



"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__SCREAMING_SNAKE_CASE  :						Dict    							=      logging.get_logger(__name__)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				feature_size
									_lowerCamelCase        =				sampling_rate
									_lowerCamelCase        =				padding_value

									_lowerCamelCase        =				kwargs.pop('''padding_side'''				,       '''right'''   )
									_lowerCamelCase        =				kwargs.pop('''return_attention_mask'''				,       lowerCamelCase__   )

									super().__init__(**lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = True				,       lowerCamelCase__ = None				,       lowerCamelCase__ = False				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       ):
									# If we have a list of dicts, let's convert it in a dict of lists
									# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
									if isinstance(lowerCamelCase__				,       (list, tuple)   ) and isinstance(processed_features[0]				,       (dict, BatchFeature)   ):
													_lowerCamelCase        =				{
													    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
													}

									# The model's main input name, usually `input_values`, has be passed for padding
									if self.model_input_names[0] not in processed_features:
													raise ValueError(
													    '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
													    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
													    F""" {list(processed_features.keys()   )}"""   )

									_lowerCamelCase        =				processed_features[self.model_input_names[0]]
									_lowerCamelCase        =				(
									    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
									)

									if len(lowerCamelCase__   ) == 0:
													if return_attention_mask:
																	_lowerCamelCase        =				[]
													return processed_features

									# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
									# and rebuild them afterwards if no return_tensors is specified
									# Note that we lose the specific device the tensor may be on for PyTorch

									_lowerCamelCase        =				required_input[0]
									if isinstance(lowerCamelCase__				,       (list, tuple)   ):
													# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
													_lowerCamelCase        =				0
													while len(required_input[index]   ) == 0:
																	index += 1
													if index < len(lowerCamelCase__   ):
																	_lowerCamelCase        =				required_input[index][0]

									if return_tensors is None:
													if is_tf_tensor(lowerCamelCase__   ):
																	_lowerCamelCase        =				'''tf'''
													elif is_torch_tensor(lowerCamelCase__   ):
																	_lowerCamelCase        =				'''pt'''
													elif isinstance(lowerCamelCase__				,       (int, float, list, tuple, np.ndarray)   ):
																	_lowerCamelCase        =				'''np'''
													else:
																	raise ValueError(
																	    F"""type of {first_element} unknown: {type(lowerCamelCase__   )}. """
																	    '''Should be one of a python, numpy, pytorch or tensorflow object.'''   )

									for key, value in processed_features.items():
													if isinstance(value[0]				,       (int, float)   ):
																	_lowerCamelCase        =				to_numpy(lowerCamelCase__   )
													else:
																	_lowerCamelCase        =				[to_numpy(lowerCamelCase__   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
									_lowerCamelCase        =				self._get_padding_strategies(padding=lowerCamelCase__				,       max_length=lowerCamelCase__   )

									_lowerCamelCase        =				processed_features[self.model_input_names[0]]

									_lowerCamelCase        =				len(lowerCamelCase__   )
									if not all(len(lowerCamelCase__   ) == batch_size for v in processed_features.values()   ):
													raise ValueError('''Some items in the output dictionary have a different batch size than others.'''   )

									_lowerCamelCase        =				[]
									for i in range(lowerCamelCase__   ):
													_lowerCamelCase        =				{k: v[i] for k, v in processed_features.items()}
													# truncation
													_lowerCamelCase        =				self._truncate(
													    lowerCamelCase__				,       max_length=lowerCamelCase__				,       pad_to_multiple_of=lowerCamelCase__				,       truncation=lowerCamelCase__				,       )
													truncated_inputs.append(lowerCamelCase__   )

									if padding_strategy == PaddingStrategy.LONGEST:
													# make sure that `max_length` cannot be longer than the longest truncated length
													_lowerCamelCase        =				max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
													_lowerCamelCase        =				PaddingStrategy.MAX_LENGTH

									_lowerCamelCase        =				{}
									for i in range(lowerCamelCase__   ):
													# padding
													_lowerCamelCase        =				self._pad(
													    truncated_inputs[i]				,       max_length=lowerCamelCase__				,       padding_strategy=lowerCamelCase__				,       pad_to_multiple_of=lowerCamelCase__				,       return_attention_mask=lowerCamelCase__				,       )

													for key, value in outputs.items():
																	if key not in batch_outputs:
																					_lowerCamelCase        =				[]
																	if value.dtype is np.dtype(np.floataa   ):
																					_lowerCamelCase        =				value.astype(np.floataa   )
																	batch_outputs[key].append(lowerCamelCase__   )

