



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

							# Load in the weights from the checkpoint_path
							__SCREAMING_SNAKE_CASE									= torch.load(_lowerCAmelCase							,       map_location='cpu'       )["""module"""]

							# Load the entity vocab file
							__SCREAMING_SNAKE_CASE									= load_original_entity_vocab(_lowerCAmelCase       )
							# add an entry for [MASK2]
							__SCREAMING_SNAKE_CASE									= max(entity_vocab.values()       ) + 1
							config.entity_vocab_size += 1

							__SCREAMING_SNAKE_CASE									= XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']       )

							# Add special tokens to the token vocabulary for downstream tasks
							__SCREAMING_SNAKE_CASE									= AddedToken('<ent>'							,       lstrip=_lowerCAmelCase							,       rstrip=_lowerCAmelCase       )
							__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_SNAKE_CASE									= json.load(_lowerCAmelCase       )
							__SCREAMING_SNAKE_CASE									= """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       )

							__SCREAMING_SNAKE_CASE									= MLukeTokenizer.from_pretrained(_lowerCAmelCase       )

							# Initialize the embeddings of the special tokens
							__SCREAMING_SNAKE_CASE									= tokenizer.convert_tokens_to_ids(['@']       )[0]
							__SCREAMING_SNAKE_CASE									= tokenizer.convert_tokens_to_ids(['#']       )[0]

							__SCREAMING_SNAKE_CASE									= state_dict["""embeddings.word_embeddings.weight"""]
							__SCREAMING_SNAKE_CASE									= word_emb[ent_init_index].unsqueeze(0       )
							__SCREAMING_SNAKE_CASE									= word_emb[enta_init_index].unsqueeze(0       )
							__SCREAMING_SNAKE_CASE									= 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"]:
														__SCREAMING_SNAKE_CASE									= state_dict[bias_name]
														__SCREAMING_SNAKE_CASE									= decoder_bias[ent_init_index].unsqueeze(0       )
														__SCREAMING_SNAKE_CASE									= decoder_bias[enta_init_index].unsqueeze(0       )
														__SCREAMING_SNAKE_CASE									= 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"]:
																					__SCREAMING_SNAKE_CASE									= f"""encoder.layer.{layer_index}.attention.self."""
																					__SCREAMING_SNAKE_CASE									= state_dict[prefix + matrix_name]
																					__SCREAMING_SNAKE_CASE									= state_dict[prefix + matrix_name]
																					__SCREAMING_SNAKE_CASE									= state_dict[prefix + matrix_name]

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

							__SCREAMING_SNAKE_CASE									= 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'       )
							__SCREAMING_SNAKE_CASE									= OrderedDict()
							for key, value in state_dict.items():
														if not (key.startswith('lm_head'       ) or key.startswith('entity_predictions'       )):
																					__SCREAMING_SNAKE_CASE									= state_dict[key]
														else:
																					__SCREAMING_SNAKE_CASE									= state_dict[key]

							__SCREAMING_SNAKE_CASE									= 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
							__SCREAMING_SNAKE_CASE									= MLukeTokenizer.from_pretrained(_lowerCAmelCase							,       task='entity_classification'       )

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

							__SCREAMING_SNAKE_CASE									= model(**_lowerCAmelCase       )

							# Verify word hidden states
							if model_size == "large":
														raise NotImplementedError
							else:  # base
														__SCREAMING_SNAKE_CASE									= torch.Size((1, 33, 7_68)       )
														__SCREAMING_SNAKE_CASE									= 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
														__SCREAMING_SNAKE_CASE									= torch.Size((1, 1, 7_68)       )
														__SCREAMING_SNAKE_CASE									= 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
							__SCREAMING_SNAKE_CASE									= MLukeTokenizer.from_pretrained(_lowerCAmelCase       )
							__SCREAMING_SNAKE_CASE									= """Tokyo is the capital of <mask>."""
							__SCREAMING_SNAKE_CASE									= (24, 30)
							__SCREAMING_SNAKE_CASE									= tokenizer(_lowerCAmelCase							,       entity_spans=[span]							,       return_tensors='pt'       )

							__SCREAMING_SNAKE_CASE									= model(**_lowerCAmelCase       )

							__SCREAMING_SNAKE_CASE									= encoding["""input_ids"""][0].tolist()
							__SCREAMING_SNAKE_CASE									= input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'       )       )
							__SCREAMING_SNAKE_CASE									= outputs.logits[0][mask_position_id].argmax(dim=-1       )
							assert "Japan" == tokenizer.decode(_lowerCAmelCase       )

							__SCREAMING_SNAKE_CASE									= outputs.entity_logits[0][0].argmax().item()
							__SCREAMING_SNAKE_CASE									= [
							    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      __lowercase       (			a__       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= ["""[MASK]""", """[PAD]""", """[UNK]"""]

							__SCREAMING_SNAKE_CASE									= [json.loads(_lowerCAmelCase       ) for line in open(_lowerCAmelCase       )]

							__SCREAMING_SNAKE_CASE									= {}
							for entry in data:
														__SCREAMING_SNAKE_CASE									= entry["""id"""]
														for entity_name, language in entry["entities"]:
																					if entity_name in SPECIAL_TOKENS:
																												__SCREAMING_SNAKE_CASE									= entity_id
																												break
																					__SCREAMING_SNAKE_CASE									= f"""{language}:{entity_name}"""
																					__SCREAMING_SNAKE_CASE									= entity_id
							return new_mapping


if __name__ == "__main__":
			lowerCAmelCase__		:					List[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.'''
			)
			lowerCAmelCase__		:					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,
			)











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=1e-5				,							_A="group"				,							_A="gelu"				,							_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							_A=False				,							_A=128				,							_A=16				,							_A=True				,							_A=0.0_5				,							_A=10				,							_A=2				,							_A=0.0				,							_A=10				,							_A=0				,							_A="mean"				,							_A=False				,							_A=False				,							_A=256				,							_A=0				,							_A=1				,							_A=2				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= vocab_size

													if (
													    (len(self.conv_stride		) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
													    or (len(self.conv_dim		) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    'Configuration for convolutional layers is incorrect.'
																				    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
																				    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
																				    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													__SCREAMING_SNAKE_CASE									= apply_spec_augment
													__SCREAMING_SNAKE_CASE									= mask_time_prob
													__SCREAMING_SNAKE_CASE									= mask_time_length
													__SCREAMING_SNAKE_CASE									= mask_time_min_masks
													__SCREAMING_SNAKE_CASE									= mask_feature_prob
													__SCREAMING_SNAKE_CASE									= mask_feature_length
													__SCREAMING_SNAKE_CASE									= mask_feature_min_masks

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return functools.reduce(operator.mul				,							self.conv_stride				,							1		)






