




"""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     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=None	):
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
			__lowercase			:  List[Any]								=       nn.Parameter(__UpperCamelCase	)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
						__lowercase			:  Optional[int]								=       nn.Parameter(__UpperCamelCase	)







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

			# lsh weights + output
			__lowercase			:  Tuple								=       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
			__lowercase			:  Any								=       weights[2][0][1][2]

			# Chunked Feed Forward
			if len(__UpperCamelCase	) == 4:
						__lowercase			:  List[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(__UpperCamelCase	)	,				torch.tensor(__UpperCamelCase	)	,				)

			# intermediate dense
			__lowercase			:  Optional[Any]								=       np.asarray(intermediate_weights[1][0]	)
			__lowercase			:  Tuple								=       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
			__lowercase			:  Any								=       np.asarray(intermediate_weights[4][0]	)
			__lowercase			:  List[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      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			# reformer model
			__lowercase			:  str								=       torch_model.reformer

			# word embeds
			__lowercase			:  Optional[int]								=       np.asarray(weights[1]	)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings	,				torch.tensor(__UpperCamelCase	)	,				)

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

			__lowercase			:  List[str]								=       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	):
						__lowercase			:  Union[str, Any]								=       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

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

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







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			# Initialise PyTorch model
			__lowercase			:  Tuple								=       ReformerConfig.from_json_file(__UpperCamelCase	)
			print(f"""Building PyTorch model from configuration: {config}"""	)
			__lowercase			:  Tuple								=       ReformerModelWithLMHead(__UpperCamelCase	)

			with open(__UpperCamelCase	,				'''rb'''	) as f:
						__lowercase			:  int								=       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_        =       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_        =       parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	img.shape[0], img.shape[1]

 # converting each pixel's color to its negative
 for i in range(lowerCamelCase_			):
  for j in range(lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	[2_5_5, 2_5_5, 2_5_5] - img[i][j]

 return img


if __name__ == "__main__":
     # read original image
     snake_case									=    imread("""image_data/lena.jpg""", 1)

     # convert to its negative
     snake_case									=    convert_to_negative(img)

     # show result image
     imshow("""negative of original image""", img)
     waitKey(0)
     destroyAllWindows()





