


'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a__  :					List[str]        =       logging.get_logger(__name__)

a__  :					Optional[int]        =       {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'deta'
    __UpperCAmelCase   			=	{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }


    def __init__(     self  ,					a=None  ,					a=9_00  ,					a=20_48  ,					a=6  ,					a=20_48  ,					a=8  ,					a=6  ,					a=10_24  ,					a=8  ,					a=0.0  ,					a=True  ,					a="relu"  ,					a=2_56  ,					a=0.1  ,					a=0.0  ,					a=0.0  ,					a=0.02  ,					a=1.0  ,					a=True  ,					a=False  ,					a="sine"  ,					a=5  ,					a=4  ,					a=4  ,					a=True  ,					a=3_00  ,					a=True  ,					a=True  ,					a=1  ,					a=5  ,					a=2  ,					a=1  ,					a=1  ,					a=5  ,					a=2  ,					a=0.1  ,					a=0.25  ,					**a  ,					):
          if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."    )
                UpperCamelCase__										=				CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]    )
          else:
                if isinstance(a  ,					a    ):
                      UpperCamelCase__										=				backbone_config.pop("model_type"    )
                      UpperCamelCase__										=				CONFIG_MAPPING[backbone_model_type]
                      UpperCamelCase__										=				config_class.from_dict(a    )

          UpperCamelCase__										=				backbone_config
          UpperCamelCase__										=				num_queries
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				d_model
          UpperCamelCase__										=				encoder_ffn_dim
          UpperCamelCase__										=				encoder_layers
          UpperCamelCase__										=				encoder_attention_heads
          UpperCamelCase__										=				decoder_ffn_dim
          UpperCamelCase__										=				decoder_layers
          UpperCamelCase__										=				decoder_attention_heads
          UpperCamelCase__										=				dropout
          UpperCamelCase__										=				attention_dropout
          UpperCamelCase__										=				activation_dropout
          UpperCamelCase__										=				activation_function
          UpperCamelCase__										=				init_std
          UpperCamelCase__										=				init_xavier_std
          UpperCamelCase__										=				encoder_layerdrop
          UpperCamelCase__										=				auxiliary_loss
          UpperCamelCase__										=				position_embedding_type
          # deformable attributes
          UpperCamelCase__										=				num_feature_levels
          UpperCamelCase__										=				encoder_n_points
          UpperCamelCase__										=				decoder_n_points
          UpperCamelCase__										=				two_stage
          UpperCamelCase__										=				two_stage_num_proposals
          UpperCamelCase__										=				with_box_refine
          UpperCamelCase__										=				assign_first_stage
          if two_stage is True and with_box_refine is False:
                raise ValueError("If two_stage is True, with_box_refine must be True."    )
          # Hungarian matcher
          UpperCamelCase__										=				class_cost
          UpperCamelCase__										=				bbox_cost
          UpperCamelCase__										=				giou_cost
          # Loss coefficients
          UpperCamelCase__										=				mask_loss_coefficient
          UpperCamelCase__										=				dice_loss_coefficient
          UpperCamelCase__										=				bbox_loss_coefficient
          UpperCamelCase__										=				giou_loss_coefficient
          UpperCamelCase__										=				eos_coefficient
          UpperCamelCase__										=				focal_alpha
          super().__init__(is_encoder_decoder=a  ,					**a    )


    @property
    def    __a							(     self    ):
          return self.encoder_attention_heads


    @property
    def    __a							(     self    ):
          return self.d_model


    def    __a							(     self    ):
          UpperCamelCase__										=				copy.deepcopy(self.__dict__    )
          UpperCamelCase__										=				self.backbone_config.to_dict()
          UpperCamelCase__										=				self.__class__.model_type
          return output







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)