'''simple docstring'''






import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)





class     A__							(       nn.Module   ):





   """simple docstring"""





   def __init__( self     :		List[str]			)       ->    List[Any]:







      """simple docstring"""




      super().__init__()
      _UpperCAmelCase							:	List[Any]							=   nn.Linear(3						,     4			)
      _UpperCAmelCase							:	int							=   nn.BatchNormad(4			)
      _UpperCAmelCase							:	Optional[int]							=   nn.Linear(4						,     5			)




   def 			_lowerCAmelCase		( self     :		List[str]						,     lowerCAmelCase__     :		Optional[Any]			)       ->    Any:







      """simple docstring"""




      return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__			)			)			)





class     A__							(       unittest.TestCase   ):





   """simple docstring"""





   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   ModelForTest()
      with TemporaryDirectory() as tmp_dir:
         offload_state_dict(lowerCAmelCase__						,     model.state_dict()			)
         _UpperCAmelCase							:	Union[str, Any]							=   os.path.join(lowerCAmelCase__						,     "index.json"			)
         self.assertTrue(os.path.isfile(lowerCAmelCase__			)			)
         # TODO: add tests on what is inside the index

         for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
            _UpperCAmelCase							:	int							=   os.path.join(lowerCAmelCase__						,     F"""{key}.dat"""			)
            self.assertTrue(os.path.isfile(lowerCAmelCase__			)			)
            # TODO: add tests on the fact weights are properly loaded




   def 			_lowerCAmelCase		( self     :		str			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   [torch.floataa, torch.floataa, torch.bfloataa]

      for dtype in dtypes:
         _UpperCAmelCase							:	List[Any]							=   torch.randn(2						,     3						,     dtype=lowerCAmelCase__			)
         with TemporaryDirectory() as tmp_dir:
            _UpperCAmelCase							:	str							=   offload_weight(lowerCAmelCase__						,     "weight"						,     lowerCAmelCase__						,     {}			)
            _UpperCAmelCase							:	List[Any]							=   os.path.join(lowerCAmelCase__						,     "weight.dat"			)
            self.assertTrue(os.path.isfile(lowerCAmelCase__			)			)
            self.assertDictEqual(lowerCAmelCase__						,     {"weight": {"shape": [2, 3], "dtype": str(lowerCAmelCase__			).split("."			)[1]}}			)

            _UpperCAmelCase							:	Union[str, Any]							=   load_offloaded_weight(lowerCAmelCase__						,     index["weight"]			)
            self.assertTrue(torch.equal(lowerCAmelCase__						,     lowerCAmelCase__			)			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   ModelForTest()
      _UpperCAmelCase							:	Optional[int]							=   model.state_dict()
      _UpperCAmelCase							:	Union[str, Any]							=   {k: v for k, v in state_dict.items() if "linear2" not in k}
      _UpperCAmelCase							:	List[str]							=   {k: v for k, v in state_dict.items() if "linear2" in k}

      with TemporaryDirectory() as tmp_dir:
         offload_state_dict(lowerCAmelCase__						,     lowerCAmelCase__			)
         _UpperCAmelCase							:	int							=   OffloadedWeightsLoader(state_dict=lowerCAmelCase__						,     save_folder=lowerCAmelCase__			)

         # Every key is there with the right value
         self.assertEqual(sorted(lowerCAmelCase__			)						,     sorted(state_dict.keys()			)			)
         for key, param in state_dict.items():
            self.assertTrue(torch.allclose(lowerCAmelCase__						,     weight_map[key]			)			)

      _UpperCAmelCase							:	List[str]							=   {k: v for k, v in state_dict.items() if "weight" in k}
      _UpperCAmelCase							:	Dict							=   {k: v for k, v in state_dict.items() if "weight" not in k}

      with TemporaryDirectory() as tmp_dir:
         offload_state_dict(lowerCAmelCase__						,     lowerCAmelCase__			)
         _UpperCAmelCase							:	Union[str, Any]							=   OffloadedWeightsLoader(state_dict=lowerCAmelCase__						,     save_folder=lowerCAmelCase__			)

         # Every key is there with the right value
         self.assertEqual(sorted(lowerCAmelCase__			)						,     sorted(state_dict.keys()			)			)
         for key, param in state_dict.items():
            self.assertTrue(torch.allclose(lowerCAmelCase__						,     weight_map[key]			)			)

      with TemporaryDirectory() as tmp_dir:
         offload_state_dict(lowerCAmelCase__						,     lowerCAmelCase__			)
         # Duplicates are removed
         _UpperCAmelCase							:	List[str]							=   OffloadedWeightsLoader(state_dict=lowerCAmelCase__						,     save_folder=lowerCAmelCase__			)

         # Every key is there with the right value
         self.assertEqual(sorted(lowerCAmelCase__			)						,     sorted(state_dict.keys()			)			)
         for key, param in state_dict.items():
            self.assertTrue(torch.allclose(lowerCAmelCase__						,     weight_map[key]			)			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   {"a.1": 0, "a.10": 1, "a.2": 2}
      _UpperCAmelCase							:	List[Any]							=   extract_submodules_state_dict(lowerCAmelCase__						,     ["a.1", "a.2"]			)
      self.assertDictEqual(lowerCAmelCase__						,     {"a.1": 0, "a.2": 2}			)

      _UpperCAmelCase							:	Any							=   {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
      _UpperCAmelCase							:	Any							=   extract_submodules_state_dict(lowerCAmelCase__						,     ["a.1", "a.2"]			)
      self.assertDictEqual(lowerCAmelCase__						,     {"a.1.a": 0, "a.2.a": 2}			)

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_		(       __lowercase   :	Dict       ,					__lowercase   :	int       ,					__lowercase   :	Optional[Any]=None ) ->  Any:
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
			_snake_case		=			nn.Parameter(__lowercase )
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
						_snake_case		=			nn.Parameter(__lowercase )






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

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






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

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






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

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

			# intermediate weighs
			_snake_case		=			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(__lowercase ) == 4:
						_snake_case		=			intermediate_weights[2]

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

			# intermediate dense
			_snake_case		=			np.asarray(intermediate_weights[1][0] )
			_snake_case		=			np.asarray(intermediate_weights[1][1] )
			set_param(
			    torch_block.feed_forward.dense.dense       ,					torch.tensor(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)

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






def        a_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict ) ->  Optional[int]:
			# reformer model
			_snake_case		=			torch_model.reformer

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

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

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

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

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






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Dict       ,					__lowercase   :	List[Any] ) ->  Optional[int]:
			# Initialise PyTorch model
			_snake_case		=			ReformerConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			_snake_case		=			ReformerModelWithLMHead(__lowercase )

			with open(__lowercase       ,					'rb' ) as f:
						_snake_case		=			pickle.load(__lowercase )['weights']

			set_model_weights_in_torch(__lowercase       ,					__lowercase       ,					config.hidden_size )

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


if __name__ == "__main__":
			_lowerCamelCase       :				Union[str, 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.'''
			)
			_lowerCamelCase       :				List[Any]       =			parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)