[int]   =      classifier_proj_size







		@property
		def  A__      (					self		):
						return functools.reduce(operator.mul       ,self.conv_stride       ,1		)



