


"""simple docstring"""


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__lowerCamelCase      :Optional[Any]	    =		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def   snake_case		(     UpperCamelCase__       :						Optional[int]    )   ->      Union[str, Any]:
      if isinstance(UpperCamelCase__			,				torch.Tensor    ):
            return image
      elif isinstance(UpperCamelCase__			,				PIL.Image.Image    ):
            lowerCamelCase					:					int        =	[image]

      lowerCamelCase					:					int        =	[trans(img.convert("""RGB"""    )    ) for img in image]
      lowerCamelCase					:					Dict        =	torch.stack(UpperCamelCase__    )
      return image






class 			A__	(						snake_case__):


     """simple docstring"""

     def __init__(	self:						str     ,		__a:						Any     ,		__a:						int	)->    str:
           super().__init__()

           # make sure scheduler can always be converted to DDIM
           lowerCamelCase					:					Tuple        =	DDIMScheduler.from_config(scheduler.config	)

           self.register_modules(unet=_A     ,		scheduler=_A	)

     def 		a__						(	self:						Optional[int]     ,		__a:						List[Any]	)->    Optional[Any]:
           if strength < 0 or strength > 1:
                 raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						str     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]	)->    str:
           # get the original timestep using init_timestep
           lowerCamelCase					:					Optional[int]        =	min(int(num_inference_steps * strength	)     ,		_A	)

           lowerCamelCase					:					str        =	max(num_inference_steps - init_timestep     ,		0	)
           lowerCamelCase					:					Optional[int]        =	self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start

     def 		a__						(	self:						Optional[Any]     ,		__a:						str     ,		__a:						Tuple     ,		__a:						Union[str, Any]     ,		__a:						Any     ,		__a:						Dict     ,		__a:						Union[str, Any]=None	)->    Optional[int]:
           if not isinstance(_A     ,		(torch.Tensor, PIL.Image.Image, list)	):
                 raise ValueError(
                     f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A	)}'	)

           lowerCamelCase					:					Union[str, Any]        =	image.to(device=_A     ,		dtype=_A	)

           if isinstance(_A     ,		_A	) and len(_A	) != batch_size:
                 raise ValueError(
                     f'You have passed a list of generators of length {len(_A	)}, but requested an effective batch'
                     f' size of {batch_size}. Make sure the batch size matches the length of the generators.'	)

           lowerCamelCase					:					Dict        =	init_latents.shape
           lowerCamelCase					:					Optional[int]        =	randn_tensor(_A     ,		generator=_A     ,		device=_A     ,		dtype=_A	)

           # get latents
           print("""add noise to latents at timestep"""     ,		_A	)
           lowerCamelCase					:					List[Any]        =	self.scheduler.add_noise(_A     ,		_A     ,		_A	)
           lowerCamelCase					:					Optional[int]        =	init_latents

           return latents







     @torch.no_grad()
     def __call__(	self:						Union[str, Any]     ,		__a:						Tuple = None     ,		__a:						int = 0.8     ,		__a:						Dict = 1     ,		__a:						Optional[int] = None     ,		__a:						Optional[Any] = 0.0     ,		__a:						Any = 50     ,		__a:						List[str] = None     ,		__a:						Optional[int] = "pil"     ,		__a:						str = True     ,		)->    str:
           self.check_inputs(_A	)

           # 2. Preprocess image
           lowerCamelCase					:					int        =	preprocess(_A	)

           # 3. set timesteps
           self.scheduler.set_timesteps(_A     ,		device=self.device	)
           lowerCamelCase					:					Tuple        =	self.get_timesteps(_A     ,		_A     ,		self.device	)
           lowerCamelCase					:					int        =	timesteps[:1].repeat(_A	)

           # 4. Prepare latent variables
           lowerCamelCase					:					Dict        =	self.prepare_latents(_A     ,		_A     ,		_A     ,		self.unet.dtype     ,		self.device     ,		_A	)
           lowerCamelCase					:					List[Any]        =	latents

           # 5. Denoising loop
           for t in self.progress_bar(_A	):
                 # 1. predict noise model_output
                 lowerCamelCase					:					Optional[Any]        =	self.unet(_A     ,		_A	).sample

                 # 2. predict previous mean of image x_t-1 and add variance depending on eta
                 # eta corresponds to η in paper and should be between [0, 1]
                 # do x_t -> x_t-1
                 lowerCamelCase					:					List[Any]        =	self.scheduler.step(
                     _A     ,		_A     ,		_A     ,		eta=_A     ,		use_clipped_model_output=_A     ,		generator=_A     ,		).prev_sample

           lowerCamelCase					:					List[Any]        =	(image / 2 + 0.5).clamp(0     ,		1	)
           lowerCamelCase					:					List[str]        =	image.cpu().permute(0     ,		2     ,		3     ,		1	).numpy()
           if output_type == "pil":
                 lowerCamelCase					:					Optional[int]        =	self.numpy_to_pil(_A	)

           if not return_dict:
                 return (image, latent_timestep.item())

           return ImagePipelineOutput(images=_A	)








"""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   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Optional[Any]=None    )   ->      Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
      lowerCamelCase					:					Dict        =	nn.Parameter(UpperCamelCase__    )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
            lowerCamelCase					:					Any        =	nn.Parameter(UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Tuple    )   ->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Dict        =	np.asarray(weights[0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[2]    )

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




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Any    )   ->      List[Any]:
      # set torch weights for 1-to-1 comparison
      lowerCamelCase					:					Tuple        =	np.asarray(weights[0]    )
      lowerCamelCase					:					Any        =	np.asarray(weights[1]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[2]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[3]    )

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




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Any    )   ->      Optional[Any]:
      # layernorm 1
      lowerCamelCase					:					str        =	weights[0][0][0]
      lowerCamelCase					:					Optional[int]        =	np.asarray(layer_norm_a[0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(layer_norm_a[1]    )
      set_param(
          torch_block.attention.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

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

      # intermediate weighs
      lowerCamelCase					:					int        =	weights[2][0][1][2]

      # Chunked Feed Forward
      if len(UpperCamelCase__    ) == 4:
            lowerCamelCase					:					Dict        =	intermediate_weights[2]

      # layernorm 2
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[0][0]    )
      lowerCamelCase					:					Tuple        =	np.asarray(intermediate_weights[0][1]    )
      set_param(
          torch_block.feed_forward.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate dense
      lowerCamelCase					:					Optional[Any]        =	np.asarray(intermediate_weights[1][0]    )
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(intermediate_weights[1][1]    )
      set_param(
          torch_block.feed_forward.dense.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)

      # intermediate out
      lowerCamelCase					:					Optional[int]        =	np.asarray(intermediate_weights[4][0]    )
      lowerCamelCase					:					List[Any]        =	np.asarray(intermediate_weights[4][1]    )
      set_param(
          torch_block.feed_forward.output.dense			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						int    )   ->      List[Any]:
      # reformer model
      lowerCamelCase					:					List[Any]        =	torch_model.reformer

      # word embeds
      lowerCamelCase					:					Union[str, Any]        =	np.asarray(weights[1]    )
      set_param(
          torch_model_reformer.embeddings.word_embeddings			,				torch.tensor(UpperCamelCase__    )			,				)

      if isinstance(weights[3]			,				UpperCamelCase__    ):
            lowerCamelCase					:					Union[str, Any]        =	torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights    )    ):
                  lowerCamelCase					:					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'
                  lowerCamelCase					:					Dict        =	nn.Parameter(torch.tensor(UpperCamelCase__    )    )

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

      # output layer norm
      lowerCamelCase					:					Any        =	np.asarray(weights[7][0]    )
      lowerCamelCase					:					List[str]        =	np.asarray(weights[7][1]    )
      set_param(
          torch_model_reformer.encoder.layer_norm			,				torch.tensor(UpperCamelCase__    )			,				torch.tensor(UpperCamelCase__    )			,				)

      # output embeddings
      lowerCamelCase					:					List[Any]        =	np.asarray(weights[9][0]    )
      lowerCamelCase					:					Optional[int]        =	np.asarray(weights[9][1]    )
      set_param(
          torch_model.lm_head.decoder			,				torch.tensor(UpperCamelCase__    ).transpose(0			,				1    ).contiguous()			,				torch.tensor(UpperCamelCase__    )			,				)




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      # Initialise PyTorch model
      lowerCamelCase					:					Union[str, Any]        =	ReformerConfig.from_json_file(UpperCamelCase__    )
      print(F'Building PyTorch model from configuration: {config}'    )
      lowerCamelCase					:					str        =	ReformerModelWithLMHead(UpperCamelCase__    )

      with open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					str        =	pickle.load(UpperCamelCase__    )["""weights"""]

      set_model_weights_in_torch(UpperCamelCase__			,				UpperCamelCase__			,				config.hidden_size    )

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


if __name__ == "__main__":
    __lowerCamelCase      :Dict	    =		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      :Optional[int]	    =		parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




