




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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Optional[Any]				)  ->							str:

				with open(__lowerCAmelCase				) as metadata_file:
								UpperCAmelCase_										=					json.load(__lowerCAmelCase				)
				UpperCAmelCase_										=					LukeConfig(use_entity_aware_attention=__lowerCAmelCase	,					**metadata['''model_config''']				)

				# Load in the weights from the checkpoint_path
				UpperCAmelCase_										=					torch.load(__lowerCAmelCase	,					map_location='''cpu'''				)

				# Load the entity vocab file
				UpperCAmelCase_										=					load_entity_vocab(__lowerCAmelCase				)

				UpperCAmelCase_										=					RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']				)

				# Add special tokens to the token vocabulary for downstream tasks
				UpperCAmelCase_										=					AddedToken('''<ent>'''	,					lstrip=__lowerCAmelCase	,					rstrip=__lowerCAmelCase				)
				UpperCAmelCase_										=					AddedToken('''<ent2>'''	,					lstrip=__lowerCAmelCase	,					rstrip=__lowerCAmelCase				)
				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(__lowerCAmelCase				)
				with open(os.path.join(__lowerCAmelCase	,					LukeTokenizer.vocab_files_names['''entity_vocab_file''']				)	,					'''w'''				) as f:
								json.dump(__lowerCAmelCase	,					__lowerCAmelCase				)

				UpperCAmelCase_										=					LukeTokenizer.from_pretrained(__lowerCAmelCase				)

				# Initialize the embeddings of the special tokens
				UpperCAmelCase_										=					state_dict['''embeddings.word_embeddings.weight''']
				UpperCAmelCase_										=					word_emb[tokenizer.convert_tokens_to_ids(['''@''']				)[0]].unsqueeze(0				)
				UpperCAmelCase_										=					word_emb[tokenizer.convert_tokens_to_ids(['''#''']				)[0]].unsqueeze(0				)
				UpperCAmelCase_										=					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"]:
												UpperCAmelCase_										=					f'encoder.layer.{layer_index}.attention.self.'
												UpperCAmelCase_										=					state_dict[prefix + matrix_name]
												UpperCAmelCase_										=					state_dict[prefix + matrix_name]
												UpperCAmelCase_										=					state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				UpperCAmelCase_										=					state_dict['''entity_embeddings.entity_embeddings.weight''']
				UpperCAmelCase_										=					entity_emb[entity_vocab['''[MASK]''']]

				UpperCAmelCase_										=					LukeModel(config=__lowerCAmelCase				).eval()

				UpperCAmelCase_										=					model.load_state_dict(__lowerCAmelCase	,					strict=__lowerCAmelCase				)
				if not (len(__lowerCAmelCase				) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(f'Missing keys {", ".join(__lowerCAmelCase				)}. 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
				UpperCAmelCase_										=					LukeTokenizer.from_pretrained(__lowerCAmelCase	,					task='''entity_classification'''				)

				UpperCAmelCase_										=					(
				    '''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 .'''
				)
				UpperCAmelCase_										=					(39, 42)
				UpperCAmelCase_										=					tokenizer(__lowerCAmelCase	,					entity_spans=[span]	,					add_prefix_space=__lowerCAmelCase	,					return_tensors='''pt'''				)

				UpperCAmelCase_										=					model(**__lowerCAmelCase				)

				# Verify word hidden states
				if model_size == "large":
								UpperCAmelCase_										=					torch.Size((1, 42, 1024)				)
								UpperCAmelCase_										=					torch.tensor(
								    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]				)
				else:  # base
								UpperCAmelCase_										=					torch.Size((1, 42, 768)				)
								UpperCAmelCase_										=					torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]				)

				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]	,					__lowerCAmelCase	,					atol=1e-4				):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								UpperCAmelCase_										=					torch.Size((1, 1, 1024)				)
								UpperCAmelCase_										=					torch.tensor([[0.0_466, -0.0_106, -0.0_179]]				)
				else:  # base
								UpperCAmelCase_										=					torch.Size((1, 1, 768)				)
								UpperCAmelCase_										=					torch.tensor([[0.1_457, 0.1_044, 0.0_174]]				)

				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]	,					__lowerCAmelCase	,					atol=1e-4				):
								raise ValueError

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Optional[Any]:
				UpperCAmelCase_										=					{}
				with open(__lowerCAmelCase	,					'''r'''	,					encoding='''utf-8'''				) as f:
								for index, line in enumerate(__lowerCAmelCase				):
												UpperCAmelCase_										=					line.rstrip().split('''\t'''				)
												UpperCAmelCase_										=					index

				return entity_vocab


if __name__ == "__main__":
	_lowerCamelCase      			=						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.'
	)
	_lowerCamelCase      			=						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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





