





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 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__   )				->			int:




				"""simple docstring"""






				with open(_snake_case   ) as metadata_file:
								snake_case   :					List[Any]    						=				json.load(_snake_case   )
				snake_case   :					Optional[int]    						=				LukeConfig(use_entity_aware_attention=_snake_case		,     **metadata['''model_config''']   )

				# Load in the weights from the checkpoint_path
				snake_case   :					str    						=				torch.load(_snake_case		,     map_location='''cpu'''   )

				# Load the entity vocab file
				snake_case   :					List[Any]    						=				load_entity_vocab(_snake_case   )

				snake_case   :					Tuple    						=				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']   )

				# Add special tokens to the token vocabulary for downstream tasks
				snake_case   :					Optional[Any]    						=				AddedToken('''<ent>'''		,     lstrip=_snake_case		,     rstrip=_snake_case   )
				snake_case   :					str    						=				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   )

				snake_case   :					Optional[int]    						=				LukeTokenizer.from_pretrained(_snake_case   )

				# Initialize the embeddings of the special tokens
				snake_case   :					int    						=				state_dict['''embeddings.word_embeddings.weight''']
				snake_case   :					Dict    						=				word_emb[tokenizer.convert_tokens_to_ids(['''@''']   )[0]].unsqueeze(0   )
				snake_case   :					Dict    						=				word_emb[tokenizer.convert_tokens_to_ids(['''#''']   )[0]].unsqueeze(0   )
				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"]:
												snake_case   :					Optional[int]    						=				F"encoder.layer.{layer_index}.attention.self."
												snake_case   :					Optional[Any]    						=				state_dict[prefix + matrix_name]
												snake_case   :					Union[str, Any]    						=				state_dict[prefix + matrix_name]
												snake_case   :					int    						=				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				snake_case   :					Optional[Any]    						=				state_dict['''entity_embeddings.entity_embeddings.weight''']
				snake_case   :					List[str]    						=				entity_emb[entity_vocab['''[MASK]''']]

				snake_case   :					List[str]    						=				LukeModel(config=_snake_case   ).eval()

				snake_case						,						snake_case   :					List[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
				snake_case   :					Optional[int]    						=				LukeTokenizer.from_pretrained(_snake_case		,     task='''entity_classification'''   )

				snake_case   :					Optional[int]    						=				(
				    '''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 .'''
				)
				snake_case   :					Optional[int]    						=				(39, 42)
				snake_case   :					int    						=				tokenizer(_snake_case		,     entity_spans=[span]		,     add_prefix_space=_snake_case		,     return_tensors='''pt'''   )

				snake_case   :					str    						=				model(**_snake_case   )

				# Verify word hidden states
				if model_size == "large":
								snake_case   :					str    						=				torch.Size((1, 42, 1_024)   )
								snake_case   :					int    						=				torch.tensor(
								    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]   )
				else:  # base
								snake_case   :					str    						=				torch.Size((1, 42, 768)   )
								snake_case   :					Optional[int]    						=				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":
								snake_case   :					str    						=				torch.Size((1, 1, 1_024)   )
								snake_case   :					Optional[int]    						=				torch.tensor([[0.0466, -0.0106, -0.0179]]   )
				else:  # base
								snake_case   :					Optional[Any]    						=				torch.Size((1, 1, 768)   )
								snake_case   :					List[str]    						=				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 							a_	(    __magic_name__   )				->			Tuple:




				"""simple docstring"""






				snake_case   :					int    						=				{}
				with open(_snake_case		,     '''r'''		,     encoding='''utf-8'''   ) as f:
								for index, line in enumerate(_snake_case   ):
												snake_case						,						snake_case   :					List[str]    						=				line.rstrip().split('''\t'''   )
												snake_case   :					List[Any]    						=				index

				return entity_vocab


if __name__ == "__main__":
			_a     :    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.'
			)
			_a     :    Optional[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,
			)





'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE__          =  {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 					snake_case			(nn.Module ):



       def __init__(					self  ,UpperCAmelCase_   )	->				Dict:
           super().__init__()
           lowercase__									=    torchvision.models.resnetaaa(pretrained=UpperCAmelCase_   )
           lowercase__									=    list(model.children()   )[:-2]
           lowercase__									=    nn.Sequential(*UpperCAmelCase_   )
           lowercase__									=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    [json.loads(UpperCAmelCase_   ) for l in open(UpperCAmelCase_   )]
           lowercase__									=    os.path.dirname(UpperCAmelCase_   )
           lowercase__									=    tokenizer
           lowercase__									=    labels
           lowercase__									=    len(UpperCAmelCase_   )
           lowercase__									=    max_seq_length

           lowercase__									=    transforms



       def __len__(					self   )	->				Optional[Any]:
           return len(self.data   )



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           lowercase__							,							lowercase__							,							lowercase__									=    sentence[0], sentence[1:-1], sentence[-1]
           lowercase__									=    sentence[: self.max_seq_length]

           lowercase__									=    torch.zeros(self.n_classes   )
           lowercase__									=    1

           lowercase__									=    Image.open(os.path.join(self.data_dir  ,self.data[index]["img"]   )   ).convert("RGB"   )
           lowercase__									=    self.transforms(UpperCAmelCase_   )

           return {
               "image_start_token": start_token,
               "image_end_token": end_token,
               "sentence": sentence,
               "image": image,
               "label": label,
           }







       def 				_a   (					self   )	->				str:
           lowercase__									=    Counter()
           for row in self.data:
               label_freqs.update(row["label"]   )
           return label_freqs





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(_snake_case  ,_snake_case							)							):
        lowercase__									=    input_row["sentence"]
        lowercase__									=    1

    lowercase__									=    torch.stack([row["image"] for row in batch]							)
    lowercase__									=    torch.stack([row["label"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_start_token"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_end_token"] for row in batch]							)

    return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [
        "Crime",
        "Drama",
        "Thriller",
        "Action",
        "Comedy",
        "Romance",
        "Documentary",
        "Short",
        "Mystery",
        "History",
        "Family",
        "Adventure",
        "Fantasy",
        "Sci-Fi",
        "Western",
        "Horror",
        "Sport",
        "War",
        "Music",
        "Musical",
        "Animation",
        "Biography",
        "Film-Noir",
    ]




def 							lowerCamelCase	(					):





    '''simple docstring'''


    return transforms.Compose(
        [
            transforms.Resize(256							),
            transforms.CenterCrop(224							),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]  ,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]  ,),
        ]							)




