




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 				A__ (snake_case   :		Dict		,	snake_case   :		List[Any]		,	snake_case   :		Dict		,	snake_case   :		List[Any]		,	snake_case   :		List[Any]				)     ->						List[str]:
					# Load configuration defined in the metadata file
					with open(snake_case				) as metadata_file:
										__UpperCamelCase					:	Optional[int]             =      json.load(snake_case				)
					__UpperCamelCase					:	List[str]             =      LukeConfig(use_entity_aware_attention=snake_case		,	**metadata["""model_config"""]				)

					# Load in the weights from the checkpoint_path
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case		,	map_location="""cpu"""				)["""module"""]

					# Load the entity vocab file
					__UpperCamelCase					:	Union[str, Any]             =      load_original_entity_vocab(snake_case				)
					# add an entry for [MASK2]
					__UpperCamelCase					:	Any             =      max(entity_vocab.values()				) + 1
					config.entity_vocab_size += 1

					__UpperCamelCase					:	Any             =      XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]				)

					# Add special tokens to the token vocabulary for downstream tasks
					__UpperCamelCase					:	Optional[int]             =      AddedToken("""<ent>"""		,	lstrip=snake_case		,	rstrip=snake_case				)
					__UpperCamelCase					:	List[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		,	"""tokenizer_config.json"""				)		,	"""r"""				) as f:
										__UpperCamelCase					:	int             =      json.load(snake_case				)
					__UpperCamelCase					:	Optional[Any]             =      """MLukeTokenizer"""
					with open(os.path.join(snake_case		,	"""tokenizer_config.json"""				)		,	"""w"""				) as f:
										json.dump(snake_case		,	snake_case				)

					with open(os.path.join(snake_case		,	MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]				)		,	"""w"""				) as f:
										json.dump(snake_case		,	snake_case				)

					__UpperCamelCase					:	List[str]             =      MLukeTokenizer.from_pretrained(snake_case				)

					# Initialize the embeddings of the special tokens
					__UpperCamelCase					:	Tuple             =      tokenizer.convert_tokens_to_ids(["""@"""]				)[0]
					__UpperCamelCase					:	Any             =      tokenizer.convert_tokens_to_ids(["""#"""]				)[0]

					__UpperCamelCase					:	str             =      state_dict["""embeddings.word_embeddings.weight"""]
					__UpperCamelCase					:	List[str]             =      word_emb[ent_init_index].unsqueeze(0				)
					__UpperCamelCase					:	List[Any]             =      word_emb[enta_init_index].unsqueeze(0				)
					__UpperCamelCase					:	int             =      torch.cat([word_emb, ent_emb, enta_emb]				)
					# add special tokens for 'entity_predictions.bias'
					for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
										__UpperCamelCase					:	List[Any]             =      state_dict[bias_name]
										__UpperCamelCase					:	Optional[int]             =      decoder_bias[ent_init_index].unsqueeze(0				)
										__UpperCamelCase					:	Any             =      decoder_bias[enta_init_index].unsqueeze(0				)
										__UpperCamelCase					:	Dict             =      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]				)

					# 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"]:
															__UpperCamelCase					:	Any             =      F'''encoder.layer.{layer_index}.attention.self.'''
															__UpperCamelCase					:	Union[str, Any]             =      state_dict[prefix + matrix_name]
															__UpperCamelCase					:	Any             =      state_dict[prefix + matrix_name]
															__UpperCamelCase					:	Optional[int]             =      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					__UpperCamelCase					:	List[str]             =      state_dict["""entity_embeddings.entity_embeddings.weight"""]
					__UpperCamelCase					:	Union[str, Any]             =      entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0				)
					__UpperCamelCase					:	str             =      torch.cat([entity_emb, entity_mask_emb]				)
					# add [MASK2] for 'entity_predictions.bias'
					__UpperCamelCase					:	Optional[int]             =      state_dict["""entity_predictions.bias"""]
					__UpperCamelCase					:	Optional[Any]             =      entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0				)
					__UpperCamelCase					:	Any             =      torch.cat([entity_prediction_bias, entity_mask_bias]				)

