

'''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 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Optional[int]=None     ):

		"""simple docstring"""

		assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
		snake_case__						:       Optional[Any]       						=  nn.Parameter(__lowerCAmelCase     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
				snake_case__						:       Optional[int]       						=  nn.Parameter(__lowerCAmelCase     )




def 	_a       (				__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  np.asarray(weights[0]     )
		snake_case__						:       Any       						=  np.asarray(weights[1]     )
		snake_case__						:       List[str]       						=  np.asarray(weights[2]     )

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




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Any     ):

		"""simple docstring"""

		snake_case__						:       Dict       						=  np.asarray(weights[0]     )
		snake_case__						:       int       						=  np.asarray(weights[1]     )
		snake_case__						:       Dict       						=  np.asarray(weights[2]     )
		snake_case__						:       Tuple       						=  np.asarray(weights[3]     )

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




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       str       						=  weights[0][0][0]
		snake_case__						:       Optional[int]       						=  np.asarray(layer_norm_a[0]     )
		snake_case__						:       str       						=  np.asarray(layer_norm_a[1]     )
		set_param(
		    torch_block.attention.layer_norm   ,							torch.tensor(__lowerCAmelCase     )   ,							torch.tensor(__lowerCAmelCase     )   ,							)

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

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

		# Chunked Feed Forward
		if len(__lowerCAmelCase     ) == 4:
				snake_case__						:       Optional[int]       						=  intermediate_weights[2]

		# layernorm 2
		snake_case__						:       Any       						=  np.asarray(intermediate_weights[0][0]     )
		snake_case__						:       List[Any]       						=  np.asarray(intermediate_weights[0][1]     )
		set_param(
		    torch_block.feed_forward.layer_norm   ,							torch.tensor(__lowerCAmelCase     )   ,							torch.tensor(__lowerCAmelCase     )   ,							)

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

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




def 	_a       (				__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      str     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  torch_model.reformer

		# word embeds
		snake_case__						:       List[Any]       						=  np.asarray(weights[1]     )
		set_param(
		    torch_model_reformer.embeddings.word_embeddings   ,							torch.tensor(__lowerCAmelCase     )   ,							)

		if isinstance(weights[3]   ,							__lowerCAmelCase     ):
				snake_case__						:       List[Any]       						=  torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights     )     ):
						snake_case__						:       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"""
						snake_case__						:       int       						=  nn.Parameter(torch.tensor(__lowerCAmelCase     )     )

		snake_case__						:       int       						=  weights[5]
		assert len(torch_model_reformer.encoder.layers     ) * 4 == len(
		    __lowerCAmelCase     ), "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__						:       List[Any]       						=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )

		# output layer norm
		snake_case__						:       Dict       						=  np.asarray(weights[7][0]     )
		snake_case__						:       Tuple       						=  np.asarray(weights[7][1]     )
		set_param(
		    torch_model_reformer.encoder.layer_norm   ,							torch.tensor(__lowerCAmelCase     )   ,							torch.tensor(__lowerCAmelCase     )   ,							)

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




def 	_a       (				__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  ReformerConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )
		snake_case__						:       Any       						=  ReformerModelWithLMHead(__lowerCAmelCase     )

		with open(__lowerCAmelCase   ,							'''rb'''     ) as f:
				snake_case__						:       int       						=  pickle.load(__lowerCAmelCase     )['''weights''']

		set_model_weights_in_torch(__lowerCAmelCase   ,							__lowerCAmelCase   ,							config.hidden_size     )

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


if __name__ == "__main__":
					lowerCAmelCase__						:       int										=						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__						:       int										=						parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor




class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """pixel_values"""
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    TimmBackboneConfig







						def __init__(							self					:      str			,			snake_case_					:      str			,			**snake_case_					:      Dict  ):



								'''simple docstring'''



								requires_backends(self			,			'''timm'''  )
								super().__init__(snake_case_  )
								snake_case__						:       List[str]       						=  config

								if config.backbone is None:
										raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
										raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(snake_case_			,			'''out_features'''  ) and config.out_features is not None:
										raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								snake_case__						:       Dict       						=  getattr(snake_case_			,			'''use_pretrained_backbone'''			,			snake_case_  )
								if pretrained is None:
										raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								snake_case__						:       int       						=  config.out_indices if getattr(snake_case_			,			'''out_indices'''			,			snake_case_  ) is not None else (-1,)

								snake_case__						:       Any       						=  timm.create_model(
								    config.backbone			,			pretrained=snake_case_			,			features_only=config.features_only			,			in_chans=config.num_channels			,			out_indices=snake_case_			,			**snake_case_			,			)
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								snake_case__						:       str       						=  self._backbone.return_layers
								snake_case__						:       List[Any]       						=  {layer['''module''']: str(snake_case_  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(snake_case_  )







						@classmethod
						def       __magic_name__							(							cls					:      Union[str, Any]			,			snake_case_					:      Union[str, Any]			,			*snake_case_					:      Optional[Any]			,			**snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								requires_backends(cls			,			['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								snake_case__						:       Dict       						=  kwargs.pop('''config'''			,			TimmBackboneConfig()  )

								snake_case__						:       Optional[Any]       						=  kwargs.pop('''use_timm_backbone'''			,			snake_case_  )
								if not use_timm:
										raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								snake_case__						:       List[Any]       						=  kwargs.pop('''num_channels'''			,			config.num_channels  )
								snake_case__						:       Optional[Any]       						=  kwargs.pop('''features_only'''			,			config.features_only  )
								snake_case__						:       Tuple       						=  kwargs.pop('''use_pretrained_backbone'''			,			config.use_pretrained_backbone  )
								snake_case__						:       Union[str, Any]       						=  kwargs.pop('''out_indices'''			,			config.out_indices  )
								snake_case__						:       int       						=  TimmBackboneConfig(
								    backbone=snake_case_			,			num_channels=snake_case_			,			features_only=snake_case_			,			use_pretrained_backbone=snake_case_			,			out_indices=snake_case_			,			)
								return super()._from_config(snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple=None			,			snake_case_					:      Tuple=None			,			snake_case_					:      Optional[Any]=None			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  return_dict if return_dict is not None else self.config.use_return_dict
								snake_case__						:       Optional[int]       						=  (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								snake_case__						:       int       						=  output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
										raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
										# We modify the return layers to include all the stages of the backbone
										snake_case__						:       Any       						=  self._all_layers
										snake_case__						:       str       						=  self._backbone(snake_case_			,			**snake_case_  )
										snake_case__						:       Tuple       						=  self._return_layers
										snake_case__						:       str       						=  tuple(hidden_states[i] for i in self.out_indices  )
								else:
										snake_case__						:       Dict       						=  self._backbone(snake_case_			,			**snake_case_  )
										snake_case__						:       Any       						=  None

								snake_case__						:       Union[str, Any]       						=  tuple(snake_case_  )
								snake_case__						:       Optional[int]       						=  tuple(snake_case_  ) if hidden_states is not None else None

								if not return_dict:
										snake_case__						:       List[str]       						=  (feature_maps,)
										if output_hidden_states:
												snake_case__						:       Optional[int]       						=  output + (hidden_states,)
										return output

								return BackboneOutput(feature_maps=snake_case_			,			hidden_states=snake_case_			,			attentions=snake_case_  )



