

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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

      # create a imagenet -> id dictionary for easier use
      SCREAMING_SNAKE_CASE_									=						{}
      if idalabel is not None:
          for key, value in idalabel.items():
              for label in value.split(','):
                  SCREAMING_SNAKE_CASE_									=						int(_A)
          SCREAMING_SNAKE_CASE_									=						dict(sorted(self.labels.items()))

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						list(_A)

      for l in label:
          if l not in self.labels:
              raise ValueError(
                  f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""")

      return [self.labels[l] for l in label]


  @torch.no_grad()
  def __call__(					self						,		_A						,		_A = 4.0						,		_A = None						,		_A = 50						,		_A = "pil"						,		_A = True						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.sample_size
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.in_channels

      SCREAMING_SNAKE_CASE_									=						randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)						,		generator=_A						,		device=self.device						,		dtype=self.transformer.dtype						,		)
      SCREAMING_SNAKE_CASE_									=						torch.cat([latents] * 2) if guidance_scale > 1 else latents

      SCREAMING_SNAKE_CASE_									=						torch.tensor(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * batch_size						,		device=self.device)
      SCREAMING_SNAKE_CASE_									=						torch.cat([class_labels, class_null]						,		0) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(_A)

      for t in self.progress_bar(self.scheduler.timesteps):
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_									=						latent_model_input[: len(_A) // 2]
              SCREAMING_SNAKE_CASE_									=						torch.cat([half, half]						,		dim=0)
          SCREAMING_SNAKE_CASE_									=						self.scheduler.scale_model_input(_A						,		_A)

          SCREAMING_SNAKE_CASE_									=						t
          if not torch.is_tensor(_A):
              # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
              # This would be a good case for the `match` statement (Python 3.10+)
              SCREAMING_SNAKE_CASE_									=						latent_model_input.device.type == 'mps'
              if isinstance(_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						torch.floataa if is_mps else torch.floataa
              else:
                  SCREAMING_SNAKE_CASE_									=						torch.intaa if is_mps else torch.intaa
              SCREAMING_SNAKE_CASE_									=						torch.tensor([timesteps]						,		dtype=_A						,		device=latent_model_input.device)
          elif len(timesteps.shape) == 0:
              SCREAMING_SNAKE_CASE_									=						timesteps[None].to(latent_model_input.device)
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          SCREAMING_SNAKE_CASE_									=						timesteps.expand(latent_model_input.shape[0])
          # predict noise model_output
          SCREAMING_SNAKE_CASE_									=						self.transformer(
              _A						,		timestep=_A						,		class_labels=_A).sample

          # perform guidance
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		len(_A) // 2						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              SCREAMING_SNAKE_CASE_									=						torch.cat([half_eps, half_eps]						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						torch.cat([eps, rest]						,		dim=1)

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		_A						,		dim=1)
          else:
              SCREAMING_SNAKE_CASE_									=						noise_pred

          # compute previous image: x_t -> x_t-1
          SCREAMING_SNAKE_CASE_									=						self.scheduler.step(_A						,		_A						,		_A).prev_sample

      if guidance_scale > 1:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						latent_model_input.chunk(2						,		dim=0)
      else:
          SCREAMING_SNAKE_CASE_									=						latent_model_input

      SCREAMING_SNAKE_CASE_									=						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

      SCREAMING_SNAKE_CASE_									=						(samples / 2 + 0.5).clamp(0						,		1)

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      SCREAMING_SNAKE_CASE_									=						samples.cpu().permute(0						,		2						,		3						,		1).float().numpy()

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

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)





import pickle

import numpy as np
from matplotlib import pyplot as plt



class       __snake_case  :

  def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=0.2						,		_A=0.2):
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						conva_get[:2]
      SCREAMING_SNAKE_CASE_									=						conva_get[2]
      SCREAMING_SNAKE_CASE_									=						size_pa
      SCREAMING_SNAKE_CASE_									=						rate_w
      SCREAMING_SNAKE_CASE_									=						rate_t
      SCREAMING_SNAKE_CASE_									=						[
          np.mat(-1 * np.random.rand(self.conva[0]						,		self.conva[0]) + 0.5)
          for i in range(self.conva[1])
      ]
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.conva[1]) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1

  def 						lowerCAmelCase__     (					self						,		_A):
      # save model dict with pickle
      SCREAMING_SNAKE_CASE_									=						{
          'num_bp1': self.num_bpa,
          'num_bp2': self.num_bpa,
          'num_bp3': self.num_bpa,
          'conv1': self.conva,
          'step_conv1': self.step_conva,
          'size_pooling1': self.size_poolinga,
          'rate_weight': self.rate_weight,
          'rate_thre': self.rate_thre,
          'w_conv1': self.w_conva,
          'wkj': self.wkj,
          'vji': self.vji,
          'thre_conv1': self.thre_conva,
          'thre_bp2': self.thre_bpa,
          'thre_bp3': self.thre_bpa,
      }
      with open(_A						,		'wb') as f:
          pickle.dump(_A						,		_A)

      print(f"""Model saved： {save_path}""")

  @classmethod
  def 						lowerCAmelCase__     (					cls						,		_A):
      # read saved model
      with open(_A						,		'rb') as f:
          SCREAMING_SNAKE_CASE_									=						pickle.load(_A)  # noqa: S301

      SCREAMING_SNAKE_CASE_									=						model_dic.get('conv1')
      conv_get.append(model_dic.get('step_conv1'))
      SCREAMING_SNAKE_CASE_									=						model_dic.get('size_pooling1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp2')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp3')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('rate_weight')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('rate_thre')
      # create model instance
      SCREAMING_SNAKE_CASE_									=						CNN(_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A)
      # modify model parameter
      SCREAMING_SNAKE_CASE_									=						model_dic.get('w_conv1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('wkj')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('vji')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_conv1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_bp2')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_bp3')
      return conv_ins

  def 						lowerCAmelCase__     (					self						,		_A):
      return 1 / (1 + np.exp(-1 * x))

  def 						lowerCAmelCase__     (					self						,		_A):
      return round(_A						,		3)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      # convolution process
      SCREAMING_SNAKE_CASE_									=						convs[0]
      SCREAMING_SNAKE_CASE_									=						convs[1]
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)[0]
      # get the data slice of original image data, data_focus
      SCREAMING_SNAKE_CASE_									=						[]
      for i_focus in range(0						,		size_data - size_conv + 1						,		_A):
          for j_focus in range(0						,		size_data - size_conv + 1						,		_A):
              SCREAMING_SNAKE_CASE_									=						data[
                  i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
              ]
              data_focus.append(_A)
        # calculate the feature map of every single kernel, and saved as list of matrix
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						int((size_data - size_conv) / conv_step + 1)
      for i_map in range(_A):
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(len(_A)):
              SCREAMING_SNAKE_CASE_									=						(
                  np.sum(np.multiply(data_focus[i_focus]						,		w_convs[i_map]))
                  - thre_convs[i_map]
              )
              featuremap.append(self.sig(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(
              _A						,		_A)
          data_featuremap.append(_A)

      # expanding the data slice to One dimenssion
      SCREAMING_SNAKE_CASE_									=						[]
      for each_focus in data_focus:
          focusa_list.extend(self.Expand_Mat(_A))
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      return focus_list, data_featuremap

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A="average_pool"):
      # pooling process
      SCREAMING_SNAKE_CASE_									=						len(featuremaps[0])
      SCREAMING_SNAKE_CASE_									=						int(size_map / size_pooling)
      SCREAMING_SNAKE_CASE_									=						[]
      for i_map in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						featuremaps[i_map]
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(0						,		_A						,		_A):
              for j_focus in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						feature_map[
                      i_focus : i_focus + size_pooling,
                      j_focus : j_focus + size_pooling,
                  ]
                  if pooling_type == "average_pool":
                      # average pooling
                      map_pooled.append(np.average(_A))
                  elif pooling_type == "max_pooling":
                      # max pooling
                      map_pooled.append(np.max(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(_A						,		_A)
          featuremap_pooled.append(_A)
      return featuremap_pooled

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding three dimension data to one dimension list
      SCREAMING_SNAKE_CASE_									=						[]
      for i in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.shape(data[i])
          SCREAMING_SNAKE_CASE_									=						data[i].reshape(1						,		shapes[0] * shapes[1])
          SCREAMING_SNAKE_CASE_									=						data_listed.getA().tolist()[0]
          data_expanded.extend(_A)
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding matrix to one dimension list
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)
      SCREAMING_SNAKE_CASE_									=						data_mat.reshape(1						,		shapes[0] * shapes[1])
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						0
      for i_map in range(_A):
          SCREAMING_SNAKE_CASE_									=						np.ones((size_map, size_map))
          for i in range(0						,		_A						,		_A):
              for j in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						pd_pool[
                      i_pool
                  ]
                  SCREAMING_SNAKE_CASE_									=						i_pool + 1
          SCREAMING_SNAKE_CASE_									=						np.multiply(
              _A						,		np.multiply(out_map[i_map]						,		(1 - out_map[i_map])))
          pd_all.append(_A)
      return pd_all

