





'''simple docstring'''




import functools
import operator

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


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)

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







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : List[str]           =							"""sew-d"""




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Tuple=3_2	,			snake_case_       :    Optional[Any]=7_6_8	,			snake_case_       :    Tuple=1_2	,			snake_case_       :    Union[str, Any]=1_2	,			snake_case_       :    Tuple=3_0_7_2	,			snake_case_       :    Tuple=2	,			snake_case_       :    int=5_1_2	,			snake_case_       :    Optional[int]=2_5_6	,			snake_case_       :    Union[str, Any]=True	,			snake_case_       :    Any=True	,			snake_case_       :    str=("p2c", "c2p")	,			snake_case_       :    Dict="layer_norm"	,			snake_case_       :    str="gelu_python"	,			snake_case_       :    Dict=0.1	,			snake_case_       :    List[str]=0.1	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Any=0.0	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Union[str, Any]=0.0_2	,			snake_case_       :    str=1e-7	,			snake_case_       :    Optional[Any]=1e-5	,			snake_case_       :    Optional[Any]="group"	,			snake_case_       :    Tuple="gelu"	,			snake_case_       :    Tuple=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,			snake_case_       :    Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,			snake_case_       :    Dict=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,			snake_case_       :    int=False	,			snake_case_       :    Union[str, Any]=1_2_8	,			snake_case_       :    int=1_6	,			snake_case_       :    Any=True	,			snake_case_       :    Tuple=0.0_5	,			snake_case_       :    Tuple=1_0	,			snake_case_       :    Dict=2	,			snake_case_       :    Tuple=0.0	,			snake_case_       :    List[Any]=1_0	,			snake_case_       :    Union[str, Any]=0	,			snake_case_       :    Any="mean"	,			snake_case_       :    Optional[Any]=False	,			snake_case_       :    Any=False	,			snake_case_       :    Tuple=2_5_6	,			snake_case_       :    int=0	,			snake_case_       :    Optional[Any]=1	,			snake_case_       :    List[str]=2	,			**snake_case_       :    List[str]	,			):
      super().__init__(**snake_case_	,			pad_token_id=snake_case_	,			bos_token_id=snake_case_	,			eos_token_id=snake_case_    )
      _UpperCAmelCase     							=   hidden_size
      _UpperCAmelCase     							=   feat_extract_norm
      _UpperCAmelCase     							=   feat_extract_activation
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   conv_bias
      _UpperCAmelCase     							=   num_conv_pos_embeddings
      _UpperCAmelCase     							=   num_conv_pos_embedding_groups
      _UpperCAmelCase     							=   len(self.conv_dim    )
      _UpperCAmelCase     							=   num_hidden_layers
      _UpperCAmelCase     							=   intermediate_size
      _UpperCAmelCase     							=   squeeze_factor
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   position_buckets
      _UpperCAmelCase     							=   share_att_key
      _UpperCAmelCase     							=   relative_attention
      _UpperCAmelCase     							=   norm_rel_ebd
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   num_attention_heads
      _UpperCAmelCase     							=   hidden_dropout
      _UpperCAmelCase     							=   attention_dropout
      _UpperCAmelCase     							=   activation_dropout
      _UpperCAmelCase     							=   feat_proj_dropout
      _UpperCAmelCase     							=   final_dropout
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   feature_layer_norm_eps
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   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
      _UpperCAmelCase     							=   apply_spec_augment
      _UpperCAmelCase     							=   mask_time_prob
      _UpperCAmelCase     							=   mask_time_length
      _UpperCAmelCase     							=   mask_time_min_masks
      _UpperCAmelCase     							=   mask_feature_prob
      _UpperCAmelCase     							=   mask_feature_length
      _UpperCAmelCase     							=   mask_feature_min_masks

      # ctc loss
      _UpperCAmelCase     							=   ctc_loss_reduction
      _UpperCAmelCase     							=   ctc_zero_infinity

      # sequence classification
      _UpperCAmelCase     							=   use_weighted_layer_sum
      _UpperCAmelCase     							=   classifier_proj_size




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











'''simple docstring'''




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 	UpperCAmelCase_							(					__lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



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







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    VQModel	,			snake_case_       :    UNetaDModel	,			snake_case_       :    Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]	,			):
      super().__init__()
      self.register_modules(vqvae=snake_case_	,			unet=snake_case_	,			scheduler=snake_case_    )




 @torch.no_grad()
 def __call__(					self       :    Any	,			snake_case_       :    Union[torch.Tensor, PIL.Image.Image] = None	,			snake_case_       :    Optional[int] = 1	,			snake_case_       :    Optional[int] = 1_0_0	,			snake_case_       :    Optional[float] = 0.0	,			snake_case_       :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			snake_case_       :    Optional[str] = "pil"	,			snake_case_       :    bool = True	,			):
      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   1
      elif isinstance(snake_case_	,			torch.Tensor    ):
           _UpperCAmelCase     							=   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    ):
           _UpperCAmelCase     							=   preprocess(snake_case_    )

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

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

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

      _UpperCAmelCase     							=   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    )
      _UpperCAmelCase     							=   self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase     							=   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]
      _UpperCAmelCase     							=   "eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      _UpperCAmelCase     							=   {}
      if accepts_eta:
           _UpperCAmelCase     							=   eta

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

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

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

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=snake_case_    )