					__UpperCamelCase					:	Any             =      LukeForMaskedLM(config=snake_case				).eval()

					state_dict.pop("""entity_predictions.decoder.weight"""				)
					state_dict.pop("""lm_head.decoder.weight"""				)
					state_dict.pop("""lm_head.decoder.bias"""				)
					__UpperCamelCase					:	str             =      OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith("""lm_head"""				) or key.startswith("""entity_predictions"""				)):
															__UpperCamelCase					:	Union[str, Any]             =      state_dict[key]
										else:
															__UpperCamelCase					:	Union[str, Any]             =      state_dict[key]

					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[Any]             =      model.load_state_dict(snake_case		,	strict=snake_case				)

					if set(snake_case				) != {"luke.embeddings.position_ids"}:
										raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''				)
					if set(snake_case				) != {
					    "lm_head.decoder.weight",
					    "lm_head.decoder.bias",
					    "entity_predictions.decoder.weight",
					}:
										raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''				)

					model.tie_weights()
					assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
					assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

					# Check outputs
					__UpperCamelCase					:	Tuple             =      MLukeTokenizer.from_pretrained(snake_case		,	task="""entity_classification"""				)

					__UpperCamelCase					:	Union[str, Any]             =      """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
					__UpperCamelCase					:	Any             =      (0, 9)
					__UpperCamelCase					:	List[Any]             =      tokenizer(snake_case		,	entity_spans=[span]		,	return_tensors="""pt"""				)

					__UpperCamelCase					:	Tuple             =      model(**snake_case				)

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										__UpperCamelCase					:	Union[str, Any]             =      torch.Size((1, 33, 7_68)				)
										__UpperCamelCase					:	List[str]             =      torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]				)

					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":
										raise NotImplementedError
					else:  # base
										__UpperCamelCase					:	int             =      torch.Size((1, 1, 7_68)				)
										__UpperCamelCase					:	List[Any]             =      torch.tensor([[-0.1482, 0.0609, 0.0322]]				)

					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

					# Verify masked word/entity prediction
					__UpperCamelCase					:	Any             =      MLukeTokenizer.from_pretrained(snake_case				)
					__UpperCamelCase					:	Tuple             =      """Tokyo is the capital of <mask>."""
					__UpperCamelCase					:	str             =      (24, 30)
					__UpperCamelCase					:	int             =      tokenizer(snake_case		,	entity_spans=[span]		,	return_tensors="""pt"""				)

					__UpperCamelCase					:	int             =      model(**snake_case				)

					__UpperCamelCase					:	str             =      encoding["""input_ids"""][0].tolist()
					__UpperCamelCase					:	Any             =      input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""				)				)
					__UpperCamelCase					:	List[str]             =      outputs.logits[0][mask_position_id].argmax(dim=-1				)
					assert "Japan" == tokenizer.decode(snake_case				)

					__UpperCamelCase					:	Dict             =      outputs.entity_logits[0][0].argmax().item()
					__UpperCamelCase					:	int             =      [
					    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
					]
					assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""				)][0] == "en:Japan"

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





def 				A__ (snake_case   :		Tuple				)     ->						Optional[int]:
					__UpperCamelCase					:	Union[str, Any]             =      ["""[MASK]""", """[PAD]""", """[UNK]"""]

					__UpperCamelCase					:	List[str]             =      [json.loads(snake_case				) for line in open(snake_case				)]

					__UpperCamelCase					:	Optional[int]             =      {}
					for entry in data:
										__UpperCamelCase					:	Tuple             =      entry["""id"""]
										for entity_name, language in entry["entities"]:
															if entity_name in SPECIAL_TOKENS:
																				__UpperCamelCase					:	Dict             =      entity_id
																				break
															__UpperCamelCase					:	List[str]             =      F'''{language}:{entity_name}'''
															__UpperCamelCase					:	int             =      entity_id
					return new_mapping


if __name__ == "__main__":
	a__		     =     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__		     =     parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)












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


a__		     =     {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    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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 				A__ ()     ->						Optional[Any]:
					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 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






