






"""simple docstring"""



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 		lowercase       (							__snake_case							:					str						,   __snake_case							:					str						,   __snake_case							:					Union[str, Any]						,   __snake_case							:					Dict						,   __snake_case							:					List[str]      ):


		with open(SCREAMING_SNAKE_CASE__      ) as metadata_file:
				lowercase_					:					str							=  json.load(SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Any							=  LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__						,   **metadata['''model_config''']      )

		# Load in the weights from the checkpoint_path
		lowercase_					:					int							=  torch.load(SCREAMING_SNAKE_CASE__						,   map_location='''cpu'''      )['''module''']

		# Load the entity vocab file
		lowercase_					:					Optional[Any]							=  load_original_entity_vocab(SCREAMING_SNAKE_CASE__      )
		# add an entry for [MASK2]
		lowercase_					:					Optional[int]							=  max(entity_vocab.values()      ) + 1
		config.entity_vocab_size += 1

		lowercase_					:					Optional[int]							=  XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

		# Add special tokens to the token vocabulary for downstream tasks
		lowercase_					:					int							=  AddedToken('''<ent>'''						,   lstrip=SCREAMING_SNAKE_CASE__						,   rstrip=SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Optional[Any]							=  AddedToken('''<ent2>'''						,   lstrip=SCREAMING_SNAKE_CASE__						,   rstrip=SCREAMING_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(SCREAMING_SNAKE_CASE__      )
		with open(os.path.join(SCREAMING_SNAKE_CASE__						,   '''tokenizer_config.json'''      )						,   '''r'''      ) as f:
				lowercase_					:					Optional[Any]							=  json.load(SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Optional[Any]							=  '''MLukeTokenizer'''
		with open(os.path.join(SCREAMING_SNAKE_CASE__						,   '''tokenizer_config.json'''      )						,   '''w'''      ) as f:
				json.dump(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__      )

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

		lowercase_					:					int							=  MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      )

		# Initialize the embeddings of the special tokens
		lowercase_					:					int							=  tokenizer.convert_tokens_to_ids(['''@''']      )[0]
		lowercase_					:					List[Any]							=  tokenizer.convert_tokens_to_ids(['''#''']      )[0]

		lowercase_					:					Optional[int]							=  state_dict['''embeddings.word_embeddings.weight''']
		lowercase_					:					List[str]							=  word_emb[ent_init_index].unsqueeze(0      )
		lowercase_					:					Optional[int]							=  word_emb[enta_init_index].unsqueeze(0      )
		lowercase_					:					Any							=  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"]:
				lowercase_					:					Union[str, Any]							=  state_dict[bias_name]
				lowercase_					:					Dict							=  decoder_bias[ent_init_index].unsqueeze(0      )
				lowercase_					:					List[Any]							=  decoder_bias[enta_init_index].unsqueeze(0      )
				lowercase_					:					int							=  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"]:
						lowercase_					:					List[Any]							=  F'''encoder.layer.{layer_index}.attention.self.'''
						lowercase_					:					int							=  state_dict[prefix + matrix_name]
						lowercase_					:					Optional[Any]							=  state_dict[prefix + matrix_name]
						lowercase_					:					Optional[int]							=  state_dict[prefix + matrix_name]

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

		lowercase_					:					Union[str, Any]							=  LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__      ).eval()

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

		lowercase_					:					str							=  model.load_state_dict(SCREAMING_SNAKE_CASE__						,   strict=SCREAMING_SNAKE_CASE__      )

		if set(SCREAMING_SNAKE_CASE__      ) != {"luke.embeddings.position_ids"}:
				raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''      )
		if set(SCREAMING_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
		lowercase_					:					List[Any]							=  MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						,   task='''entity_classification'''      )

		lowercase_					:					Tuple							=  '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
		lowercase_					:					Dict							=  (0, 9)
		lowercase_					:					Optional[int]							=  tokenizer(SCREAMING_SNAKE_CASE__						,   entity_spans=[span]						,   return_tensors='''pt'''      )

		lowercase_					:					Tuple							=  model(**SCREAMING_SNAKE_CASE__      )

