

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Union[str, Any]:
	# Load configuration defined in the metadata file
	with open(lowerCamelCase__      ) as metadata_file:
		__lowerCamelCase    :    Dict 	=			json.load(lowerCamelCase__      )
	__lowerCamelCase    :    List[str] 	=			LukeConfig(use_entity_aware_attention=lowerCamelCase__     ,			**metadata['model_config']      )

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

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

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

	# Add special tokens to the token vocabulary for downstream tasks
	__lowerCamelCase    :    Tuple 	=			AddedToken('<ent>'     ,			lstrip=lowerCamelCase__     ,			rstrip=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			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__     ,			'tokenizer_config.json'      )     ,			'r'      ) as f:
		__lowerCamelCase    :    Optional[int] 	=			json.load(lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			'MLukeTokenizer'
	with open(os.path.join(lowerCamelCase__     ,			'tokenizer_config.json'      )     ,			'w'      ) as f:
		json.dump(lowerCamelCase__     ,			lowerCamelCase__      )

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

	__lowerCamelCase    :    int 	=			MLukeTokenizer.from_pretrained(lowerCamelCase__      )

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

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

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

	__lowerCamelCase    :    Tuple 	=			LukeForMaskedLM(config=lowerCamelCase__      ).eval()

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

	__lowerCamelCase	,			__lowerCamelCase    :    str 	=			model.load_state_dict(lowerCamelCase__     ,			strict=lowerCamelCase__      )

	if set(lowerCamelCase__      ) != {"luke.embeddings.position_ids"}:
		raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"      )
	if set(lowerCamelCase__      ) != {
	    "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    :    List[Any] 	=			MLukeTokenizer.from_pretrained(lowerCamelCase__     ,			task='entity_classification'      )

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

	__lowerCamelCase    :    Any 	=			model(**lowerCamelCase__      )

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		__lowerCamelCase    :    Any 	=			torch.Size((1, 3_3, 7_6_8)      )
		__lowerCamelCase    :    Optional[int] 	=			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]     ,			lowerCamelCase__     ,			atol=1e-4      ):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		__lowerCamelCase    :    str 	=			torch.Size((1, 1, 7_6_8)      )
		__lowerCamelCase    :    List[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]     ,			lowerCamelCase__     ,			atol=1e-4      ):
		raise ValueError

	# Verify masked word/entity prediction
	__lowerCamelCase    :    List[str] 	=			MLukeTokenizer.from_pretrained(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			'Tokyo is the capital of <mask>.'
	__lowerCamelCase    :    Optional[int] 	=			(2_4, 3_0)
	__lowerCamelCase    :    List[Any] 	=			tokenizer(lowerCamelCase__     ,			entity_spans=[span]     ,			return_tensors='pt'      )

	__lowerCamelCase    :    str 	=			model(**lowerCamelCase__      )

	__lowerCamelCase    :    Tuple 	=			encoding['input_ids'][0].tolist()
	__lowerCamelCase    :    List[str] 	=			input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'      )      )
	__lowerCamelCase    :    Optional[Any] 	=			outputs.logits[0][mask_position_id].argmax(dim=-1      )
	assert "Japan" == tokenizer.decode(lowerCamelCase__      )

	__lowerCamelCase    :    str 	=			outputs.entity_logits[0][0].argmax().item()
	__lowerCamelCase    :    Optional[Any] 	=			[
	    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(lowerCamelCase__      )      )
	model.save_pretrained(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Tuple 	=			['[MASK]', '[PAD]', '[UNK]']

	__lowerCamelCase    :    List[Any] 	=			[json.loads(lowerCamelCase__      ) for line in open(lowerCamelCase__      )]

	__lowerCamelCase    :    Dict 	=			{}
	for entry in data:
		__lowerCamelCase    :    Tuple 	=			entry['id']
		for entity_name, language in entry["entities"]:
			if entity_name in SPECIAL_TOKENS:
				__lowerCamelCase    :    Tuple 	=			entity_id
				break
			__lowerCamelCase    :    Optional[int] 	=			F"{language}:{entity_name}"
			__lowerCamelCase    :    Any 	=			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,
					)







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
a		=parser.parse_args()


a		="""cpu"""
a		="""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a		="""path-to-your-trained-model"""
a		=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					a		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a		=pipe.to(device)

# to channels last
a		=pipe.unet.to(memory_format=torch.channels_last)
a		=pipe.vae.to(memory_format=torch.channels_last)
a		=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					a		=pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		=torch.randn(2, 4, 64, 64)
a		=torch.rand(1) * 999
a		=torch.randn(2, 77, 768)
a		=(sample, timestep, encoder_hidden_status)
try:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					a		=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a		=666
a		=torch.Generator(device).manual_seed(seed)
a		={"""generator""": generator}
if args.steps is not None:
					a		=args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					a		=pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")




