
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Tuple       ,					__lowercase   :	List[Any]       ,					__lowercase   :	List[Any]="attention" ) ->  Tuple:
			_snake_case		=			params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			_snake_case		=			params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			_snake_case		=			params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			_snake_case		=			params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v






def        a_		(       __lowercase   :	Union[str, Any]       ,					__lowercase   :	Union[str, Any]       ,					__lowercase   :	Optional[int]       ,					__lowercase   :	Union[str, Any]=False ) ->  List[str]:
			if split_mlp_wi:
						_snake_case		=			params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
						_snake_case		=			params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
						_snake_case		=			(wi_a, wi_a)
			else:
						_snake_case		=			params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

			_snake_case		=			params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
			return wi, wo






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	str       ,					__lowercase   :	List[Any]       ,					__lowercase   :	Dict ) ->  str:
			return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']






def        a_		(       __lowercase   :	dict       ,					*, __lowercase   :	int       ,					__lowercase   :	bool ) ->  Union[str, Any]:
			_snake_case		=			traverse_util.flatten_dict(variables['target'] )
			_snake_case		=			{'/'.join(__lowercase ): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			_snake_case		=			'encoder/layers_0/mlp/wi_0/kernel' in old
			print('Split MLP:'       ,					__lowercase )

			_snake_case		=			collections.OrderedDict()

			# Shared embeddings.
			_snake_case		=			old['token_embedder/embedding']

			# Encoder.
			for i in range(__lowercase ):
						# Block i, layer 0 (Self Attention).
						_snake_case		=			tax_layer_norm_lookup(__lowercase       ,					__lowercase       ,					'encoder'       ,					'pre_attention_layer_norm' )
						_snake_case	,					_snake_case	,					_snake_case	,					_snake_case		=			tax_attention_lookup(__lowercase       ,					__lowercase       ,					'encoder'       ,					'attention' )
						_snake_case		=			layer_norm
						_snake_case		=			k.T
						_snake_case		=			o.T
						_snake_case		=			q.T
						_snake_case		=			v.T

						# Block i, layer 1 (MLP).
						_snake_case		=			tax_layer_norm_lookup(__lowercase       ,					__lowercase       ,					'encoder'       ,					'pre_mlp_layer_norm' )
						_snake_case	,					_snake_case		=			tax_mlp_lookup(__lowercase       ,					__lowercase       ,					'encoder'       ,					__lowercase )
						_snake_case		=			layer_norm
						if split_mlp_wi:
									_snake_case		=			wi[0].T
									_snake_case		=			wi[1].T
						else:
									_snake_case		=			wi.T
						_snake_case		=			wo.T

			_snake_case		=			old[
			    'encoder/relpos_bias/rel_embedding'
			].T
			_snake_case		=			old['encoder/encoder_norm/scale']

			if not is_encoder_only:
						# Decoder.
						for i in range(__lowercase ):
									# Block i, layer 0 (Self Attention).
									_snake_case		=			tax_layer_norm_lookup(__lowercase       ,					__lowercase       ,					'decoder'       ,					'pre_self_attention_layer_norm' )
									_snake_case	,					_snake_case	,					_snake_case	,					_snake_case		=			tax_attention_lookup(__lowercase       ,					__lowercase       ,					'decoder'       ,					'self_attention' )
									_snake_case		=			layer_norm
									_snake_case		=			k.T
									_snake_case		=			o.T
									_snake_case		=			q.T
									_snake_case		=			v.T

									# Block i, layer 1 (Cross Attention).
									_snake_case		=			tax_layer_norm_lookup(__lowercase       ,					__lowercase       ,					'decoder'       ,					'pre_cross_attention_layer_norm' )
									_snake_case	,					_snake_case	,					_snake_case	,					_snake_case		=			tax_attention_lookup(__lowercase       ,					__lowercase       ,					'decoder'       ,					'encoder_decoder_attention' )
									_snake_case		=			layer_norm
									_snake_case		=			k.T
									_snake_case		=			o.T
									_snake_case		=			q.T
									_snake_case		=			v.T

									# Block i, layer 2 (MLP).
									_snake_case		=			tax_layer_norm_lookup(__lowercase       ,					__lowercase       ,					'decoder'       ,					'pre_mlp_layer_norm' )
									_snake_case	,					_snake_case		=			tax_mlp_lookup(__lowercase       ,					__lowercase       ,					'decoder'       ,					__lowercase )
									_snake_case		=			layer_norm
									if split_mlp_wi:
												_snake_case		=			wi[0].T
												_snake_case		=			wi[1].T
									else:
												_snake_case		=			wi.T
									_snake_case		=			wo.T

						_snake_case		=			old['decoder/decoder_norm/scale']
						_snake_case		=			old[
						    'decoder/relpos_bias/rel_embedding'
						].T

						# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
						if "decoder/logits_dense/kernel" in old:
									_snake_case		=			old['decoder/logits_dense/kernel'].T

			return new






def        a_		(       __lowercase   :	Union[str, Any]       ,					__lowercase   :	bool ) ->  Optional[int]:
			_snake_case		=			collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

			# Add what is missing.
			if "encoder.embed_tokens.weight" not in state_dict:
						_snake_case		=			state_dict['shared.weight']

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									_snake_case		=			state_dict['shared.weight']

						if "lm_head.weight" not in state_dict:  # For old 1.0 models.
									print('Using shared word embeddings as lm_head.' )
									_snake_case		=			state_dict['shared.weight']

			return state_dict






def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	int       ,					__lowercase   :	List[str]       ,					__lowercase   :	int ) ->  List[Any]:
			_snake_case		=			checkpoints.load_tax_checkpoint(__lowercase )
			_snake_case		=			convert_tax_to_pytorch(__lowercase       ,					num_layers=config.num_layers       ,					is_encoder_only=__lowercase )
			_snake_case		=			make_state_dict(__lowercase       ,					__lowercase )
			model.load_state_dict(__lowercase       ,					strict=__lowercase )






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict       ,					__lowercase   :	bool = False ) ->  Optional[Any]:
			_snake_case		=			TaConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			# Non-v1.1 checkpoints could also use T5Model, but this works for all.
			# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
			if is_encoder_only:
						_snake_case		=			TaEncoderModel(__lowercase )
			else:
						_snake_case		=			TaForConditionalGeneration(__lowercase )

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )

			# Save pytorch-model
			print(f'''Save PyTorch model to {pytorch_dump_path}''' )
			model.save_pretrained(__lowercase )

			# Verify that we can load the checkpoint.
			model.from_pretrained(__lowercase )
			print('Done' )


if __name__ == "__main__":
			_lowerCamelCase       :				Optional[int]       =			argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
			# Required parameters
			parser.add_argument(
			    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
			)
			_lowerCamelCase       :				Dict       =			parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)

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)