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowercase_					:					Optional[Any]							=  torch.Size((1, 3_3, 7_6_8)      )
				lowercase_					:					Tuple							=  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]						,   SCREAMING_SNAKE_CASE__						,   atol=1e-4      ):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowercase_					:					int							=  torch.Size((1, 1, 7_6_8)      )
				lowercase_					:					int							=  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]						,   SCREAMING_SNAKE_CASE__						,   atol=1e-4      ):
				raise ValueError

		# Verify masked word/entity prediction
		lowercase_					:					Optional[Any]							=  MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      )
		lowercase_					:					List[str]							=  '''Tokyo is the capital of <mask>.'''
		lowercase_					:					Union[str, Any]							=  (2_4, 3_0)
		lowercase_					:					str							=  tokenizer(SCREAMING_SNAKE_CASE__						,   entity_spans=[span]						,   return_tensors='''pt'''      )

		lowercase_					:					List[Any]							=  model(**SCREAMING_SNAKE_CASE__      )

		lowercase_					:					Optional[int]							=  encoding['''input_ids'''][0].tolist()
		lowercase_					:					int							=  input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''      )      )
		lowercase_					:					List[str]							=  outputs.logits[0][mask_position_id].argmax(dim=-1      )
		assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__      )

		lowercase_					:					List[Any]							=  outputs.entity_logits[0][0].argmax().item()
		lowercase_					:					List[Any]							=  [
		    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(SCREAMING_SNAKE_CASE__      )      )
		model.save_pretrained(SCREAMING_SNAKE_CASE__      )



def 		lowercase       (							__snake_case							:					Dict      ):
		lowercase_					:					int							=  ['''[MASK]''', '''[PAD]''', '''[UNK]''']

		lowercase_					:					str							=  [json.loads(SCREAMING_SNAKE_CASE__      ) for line in open(SCREAMING_SNAKE_CASE__      )]

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


if __name__ == "__main__":
						__A						:			int    							=						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						:			Union[str, 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'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""yolos"""



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Optional[Any]=768  ,	UpperCamelCase__				:			Any=12  ,	UpperCamelCase__				:			Optional[int]=12  ,	UpperCamelCase__				:			Union[str, Any]=3072  ,	UpperCamelCase__				:			str="gelu"  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.02  ,	UpperCamelCase__				:			List[Any]=1e-1_2  ,	UpperCamelCase__				:			List[Any]=[512, 864]  ,	UpperCamelCase__				:			int=16  ,	UpperCamelCase__				:			Union[str, Any]=3  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			int=100  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			int=False  ,	UpperCamelCase__				:			Dict=1  ,	UpperCamelCase__				:			Optional[int]=5  ,	UpperCamelCase__				:			List[str]=2  ,	UpperCamelCase__				:			Union[str, Any]=5  ,	UpperCamelCase__				:			Union[str, Any]=2  ,	UpperCamelCase__				:			int=0.1  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Any:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       List[str]     			=						hidden_size
				snake_case  :       Union[str, Any]     			=						num_hidden_layers
				snake_case  :       List[str]     			=						num_attention_heads
				snake_case  :       Any     			=						intermediate_size
				snake_case  :       int     			=						hidden_act
				snake_case  :       Optional[Any]     			=						hidden_dropout_prob
				snake_case  :       int     			=						attention_probs_dropout_prob
				snake_case  :       Union[str, Any]     			=						initializer_range
				snake_case  :       List[Any]     			=						layer_norm_eps
				snake_case  :       Tuple     			=						image_size
				snake_case  :       Any     			=						patch_size
				snake_case  :       str     			=						num_channels
				snake_case  :       Optional[int]     			=						qkv_bias
				snake_case  :       Optional[Any]     			=						num_detection_tokens
				snake_case  :       Dict     			=						use_mid_position_embeddings
				snake_case  :       List[Any]     			=						auxiliary_loss
				# Hungarian matcher
				snake_case  :       List[str]     			=						class_cost
				snake_case  :       Optional[Any]     			=						bbox_cost
				snake_case  :       Tuple     			=						giou_cost
				# Loss coefficients
				snake_case  :       Union[str, Any]     			=						bbox_loss_coefficient
				snake_case  :       List[Any]     			=						giou_loss_coefficient
				snake_case  :       Tuple     			=						eos_coefficient







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			float:






				"""simple docstring"""







				return 1e-4



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				return 12


