


'''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 __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: Union[str, Any]=None				)      ->     Optional[int]:







     '''simple docstring'''






     assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
     _a						=		nn.Parameter(__lowerCamelCase				)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
          _a						=		nn.Parameter(__lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: List[Any]				)      ->     Union[str, Any]:







     '''simple docstring'''






     _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(__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 __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: Optional[int]				)      ->     str:







     '''simple docstring'''






     _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(__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 __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: List[Any]				)      ->     List[Any]:







     '''simple docstring'''






     _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(__lowerCamelCase				)      ,  torch.tensor(__lowerCamelCase				)      ,  )

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

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

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

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






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: Union[str, Any]				)      ->     Dict:







     '''simple docstring'''






     _a						=		torch_model.reformer

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

     if isinstance(weights[3]      ,  __lowerCamelCase				):
          _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(__lowerCamelCase				)				)

     _a						=		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				):
          _a						=		trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)

     # 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(__lowerCamelCase				)      ,  torch.tensor(__lowerCamelCase				)      ,  )

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






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     Dict:







     '''simple docstring'''






     _a						=		ReformerConfig.from_json_file(__lowerCamelCase				)
     print(F"Building PyTorch model from configuration: {config}"				)
     _a						=		ReformerModelWithLMHead(__lowerCamelCase				)

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





'''simple docstring'''

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					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,							__UpperCamelCase     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		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__(__UpperCamelCase     ,							__UpperCamelCase		)






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    BatchEncoding:
          # 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."		)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

          # first, apply the image processor
          _a						=		self.image_processor(images=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)

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

          _a						=		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=__UpperCamelCase     ,							add_special_tokens=__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							stride=__UpperCamelCase     ,							pad_to_multiple_of=__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							return_overflowing_tokens=__UpperCamelCase     ,							return_special_tokens_mask=__UpperCamelCase     ,							return_offsets_mapping=__UpperCamelCase     ,							return_length=__UpperCamelCase     ,							verbose=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

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

          return encoded_inputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a						=		[]
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]		)

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

          return images_with_overflow






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def  a_					(			self		)       ->    Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor_class






     @property
     def  a_					(			self		)       ->    int:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor

