
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 	UpperCAmelCase__      (      UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :str ,					UpperCAmelCase__ :List[Any]=None					):






 '''simple docstring'''

 assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
 a						=		nn.Parameter(UpperCAmelCase__					)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
  a						=		nn.Parameter(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		np.asarray(weights[0]					)
 a						=		np.asarray(weights[1]					)
 a						=		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 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Any ,					UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 a						=		np.asarray(weights[0]					)
 a						=		np.asarray(weights[1]					)
 a						=		np.asarray(weights[2]					)
 a						=		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 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

 a						=		weights[0][0][0]
 a						=		np.asarray(layer_norm_a[0]					)
 a						=		np.asarray(layer_norm_a[1]					)
 set_param(
     torch_block.attention.layer_norm ,					torch.tensor(UpperCAmelCase__					) ,					torch.tensor(UpperCAmelCase__					) ,					)

 # lsh weights + output
 a						=		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
 a						=		weights[2][0][1][2]

 # Chunked Feed Forward
 if len(UpperCAmelCase__					) == 4:
  a						=		intermediate_weights[2]

 # layernorm 2
 a						=		np.asarray(intermediate_weights[0][0]					)
 a						=		np.asarray(intermediate_weights[0][1]					)
 set_param(
     torch_block.feed_forward.layer_norm ,					torch.tensor(UpperCAmelCase__					) ,					torch.tensor(UpperCAmelCase__					) ,					)

 # intermediate dense
 a						=		np.asarray(intermediate_weights[1][0]					)
 a						=		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
 a						=		np.asarray(intermediate_weights[4][0]					)
 a						=		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 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		torch_model.reformer

 # word embeds
 a						=		np.asarray(weights[1]					)
 set_param(
     torch_model_reformer.embeddings.word_embeddings ,					torch.tensor(UpperCAmelCase__					) ,					)

 if isinstance(weights[3] ,					UpperCAmelCase__					):
  a						=		torch_model_reformer.embeddings.position_embeddings
  for emb_idx in range(len(position_embeddings.weights					)					):
   a						=		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"""
   a						=		nn.Parameter(torch.tensor(UpperCAmelCase__					)					)

 a						=		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					):
  a						=		trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
  set_block_weights_in_torch(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # output layer norm
 a						=		np.asarray(weights[7][0]					)
 a						=		np.asarray(weights[7][1]					)
 set_param(
     torch_model_reformer.encoder.layer_norm ,					torch.tensor(UpperCAmelCase__					) ,					torch.tensor(UpperCAmelCase__					) ,					)

 # output embeddings
 a						=		np.asarray(weights[9][0]					)
 a						=		np.asarray(weights[9][1]					)
 set_param(
     torch_model.lm_head.decoder ,					torch.tensor(UpperCAmelCase__					).transpose(0 ,					1					).contiguous() ,					torch.tensor(UpperCAmelCase__					) ,					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Dict					):






 '''simple docstring'''

 a						=		ReformerConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		ReformerModelWithLMHead(UpperCAmelCase__					)

 with open(UpperCAmelCase__ ,					"rb"					) as f:
  a						=		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__":
     A_		:     Any        =     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.'''
     )
     A_		:     Optional[Any]        =     parser.parse_args()
     convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel

