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

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

							# Load the entity vocab file
							SCREAMING_SNAKE_CASE__						:       Dict           =   load_entity_vocab(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]						)

							# Add special tokens to the token vocabulary for downstream tasks
							SCREAMING_SNAKE_CASE__						:       int           =   AddedToken("""<ent>"""   ,lstrip=_snake_case   ,rstrip=_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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						)

							SCREAMING_SNAKE_CASE__						:       int           =   LukeTokenizer.from_pretrained(_snake_case						)

							# Initialize the embeddings of the special tokens
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   state_dict["""embeddings.word_embeddings.weight"""]
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   word_emb[tokenizer.convert_tokens_to_ids(["""@"""]						)[0]].unsqueeze(0						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   word_emb[tokenizer.convert_tokens_to_ids(["""#"""]						)[0]].unsqueeze(0						)
							SCREAMING_SNAKE_CASE__						:       str           =   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"]:
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   f'''encoder.layer.{layer_index}.attention.self.'''
																					SCREAMING_SNAKE_CASE__						:       int           =   state_dict[prefix + matrix_name]
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   state_dict[prefix + matrix_name]
																					SCREAMING_SNAKE_CASE__						:       List[Any]           =   state_dict[prefix + matrix_name]

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

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   LukeModel(config=_snake_case						).eval()

							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   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
							SCREAMING_SNAKE_CASE__						:       Dict           =   LukeTokenizer.from_pretrained(_snake_case   ,task="""entity_classification"""						)

							SCREAMING_SNAKE_CASE__						:       str           =   (
							    """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 ."""
							)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (39, 42)
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer(_snake_case   ,entity_spans=[span]   ,add_prefix_space=_snake_case   ,return_tensors="""pt"""						)

							SCREAMING_SNAKE_CASE__						:       Any           =   model(**_snake_case						)

							# Verify word hidden states
							if model_size == "large":
														SCREAMING_SNAKE_CASE__						:       Any           =   torch.Size((1, 42, 1_024)						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.tensor(
														    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]						)
							else:  # base
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.Size((1, 42, 768)						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   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":
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.Size((1, 1, 1_024)						)
														SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor([[0.0466, -0.0106, -0.0179]]						)
							else:  # base
														SCREAMING_SNAKE_CASE__						:       Dict           =   torch.Size((1, 1, 768)						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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 lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       str           =   {}
							with open(_snake_case   ,"""r"""   ,encoding="""utf-8"""						) as f:
														for index, line in enumerate(_snake_case						):
																					SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   line.rstrip().split("""\t"""						)
																					SCREAMING_SNAKE_CASE__						:       Dict           =   index

							return entity_vocab


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[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__      :    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,
					)






"""simple docstring"""




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


UpperCAmelCase__      :    Optional[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Dict    							= {
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   '''audio-spectrogram-transformer'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=10_24   ,       SCREAMING_SNAKE_CASE__=1_28   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       str           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       int           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       Tuple           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       Any           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       int           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       int           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Dict           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   frequency_stride
											SCREAMING_SNAKE_CASE__						:       Any           =   time_stride
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   max_length
											SCREAMING_SNAKE_CASE__						:       Any           =   num_mel_bins




