


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_						(			__A :			List[Any]  ,    __A :			Optional[Any]  ,    __A :			int  ,    __A :			List[Any]  ,    __A :			Union[str, Any]				)	->		str:

       """simple docstring"""

       with open(__A				) as metadata_file:
              a_					:		Optional[int]							=  json.load(__A				)
       a_					:		Any							=  LukeConfig(use_entity_aware_attention=__A  ,    **metadata['model_config']				)

       # Load in the weights from the checkpoint_path
       a_					:		List[Any]							=  torch.load(__A  ,    map_location='cpu'				)['module']

       # Load the entity vocab file
       a_					:		List[str]							=  load_original_entity_vocab(__A				)
       # add an entry for [MASK2]
       a_					:		Tuple							=  max(entity_vocab.values()				) + 1
       config.entity_vocab_size += 1

       a_					:		Dict							=  XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']				)

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

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

       a_					:		Union[str, Any]							=  MLukeTokenizer.from_pretrained(__A				)

       # Initialize the embeddings of the special tokens
       a_					:		Optional[int]							=  tokenizer.convert_tokens_to_ids(['@']				)[0]
       a_					:		Optional[int]							=  tokenizer.convert_tokens_to_ids(['#']				)[0]

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

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

       a_					:		str							=  LukeForMaskedLM(config=__A				).eval()

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

       a_							,		a_					:		Dict							=  model.load_state_dict(__A  ,    strict=__A				)

       if set(__A				) != {"luke.embeddings.position_ids"}:
              raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""				)
       if set(__A				) != {
           "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
       a_					:		Optional[int]							=  MLukeTokenizer.from_pretrained(__A  ,    task='entity_classification'				)

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

       a_					:		int							=  model(**__A				)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              a_					:		Optional[Any]							=  torch.Size((1, 33, 7_68)				)
              a_					:		Dict							=  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]  ,    __A  ,    atol=1e-4				):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              a_					:		str							=  torch.Size((1, 1, 7_68)				)
              a_					:		Optional[int]							=  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]  ,    __A  ,    atol=1e-4				):
              raise ValueError

       # Verify masked word/entity prediction
       a_					:		Union[str, Any]							=  MLukeTokenizer.from_pretrained(__A				)
       a_					:		Union[str, Any]							=  'Tokyo is the capital of <mask>.'
       a_					:		Dict							=  (24, 30)
       a_					:		Tuple							=  tokenizer(__A  ,    entity_spans=[span]  ,    return_tensors='pt'				)

       a_					:		str							=  model(**__A				)

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

       a_					:		Any							=  outputs.entity_logits[0][0].argmax().item()
       a_					:		Tuple							=  [
           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(__A				)				)
       model.save_pretrained(__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		Tuple:

       """simple docstring"""

       a_					:		List[str]							=  ['[MASK]', '[PAD]', '[UNK]']

       a_					:		Any							=  [json.loads(__A				) for line in open(__A				)]

       a_					:		Optional[Any]							=  {}
       for entry in data:
              a_					:		List[str]							=  entry['id']
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            a_					:		Optional[int]							=  entity_id
                            break
                     a_					:		List[Any]							=  F"""{language}:{entity_name}"""
                     a_					:		str							=  entity_id
       return new_mapping


if __name__ == "__main__":
   UpperCAmelCase_      : Tuple  					=       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_      : 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,
   )






import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

       # Make a hash from all this code
       a_					:		Tuple							=  full_str.encode('utf-8'				)
       return shaaaa(__A				).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase_      : List[Any]  					=       {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase_      : Dict  					=       {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase_      : Optional[int]  					=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : Dict[str, List[str]]  					=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')


