

"""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  (      A_						,     A_						,     A_						,     A_						,     A_ ):
	# Load configuration defined in the metadata file
	with open(A_ ) as metadata_file:
		lowerCAmelCase__	:						Any  				=	json.load(A_ )
	lowerCAmelCase__	:						List[str]  				=	LukeConfig(use_entity_aware_attention=A_						,     **metadata['''model_config'''] )

	# Load in the weights from the checkpoint_path
	lowerCAmelCase__	:						str  				=	torch.load(A_						,     map_location='''cpu''' )

	# Load the entity vocab file
	lowerCAmelCase__	:						int  				=	load_entity_vocab(A_ )

	lowerCAmelCase__	:						Union[str, Any]  				=	RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )

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

	lowerCAmelCase__	:						Optional[int]  				=	LukeTokenizer.from_pretrained(A_ )

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

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

	lowerCAmelCase__	:						List[Any]  				=	LukeModel(config=A_ ).eval()

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

	lowerCAmelCase__	:						Dict  				=	(
	    '''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 .'''
	)
	lowerCAmelCase__	:						Any  				=	(39, 42)
	lowerCAmelCase__	:						str  				=	tokenizer(A_						,     entity_spans=[span]						,     add_prefix_space=A_						,     return_tensors='''pt''' )

	lowerCAmelCase__	:						Optional[Any]  				=	model(**A_ )

	# Verify word hidden states
	if model_size == "large":
		lowerCAmelCase__	:						Any  				=	torch.Size((1, 42, 10_24) )
		lowerCAmelCase__	:						Union[str, Any]  				=	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
		lowerCAmelCase__	:						Dict  				=	torch.Size((1, 42, 7_68) )
		lowerCAmelCase__	:						int  				=	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]						,     A_						,     atol=1e-4 ):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		lowerCAmelCase__	:						int  				=	torch.Size((1, 1, 10_24) )
		lowerCAmelCase__	:						Any  				=	torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
	else:  # base
		lowerCAmelCase__	:						Any  				=	torch.Size((1, 1, 7_68) )
		lowerCAmelCase__	:						Dict  				=	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]						,     A_						,     atol=1e-4 ):
		raise ValueError

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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						int  				=	{}
	with open(A_						,     '''r'''						,     encoding='''utf-8''' ) as f:
		for index, line in enumerate(A_ ):
			lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	line.rstrip().split('''\t''' )
			lowerCAmelCase__	:						Dict  				=	index

	return entity_vocab


if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		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  :      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 typing import TYPE_CHECKING

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


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

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




