
"""simple docstring"""




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							# Load configuration defined in the metadata file
							with open(_snake_case						) as metadata_file:
														SCREAMING_SNAKE_CASE__						:       Dict           =   json.load(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   LukeConfig(use_entity_aware_attention=_snake_case   ,**metadata["""model_config"""]						)

							# Load in the weights from the checkpoint_path
							SCREAMING_SNAKE_CASE__						:       int           =   torch.load(_snake_case   ,map_location="""cpu"""						)

							# Load the entity vocab file
							SCREAMING_SNAKE_CASE__						:       Dict           =   load_entity_vocab(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]						)

							# Add special tokens to the token vocabulary for downstream tasks
							SCREAMING_SNAKE_CASE__						:       int           =   AddedToken("""<ent>"""   ,lstrip=_snake_case   ,rstrip=_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   AddedToken("""<ent2>"""   ,lstrip=_snake_case   ,rstrip=_snake_case						)
							tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}						)
							config.vocab_size += 2

							print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''						)
							tokenizer.save_pretrained(_snake_case						)
							with open(os.path.join(_snake_case   ,LukeTokenizer.vocab_files_names["""entity_vocab_file"""]						)   ,"""w"""						) as f:
														json.dump(_snake_case   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       int           =   LukeTokenizer.from_pretrained(_snake_case						)

							# Initialize the embeddings of the special tokens
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   state_dict["""embeddings.word_embeddings.weight"""]
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   word_emb[tokenizer.convert_tokens_to_ids(["""@"""]						)[0]].unsqueeze(0						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   word_emb[tokenizer.convert_tokens_to_ids(["""#"""]						)[0]].unsqueeze(0						)
							SCREAMING_SNAKE_CASE__						:       str           =   torch.cat([word_emb, ent_emb, enta_emb]						)

							# Initialize the query layers of the entity-aware self-attention mechanism
							for layer_index in range(config.num_hidden_layers						):
														for matrix_name in ["query.weight", "query.bias"]:
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   f'''encoder.layer.{layer_index}.attention.self.'''
																					SCREAMING_SNAKE_CASE__						:       int           =   state_dict[prefix + matrix_name]
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   state_dict[prefix + matrix_name]
																					SCREAMING_SNAKE_CASE__						:       List[Any]           =   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
							SCREAMING_SNAKE_CASE__						:       Any           =   state_dict["""entity_embeddings.entity_embeddings.weight"""]
							SCREAMING_SNAKE_CASE__						:       Dict           =   entity_emb[entity_vocab["""[MASK]"""]]

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   LukeModel(config=_snake_case						).eval()

							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   model.load_state_dict(_snake_case   ,strict=_snake_case						)
							if not (len(_snake_case						) == 1 and missing_keys[0] == "embeddings.position_ids"):
														raise ValueError(f'''Missing keys {', '.join(_snake_case						)}. Expected only missing embeddings.position_ids'''						)
							if not (all(key.startswith("""entity_predictions"""						) or key.startswith("""lm_head"""						) for key in unexpected_keys						)):
														raise ValueError(
														    """Unexpected keys"""
														    f''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'						) or key.startswith('lm_head'						))]						)}'''						)

							# Check outputs
							SCREAMING_SNAKE_CASE__						:       Dict           =   LukeTokenizer.from_pretrained(_snake_case   ,task="""entity_classification"""						)

							SCREAMING_SNAKE_CASE__						:       str           =   (
							    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
							    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
							)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (39, 42)
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer(_snake_case   ,entity_spans=[span]   ,add_prefix_space=_snake_case   ,return_tensors="""pt"""						)

							SCREAMING_SNAKE_CASE__						:       Any           =   model(**_snake_case						)

							# Verify word hidden states
							if model_size == "large":
														SCREAMING_SNAKE_CASE__						:       Any           =   torch.Size((1, 42, 1_024)						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.tensor(
														    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]						)
							else:  # base
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.Size((1, 42, 768)						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]						)

							if not (outputs.last_hidden_state.shape == expected_shape):
														raise ValueError(
														    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''						)
							if not torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,_snake_case   ,atol=1E-4						):
														raise ValueError

							# Verify entity hidden states
							if model_size == "large":
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.Size((1, 1, 1_024)						)
														SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor([[0.0466, -0.0106, -0.0179]]						)
							else:  # base
														SCREAMING_SNAKE_CASE__						:       Dict           =   torch.Size((1, 1, 768)						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.tensor([[0.1457, 0.1044, 0.0174]]						)

							if not (outputs.entity_last_hidden_state.shape != expected_shape):
														raise ValueError(
														    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
														    f''' {expected_shape}'''						)
							if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]   ,_snake_case   ,atol=1E-4						):
														raise ValueError

							# Finally, save our PyTorch model and tokenizer
							print("""Saving PyTorch model to {}""".format(_snake_case						)						)
							model.save_pretrained(_snake_case						)

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       str           =   {}
							with open(_snake_case   ,"""r"""   ,encoding="""utf-8"""						) as f:
														for index, line in enumerate(_snake_case						):
																					SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   line.rstrip().split("""\t"""						)
																					SCREAMING_SNAKE_CASE__						:       Dict           =   index

							return entity_vocab


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[Any]    							= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
					parser.add_argument(
					    '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
					)
					parser.add_argument(
					    '--entity_vocab_path',
					    default=None,
					    type=str,
					    help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
					)
					parser.add_argument(
					    '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
					)
					UpperCAmelCase__      :    Any    							= parser.parse_args()
					convert_luke_checkpoint(
					    args.checkpoint_path,
					    args.metadata_path,
					    args.entity_vocab_path,
					    args.pytorch_dump_folder_path,
					    args.model_size,
					)





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