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=bool):
      # model traning
      print('----------------------Start Training-------------------------')
      print((' - - Shape: Train_Data  ', np.shape(_A)))
      print((' - - Shape: Teach_Data  ', np.shape(_A)))
      SCREAMING_SNAKE_CASE_									=						0
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						10000
      while rp < n_repeat and mse >= error_accuracy:
          SCREAMING_SNAKE_CASE_									=						0
          print(f"""-------------Learning Time {rp}--------------""")
          for p in range(len(_A)):
              # print('------------Learning Image: %d--------------'%p)
              SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_train[p])
              SCREAMING_SNAKE_CASE_									=						np.asarray(datas_teach[p])
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
                  _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
              SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						np.shape(_A)
              SCREAMING_SNAKE_CASE_									=						self._expand(_A)
              SCREAMING_SNAKE_CASE_									=						data_bp_input

              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.wkj.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)

              # --------------Model Leaning ------------------------
              # calculate error and gradient---------------
              SCREAMING_SNAKE_CASE_									=						np.multiply(
                  (data_teach - bp_outa)						,		np.multiply(_A						,		(1 - bp_outa)))
              SCREAMING_SNAKE_CASE_									=						np.multiply(
                  np.dot(_A						,		self.wkj)						,		np.multiply(_A						,		(1 - bp_outa)))
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji)

              SCREAMING_SNAKE_CASE_									=						pd_i_all / (self.size_poolinga * self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						pd_conva_pooled.T.getA().tolist()
              SCREAMING_SNAKE_CASE_									=						self._calculate_gradient_from_pool(
                  _A						,		_A						,		shape_featuremapa[0]						,		shape_featuremapa[1]						,		self.size_poolinga						,		)
              # weight and threshold learning process---------
              # convolution layer
              for k_conv in range(self.conva[1]):
                  SCREAMING_SNAKE_CASE_									=						self._expand_mat(pd_conva_all[k_conv])
                  SCREAMING_SNAKE_CASE_									=						self.rate_weight * np.dot(_A						,		_A)

                  SCREAMING_SNAKE_CASE_									=						self.w_conva[k_conv] + delta_w.reshape(
                      (self.conva[0], self.conva[0]))

                  SCREAMING_SNAKE_CASE_									=						(
                      self.thre_conva[k_conv]
                      - np.sum(pd_conva_all[k_conv]) * self.rate_thre
                  )
              # all connected layer
              SCREAMING_SNAKE_CASE_									=						self.wkj + pd_k_all.T * bp_outa * self.rate_weight
              SCREAMING_SNAKE_CASE_									=						self.vji + pd_j_all.T * bp_outa * self.rate_weight
              SCREAMING_SNAKE_CASE_									=						self.thre_bpa - pd_k_all * self.rate_thre
              SCREAMING_SNAKE_CASE_									=						self.thre_bpa - pd_j_all * self.rate_thre
              # calculate the sum error of all single image
              SCREAMING_SNAKE_CASE_									=						np.sum(abs(data_teach - bp_outa))
              error_count += errors
              # print('   ----Teach      ',data_teach)
              # print('   ----BP_output  ',bp_out3)
          SCREAMING_SNAKE_CASE_									=						rp + 1
          SCREAMING_SNAKE_CASE_									=						error_count / patterns
          all_mse.append(_A)

      def draw_error():
          SCREAMING_SNAKE_CASE_									=						[error_accuracy for i in range(int(n_repeat * 1.2))]
          plt.plot(_A						,		'+-')
          plt.plot(_A						,		'r--')
          plt.xlabel('Learning Times')
          plt.ylabel('All_mse')
          plt.grid(_A						,		alpha=0.5)
          plt.show()

      print('------------------Training Complished---------------------')
      print((' - - Training epoch: ', rp, f"""     - - Mse: {mse:.6f}"""))
      if draw_e:
          draw_error()
      return mse

  def 						lowerCAmelCase__     (					self						,		_A):
      # model predict
      SCREAMING_SNAKE_CASE_									=						[]
      print('-------------------Start Testing-------------------------')
      print((' - - Shape: Test_Data  ', np.shape(_A)))
      for p in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_test[p])
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
              _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
          SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
          SCREAMING_SNAKE_CASE_									=						self._expand(_A)

          SCREAMING_SNAKE_CASE_									=						data_bp_input
          SCREAMING_SNAKE_CASE_									=						bp_outa * self.vji.T - self.thre_bpa
          SCREAMING_SNAKE_CASE_									=						self.sig(_A)
          SCREAMING_SNAKE_CASE_									=						bp_outa * self.wkj.T - self.thre_bpa
          SCREAMING_SNAKE_CASE_									=						self.sig(_A)
          produce_out.extend(bp_outa.getA().tolist())
      SCREAMING_SNAKE_CASE_									=						[list(map(self.do_round						,		_A)) for each in produce_out]
      return np.asarray(_A)


  def 						lowerCAmelCase__     (					self						,		_A):
      # return the data of image after convoluting process so we can check it out
      SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
          _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
      SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)

      return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


