
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__  (     SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	str=None       )  ->     Dict:
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						_lowercase					       =					nn.Parameter(SCREAMING_SNAKE_CASE_       )
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												_lowercase					       =					nn.Parameter(SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     List[str]:
						# set torch weights for 1-to-1 comparison
						_lowercase					       =					np.asarray(weights[0]       )
						_lowercase					       =					np.asarray(weights[1]       )
						_lowercase					       =					np.asarray(weights[2]       )

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




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

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     List[str]:
						# layernorm 1
						_lowercase					       =					weights[0][0][0]
						_lowercase					       =					np.asarray(layer_norm_a[0]       )
						_lowercase					       =					np.asarray(layer_norm_a[1]       )
						set_param(
						    torch_block.attention.layer_norm		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   )

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

						# intermediate weighs
						_lowercase					       =					weights[2][0][1][2]

						# Chunked Feed Forward
						if len(SCREAMING_SNAKE_CASE_       ) == 4:
												_lowercase					       =					intermediate_weights[2]

						# layernorm 2
						_lowercase					       =					np.asarray(intermediate_weights[0][0]       )
						_lowercase					       =					np.asarray(intermediate_weights[0][1]       )
						set_param(
						    torch_block.feed_forward.layer_norm		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   )

						# intermediate dense
						_lowercase					       =					np.asarray(intermediate_weights[1][0]       )
						_lowercase					       =					np.asarray(intermediate_weights[1][1]       )
						set_param(
						    torch_block.feed_forward.dense.dense		,   torch.tensor(SCREAMING_SNAKE_CASE_       ).transpose(0		,   1       ).contiguous()		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   )

						# intermediate out
						_lowercase					       =					np.asarray(intermediate_weights[4][0]       )
						_lowercase					       =					np.asarray(intermediate_weights[4][1]       )
						set_param(
						    torch_block.feed_forward.output.dense		,   torch.tensor(SCREAMING_SNAKE_CASE_       ).transpose(0		,   1       ).contiguous()		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Optional[Any]:
						# reformer model
						_lowercase					       =					torch_model.reformer

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

						if isinstance(weights[3]		,   SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights       )       ):
																		_lowercase					       =					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					       =					nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_       )       )

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

						# output layer norm
						_lowercase					       =					np.asarray(weights[7][0]       )
						_lowercase					       =					np.asarray(weights[7][1]       )
						set_param(
						    torch_model_reformer.encoder.layer_norm		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   torch.tensor(SCREAMING_SNAKE_CASE_       )		,   )

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     int:
						# Initialise PyTorch model
						_lowercase					       =					ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE_       )
						print(f"""Building PyTorch model from configuration: {config}"""       )
						_lowercase					       =					ReformerModelWithLMHead(SCREAMING_SNAKE_CASE_       )

						with open(SCREAMING_SNAKE_CASE_		,   """rb"""       ) as f:
												_lowercase					       =					pickle.load(SCREAMING_SNAKE_CASE_       )["""weights"""]

						set_model_weights_in_torch(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   config.hidden_size       )

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


if __name__ == "__main__":
		A		: List[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.'''
		)
		A		: Any								=     parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
		from .camera import create_pan_cameras
		from .pipeline_shap_e import ShapEPipeline
		from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
		from .renderer import (
		    BoundingBoxVolume,
		    ImportanceRaySampler,
		    MLPNeRFModelOutput,
		    MLPNeRSTFModel,
		    ShapEParamsProjModel,
		    ShapERenderer,
		    StratifiedRaySampler,
		    VoidNeRFModel,
		)