
'''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 A (__lowerCamelCase :str					,      __lowerCamelCase :Any					,      __lowerCamelCase :str=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 A (__lowerCamelCase :Dict					,      __lowerCamelCase :Optional[int]					,      __lowerCamelCase :Tuple						):
						# 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 A (__lowerCamelCase :Dict					,      __lowerCamelCase :List[str]					,      __lowerCamelCase :str						):
						# 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 A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Any						):
						# 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 A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict						):
						# 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 A (__lowerCamelCase :List[str]					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :Tuple						):
						# 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__":
						_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 ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												warnings.warn(
												    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use DonutImageProcessor instead."""       ,  _lowercase       ,  )
												super().__init__(*_lowercase       ,  **_lowercase )