									return BatchFeature(lowerCamelCase__				,       tensor_type=lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = PaddingStrategy.DO_NOT_PAD				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       ):
									_lowerCamelCase        =				processed_features[self.model_input_names[0]]

									if padding_strategy == PaddingStrategy.LONGEST:
													_lowerCamelCase        =				len(lowerCamelCase__   )

									if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													_lowerCamelCase        =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									_lowerCamelCase        =				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase__   ) < max_length

									if return_attention_mask and "attention_mask" not in processed_features:
													_lowerCamelCase        =				np.ones(len(lowerCamelCase__   )				,       dtype=np.intaa   )

									if needs_to_be_padded:
													_lowerCamelCase        =				max_length - len(lowerCamelCase__   )
													if self.padding_side == "right":
																	if return_attention_mask:
																					_lowerCamelCase        =				np.pad(
																					    processed_features['''attention_mask''']				,       (0, difference)   )
																	_lowerCamelCase        =				((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																	_lowerCamelCase        =				np.pad(
																	    lowerCamelCase__				,       lowerCamelCase__				,       '''constant'''				,       constant_values=self.padding_value   )
													elif self.padding_side == "left":
																	if return_attention_mask:
																					_lowerCamelCase        =				np.pad(
																					    processed_features['''attention_mask''']				,       (difference, 0)   )
																	_lowerCamelCase        =				((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																	_lowerCamelCase        =				np.pad(
																	    lowerCamelCase__				,       lowerCamelCase__				,       '''constant'''				,       constant_values=self.padding_value   )
													else:
																	raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

									return processed_features

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       ):
									if not truncation:
													return processed_features
									elif truncation and max_length is None:
													raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''   )

									_lowerCamelCase        =				processed_features[self.model_input_names[0]]

									# find `max_length` that fits `pad_to_multiple_of`
									if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													_lowerCamelCase        =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									_lowerCamelCase        =				len(lowerCamelCase__   ) > max_length

									if needs_to_be_truncated:
													_lowerCamelCase        =				processed_features[self.model_input_names[0]][:max_length]
													if "attention_mask" in processed_features:
																	_lowerCamelCase        =				processed_features['''attention_mask'''][:max_length]

									return processed_features





					def 			snake_case__      (    self				,       lowerCamelCase__=False				,       lowerCamelCase__=None   ):

									# Get padding strategy
									if padding is not False:
													if padding is True:
																	_lowerCamelCase        =				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
													elif not isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
																	_lowerCamelCase        =				PaddingStrategy(lowerCamelCase__   )
													elif isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
																	_lowerCamelCase        =				padding
									else:
													_lowerCamelCase        =				PaddingStrategy.DO_NOT_PAD

									# Set max length if needed
									if max_length is None:
													if padding_strategy == PaddingStrategy.MAX_LENGTH:
																	raise ValueError(
																	    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""   )

        # Test if we have a padding value
									if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
													raise ValueError(
													    '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
													    ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''   )

									return padding_strategy





"""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        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Tuple						,   lowercase_     :							List[str]							)	->       Dict:
				# Load configuration defined in the metadata file
				with open(lowercase_							) as metadata_file:
								_lowerCamelCase        =				json.load(lowercase_							)
				_lowerCamelCase        =				LukeConfig(use_entity_aware_attention=lowercase_						,   **metadata['''model_config''']							)

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

				# Load the entity vocab file
				_lowerCamelCase        =				load_entity_vocab(lowercase_							)

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

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

				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_							)

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

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

				_lowerCamelCase        =				LukeModel(config=lowercase_							).eval()

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

				_lowerCamelCase        =				(
				    '''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        =				(39, 42)
				_lowerCamelCase        =				tokenizer(lowercase_						,   entity_spans=[span]						,   add_prefix_space=lowercase_						,   return_tensors='''pt'''							)

				_lowerCamelCase        =				model(**lowercase_							)

				# Verify word hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 42, 10_24)							)
								_lowerCamelCase        =				torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 42, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]							)

				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]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 1, 10_24)							)
								_lowerCamelCase        =				torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 1, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]							)

				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]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

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



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Any:
				_lowerCamelCase        =				{}
				with open(lowercase_						,   '''r'''						,   encoding='''utf-8'''							) as f:
								for index, line in enumerate(lowercase_							):
												_lowerCamelCase  ,     _lowerCamelCase        =				line.rstrip().split('''\t'''							)
												_lowerCamelCase        =				index

				return entity_vocab


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      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.'''
			)
			__SCREAMING_SNAKE_CASE  :						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,
			)
