



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 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : List[str]    )				->			Union[str, Any]:






						'''simple docstring'''



						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_,       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, 10_24)    )
												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, 7_68)    )
												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, 10_24)    )
												UpperCAmelCase_=       torch.tensor([[0.0_466, -0.0_106, -0.0_179]]    )
						else:  # base
												UpperCAmelCase_=       torch.Size((1, 1, 7_68)    )
												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 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       {}
						with open(lowerCAmelCase_      ,"""r"""      ,encoding="""utf-8"""    ) as f:
												for index, line in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_,       UpperCAmelCase_=       line.rstrip().split("""\t"""    )
																		UpperCAmelCase_=       index

						return entity_vocab


if __name__ == "__main__":
	__A       =						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       =						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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A       =						{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						['''CLIPTokenizerFast''']

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						['''CLIPFeatureExtractor''']
	__A       =						['''CLIPImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						[
	    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CLIPModel''',
	    '''CLIPPreTrainedModel''',
	    '''CLIPTextModel''',
	    '''CLIPTextModelWithProjection''',
	    '''CLIPVisionModel''',
	    '''CLIPVisionModelWithProjection''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						[
	    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFCLIPModel''',
	    '''TFCLIPPreTrainedModel''',
	    '''TFCLIPTextModel''',
	    '''TFCLIPVisionModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						[
	    '''FlaxCLIPModel''',
	    '''FlaxCLIPPreTrainedModel''',
	    '''FlaxCLIPTextModel''',
	    '''FlaxCLIPTextPreTrainedModel''',
	    '''FlaxCLIPVisionModel''',
	    '''FlaxCLIPVisionPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

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




