






"""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      UpperCamelCase       ( _A  ,				_A  ,				_A=None )     ->       Tuple:
				# set parameter of one layer
				assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
				lowercase    :      Optional[Any]       = nn.Parameter(_lowerCAmelCase )
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
								lowercase    :      int       = nn.Parameter(_lowerCAmelCase )


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Union[str, Any]:
				# set torch weights for 1-to-1 comparison
				lowercase    :      List[Any]       = np.asarray(weights[0] )
				lowercase    :      Union[str, Any]       = np.asarray(weights[1] )
				lowercase    :      Union[str, Any]       = 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      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       List[str]:
				# set torch weights for 1-to-1 comparison
				lowercase    :      Dict       = np.asarray(weights[0] )
				lowercase    :      Dict       = np.asarray(weights[1] )
				lowercase    :      Optional[Any]       = np.asarray(weights[2] )
				lowercase    :      Optional[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      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       int:
				# layernorm 1
				lowercase    :      List[str]       = weights[0][0][0]
				lowercase    :      Tuple       = np.asarray(layer_norm_a[0] )
				lowercase    :      Any       = np.asarray(layer_norm_a[1] )
				set_param(
				    torch_block.attention.layer_norm  ,				torch.tensor(_lowerCAmelCase )  ,				torch.tensor(_lowerCAmelCase )  ,				)

				# lsh weights + output
				lowercase    :      Optional[Any]       = 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
				lowercase    :      int       = weights[2][0][1][2]

				# Chunked Feed Forward
				if len(_lowerCAmelCase ) == 4:
								lowercase    :      Union[str, Any]       = intermediate_weights[2]

				# layernorm 2
				lowercase    :      Tuple       = np.asarray(intermediate_weights[0][0] )
				lowercase    :      str       = np.asarray(intermediate_weights[0][1] )
				set_param(
				    torch_block.feed_forward.layer_norm  ,				torch.tensor(_lowerCAmelCase )  ,				torch.tensor(_lowerCAmelCase )  ,				)

				# intermediate dense
				lowercase    :      List[Any]       = np.asarray(intermediate_weights[1][0] )
				lowercase    :      Dict       = 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
				lowercase    :      Dict       = np.asarray(intermediate_weights[4][0] )
				lowercase    :      Optional[Any]       = 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      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[int]:
				# reformer model
				lowercase    :      Union[str, Any]       = torch_model.reformer

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

				if isinstance(weights[3]  ,				_lowerCAmelCase ):
								lowercase    :      Union[str, Any]       = torch_model_reformer.embeddings.position_embeddings
								for emb_idx in range(len(position_embeddings.weights ) ):
												lowercase    :      Union[str, 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"""
												lowercase    :      Any       = nn.Parameter(torch.tensor(_lowerCAmelCase ) )

				lowercase    :      Tuple       = 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 ):
								lowercase    :      int       = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
								set_block_weights_in_torch(_lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase )

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

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


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Dict:
				# Initialise PyTorch model
				lowercase    :      Optional[int]       = ReformerConfig.from_json_file(_lowerCAmelCase )
				print(F"""Building PyTorch model from configuration: {config}""" )
				lowercase    :      str       = ReformerModelWithLMHead(_lowerCAmelCase )

				with open(_lowerCAmelCase  ,				"""rb""" ) as f:
								lowercase    :      Any       = 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            =	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            =	parser.parse_args()
				convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''mgp-str'''




							def __init__( self    ,				snake_case_=[32, 128]    ,				snake_case_=4    ,				snake_case_=3    ,				snake_case_=27    ,				snake_case_=38    ,				snake_case_=50_257    ,				snake_case_=30_522    ,				snake_case_=768    ,				snake_case_=12    ,				snake_case_=12    ,				snake_case_=4.0    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=1e-5    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=False    ,				snake_case_=0.02    ,				**snake_case_    ,				)       ->  List[Any]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  max_token_length
													__lowerCAmelCase     =  num_character_labels
													__lowerCAmelCase     =  num_bpe_labels
													__lowerCAmelCase     =  num_wordpiece_labels
													__lowerCAmelCase     =  hidden_size
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  mlp_ratio
													__lowerCAmelCase     =  distilled
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  drop_rate
													__lowerCAmelCase     =  qkv_bias
													__lowerCAmelCase     =  attn_drop_rate
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  output_aa_attentions
													__lowerCAmelCase     =  initializer_range
