
"""simple docstring"""






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




		'''simple docstring'''






		with open(a_     ) as metadata_file:
				SCREAMING_SNAKE_CASE       :     List[str]				= json.load(a_     )
		SCREAMING_SNAKE_CASE       :     Dict				= LukeConfig(use_entity_aware_attention=a_		,		**metadata['''model_config''']     )

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

		# Load the entity vocab file
		SCREAMING_SNAKE_CASE       :     int				= load_original_entity_vocab(a_     )
		# add an entry for [MASK2]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= max(entity_vocab.values()     ) + 1
		config.entity_vocab_size += 1

		SCREAMING_SNAKE_CASE       :     Tuple				= XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']     )

		# Add special tokens to the token vocabulary for downstream tasks
		SCREAMING_SNAKE_CASE       :     Any				= AddedToken('''<ent>'''		,		lstrip=a_		,		rstrip=a_     )
		SCREAMING_SNAKE_CASE       :     Any				= 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:
				SCREAMING_SNAKE_CASE       :     Any				= json.load(a_     )
		SCREAMING_SNAKE_CASE       :     Dict				= '''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_     )

		SCREAMING_SNAKE_CASE       :     Optional[int]				= MLukeTokenizer.from_pretrained(a_     )

		# Initialize the embeddings of the special tokens
		SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer.convert_tokens_to_ids(['''@''']     )[0]
		SCREAMING_SNAKE_CASE       :     Any				= tokenizer.convert_tokens_to_ids(['''#''']     )[0]

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

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

		SCREAMING_SNAKE_CASE       :     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'''     )
		SCREAMING_SNAKE_CASE       :     List[str]				= OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith('''lm_head'''     ) or key.startswith('''entity_predictions'''     )):
						SCREAMING_SNAKE_CASE       :     Optional[int]				= state_dict[key]
				else:
						SCREAMING_SNAKE_CASE       :     str				= state_dict[key]

		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= 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
		SCREAMING_SNAKE_CASE       :     Any				= MLukeTokenizer.from_pretrained(a_		,		task='''entity_classification'''     )

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

		SCREAMING_SNAKE_CASE       :     Any				= model(**a_     )

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				SCREAMING_SNAKE_CASE       :     Dict				= torch.Size((1, 33, 7_68)     )
				SCREAMING_SNAKE_CASE       :     List[Any]				= 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
				SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.Size((1, 1, 7_68)     )
				SCREAMING_SNAKE_CASE       :     Any				= 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
		SCREAMING_SNAKE_CASE       :     Any				= MLukeTokenizer.from_pretrained(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''Tokyo is the capital of <mask>.'''
		SCREAMING_SNAKE_CASE       :     List[Any]				= (24, 30)
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer(a_		,		entity_spans=[span]		,		return_tensors='''pt'''     )

		SCREAMING_SNAKE_CASE       :     int				= model(**a_     )

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

		SCREAMING_SNAKE_CASE       :     Any				= outputs.entity_logits[0][0].argmax().item()
		SCREAMING_SNAKE_CASE       :     int				= [
		    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 __A						(   a_ :							Tuple     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= ['''[MASK]''', '''[PAD]''', '''[UNK]''']

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

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


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			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__ :       List[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"""






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

					# merge samples
					if i == 0:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= down_samples, mid_sample
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= [
							    samples_prev + samples_curr
							    for samples_prev, samples_curr in zip(lowerCamelCase_		, lowerCamelCase_					)
							]
							mid_block_res_sample += mid_sample

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# load controlnet and append to list until no controlnet directory exists anymore
			# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
			# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
			SCREAMING_SNAKE_CASE       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

			if len(lowerCamelCase_					) == 0:
					raise ValueError(
					    f"No ControlNets found under {os.path.dirname(lowerCamelCase_					)}. Expected at least {pretrained_model_path + '_0'}."					)

			return cls(lowerCamelCase_					)





