


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 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				str					,   _A    :				str					,   _A    :				Any					,   _A    :				Any				)    ->      int:




	"""simple docstring"""





	with open(lowerCamelCase_				) as metadata_file:
		lowerCAmelCase					:    Tuple      							= json.load(lowerCamelCase_				)
	lowerCAmelCase					:    str      							= LukeConfig(use_entity_aware_attention=lowerCamelCase_					,   **metadata['model_config']				)

	# Load in the weights from the checkpoint_path
	lowerCAmelCase					:    List[Any]      							= 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					:    List[str]      							= max(entity_vocab.values()				) + 1
	config.entity_vocab_size += 1

	lowerCAmelCase					:    List[Any]      							= XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']				)

	# Add special tokens to the token vocabulary for downstream tasks
	lowerCAmelCase					:    str      							= AddedToken('<ent>'					,   lstrip=lowerCamelCase_					,   rstrip=lowerCamelCase_				)
	lowerCAmelCase					:    Tuple      							= 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					:    Dict      							= json.load(lowerCamelCase_				)
	lowerCAmelCase					:    Union[str, Any]      							= """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					:    Any      							= MLukeTokenizer.from_pretrained(lowerCamelCase_				)

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

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

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

	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					:    str      							= MLukeTokenizer.from_pretrained(lowerCamelCase_					,   task='entity_classification'				)

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

	lowerCAmelCase					:    List[Any]      							= model(**lowerCamelCase_				)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		lowerCAmelCase					:    List[Any]      							= torch.Size((1, 33, 7_68)				)
		lowerCAmelCase					:    Any      							= 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]					,   lowerCamelCase_					,   atol=1e-4				):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		lowerCAmelCase					:    List[Any]      							= torch.Size((1, 1, 7_68)				)
		lowerCAmelCase					:    Union[str, Any]      							= 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]					,   lowerCamelCase_					,   atol=1e-4				):
		raise ValueError

	# Verify masked word/entity prediction
	lowerCAmelCase					:    Tuple      							= MLukeTokenizer.from_pretrained(lowerCamelCase_				)
	lowerCAmelCase					:    Dict      							= """Tokyo is the capital of <mask>."""
	lowerCAmelCase					:    Optional[int]      							= (24, 30)
	lowerCAmelCase					:    Optional[Any]      							= tokenizer(lowerCamelCase_					,   entity_spans=[span]					,   return_tensors='pt'				)

	lowerCAmelCase					:    Dict      							= model(**lowerCamelCase_				)

	lowerCAmelCase					:    List[str]      							= encoding["""input_ids"""][0].tolist()
	lowerCAmelCase					:    Dict      							= input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'				)				)
	lowerCAmelCase					:    str      							= outputs.logits[0][mask_position_id].argmax(dim=-1				)
	assert "Japan" == tokenizer.decode(lowerCamelCase_				)

	lowerCAmelCase					:    Dict      							= outputs.entity_logits[0][0].argmax().item()
	lowerCAmelCase					:    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 			__UpperCamelCase    (    _A    :				List[str]				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= ["""[MASK]""", """[PAD]""", """[UNK]"""]

	lowerCAmelCase					:    List[Any]      							= [json.loads(lowerCamelCase_				) for line in open(lowerCamelCase_				)]

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


if __name__ == "__main__":
				_lowerCAmelCase  :		Optional[int]		     =	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[str]		     =	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 gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[Any]			      = LDMTextToImagePipeline
	_lowerCamelCase   :					Optional[Any]			      = TEXT_TO_IMAGE_PARAMS - {
	    """negative_prompt""",
	    """negative_prompt_embeds""",
	    """cross_attention_kwargs""",
	    """prompt_embeds""",
	}
	_lowerCamelCase   :					List[str]			      = PipelineTesterMixin.required_optional_params - {
	    """num_images_per_prompt""",
	    """callback""",
	    """callback_steps""",
	}
	_lowerCamelCase   :					Union[str, Any]			      = TEXT_TO_IMAGE_BATCH_PARAMS
	_lowerCamelCase   :					Optional[int]			      = False



