




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   :							Dict					,	__snake_case   :							Tuple					,	__snake_case   :							List[str]					,	__snake_case   :							Optional[Any]					,	__snake_case   :							Any  ):


					'''simple docstring'''


					with open(snake_case__  ) as metadata_file:
										UpperCAmelCase_   :				List[str]						=						json.load(snake_case__  )
					UpperCAmelCase_   :				Union[str, Any]						=						LukeConfig(use_entity_aware_attention=snake_case__					,	**metadata['model_config']  )

					# Load in the weights from the checkpoint_path
					UpperCAmelCase_   :				Any						=						torch.load(snake_case__					,	map_location='cpu'  )['module']

					# Load the entity vocab file
					UpperCAmelCase_   :				Optional[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_   :				str						=						XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']  )

					# Add special tokens to the token vocabulary for downstream tasks
					UpperCAmelCase_   :				Tuple						=						AddedToken('<ent>'					,	lstrip=snake_case__					,	rstrip=snake_case__  )
					UpperCAmelCase_   :				Optional[int]						=						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_   :				List[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_   :				Union[str, Any]						=						MLukeTokenizer.from_pretrained(snake_case__  )

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

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

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

					UpperCAmelCase_   :				Tuple						=						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_   :				Optional[int]						=						state_dict[key]
										else:
															UpperCAmelCase_   :				List[Any]						=						state_dict[key]

					UpperCAmelCase_   :				int						=						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_   :				int						=						MLukeTokenizer.from_pretrained(snake_case__					,	task='entity_classification'  )

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

					UpperCAmelCase_   :				str						=						model(**snake_case__  )

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										UpperCAmelCase_   :				Optional[int]						=						torch.Size((1, 33, 768)  )
										UpperCAmelCase_   :				Union[str, Any]						=						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_   :				Tuple						=						torch.Size((1, 1, 768)  )
										UpperCAmelCase_   :				Optional[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]					,	snake_case__					,	atol=1E-4  ):
										raise ValueError

					# Verify masked word/entity prediction
					UpperCAmelCase_   :				str						=						MLukeTokenizer.from_pretrained(snake_case__  )
					UpperCAmelCase_   :				int						=						'Tokyo is the capital of <mask>.'
					UpperCAmelCase_   :				str						=						(24, 30)
					UpperCAmelCase_   :				List[str]						=						tokenizer(snake_case__					,	entity_spans=[span]					,	return_tensors='pt'  )

					UpperCAmelCase_   :				Any						=						model(**snake_case__  )

					UpperCAmelCase_   :				Tuple						=						encoding['input_ids'][0].tolist()
					UpperCAmelCase_   :				int						=						input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'  )  )
					UpperCAmelCase_   :				Dict						=						outputs.logits[0][mask_position_id].argmax(dim=-1  )
					assert "Japan" == tokenizer.decode(snake_case__  )

					UpperCAmelCase_   :				List[Any]						=						outputs.entity_logits[0][0].argmax().item()
					UpperCAmelCase_   :				Optional[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 					lowercase__				(					__snake_case   :							List[str]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Dict						=						['[MASK]', '[PAD]', '[UNK]']

					UpperCAmelCase_   :				Optional[Any]						=						[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_   :				int						=						entity_id
																				break
															UpperCAmelCase_   :				Union[str, Any]						=						F"{language}:{entity_name}"
															UpperCAmelCase_   :				str						=						entity_id
					return new_mapping


if __name__ == "__main__":
		__UpperCAmelCase     							=						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     							=						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 Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from ..image_utils import load_image


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase     							=						logging.get_logger(__name__)


__UpperCAmelCase     							=						Dict[str, Any]
__UpperCAmelCase     							=						List[Prediction]



@add_end_docstrings(_snake_case      )
class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Optional[int]:
							super().__init__(*_UpperCamelCase    ,		**_UpperCamelCase       )

							if self.framework == "tf":
												raise ValueError(f"The {self.__class__} is only available in PyTorch."       )

							requires_backends(self    ,		'vision'       )
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()       )       )



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						{}
							if "threshold" in kwargs:
												UpperCAmelCase_   :				Tuple						=						kwargs['threshold']
							return {}, {}, postprocess_kwargs



		def __call__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[Predictions, List[Prediction]]:
							return super().__call__(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Any						=						load_image(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						torch.IntTensor([[image.height, image.width]]       )
							UpperCAmelCase_   :				str						=						self.image_processor(images=[image]    ,		return_tensors='pt'       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						self.tokenizer(text=inputs['words']    ,		boxes=inputs['boxes']    ,		return_tensors='pt'       )
							UpperCAmelCase_   :				Any						=						target_size
							return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						model_inputs.pop('target_size'       )
							UpperCAmelCase_   :				Optional[Any]						=						self.model(**_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						outputs.__class__({'target_size': target_size, **outputs}       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						model_inputs['bbox']
							return model_outputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0.9       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						model_outputs['target_size']
							if self.tokenizer is not None:
												# This is a LayoutLMForTokenClassification variant.
												# The OCR got the boxes and the model classified the words.
												UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						target_size[0].tolist()

												def unnormalize(_UpperCamelCase       ):
																	return self._get_bounding_box(
																	    torch.Tensor(
																	        [
																	            (width * bbox[0] / 1_0_0_0),
																	            (height * bbox[1] / 1_0_0_0),
																	            (width * bbox[2] / 1_0_0_0),
																	            (height * bbox[3] / 1_0_0_0),
																	        ]       )       )

												UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						model_outputs['logits'].squeeze(0       ).softmax(dim=-1       ).max(dim=-1       )
												UpperCAmelCase_   :				Union[str, Any]						=						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
												UpperCAmelCase_   :				Tuple						=						[unnormalize(_UpperCamelCase       ) for bbox in model_outputs['bbox'].squeeze(0       )]
												UpperCAmelCase_   :				List[str]						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Any						=						[dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       ) for vals in zip(scores.tolist()    ,		_UpperCamelCase    ,		_UpperCamelCase       ) if vals[0] > threshold]
							else:
												# This is a regular ForObjectDetectionModel
												UpperCAmelCase_   :				Union[str, Any]						=						self.image_processor.post_process_object_detection(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						raw_annotations[0]
												UpperCAmelCase_   :				str						=						raw_annotation['scores']
												UpperCAmelCase_   :				Tuple						=						raw_annotation['labels']
												UpperCAmelCase_   :				List[Any]						=						raw_annotation['boxes']

												UpperCAmelCase_   :				Union[str, Any]						=						scores.tolist()
												UpperCAmelCase_   :				int						=						[self.model.config.idalabel[label.item()] for label in labels]
												UpperCAmelCase_   :				Any						=						[self._get_bounding_box(_UpperCamelCase       ) for box in boxes]

												# {"scores": [...], ...} --> [{"score":x, ...}, ...]
												UpperCAmelCase_   :				int						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Dict						=						[
												    dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       )
												    for vals in zip(raw_annotation['scores']    ,		raw_annotation['labels']    ,		raw_annotation['boxes']       )
												]

							return annotation



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict[str, int]:
							if self.framework != "pt":
												raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'       )
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						box.int().tolist()
							UpperCAmelCase_   :				str						=						{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox






