




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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None)      ->					List[Any]:
					# set parameter of one layer
					assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
					a     =				nn.Parameter(__UpperCamelCase)
					if bias is not None:
										assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
										a     =				nn.Parameter(__UpperCamelCase)

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

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

					# Chunked Feed Forward
					if len(__UpperCamelCase) == 4:
										a     =				intermediate_weights[2]

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

					# intermediate dense
					a     =				np.asarray(intermediate_weights[1][0])
					a     =				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
					a     =				np.asarray(intermediate_weights[4][0])
					a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
					# reformer model
					a     =				torch_model.reformer

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

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

					a     =				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):
										a     =				trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
										set_block_weights_in_torch(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)

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

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Union[str, Any]:
					# Initialise PyTorch model
					a     =				ReformerConfig.from_json_file(__UpperCamelCase)
					print(f'''Building PyTorch model from configuration: {config}''')
					a     =				ReformerModelWithLMHead(__UpperCamelCase)

					with open(__UpperCamelCase		, "rb") as f:
										a     =				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__":
			lowercase__				:      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."
			)
			lowercase__				:      Dict	   = parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)

lowercase__				:      str	   = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''simple docstring'''



          if backbone_config is not None and use_timm_backbone:
               raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."			)

          if not use_timm_backbone:
               if backbone_config is None:
                    logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

          a     =				use_timm_backbone
          a     =				backbone_config
          a     =				num_channels
          a     =				num_queries
          a     =				d_model
          a     =				encoder_ffn_dim
          a     =				encoder_layers
          a     =				encoder_attention_heads
          a     =				decoder_ffn_dim
          a     =				decoder_layers
          a     =				decoder_attention_heads
          a     =				dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				activation_function
          a     =				init_std
          a     =				init_xavier_std
          a     =				encoder_layerdrop
          a     =				decoder_layerdrop
          a     =				encoder_layers
          a     =				auxiliary_loss
          a     =				position_embedding_type
          a     =				backbone
          a     =				use_pretrained_backbone
          a     =				dilation
          # Hungarian matcher
          a     =				class_cost
          a     =				bbox_cost
          a     =				giou_cost
          # Loss coefficients
          a     =				mask_loss_coefficient
          a     =				dice_loss_coefficient
          a     =				bbox_loss_coefficient
          a     =				giou_loss_coefficient
          a     =				eos_coefficient
          super().__init__(is_encoder_decoder=A  ,		**A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

