




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      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: List[Any]					,	UpperCamelCase__							: List[Any] )	->			Tuple:





				"""simple docstring"""


				with open(UpperCamelCase__ ) as metadata_file:
								__lowerCamelCase									=       json.load(UpperCamelCase__ )
				__lowerCamelCase									=       LukeConfig(use_entity_aware_attention=UpperCamelCase__					,	**metadata['model_config'] )

				# Load in the weights from the checkpoint_path
				__lowerCamelCase									=       torch.load(UpperCamelCase__					,	map_location='cpu' )['module']

				# Load the entity vocab file
				__lowerCamelCase									=       load_original_entity_vocab(UpperCamelCase__ )
				# add an entry for [MASK2]
				__lowerCamelCase									=       max(entity_vocab.values() ) + 1
				config.entity_vocab_size += 1

				__lowerCamelCase									=       XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )

				# Add special tokens to the token vocabulary for downstream tasks
				__lowerCamelCase									=       AddedToken('<ent>'					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__ )
				__lowerCamelCase									=       AddedToken('<ent2>'					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__ )
				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(UpperCamelCase__ )
				with open(os.path.join(UpperCamelCase__					,	'tokenizer_config.json' )					,	'r' ) as f:
								__lowerCamelCase									=       json.load(UpperCamelCase__ )
				__lowerCamelCase									=       'MLukeTokenizer'
				with open(os.path.join(UpperCamelCase__					,	'tokenizer_config.json' )					,	'w' ) as f:
								json.dump(UpperCamelCase__					,	UpperCamelCase__ )

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

				__lowerCamelCase									=       MLukeTokenizer.from_pretrained(UpperCamelCase__ )

				# Initialize the embeddings of the special tokens
				__lowerCamelCase									=       tokenizer.convert_tokens_to_ids(['@'] )[0]
				__lowerCamelCase									=       tokenizer.convert_tokens_to_ids(['#'] )[0]

				__lowerCamelCase									=       state_dict['embeddings.word_embeddings.weight']
				__lowerCamelCase									=       word_emb[ent_init_index].unsqueeze(0 )
				__lowerCamelCase									=       word_emb[enta_init_index].unsqueeze(0 )
				__lowerCamelCase									=       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"]:
								__lowerCamelCase									=       state_dict[bias_name]
								__lowerCamelCase									=       decoder_bias[ent_init_index].unsqueeze(0 )
								__lowerCamelCase									=       decoder_bias[enta_init_index].unsqueeze(0 )
								__lowerCamelCase									=       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"]:
												__lowerCamelCase									=       F"""encoder.layer.{layer_index}.attention.self."""
												__lowerCamelCase									=       state_dict[prefix + matrix_name]
												__lowerCamelCase									=       state_dict[prefix + matrix_name]
												__lowerCamelCase									=       state_dict[prefix + matrix_name]

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

				__lowerCamelCase									=       LukeForMaskedLM(config=UpperCamelCase__ ).eval()

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

				__lowerCamelCase			,		__lowerCamelCase									=       model.load_state_dict(UpperCamelCase__					,	strict=UpperCamelCase__ )

				if set(UpperCamelCase__ ) != {"luke.embeddings.position_ids"}:
								raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
				if set(UpperCamelCase__ ) != {
				    "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
				__lowerCamelCase									=       MLukeTokenizer.from_pretrained(UpperCamelCase__					,	task='entity_classification' )

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

				__lowerCamelCase									=       model(**UpperCamelCase__ )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								__lowerCamelCase									=       torch.Size((1, 33, 768) )
								__lowerCamelCase									=       torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )

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

				# Verify entity hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								__lowerCamelCase									=       torch.Size((1, 1, 768) )
								__lowerCamelCase									=       torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )

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

				# Verify masked word/entity prediction
				__lowerCamelCase									=       MLukeTokenizer.from_pretrained(UpperCamelCase__ )
				__lowerCamelCase									=       'Tokyo is the capital of <mask>.'
				__lowerCamelCase									=       (24, 30)
				__lowerCamelCase									=       tokenizer(UpperCamelCase__					,	entity_spans=[span]					,	return_tensors='pt' )

				__lowerCamelCase									=       model(**UpperCamelCase__ )

				__lowerCamelCase									=       encoding['input_ids'][0].tolist()
				__lowerCamelCase									=       input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
				__lowerCamelCase									=       outputs.logits[0][mask_position_id].argmax(dim=-1 )
				assert "Japan" == tokenizer.decode(UpperCamelCase__ )

				__lowerCamelCase									=       outputs.entity_logits[0][0].argmax().item()
				__lowerCamelCase									=       [
				    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(UpperCamelCase__ ) )
				model.save_pretrained(UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       ['[MASK]', '[PAD]', '[UNK]']

