
'''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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							# Load configuration defined in the metadata file
							with open(__snake_case ) as metadata_file:
														_snake_case  =    json.load(__snake_case )
							_snake_case  =    LukeConfig(use_entity_aware_attention=__snake_case				,   **metadata["""model_config"""] )

							# Load in the weights from the checkpoint_path
							_snake_case  =    torch.load(__snake_case				,   map_location="""cpu""" )

							# Load the entity vocab file
							_snake_case  =    load_entity_vocab(__snake_case )

							_snake_case  =    RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] )

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

							_snake_case  =    LukeTokenizer.from_pretrained(__snake_case )

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

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

							_snake_case  =    LukeModel(config=__snake_case ).eval()

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

							_snake_case  =    (
							    """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 ."""
							)
							_snake_case  =    (39, 42)
							_snake_case  =    tokenizer(__snake_case				,   entity_spans=[span]				,   add_prefix_space=__snake_case				,   return_tensors="""pt""" )

							_snake_case  =    model(**__snake_case )

							# Verify word hidden states
							if model_size == "large":
														_snake_case  =    torch.Size((1, 42, 1024) )
														_snake_case  =    torch.tensor(
														    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
							else:  # base
														_snake_case  =    torch.Size((1, 42, 768) )
														_snake_case  =    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )

							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]				,   __snake_case				,   atol=1E-4 ):
														raise ValueError

							# Verify entity hidden states
							if model_size == "large":
														_snake_case  =    torch.Size((1, 1, 1024) )
														_snake_case  =    torch.tensor([[0.0466, -0.0106, -0.0179]] )
							else:  # base
														_snake_case  =    torch.Size((1, 1, 768) )
														_snake_case  =    torch.tensor([[0.1457, 0.1044, 0.0174]] )

							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]				,   __snake_case				,   atol=1E-4 ):
														raise ValueError

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






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case  =    {}
							with open(__snake_case				,   """r"""				,   encoding="""utf-8""" ) as f:
														for index, line in enumerate(__snake_case ):
																					_snake_case,				_snake_case  =    line.rstrip().split("""\t""" )
																					_snake_case  =    index

							return entity_vocab


if __name__ == "__main__":
				__lowerCAmelCase					=			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					=			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 argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






