






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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  str	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Union[str, Any]					)		->			Tuple:






	"""simple docstring"""



	with open(lowerCAmelCase__					) as metadata_file:
		UpperCamelCase     :str          =	json.load(lowerCAmelCase__					)
	UpperCamelCase     :int          =	LukeConfig(use_entity_aware_attention=lowerCAmelCase__	,			**metadata["""model_config"""]					)

	# Load in the weights from the checkpoint_path
	UpperCamelCase     :List[Any]          =	torch.load(lowerCAmelCase__	,			map_location="""cpu"""					)["""module"""]

	# Load the entity vocab file
	UpperCamelCase     :Tuple          =	load_original_entity_vocab(lowerCAmelCase__					)
	# add an entry for [MASK2]
	UpperCamelCase     :Any          =	max(entity_vocab.values()					) + 1
	config.entity_vocab_size += 1

	UpperCamelCase     :List[Any]          =	XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]					)

	# Add special tokens to the token vocabulary for downstream tasks
	UpperCamelCase     :Union[str, Any]          =	AddedToken("""<ent>"""	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					)
	UpperCamelCase     :Tuple          =	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__	,			"""tokenizer_config.json"""					)	,			"""r"""					) as f:
		UpperCamelCase     :List[Any]          =	json.load(lowerCAmelCase__					)
	UpperCamelCase     :List[str]          =	"""MLukeTokenizer"""
	with open(os.path.join(lowerCAmelCase__	,			"""tokenizer_config.json"""					)	,			"""w"""					) as f:
		json.dump(lowerCAmelCase__	,			lowerCAmelCase__					)

	with open(os.path.join(lowerCAmelCase__	,			MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]					)	,			"""w"""					) as f:
		json.dump(lowerCAmelCase__	,			lowerCAmelCase__					)

	UpperCamelCase     :List[Any]          =	MLukeTokenizer.from_pretrained(lowerCAmelCase__					)

	# Initialize the embeddings of the special tokens
	UpperCamelCase     :Optional[Any]          =	tokenizer.convert_tokens_to_ids(["""@"""]					)[0]
	UpperCamelCase     :List[Any]          =	tokenizer.convert_tokens_to_ids(["""#"""]					)[0]

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

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

	UpperCamelCase     :Optional[int]          =	LukeForMaskedLM(config=lowerCAmelCase__					).eval()

	state_dict.pop("""entity_predictions.decoder.weight"""					)
	state_dict.pop("""lm_head.decoder.weight"""					)
	state_dict.pop("""lm_head.decoder.bias"""					)
	UpperCamelCase     :List[str]          =	OrderedDict()
	for key, value in state_dict.items():
		if not (key.startswith("""lm_head"""					) or key.startswith("""entity_predictions"""					)):
			UpperCamelCase     :str          =	state_dict[key]
		else:
			UpperCamelCase     :List[Any]          =	state_dict[key]

	UpperCamelCase     ,      UpperCamelCase     :int          =	model.load_state_dict(lowerCAmelCase__	,			strict=lowerCAmelCase__					)

	if set(lowerCAmelCase__					) != {"luke.embeddings.position_ids"}:
		raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""					)
	if set(lowerCAmelCase__					) != {
	    "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     :Tuple          =	MLukeTokenizer.from_pretrained(lowerCAmelCase__	,			task="""entity_classification"""					)

	UpperCamelCase     :Tuple          =	"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
	UpperCamelCase     :Any          =	(0, 9)
	UpperCamelCase     :Tuple          =	tokenizer(lowerCAmelCase__	,			entity_spans=[span]	,			return_tensors="""pt"""					)

	UpperCamelCase     :List[Any]          =	model(**lowerCAmelCase__					)

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

	# Verify entity hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		UpperCamelCase     :str          =	torch.Size((1, 1, 768)					)
		UpperCamelCase     :List[Any]          =	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]	,			lowerCAmelCase__	,			atol=1E-4					):
		raise ValueError

	# Verify masked word/entity prediction
	UpperCamelCase     :Optional[Any]          =	MLukeTokenizer.from_pretrained(lowerCAmelCase__					)
	UpperCamelCase     :str          =	"""Tokyo is the capital of <mask>."""
	UpperCamelCase     :Optional[Any]          =	(24, 30)
	UpperCamelCase     :Union[str, Any]          =	tokenizer(lowerCAmelCase__	,			entity_spans=[span]	,			return_tensors="""pt"""					)

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

	UpperCamelCase     :Any          =	encoding["""input_ids"""][0].tolist()
	UpperCamelCase     :List[str]          =	input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""					)					)
	UpperCamelCase     :int          =	outputs.logits[0][mask_position_id].argmax(dim=-1					)
	assert "Japan" == tokenizer.decode(lowerCAmelCase__					)

	UpperCamelCase     :str          =	outputs.entity_logits[0][0].argmax().item()
	UpperCamelCase     :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(lowerCAmelCase__					)					)
	model.save_pretrained(lowerCAmelCase__					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict					)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :str          =	["""[MASK]""", """[PAD]""", """[UNK]"""]

	UpperCamelCase     :Union[str, Any]          =	[json.loads(lowerCAmelCase__					) for line in open(lowerCAmelCase__					)]

	UpperCamelCase     :Any          =	{}
	for entry in data:
		UpperCamelCase     :Optional[int]          =	entry["""id"""]
		for entity_name, language in entry["entities"]:
			if entity_name in SPECIAL_TOKENS:
				UpperCamelCase     :str          =	entity_id
				break
			UpperCamelCase     :Any          =	f"""{language}:{entity_name}"""
			UpperCamelCase     :Dict          =	entity_id
	return new_mapping


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_ :						int      =      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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__		:int   =    logging.get_logger(__name__)

lowercase__		:Any   =    {
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[Any]       					='''poolformer'''




							def __init__(				self   ,A__=3   ,A__=1_6   ,A__=1_6   ,A__=3   ,A__=4.0   ,A__=[2, 2, 6, 2]   ,A__=[6_4, 1_2_8, 3_2_0, 5_1_2]   ,A__=[7, 3, 3, 3]   ,A__=[4, 2, 2, 2]   ,A__=[2, 1, 1, 1]   ,A__=4   ,A__=0.0   ,A__="gelu"   ,A__=True   ,A__=1E-5   ,A__=0.02   ,**A__   ,):
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						stride
												lowercase							=						padding
												lowercase							=						pool_size
												lowercase							=						hidden_sizes
												lowercase							=						mlp_ratio
												lowercase							=						depths
												lowercase							=						patch_sizes
												lowercase							=						strides
												lowercase							=						num_encoder_blocks
												lowercase							=						drop_path_rate
												lowercase							=						hidden_act
												lowercase							=						use_layer_scale
												lowercase							=						layer_scale_init_value
												lowercase							=						initializer_range
												super().__init__(**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       List[Any]       					=version.parse('''1.11'''		)




							@property
							def  A__      (				self):
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ])





							@property
							def  A__      (				self):
												return 2E-3




