


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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    Dict=None       ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
							a__					=					nn.Parameter(__lowerCAmelCase       )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
														a__					=					nn.Parameter(__lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    List[Any]       ):
							# set torch weights for 1-to-1 comparison
							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(__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 							__lowercase       (			__lowerCAmelCase							:    Tuple ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[str]       ):
							# set torch weights for 1-to-1 comparison
							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(__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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    List[Any]       ):
							# layernorm 1
							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(__lowerCAmelCase       ) ,   torch.tensor(__lowerCAmelCase       ) ,   )

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

							# Chunked Feed Forward
							if len(__lowerCAmelCase       ) == 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(__lowerCAmelCase       ) ,   torch.tensor(__lowerCAmelCase       ) ,   )

							# 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(__lowerCAmelCase       ).transpose(0 ,   1       ).contiguous() ,   torch.tensor(__lowerCAmelCase       ) ,   )

							# 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(__lowerCAmelCase       ).transpose(0 ,   1       ).contiguous() ,   torch.tensor(__lowerCAmelCase       ) ,   )






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							# reformer model
							a__					=					torch_model.reformer

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

							if isinstance(weights[3] ,   __lowerCAmelCase       ):
														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(__lowerCAmelCase       )       )

							a__					=					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       ):
														a__					=					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# 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(__lowerCAmelCase       ) ,   torch.tensor(__lowerCAmelCase       ) ,   )

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






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							# Initialise PyTorch model
							a__					=					ReformerConfig.from_json_file(__lowerCAmelCase       )
							print(F'Building PyTorch model from configuration: {config}'       )
							a__					=					ReformerModelWithLMHead(__lowerCAmelCase       )

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








from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)

snake_case							:	List[Any]					      =				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      '''efficientformer'''
					def __init__(				self       :List[str]      ,__snake_case       :List[int] = [3, 2, 6, 4]      ,__snake_case       :List[int] = [48, 96, 2_24, 4_48]      ,__snake_case       :List[bool] = [True, True, True, True]      ,__snake_case       :int = 4_48      ,__snake_case       :int = 32      ,__snake_case       :int = 4      ,__snake_case       :int = 7      ,__snake_case       :int = 5      ,__snake_case       :int = 8      ,__snake_case       :int = 4      ,__snake_case       :float = 0.0      ,__snake_case       :int = 16      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 2      ,__snake_case       :int = 1      ,__snake_case       :float = 0.0      ,__snake_case       :int = 1      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,__snake_case       :float = 1E-5      ,__snake_case       :str = "gelu"      ,__snake_case       :float = 0.02      ,__snake_case       :float = 1E-12      ,__snake_case       :int = 2_24      ,__snake_case       :float = 1E-05      ,**__snake_case       :Dict      ,) ->   None:
												super().__init__(**__snake_case				)

												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					hidden_sizes
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					initializer_range
												a__					=					layer_norm_eps
												a__					=					patch_size
												a__					=					num_channels
												a__					=					depths
												a__					=					mlp_expansion_ratio
												a__					=					downsamples
												a__					=					dim
												a__					=					key_dim
												a__					=					attention_ratio
												a__					=					resolution
												a__					=					pool_size
												a__					=					downsample_patch_size
												a__					=					downsample_stride
												a__					=					downsample_pad
												a__					=					drop_path_rate
												a__					=					num_metaad_blocks
												a__					=					distillation
												a__					=					use_layer_scale
												a__					=					layer_scale_init_value
												a__					=					image_size
												a__					=					batch_norm_eps




