

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     (				a_				:					Union[str, Any]					,a_				:					Optional[Any]					,a_				:					Dict=None				)				->	List[Any]:




					'''simple docstring'''







					assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
					__magic_name__							=    nn.Parameter(a_				)
					if bias is not None:
										assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
										__magic_name__							=    nn.Parameter(a_				)




def 					_lowercase     (				a_				:					Dict					,a_				:					Dict					,a_				:					Dict				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    np.asarray(weights[0]				)
					__magic_name__							=    np.asarray(weights[1]				)
					__magic_name__							=    np.asarray(weights[2]				)

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




def 					_lowercase     (				a_				:					List[Any]					,a_				:					int					,a_				:					int				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    np.asarray(weights[0]				)
					__magic_name__							=    np.asarray(weights[1]				)
					__magic_name__							=    np.asarray(weights[2]				)
					__magic_name__							=    np.asarray(weights[3]				)

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




def 					_lowercase     (				a_				:					Any					,a_				:					Dict					,a_				:					Optional[int]				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    weights[0][0][0]
					__magic_name__							=    np.asarray(layer_norm_a[0]				)
					__magic_name__							=    np.asarray(layer_norm_a[1]				)
					set_param(
					    torch_block.attention.layer_norm					,torch.tensor(a_				)					,torch.tensor(a_				)					,)

					# lsh weights + output
					__magic_name__							=    weights[0][1]
					if len(a_				) < 4:
										set_layer_weights_in_torch_lsh(a_					,torch_block.attention					,a_				)
					else:
										set_layer_weights_in_torch_local(a_					,torch_block.attention					,a_				)

					# intermediate weighs
					__magic_name__							=    weights[2][0][1][2]

					# Chunked Feed Forward
					if len(a_				) == 4:
										__magic_name__							=    intermediate_weights[2]

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

					# intermediate dense
					__magic_name__							=    np.asarray(intermediate_weights[1][0]				)
					__magic_name__							=    np.asarray(intermediate_weights[1][1]				)
					set_param(
					    torch_block.feed_forward.dense.dense					,torch.tensor(a_				).transpose(0					,1				).contiguous()					,torch.tensor(a_				)					,)

					# intermediate out
					__magic_name__							=    np.asarray(intermediate_weights[4][0]				)
					__magic_name__							=    np.asarray(intermediate_weights[4][1]				)
					set_param(
					    torch_block.feed_forward.output.dense					,torch.tensor(a_				).transpose(0					,1				).contiguous()					,torch.tensor(a_				)					,)




def 					_lowercase     (				a_				:					List[Any]					,a_				:					Tuple					,a_				:					Dict				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    torch_model.reformer

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

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

					__magic_name__							=    weights[5]
					assert len(torch_model_reformer.encoder.layers				) * 4 == len(
					    a_				), "HF and trax model do not have the same number of layers"
					for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers				):
										__magic_name__							=    trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
										set_block_weights_in_torch(a_					,a_					,a_				)

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

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




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Any					,a_				:					Any				)				->	Tuple:




					'''simple docstring'''







					__magic_name__							=    ReformerConfig.from_json_file(a_				)
					print(F'Building PyTorch model from configuration: {config}'				)
					__magic_name__							=    ReformerModelWithLMHead(a_				)

					with open(a_					,'rb'				) as f:
										__magic_name__							=    pickle.load(a_				)['weights']

					set_model_weights_in_torch(a_					,a_					,config.hidden_size				)

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


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)







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType


A__						=							logging.get_logger(__name__)

A__						=							{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "layoutlmv3"







	def __init__(   self: Tuple  , __UpperCamelCase: List[Any]=5_02_65  , __UpperCamelCase: Union[str, Any]=7_68  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=12  , __UpperCamelCase: List[Any]=30_72  , __UpperCamelCase: Union[str, Any]="gelu"  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: List[Any]=5_12  , __UpperCamelCase: int=2  , __UpperCamelCase: Union[str, Any]=0.02  , __UpperCamelCase: Dict=1E-5  , __UpperCamelCase: int=1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Tuple=2  , __UpperCamelCase: Any=10_24  , __UpperCamelCase: Union[str, Any]=1_28  , __UpperCamelCase: Dict=1_28  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=1_28  , __UpperCamelCase: List[str]=64  , __UpperCamelCase: Union[str, Any]=2_56  , __UpperCamelCase: int=True  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Union[str, Any]=2_24  , __UpperCamelCase: Tuple=3  , __UpperCamelCase: Any=16  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





						super().__init__(
						    vocab_size=__UpperCamelCase  , hidden_size=__UpperCamelCase  , num_hidden_layers=__UpperCamelCase  , num_attention_heads=__UpperCamelCase  , intermediate_size=__UpperCamelCase  , hidden_act=__UpperCamelCase  , hidden_dropout_prob=__UpperCamelCase  , attention_probs_dropout_prob=__UpperCamelCase  , max_position_embeddings=__UpperCamelCase  , type_vocab_size=__UpperCamelCase  , initializer_range=__UpperCamelCase  , layer_norm_eps=__UpperCamelCase  , pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase  , )
						__magic_name__							=    max_ad_position_embeddings
						__magic_name__							=    coordinate_size
						__magic_name__							=    shape_size
						__magic_name__							=    has_relative_attention_bias
						__magic_name__							=    rel_pos_bins
						__magic_name__							=    max_rel_pos
						__magic_name__							=    has_spatial_attention_bias
						__magic_name__							=    rel_ad_pos_bins
						__magic_name__							=    max_rel_ad_pos
						__magic_name__							=    text_embed
						__magic_name__							=    visual_embed
						__magic_name__							=    input_size
						__magic_name__							=    num_channels
						__magic_name__							=    patch_size
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  version.parse("1.12"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]      )
						else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return 1E-5







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "ProcessorMixin"  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional["TensorType"] = None  , __UpperCamelCase: int = 3  , __UpperCamelCase: int = 40  , __UpperCamelCase: int = 40  , ):

						'''simple docstring'''





						setattr(processor.image_processor  , 'apply_ocr'  , __UpperCamelCase      )

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_batch  , num_token_to_add=0      )
						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__magic_name__							=    processor.tokenizer.num_special_tokens_to_add(__UpperCamelCase      )
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_sequence  , num_token_to_add=__UpperCamelCase      )
						# Generate dummy inputs according to compute batch and sequence
						__magic_name__							=    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						__magic_name__							=    [[[48, 84, 73, 1_28]]] * batch_size

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
						__magic_name__							=    self._generate_dummy_images(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						__magic_name__							=    dict(
						    processor(
						        __UpperCamelCase  , text=__UpperCamelCase  , boxes=__UpperCamelCase  , return_tensors=__UpperCamelCase  , )      )

						return inputs




