
'''simple docstring'''




from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class  A				(							__snake_case						):
     __magic_name__						=		['''vqvae''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				str:






         """simple docstring"""






         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__						,			scheduler=SCREAMING_SNAKE_CASE__						,			mel=SCREAMING_SNAKE_CASE__						,			vqvae=SCREAMING_SNAKE_CASE__	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return 50 if isinstance(self.scheduler						,			SCREAMING_SNAKE_CASE__	) else 1000





     @torch.no_grad()
     def __call__(							self						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE=True						,			) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






         """simple docstring"""






         A       :   Union[str, Any]					=						steps or self.get_default_steps()
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__	)
         A       :   Tuple					=						step_generator or generator
         # For backwards compatibility
         if type(self.unet.config.sample_size	) == int:
             A       :   List[str]					=						(self.unet.config.sample_size, self.unet.config.sample_size)
         if noise is None:
             A       :   Dict					=						randn_tensor(
                 (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size[0],
                     self.unet.config.sample_size[1],
                 )						,			generator=SCREAMING_SNAKE_CASE__						,			device=self.device						,			)
         A       :   Optional[Any]					=						noise
         A       :   Any					=						None

         if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__	)
             A       :   List[str]					=						self.mel.audio_slice_to_image(SCREAMING_SNAKE_CASE__	)
             A       :   Dict					=						np.frombuffer(input_image.tobytes()						,			dtype='''uint8'''	).reshape(
                 (input_image.height, input_image.width)	)
             A       :   List[str]					=						(input_image / 255) * 2 - 1
             A       :   Optional[int]					=						torch.tensor(input_image[np.newaxis, :, :]						,			dtype=torch.float	).to(self.device	)

             if self.vqvae is not None:
                 A       :   Dict					=						self.vqvae.encode(torch.unsqueeze(SCREAMING_SNAKE_CASE__						,			0	)	).latent_dist.sample(
                     generator=SCREAMING_SNAKE_CASE__	)[0]
                 A       :   int					=						self.vqvae.config.scaling_factor * input_images

             if start_step > 0:
                 A       :   Optional[int]					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__						,			self.scheduler.timesteps[start_step - 1]	)

             A       :   int					=						(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             A       :   str					=						int(mask_start_secs * pixels_per_second	)
             A       :   Tuple					=						int(mask_end_secs * pixels_per_second	)
             A       :   Tuple					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__						,			torch.tensor(self.scheduler.timesteps[start_step:]	)	)

         for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]	)	):
             if isinstance(self.unet						,			SCREAMING_SNAKE_CASE__	):
                 A       :   Any					=						self.unet(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__	)['''sample''']
             else:
                 A       :   Any					=						self.unet(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__	)['''sample''']

             if isinstance(self.scheduler						,			SCREAMING_SNAKE_CASE__	):
                 A       :   List[Any]					=						self.scheduler.step(
                     model_output=SCREAMING_SNAKE_CASE__						,			timestep=SCREAMING_SNAKE_CASE__						,			sample=SCREAMING_SNAKE_CASE__						,			eta=SCREAMING_SNAKE_CASE__						,			generator=SCREAMING_SNAKE_CASE__						,			)['''prev_sample''']
             else:
                 A       :   Tuple					=						self.scheduler.step(
                     model_output=SCREAMING_SNAKE_CASE__						,			timestep=SCREAMING_SNAKE_CASE__						,			sample=SCREAMING_SNAKE_CASE__						,			generator=SCREAMING_SNAKE_CASE__						,			)['''prev_sample''']

             if mask is not None:
                 if mask_start > 0:
                     A       :   str					=						mask[:, step, :, :mask_start]
                 if mask_end > 0:
                     A       :   Optional[int]					=						mask[:, step, :, -mask_end:]

         if self.vqvae is not None:
             # 0.18215 was scaling factor used in training to ensure unit variance
             A       :   Any					=						1 / self.vqvae.config.scaling_factor * images
             A       :   Dict					=						self.vqvae.decode(SCREAMING_SNAKE_CASE__	)['''sample''']

         A       :   int					=						(images / 2 + 0.5).clamp(0						,			1	)
         A       :   int					=						images.cpu().permute(0						,			2						,			3						,			1	).numpy()
         A       :   Dict					=						(images * 255).round().astype('''uint8'''	)
         A       :   Optional[int]					=						list(
             (Image.fromarray(_[:, :, 0]	) for _ in images)
             if images.shape[3] == 1
             else (Image.fromarray(SCREAMING_SNAKE_CASE__						,			mode='''RGB'''	).convert('''L'''	) for _ in images)	)

         A       :   Union[str, Any]					=						[self.mel.image_to_audio(SCREAMING_SNAKE_CASE__	) for _ in images]
         if not return_dict:
             return images, (self.mel.get_sample_rate(), audios)

         return BaseOutput(**AudioPipelineOutput(np.array(SCREAMING_SNAKE_CASE__	)[:, np.newaxis, :]	)						,			**ImagePipelineOutput(SCREAMING_SNAKE_CASE__	)	)





     @torch.no_grad()
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 50	) ->				np.ndarray:






