






'''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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None   )   -> List[str]:
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
							lowerCamelCase__      :			Union[str, Any] 						= nn.Parameter(UpperCamelCase   )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
														lowerCamelCase__      :			Union[str, Any] 						= nn.Parameter(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> str:
							# set torch weights for 1-to-1 comparison
							lowerCamelCase__      :			Tuple 						= np.asarray(weights[0]   )
							lowerCamelCase__      :			Dict 						= np.asarray(weights[1]   )
							lowerCamelCase__      :			Tuple 						= np.asarray(weights[2]   )

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Any:
							# set torch weights for 1-to-1 comparison
							lowerCamelCase__      :			str 						= np.asarray(weights[0]   )
							lowerCamelCase__      :			List[str] 						= np.asarray(weights[1]   )
							lowerCamelCase__      :			str 						= np.asarray(weights[2]   )
							lowerCamelCase__      :			Union[str, Any] 						= np.asarray(weights[3]   )

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> int:
							# layernorm 1
							lowerCamelCase__      :			Optional[Any] 						= weights[0][0][0]
							lowerCamelCase__      :			Dict 						= np.asarray(layer_norm_a[0]   )
							lowerCamelCase__      :			int 						= np.asarray(layer_norm_a[1]   )
							set_param(
							    torch_block.attention.layer_norm       ,    torch.tensor(UpperCamelCase   )       ,    torch.tensor(UpperCamelCase   )       ,    )

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

							# intermediate weighs
							lowerCamelCase__      :			Optional[int] 						= weights[2][0][1][2]

							# Chunked Feed Forward
							if len(UpperCamelCase   ) == 4:
														lowerCamelCase__      :			str 						= intermediate_weights[2]

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

							# intermediate dense
							lowerCamelCase__      :			Optional[int] 						= np.asarray(intermediate_weights[1][0]   )
							lowerCamelCase__      :			Union[str, Any] 						= np.asarray(intermediate_weights[1][1]   )
							set_param(
							    torch_block.feed_forward.dense.dense       ,    torch.tensor(UpperCamelCase   ).transpose(0       ,    1   ).contiguous()       ,    torch.tensor(UpperCamelCase   )       ,    )

							# intermediate out
							lowerCamelCase__      :			str 						= np.asarray(intermediate_weights[4][0]   )
							lowerCamelCase__      :			Union[str, Any] 						= np.asarray(intermediate_weights[4][1]   )
							set_param(
							    torch_block.feed_forward.output.dense       ,    torch.tensor(UpperCamelCase   ).transpose(0       ,    1   ).contiguous()       ,    torch.tensor(UpperCamelCase   )       ,    )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> int:
							# reformer model
							lowerCamelCase__      :			int 						= torch_model.reformer

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

							if isinstance(weights[3]       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights   )   ):
																					lowerCamelCase__      :			List[str] 						= 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'''
																					lowerCamelCase__      :			Optional[int] 						= nn.Parameter(torch.tensor(UpperCamelCase   )   )

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

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

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> List[str]:
							# Initialise PyTorch model
							lowerCamelCase__      :			Optional[Any] 						= ReformerConfig.from_json_file(UpperCamelCase   )
							print(f'''Building PyTorch model from configuration: {config}'''   )
							lowerCamelCase__      :			int 						= ReformerModelWithLMHead(UpperCamelCase   )

							with open(UpperCamelCase       ,    """rb"""   ) as f:
														lowerCamelCase__      :			Union[str, Any] 						= pickle.load(UpperCamelCase   )["""weights"""]

							set_model_weights_in_torch(UpperCamelCase       ,    UpperCamelCase       ,    config.hidden_size   )

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


if __name__ == "__main__":
			_A			:    str					  =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.'''
			)
			_A			:    Tuple					  =parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

