
"""simple docstring"""
import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE_					     =	{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''mask2former'''
							_snake_case						      =		['''swin''']
							_snake_case						      =		{'''hidden_size''': '''hidden_dim'''}




							def __init__( self    ,				snake_case_ = None    ,				snake_case_ = 256    ,				snake_case_ = 256    ,				snake_case_ = 256    ,				snake_case_ = 1_024    ,				snake_case_ = "relu"    ,				snake_case_ = 6    ,				snake_case_ = 10    ,				snake_case_ = 8    ,				snake_case_ = 0.0    ,				snake_case_ = 2_048    ,				snake_case_ = False    ,				snake_case_ = False    ,				snake_case_ = 4    ,				snake_case_ = 255    ,				snake_case_ = 100    ,				snake_case_ = 0.1    ,				snake_case_ = 2.0    ,				snake_case_ = 5.0    ,				snake_case_ = 5.0    ,				snake_case_ = 12_544    ,				snake_case_ = 3.0    ,				snake_case_ = 0.75    ,				snake_case_ = 0.02    ,				snake_case_ = 1.0    ,				snake_case_ = True    ,				snake_case_ = [4, 8, 16, 32]    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  List[Any]:
													if backbone_config is None:
																			logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""				)
																			__lowerCAmelCase     =  CONFIG_MAPPING["""swin"""](
																			    image_size=224    ,				in_channels=3    ,				patch_size=4    ,				embed_dim=96    ,				depths=[2, 2, 18, 2]    ,				num_heads=[3, 6, 12, 24]    ,				window_size=7    ,				drop_path_rate=0.3    ,				use_absolute_embeddings=snake_case_    ,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,				)

													if isinstance(snake_case_    ,				snake_case_				):
																			__lowerCAmelCase     =  backbone_config.pop("""model_type"""				)
																			__lowerCAmelCase     =  CONFIG_MAPPING[backbone_model_type]
																			__lowerCAmelCase     =  config_class.from_dict(snake_case_				)

													# verify that the backbone is supported
													if backbone_config.model_type not in self.backbones_supported:
																			logger.warning_once(
																			    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
																			    f"""Supported model types: {",".join(self.backbones_supported				)}"""				)

													__lowerCAmelCase     =  backbone_config
													__lowerCAmelCase     =  feature_size
													__lowerCAmelCase     =  mask_feature_size
													__lowerCAmelCase     =  hidden_dim
													__lowerCAmelCase     =  encoder_feedforward_dim
													__lowerCAmelCase     =  activation_function
													__lowerCAmelCase     =  encoder_layers
													__lowerCAmelCase     =  decoder_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  dropout
													__lowerCAmelCase     =  dim_feedforward
													__lowerCAmelCase     =  pre_norm
													__lowerCAmelCase     =  enforce_input_projection
													__lowerCAmelCase     =  common_stride
													__lowerCAmelCase     =  ignore_value
													__lowerCAmelCase     =  num_queries
													__lowerCAmelCase     =  no_object_weight
													__lowerCAmelCase     =  class_weight
													__lowerCAmelCase     =  mask_weight
													__lowerCAmelCase     =  dice_weight
													__lowerCAmelCase     =  train_num_points
													__lowerCAmelCase     =  oversample_ratio
													__lowerCAmelCase     =  importance_sample_ratio
													__lowerCAmelCase     =  init_std
													__lowerCAmelCase     =  init_xavier_std
													__lowerCAmelCase     =  use_auxiliary_loss
													__lowerCAmelCase     =  feature_strides
													__lowerCAmelCase     =  output_auxiliary_logits
													__lowerCAmelCase     =  decoder_layers

													super().__init__(**snake_case_				)




							@classmethod
							def    A__						( cls    ,				snake_case_    ,				**snake_case_				)       ->  Any:
													return cls(
													    backbone_config=snake_case_    ,				**snake_case_    ,				)




							def    A__						( self				)       ->  Dict[str, any]:
													__lowerCAmelCase     =  copy.deepcopy(self.__dict__				)
													__lowerCAmelCase     =  self.backbone_config.to_dict()
													__lowerCAmelCase     =  self.__class__.model_type
													return output


"""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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  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
						__lowerCAmelCase     =  weights[2][0][1][2]

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

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

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  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
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

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

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

						__lowerCAmelCase     =  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						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  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__":
			SCREAMING_SNAKE_CASE_					     =	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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
