






"""simple docstring"""







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       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# Load configuration defined in the metadata file
					with open(lowerCamelCase_) as metadata_file:
										a__          =  json.load(lowerCamelCase_)
					a__          =  LukeConfig(use_entity_aware_attention=lowerCamelCase_ ,     **metadata['''model_config'''])

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

					# Load the entity vocab file
					a__          =  load_entity_vocab(lowerCamelCase_)

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

					# Add special tokens to the token vocabulary for downstream tasks
					a__          =  AddedToken('''<ent>''' ,     lstrip=lowerCamelCase_ ,     rstrip=lowerCamelCase_)
					a__          =  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_)

					a__          =  LukeTokenizer.from_pretrained(lowerCamelCase_)

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

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

					a__          =  LukeModel(config=lowerCamelCase_).eval()

					a__       ,a__          =  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
					a__          =  LukeTokenizer.from_pretrained(lowerCamelCase_ ,     task='''entity_classification''')

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

					a__          =  model(**lowerCamelCase_)

					# Verify word hidden states
					if model_size == "large":
										a__          =  torch.Size((1, 42, 1024))
										a__          =  torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
					else:  # base
										a__          =  torch.Size((1, 42, 768))
										a__          =  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] ,     lowerCamelCase_ ,     atol=1E-4):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										a__          =  torch.Size((1, 1, 1024))
										a__          =  torch.tensor([[0.0466, -0.0106, -0.0179]])
					else:  # base
										a__          =  torch.Size((1, 1, 768))
										a__          =  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] ,     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       (      lowerCamelCase_):
					a__          =  {}
					with open(lowerCamelCase_ ,     '''r''' ,     encoding='''utf-8''') as f:
										for index, line in enumerate(lowerCamelCase_):
															a__       ,a__          =  line.rstrip().split('''\t''')
															a__          =  index

					return entity_vocab


if __name__ == "__main__":
	__a       :	Dict             = 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       :	List[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


