

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

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

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

def 			a__     (		UpperCAmelCase							:			str				, UpperCAmelCase							:			Optional[Any]				, UpperCAmelCase							:			Optional[Any]			)  ->						Optional[int]:
  # set torch weights for 1-to-1 comparison
  UpperCAmelCase       :							Tuple  = np.asarray(weights[0]			)
  UpperCAmelCase       :							Dict  = np.asarray(weights[1]			)
  UpperCAmelCase       :							List[str]  = np.asarray(weights[2]			)
  UpperCAmelCase       :							Union[str, Any]  = np.asarray(weights[3]			)

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

def 			a__     (		UpperCAmelCase							:			Optional[int]				, UpperCAmelCase							:			Union[str, Any]				, UpperCAmelCase							:			Union[str, Any]			)  ->						Union[str, Any]:
  # layernorm 1
  UpperCAmelCase       :							str  = weights[0][0][0]
  UpperCAmelCase       :							int  = np.asarray(layer_norm_a[0]			)
  UpperCAmelCase       :							Tuple  = np.asarray(layer_norm_a[1]			)
  set_param(
      torch_block.attention.layer_norm				, torch.tensor(_lowerCAmelCase			)				, torch.tensor(_lowerCAmelCase			)				, )

  # lsh weights + output
  UpperCAmelCase       :							int  = weights[0][1]
  if len(_lowerCAmelCase			) < 4:
    set_layer_weights_in_torch_lsh(_lowerCAmelCase				, torch_block.attention				, _lowerCAmelCase			)
  else:
    set_layer_weights_in_torch_local(_lowerCAmelCase				, torch_block.attention				, _lowerCAmelCase			)

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

  # Chunked Feed Forward
  if len(_lowerCAmelCase			) == 4:
    UpperCAmelCase       :							int  = intermediate_weights[2]

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

  # intermediate dense
  UpperCAmelCase       :							List[str]  = np.asarray(intermediate_weights[1][0]			)
  UpperCAmelCase       :							Any  = np.asarray(intermediate_weights[1][1]			)
  set_param(
      torch_block.feed_forward.dense.dense				, torch.tensor(_lowerCAmelCase			).transpose(0				, 1			).contiguous()				, torch.tensor(_lowerCAmelCase			)				, )

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

def 			a__     (		UpperCAmelCase							:			Optional[int]				, UpperCAmelCase							:			List[str]				, UpperCAmelCase							:			List[str]			)  ->						Tuple:
  # reformer model
  UpperCAmelCase       :							Optional[int]  = torch_model.reformer

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

  if isinstance(weights[3]				, _lowerCAmelCase			):
    UpperCAmelCase       :							Dict  = torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights			)			):
      UpperCAmelCase       :							Any  = 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'''
      UpperCAmelCase       :							List[Any]  = nn.Parameter(torch.tensor(_lowerCAmelCase			)			)

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

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

  # output embeddings
  UpperCAmelCase       :							Tuple  = np.asarray(weights[9][0]			)
  UpperCAmelCase       :							str  = np.asarray(weights[9][1]			)
  set_param(
      torch_model.lm_head.decoder				, torch.tensor(_lowerCAmelCase			).transpose(0				, 1			).contiguous()				, torch.tensor(_lowerCAmelCase			)				, )

def 			a__     (		UpperCAmelCase							:			Optional[int]				, UpperCAmelCase							:			str				, UpperCAmelCase							:			Optional[Any]			)  ->						Optional[Any]:
  # Initialise PyTorch model
  UpperCAmelCase       :							List[Any]  = ReformerConfig.from_json_file(_lowerCAmelCase			)
  print(f'''Building PyTorch model from configuration: {config}'''			)
  UpperCAmelCase       :							Tuple  = ReformerModelWithLMHead(_lowerCAmelCase			)

  with open(_lowerCAmelCase				, '''rb'''			) as f:
    UpperCAmelCase       :							List[str]  = pickle.load(_lowerCAmelCase			)['weights']

  set_model_weights_in_torch(_lowerCAmelCase				, _lowerCAmelCase				, config.hidden_size			)

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


if __name__ == "__main__":
     _lowerCamelCase				:   Tuple					   =							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				:   int					   =							parser.parse_args()
     convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










'''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        a_     (							_lowerCAmelCase       )      ->      str:
  __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = image.size
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __lowerCamelCase       :       Optional[Any]           = image.resize((w, h)       ,resample=PIL_INTERPOLATION['lanczos']       )
  __lowerCamelCase       :       List[Any]           = np.array(_lowerCAmelCase       ).astype(np.floataa       ) / 255.0
  __lowerCamelCase       :       Optional[Any]           = image[None].transpose(0       ,3       ,1       ,2       )
  __lowerCamelCase       :       int           = torch.from_numpy(_lowerCAmelCase       )
  return 2.0 * image - 1.0






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




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




  @torch.no_grad()
  def __call__( self      :	List[Any]			,	_a      :	Union[torch.Tensor, PIL.Image.Image] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[int] = 100			,	_a      :	Optional[float] = 0.0			,	_a      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	)					->					Union[Tuple, ImagePipelineOutput]:
    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       Any           = 1
    elif isinstance(_a			,	torch.Tensor   ):
      __lowerCamelCase       :       Any           = image.shape[0]
    else:
      raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a   )}'   )

    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       List[str]           = preprocess(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = image.shape[-2:]

    # in_channels should be 6: 3 for latents, 3 for low resolution image
    __lowerCamelCase       :       Union[str, Any]           = (batch_size, self.unet.config.in_channels // 2, height, width)
    __lowerCamelCase       :       Tuple           = next(self.unet.parameters()   ).dtype

    __lowerCamelCase       :       Optional[int]           = randn_tensor(_a			,	generator=_a			,	device=self.device			,	dtype=_a   )

    __lowerCamelCase       :       Optional[int]           = image.to(device=self.device			,	dtype=_a   )

    # set timesteps and move to the correct device
    self.scheduler.set_timesteps(_a			,	device=self.device   )
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler.timesteps

    # scale the initial noise by the standard deviation required by the scheduler
    __lowerCamelCase       :       List[Any]           = 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]
    __lowerCamelCase       :       int           = 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
    __lowerCamelCase       :       List[str]           = {}
    if accepts_eta:
      __lowerCamelCase       :       str           = eta

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

    # decode the image latents with the VQVAE
    __lowerCamelCase       :       Union[str, Any]           = self.vqvae.decode(_a   ).sample
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_a			,	-1.0			,	1.0   )
    __lowerCamelCase       :       List[str]           = image / 2 + 0.5
    __lowerCamelCase       :       Optional[Any]           = image.cpu().permute(0			,	2			,	3			,	1   ).numpy()

    if output_type == "pil":
      __lowerCamelCase       :       Tuple           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






