






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''image_processor''', '''tokenizer''']
  _lowerCamelCase    =  '''LayoutLMv3ImageProcessor'''
  _lowerCamelCase    =  ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')


  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			Any=None       ,				**lowerCamelCase		:			Dict							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead."""       ,				lowerCamelCase       ,				)
                _UpperCAmelCase							=    kwargs.pop("""feature_extractor"""							)

         _UpperCAmelCase							=    image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""							)
         if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""							)

         super().__init__(lowerCamelCase       ,				lowerCamelCase							)


  def __call__(      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,				lowerCamelCase		:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,				lowerCamelCase		:			Union[List[List[int]], List[List[List[int]]]] = None       ,				lowerCamelCase		:			Optional[Union[List[int], List[List[int]]]] = None       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Union[bool, str, PaddingStrategy] = False       ,				lowerCamelCase		:			Union[bool, str, TruncationStrategy] = None       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			int = 0       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				**lowerCamelCase		:			List[str]       ,				)						->	BatchEncoding:


         """simple docstring"""
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
                raise ValueError(
                    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""							)

         if self.image_processor.apply_ocr and (word_labels is not None):
                raise ValueError(
                    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""							)

         # first, apply the image processor
         _UpperCAmelCase							=    self.image_processor(images=lowerCamelCase       ,				return_tensors=lowerCamelCase							)

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
                _UpperCAmelCase							=    features["""words"""]

         _UpperCAmelCase							=    self.tokenizer(
             text=text if text is not None else features["""words"""]       ,				text_pair=text_pair if text_pair is not None else None       ,				boxes=boxes if boxes is not None else features["""boxes"""]       ,				word_labels=lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase       ,				max_length=lowerCamelCase       ,				stride=lowerCamelCase       ,				pad_to_multiple_of=lowerCamelCase       ,				return_token_type_ids=lowerCamelCase       ,				return_attention_mask=lowerCamelCase       ,				return_overflowing_tokens=lowerCamelCase       ,				return_special_tokens_mask=lowerCamelCase       ,				return_offsets_mapping=lowerCamelCase       ,				return_length=lowerCamelCase       ,				verbose=lowerCamelCase       ,				return_tensors=lowerCamelCase       ,				**lowerCamelCase       ,				)

         # add pixel values
         _UpperCAmelCase							=    features.pop("""pixel_values"""							)
         if return_overflowing_tokens is True:
                _UpperCAmelCase							=    self.get_overflowing_images(lowerCamelCase       ,				encoded_inputs["""overflow_to_sample_mapping"""]							)
         _UpperCAmelCase							=    images

         return encoded_inputs


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         _UpperCAmelCase							=    []
         for sample_idx in overflow_to_sample_mapping:
                images_with_overflow.append(images[sample_idx]							)

         if len(lowerCamelCase							) != len(lowerCamelCase							):
                raise ValueError(
                    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                    f""" {len(lowerCamelCase							)} and {len(lowerCamelCase							)}"""							)

         return images_with_overflow


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				*lowerCamelCase		:			Tuple       ,				**lowerCamelCase		:			Union[str, Any]							)						->	int:


         """simple docstring"""
         return self.tokenizer.batch_decode(*lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				*lowerCamelCase		:			Dict       ,				**lowerCamelCase		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         return self.tokenizer.decode(*lowerCamelCase       ,				**lowerCamelCase							)


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         return ["input_ids", "bbox", "attention_mask", "pixel_values"]


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,				lowerCamelCase       ,				)
         return self.image_processor_class





  @property
  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,				lowerCamelCase       ,				)
         return self.image_processor






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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[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(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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[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(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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# 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(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

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

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 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(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# 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(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# 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(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# 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(UpperCAmelCase_							)					,				)

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		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(UpperCAmelCase_							)							)

	snake_case__	       =   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							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# 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(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# 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(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   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__":
			a__					:			Optional[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.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