	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Dict      							= UNetaDConditionModel(
		    block_out_channels=(32, 64) ,					layers_per_block=2 ,					sample_size=32 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,					cross_attention_dim=32 ,					)
		lowerCAmelCase					:    int      							= DDIMScheduler(
		    beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					clip_sample=snake_case__ ,					set_alpha_to_one=snake_case__ ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    str      							= AutoencoderKL(
		    block_out_channels=(32, 64) ,					in_channels=3 ,					out_channels=3 ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,					latent_channels=4 ,					)
		torch.manual_seed(0    )
		lowerCAmelCase					:    Any      							= CLIPTextConfig(
		    bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=32 ,					intermediate_size=37 ,					layer_norm_eps=1e-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1000 ,					)
		lowerCAmelCase					:    str      							= CLIPTextModel(snake_case__    )
		lowerCAmelCase					:    str      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

		lowerCAmelCase					:    List[Any]      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'vqvae': vae,
		    'bert': text_encoder,
		    'tokenizer': tokenizer,
		}
		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    Optional[int]      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    str      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
		lowerCAmelCase					:    Tuple      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'generator': generator,
		    'num_inference_steps': 2,
		    'guidance_scale': 6.0,
		    'output_type': 'numpy',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= 'cpu'  # ensure determinism for the device-dependent torch.Generator

		lowerCAmelCase					:    Optional[Any]      							= self.get_dummy_components()
		lowerCAmelCase					:    Optional[Any]      							= LDMTextToImagePipeline(**snake_case__    )
		pipe.to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Tuple      							= self.get_dummy_inputs(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= pipe(**snake_case__    ).images
		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1]

		assert image.shape == (1, 16, 16, 3)
		lowerCAmelCase					:    List[Any]      							= np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]    )

		assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3



@slow
@require_torch_gpu
class  lowerCAmelCase				(				unittest.TestCase							):



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=torch.floataa ,					snake_case__=0    ):
		lowerCAmelCase					:    List[str]      							= torch.manual_seed(snake_case__    )
		lowerCAmelCase					:    int      							= np.random.RandomState(snake_case__    ).standard_normal((1, 4, 32, 32)    )
		lowerCAmelCase					:    Optional[Any]      							= torch.from_numpy(snake_case__    ).to(device=snake_case__ ,					dtype=snake_case__    )
		lowerCAmelCase					:    List[str]      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'latents': latents,
		    'generator': generator,
		    'num_inference_steps': 3,
		    'guidance_scale': 6.0,
		    'output_type': 'numpy',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= self.get_inputs(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= pipe(**snake_case__    ).images
		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 256, 256, 3)
		lowerCAmelCase					:    Tuple      							= np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]    )
		lowerCAmelCase					:    int      							= np.abs(expected_slice - image_slice    ).max()
		assert max_diff < 1e-3



@nightly
@require_torch_gpu
class  lowerCAmelCase				(				unittest.TestCase							):



	def 				lowercase     (					self    ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=torch.floataa ,					snake_case__=0    ):
		lowerCAmelCase					:    List[str]      							= torch.manual_seed(snake_case__    )
		lowerCAmelCase					:    Any      							= np.random.RandomState(snake_case__    ).standard_normal((1, 4, 32, 32)    )
		lowerCAmelCase					:    List[Any]      							= torch.from_numpy(snake_case__    ).to(device=snake_case__ ,					dtype=snake_case__    )
		lowerCAmelCase					:    List[str]      							= {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'latents': latents,
		    'generator': generator,
		    'num_inference_steps': 50,
		    'guidance_scale': 6.0,
		    'output_type': 'numpy',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'    ).to(snake_case__    )
		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    int      							= self.get_inputs(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= pipe(**snake_case__    ).images[0]

		lowerCAmelCase					:    Optional[int]      							= load_numpy(
		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'    )
		lowerCAmelCase					:    List[str]      							= np.abs(expected_image - image    ).max()
		assert max_diff < 1e-3
