






"""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 				snake_case      (   A__	,A__	,A__=None						):
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
			UpperCAmelCase_     : List[str]			     =					nn.Parameter(A__						)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
						UpperCAmelCase_     : Any			     =					nn.Parameter(A__						)

def 				snake_case      (   A__	,A__	,A__						):
			# set torch weights for 1-to-1 comparison
			UpperCAmelCase_     : Union[str, Any]			     =					np.asarray(weights[0]						)
			UpperCAmelCase_     : Optional[int]			     =					np.asarray(weights[1]						)
			UpperCAmelCase_     : Dict			     =					np.asarray(weights[2]						)

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

def 				snake_case      (   A__	,A__	,A__						):
			# set torch weights for 1-to-1 comparison
			UpperCAmelCase_     : Optional[int]			     =					np.asarray(weights[0]						)
			UpperCAmelCase_     : Union[str, Any]			     =					np.asarray(weights[1]						)
			UpperCAmelCase_     : str			     =					np.asarray(weights[2]						)
			UpperCAmelCase_     : Dict			     =					np.asarray(weights[3]						)

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

def 				snake_case      (   A__	,A__	,A__						):
			# layernorm 1
			UpperCAmelCase_     : int			     =					weights[0][0][0]
			UpperCAmelCase_     : Union[str, Any]			     =					np.asarray(layer_norm_a[0]						)
			UpperCAmelCase_     : Dict			     =					np.asarray(layer_norm_a[1]						)
			set_param(
			    torch_block.attention.layer_norm	,torch.tensor(A__						)	,torch.tensor(A__						)	,)

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

			# intermediate weighs
			UpperCAmelCase_     : List[str]			     =					weights[2][0][1][2]

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

			# layernorm 2
			UpperCAmelCase_     : str			     =					np.asarray(intermediate_weights[0][0]						)
			UpperCAmelCase_     : Optional[Any]			     =					np.asarray(intermediate_weights[0][1]						)
			set_param(
			    torch_block.feed_forward.layer_norm	,torch.tensor(A__						)	,torch.tensor(A__						)	,)

			# intermediate dense
			UpperCAmelCase_     : int			     =					np.asarray(intermediate_weights[1][0]						)
			UpperCAmelCase_     : Tuple			     =					np.asarray(intermediate_weights[1][1]						)
			set_param(
			    torch_block.feed_forward.dense.dense	,torch.tensor(A__						).transpose(0	,1						).contiguous()	,torch.tensor(A__						)	,)

			# intermediate out
			UpperCAmelCase_     : List[Any]			     =					np.asarray(intermediate_weights[4][0]						)
			UpperCAmelCase_     : Optional[int]			     =					np.asarray(intermediate_weights[4][1]						)
			set_param(
			    torch_block.feed_forward.output.dense	,torch.tensor(A__						).transpose(0	,1						).contiguous()	,torch.tensor(A__						)	,)

def 				snake_case      (   A__	,A__	,A__						):
			# reformer model
			UpperCAmelCase_     : Tuple			     =					torch_model.reformer

			# word embeds
			UpperCAmelCase_     : Tuple			     =					np.asarray(weights[1]						)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings	,torch.tensor(A__						)	,)

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

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

			# output layer norm
			UpperCAmelCase_     : str			     =					np.asarray(weights[7][0]						)
			UpperCAmelCase_     : List[Any]			     =					np.asarray(weights[7][1]						)
			set_param(
			    torch_model_reformer.encoder.layer_norm	,torch.tensor(A__						)	,torch.tensor(A__						)	,)

			# output embeddings
			UpperCAmelCase_     : Union[str, Any]			     =					np.asarray(weights[9][0]						)
			UpperCAmelCase_     : List[Any]			     =					np.asarray(weights[9][1]						)
			set_param(
			    torch_model.lm_head.decoder	,torch.tensor(A__						).transpose(0	,1						).contiguous()	,torch.tensor(A__						)	,)

def 				snake_case      (   A__	,A__	,A__						):
			# Initialise PyTorch model
			UpperCAmelCase_     : int			     =					ReformerConfig.from_json_file(A__						)
			print(F"""Building PyTorch model from configuration: {config}"""						)
			UpperCAmelCase_     : List[str]			     =					ReformerModelWithLMHead(A__						)

			with open(A__	,"rb"						) as f:
						UpperCAmelCase_     : Any			     =					pickle.load(A__						)["weights"]

			set_model_weights_in_torch(A__	,A__	,config.hidden_size						)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
			torch.save(model.state_dict()	,A__						)


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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''rwkv'''
	__magic_name__	      =				{'''max_position_embeddings''': '''context_length'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: str=50_277	,	lowerCAmelCase_					: Optional[int]=1_024	,	lowerCAmelCase_					: Optional[int]=4_096	,	lowerCAmelCase_					: Union[str, Any]=32	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: List[Any]=1e-5	,	lowerCAmelCase_					: str=0	,	lowerCAmelCase_					: Tuple=0	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Optional[Any]=False	,	lowerCAmelCase_					: Any=True	,	**lowerCAmelCase_					: List[Any]	,	)						->			List[str]:
				UpperCAmelCase_     : Tuple			     =					vocab_size
				UpperCAmelCase_     : List[str]			     =					context_length
				UpperCAmelCase_     : Dict			     =					hidden_size
				UpperCAmelCase_     : Optional[int]			     =					num_hidden_layers
				UpperCAmelCase_     : Optional[int]			     =					attention_hidden_size if attention_hidden_size is not None else hidden_size
				UpperCAmelCase_     : Dict			     =					intermediate_size if intermediate_size is not None else 4 * hidden_size
				UpperCAmelCase_     : Any			     =					layer_norm_epsilon
				UpperCAmelCase_     : List[Any]			     =					rescale_every
				UpperCAmelCase_     : List[str]			     =					use_cache

				UpperCAmelCase_     : List[str]			     =					bos_token_id
				UpperCAmelCase_     : Union[str, Any]			     =					eos_token_id

				super().__init__(
				    tie_word_embeddings=lowerCAmelCase_	,	bos_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	**lowerCAmelCase_						)



