






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

				# Load in the weights from the checkpoint_path
				_A	:				Dict   =      torch.load(__lowerCamelCase					,  map_location='''cpu'''   )

				# Load the entity vocab file
				_A	:				Any   =      load_entity_vocab(__lowerCamelCase   )

				_A	:				Tuple   =      RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']   )

				# Add special tokens to the token vocabulary for downstream tasks
				_A	:				Tuple   =      AddedToken('''<ent>'''					,  lstrip=__lowerCamelCase					,  rstrip=__lowerCamelCase   )
				_A	:				str   =      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					,  LukeTokenizer.vocab_files_names['''entity_vocab_file''']   )					,  '''w'''   ) as f:
								json.dump(__lowerCamelCase					,  __lowerCamelCase   )

				_A	:				Dict   =      LukeTokenizer.from_pretrained(__lowerCamelCase   )

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				_A	:				List[Any]   =      state_dict['''entity_embeddings.entity_embeddings.weight''']
				_A	:				List[str]   =      entity_emb[entity_vocab['''[MASK]''']]

				_A	:				Optional[int]   =      LukeModel(config=__lowerCamelCase   ).eval()

				_A  ,				_A	:				int   =      model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase   )
				if not (len(__lowerCamelCase   ) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(f"""Missing keys {', '.join(__lowerCamelCase   )}. 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
				_A	:				Union[str, Any]   =      LukeTokenizer.from_pretrained(__lowerCamelCase					,  task='''entity_classification'''   )

				_A	:				Tuple   =      (
				    '''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 .'''
				)
				_A	:				Tuple   =      (39, 42)
				_A	:				str   =      tokenizer(__lowerCamelCase					,  entity_spans=[span]					,  add_prefix_space=__lowerCamelCase					,  return_tensors='''pt'''   )

				_A	:				List[Any]   =      model(**__lowerCamelCase   )

				# Verify word hidden states
				if model_size == "large":
								_A	:				int   =      torch.Size((1, 42, 1024)   )
								_A	:				Tuple   =      torch.tensor(
								    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]   )
				else:  # base
								_A	:				int   =      torch.Size((1, 42, 768)   )
								_A	:				Dict   =      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]					,  __lowerCamelCase					,  atol=1e-4   ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_A	:				Union[str, Any]   =      torch.Size((1, 1, 1024)   )
								_A	:				Union[str, Any]   =      torch.tensor([[0.0466, -0.0106, -0.0179]]   )
				else:  # base
								_A	:				str   =      torch.Size((1, 1, 768)   )
								_A	:				Dict   =      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]					,  __lowerCamelCase					,  atol=1e-4   ):
								raise ValueError

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



def  a__ (__lowercase							:str   )		-> str:
				_A	:				Optional[int]   =      {}
				with open(__lowerCamelCase					,  '''r'''					,  encoding='''utf-8'''   ) as f:
								for index, line in enumerate(__lowerCamelCase   ):
												_A  ,				_A	:				Any   =      line.rstrip().split('''\t'''   )
												_A	:				Any   =      index

				return entity_vocab


if __name__ == "__main__":
	_UpperCamelCase			:	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,
	)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