         """simple docstring"""






         assert isinstance(self.scheduler						,			SCREAMING_SNAKE_CASE__	)
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__	)
         A       :   Dict					=						np.array(
             [np.frombuffer(image.tobytes()						,			dtype='''uint8'''	).reshape((1, image.height, image.width)	) for image in images]	)
         A       :   Optional[Any]					=						(sample / 255) * 2 - 1
         A       :   List[Any]					=						torch.Tensor(SCREAMING_SNAKE_CASE__	).to(self.device	)

         for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,			(0,)	)	):
             A       :   str					=						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             A       :   Union[str, Any]					=						self.scheduler.alphas_cumprod[t]
             A       :   Tuple					=						(
                 self.scheduler.alphas_cumprod[prev_timestep]
                 if prev_timestep >= 0
                 else self.scheduler.final_alpha_cumprod
             )
             A       :   Dict					=						1 - alpha_prod_t
             A       :   Dict					=						self.unet(SCREAMING_SNAKE_CASE__						,			SCREAMING_SNAKE_CASE__	)['''sample''']
             A       :   List[str]					=						(1 - alpha_prod_t_prev) ** 0.5 * model_output
             A       :   int					=						(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
             A       :   Tuple					=						sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

         return sample





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				torch.Tensor:






         """simple docstring"""






         A       :   int					=						acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE__	)						,			torch.flatten(SCREAMING_SNAKE_CASE__	)	) / torch.norm(SCREAMING_SNAKE_CASE__	) / torch.norm(SCREAMING_SNAKE_CASE__	)	)
         return sin((1 - alpha) * theta	) * xa / sin(SCREAMING_SNAKE_CASE__	) + sin(alpha * theta	) * xa / sin(SCREAMING_SNAKE_CASE__	)











"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		str=None							)			->      Union[str, Any]:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      __SCREAMING_SNAKE_CASE   :Dict						=  nn.Parameter(a_							)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  nn.Parameter(a_							)



def      __lowerCamelCase   (							a_     :		Dict ,   a_     :		str ,   a_     :		Optional[int]							)			->      Any:
      # set torch weights for 1-to-1 comparison
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[0]							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[1]							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[2]							)

      set_param(
          torch_layer.self_attention.query_key ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.self_attention.value ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.output.dense ,   torch.tensor(a_							).view(-1 ,   a_							).contiguous().transpose(0 ,   1							) ,   )



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Dict ,   a_     :		List[str]							)			->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(weights[0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(weights[1]							)
      __SCREAMING_SNAKE_CASE   :Any						=  np.asarray(weights[2]							)
      __SCREAMING_SNAKE_CASE   :Dict						=  np.asarray(weights[3]							)

      set_param(
          torch_layer.self_attention.query ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.self_attention.key ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.self_attention.value ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.output.dense ,   torch.tensor(a_							).view(-1 ,   a_							).contiguous().transpose(0 ,   1							) ,   )



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		List[str] ,   a_     :		Optional[int]							)			->      Union[str, Any]:
      # layernorm 1
      __SCREAMING_SNAKE_CASE   :Any						=  weights[0][0][0]
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(layer_norm_a[0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(layer_norm_a[1]							)
      set_param(
          torch_block.attention.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # lsh weights + output
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[0][1]
      if len(a_							) < 4:
            set_layer_weights_in_torch_lsh(a_ ,   torch_block.attention ,   a_							)
      else:
            set_layer_weights_in_torch_local(a_ ,   torch_block.attention ,   a_							)

      # intermediate weighs
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[2][0][1][2]

      # Chunked Feed Forward
      if len(a_							) == 4:
            __SCREAMING_SNAKE_CASE   :List[str]						=  intermediate_weights[2]

      # layernorm 2
      __SCREAMING_SNAKE_CASE   :Tuple						=  np.asarray(intermediate_weights[0][0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(intermediate_weights[0][1]							)
      set_param(
          torch_block.feed_forward.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # intermediate dense
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][0]							)
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][1]							)
      set_param(
          torch_block.feed_forward.dense.dense ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )

      # intermediate out
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][1]							)
      set_param(
          torch_block.feed_forward.output.dense ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )



def      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		str ,   a_     :		List[Any]							)			->      Optional[Any]:
      # reformer model
      __SCREAMING_SNAKE_CASE   :Dict						=  torch_model.reformer

      # word embeds
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[1]							)
      set_param(
          torch_model_reformer.embeddings.word_embeddings ,   torch.tensor(a_							) ,   )

      if isinstance(weights[3] ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[Any]						=  torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights							)							):
                  __SCREAMING_SNAKE_CASE   :List[str]						=  np.asarray(weights[3][emb_idx][0]							)
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), f'''{position_embeddings[emb_idx]} emb does not match'''
                  __SCREAMING_SNAKE_CASE   :str						=  nn.Parameter(torch.tensor(a_							)							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  weights[5]
      assert len(torch_model_reformer.encoder.layers							) * 4 == len(
          a_							), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
            __SCREAMING_SNAKE_CASE   :Dict						=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(a_ ,   a_ ,   a_							)

      # output layer norm
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[7][0]							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[7][1]							)
      set_param(
          torch_model_reformer.encoder.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # output embeddings
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[9][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(weights[9][1]							)
      set_param(
          torch_model.lm_head.decoder ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		Dict ,   a_     :		Dict							)			->      Tuple:
      # Initialise PyTorch model
      __SCREAMING_SNAKE_CASE   :List[str]						=  ReformerConfig.from_json_file(a_							)
      print(f'''Building PyTorch model from configuration: {config}'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  ReformerModelWithLMHead(a_							)

      with open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Any						=  pickle.load(a_							)['''weights''']

      set_model_weights_in_torch(a_ ,   a_ ,   config.hidden_size							)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
      torch.save(model.state_dict() ,   a_							)


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained Reformer model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase_								=		parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)