				__lowerCamelCase									=       [json.loads(UpperCamelCase__ ) for line in open(UpperCamelCase__ )]

				__lowerCamelCase									=       {}
				for entry in data:
								__lowerCamelCase									=       entry['id']
								for entity_name, language in entry["entities"]:
												if entity_name in SPECIAL_TOKENS:
																__lowerCamelCase									=       entity_id
																break
												__lowerCamelCase									=       F"""{language}:{entity_name}"""
												__lowerCamelCase									=       entity_id
				return new_mapping


if __name__ == "__main__":
					__A								=       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."
					)
					__A								=       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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42







class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 65_536     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 0     ,		lowerCamelCase__ = "fourier"     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = 0.0     ,		lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,		lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,		lowerCamelCase__ = "UNetMidBlock1D"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = (32, 32, 64)     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 8     ,		lowerCamelCase__ = 1     ,		lowerCamelCase__ = False     ,		)       -> Dict:



							'''simple docstring'''
							super().__init__()
							__lowerCamelCase									=       sample_size

							# time
							if time_embedding_type == "fourier":
											__lowerCamelCase									=       GaussianFourierProjection(
											    embedding_size=8     ,		set_W_to_weight=lowerCamelCase__     ,		log=lowerCamelCase__     ,		flip_sin_to_cos=lowerCamelCase__				)
											__lowerCamelCase									=       2 * block_out_channels[0]
							elif time_embedding_type == "positional":
											__lowerCamelCase									=       Timesteps(
											    block_out_channels[0]     ,		flip_sin_to_cos=lowerCamelCase__     ,		downscale_freq_shift=lowerCamelCase__				)
											__lowerCamelCase									=       block_out_channels[0]

							if use_timestep_embedding:
											__lowerCamelCase									=       block_out_channels[0] * 4
											__lowerCamelCase									=       TimestepEmbedding(
											    in_channels=lowerCamelCase__     ,		time_embed_dim=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		out_dim=block_out_channels[0]     ,		)

							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None
							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None

							# down
							__lowerCamelCase									=       in_channels
							for i, down_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       block_out_channels[i]

											if i == 0:
															input_channel += extra_in_channels

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_down_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_downsample=not is_final_block or downsample_each_block     ,		)
											self.down_blocks.append(lowerCamelCase__				)

							# mid
							__lowerCamelCase									=       get_mid_block(
							    lowerCamelCase__     ,		in_channels=block_out_channels[-1]     ,		mid_channels=block_out_channels[-1]     ,		out_channels=block_out_channels[-1]     ,		embed_dim=block_out_channels[0]     ,		num_layers=lowerCamelCase__     ,		add_downsample=lowerCamelCase__     ,		)

							# up
							__lowerCamelCase									=       list(reversed(lowerCamelCase__				)				)
							__lowerCamelCase									=       reversed_block_out_channels[0]
							if out_block_type is None:
											__lowerCamelCase									=       out_channels
							else:
											__lowerCamelCase									=       block_out_channels[0]

							for i, up_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       (
											    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__				) - 1 else final_upsample_channels
											)

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_up_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_upsample=not is_final_block     ,		)
											self.up_blocks.append(lowerCamelCase__				)
											__lowerCamelCase									=       output_channel

							# out
							__lowerCamelCase									=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,		32				)
							__lowerCamelCase									=       get_out_block(
							    out_block_type=lowerCamelCase__     ,		num_groups_out=lowerCamelCase__     ,		embed_dim=block_out_channels[0]     ,		out_channels=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		fc_dim=block_out_channels[-1] // 4     ,		)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = True     ,		)       -> Union[UNetaDOutput, Tuple]:



							'''simple docstring'''
							__lowerCamelCase									=       timestep
							if not torch.is_tensor(lowerCamelCase__				):
											__lowerCamelCase									=       torch.tensor([timesteps]     ,		dtype=torch.long     ,		device=sample.device				)
							elif torch.is_tensor(lowerCamelCase__				) and len(timesteps.shape				) == 0:
											__lowerCamelCase									=       timesteps[None].to(sample.device				)

							__lowerCamelCase									=       self.time_proj(lowerCamelCase__				)
							if self.config.use_timestep_embedding:
											__lowerCamelCase									=       self.time_mlp(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       timestep_embed[..., None]
											__lowerCamelCase									=       timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
											__lowerCamelCase									=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

							# 2. down
							__lowerCamelCase									=       ()
							for downsample_block in self.down_blocks:
											__lowerCamelCase			,		__lowerCamelCase									=       downsample_block(hidden_states=lowerCamelCase__     ,		temb=lowerCamelCase__				)
											down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
											__lowerCamelCase									=       self.mid_block(lowerCamelCase__     ,		lowerCamelCase__				)

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks				):
											__lowerCamelCase									=       down_block_res_samples[-1:]
											__lowerCamelCase									=       down_block_res_samples[:-1]
											__lowerCamelCase									=       upsample_block(lowerCamelCase__     ,		res_hidden_states_tuple=lowerCamelCase__     ,		temb=lowerCamelCase__				)

							# 5. post-process
							if self.out_block:
											__lowerCamelCase									=       self.out_block(lowerCamelCase__     ,		lowerCamelCase__				)

							if not return_dict:
											return (sample,)

							return UNetaDOutput(sample=lowerCamelCase__				)


