





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__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Union[str, Any]:







	'''simple docstring'''







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

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

	# Load the entity vocab file
	lowerCamelCase__								=		load_original_entity_vocab(a_							)
	# 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=a_       ,rstrip=a_							)
	lowerCamelCase__								=		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:
		lowerCamelCase__								=		json.load(a_							)
	lowerCamelCase__								=		'''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_							)

	lowerCamelCase__								=		MLukeTokenizer.from_pretrained(a_							)

	# 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=a_							).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(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
	lowerCamelCase__								=		MLukeTokenizer.from_pretrained(a_       ,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(a_       ,entity_spans=[span]       ,return_tensors='''pt'''							)

	lowerCamelCase__								=		model(**a_							)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		lowerCamelCase__								=		torch.Size((1, 33, 768)							)
		lowerCamelCase__								=		torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]							)

	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
		lowerCamelCase__								=		torch.Size((1, 1, 768)							)
		lowerCamelCase__								=		torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]							)

	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
	lowerCamelCase__								=		MLukeTokenizer.from_pretrained(a_							)
	lowerCamelCase__								=		'''Tokyo is the capital of <mask>.'''
	lowerCamelCase__								=		(24, 30)
	lowerCamelCase__								=		tokenizer(a_       ,entity_spans=[span]       ,return_tensors='''pt'''							)

	lowerCamelCase__								=		model(**a_							)

	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(a_							)

	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(a_							)							)
	model.save_pretrained(a_							)





def lowerCAmelCase__(__snake_case							)				->      Union[str, Any]:







	'''simple docstring'''







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

	lowerCamelCase__								=		[json.loads(a_							) for line in open(a_							)]

	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 typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase  							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase  							=					"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"





def 						__UpperCAmelCase			(     a_							,     a_							,     a_=8):
							snake_case_				=							height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							snake_case_				=							width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a       ,			)       ->    Tuple:
								super().__init__()

								self.register_modules(
								    unet=a       ,			scheduler=a       ,			movq=a       ,			)
								snake_case_				=							2 ** (len(self.movq.config.block_out_channels					) - 1)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Any:
								if latents is None:
															snake_case_				=							randn_tensor(a       ,			generator=a       ,			device=a       ,			dtype=a					)
								else:
															if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
															snake_case_				=							latents.to(a					)

								snake_case_				=							latents * scheduler.init_noise_sigma
								return latents




	def 							_UpperCamelCase			(       self       ,			a=0					)       ->    str:
								if is_accelerate_available():
															from accelerate import cpu_offload
								else:
															raise ImportError('Please install accelerate via `pip install accelerate`'					)

								snake_case_				=							torch.device(F'''cuda:{gpu_id}'''					)

								snake_case_				=							[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																						cpu_offload(a       ,			a					)




	def 							_UpperCamelCase			(       self       ,			a=0					)       ->    List[str]:
								if is_accelerate_available() and is_accelerate_version('>='       ,			'0.17.0.dev0'					):
															from accelerate import cpu_offload_with_hook
								else:
															raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'					)

								snake_case_				=							torch.device(F'''cuda:{gpu_id}'''					)

								if self.device.type != "cpu":
															self.to('cpu'       ,			silence_dtype_warnings=a					)
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

								snake_case_				=							None
								for cpu_offloaded_model in [self.unet, self.movq]:
															snake_case_			,		snake_case_				=							cpu_offload_with_hook(a       ,			a       ,			prev_module_hook=a					)

								# We'll offload the last model manually.
								snake_case_				=							hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 							_UpperCamelCase			(       self					)       ->    Any:
								if not hasattr(self.unet       ,			'_hf_hook'					):
															return self.device
								for module in self.unet.modules():
															if (
															    hasattr(a       ,			'_hf_hook'					)
															    and hasattr(module._hf_hook       ,			'execution_device'					)
															    and module._hf_hook.execution_device is not None
															):
																						return torch.device(module._hf_hook.execution_device					)
								return self.device






	@torch.no_grad()
	@replace_example_docstring(a					)
	def __call__(       self       ,			a       ,			a       ,			a       ,			a = 5_12       ,			a = 5_12       ,			a = 1_00       ,			a = 4.0       ,			a = 1       ,			a = None       ,			a = None       ,			a = "pil"       ,			a = True       ,			)       ->    List[str]:
								snake_case_				=							self._execution_device

								snake_case_				=							guidance_scale > 1.0

								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)
								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)
								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)

								snake_case_				=							image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
															snake_case_				=							image_embeds.repeat_interleave(a       ,			dim=0					)
															snake_case_				=							negative_image_embeds.repeat_interleave(a       ,			dim=0					)
															snake_case_				=							hint.repeat_interleave(a       ,			dim=0					)

								snake_case_				=							torch.cat([negative_image_embeds, image_embeds]       ,			dim=0					).to(dtype=self.unet.dtype       ,			device=a					)
								snake_case_				=							torch.cat([hint, hint]       ,			dim=0					).to(dtype=self.unet.dtype       ,			device=a					)
								self.scheduler.set_timesteps(a       ,			device=a					)
								snake_case_				=							self.scheduler.timesteps

								snake_case_				=							self.movq.config.latent_channels

								snake_case_			,		snake_case_				=							downscale_height_and_width(a       ,			a       ,			self.movq_scale_factor					)

								# create initial latent
								snake_case_				=							self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)       ,			image_embeds.dtype       ,			a       ,			a       ,			a       ,			self.scheduler       ,			)

								for i, t in enumerate(self.progress_bar(a					)					):
															# expand the latents if we are doing classifier free guidance
															snake_case_				=							torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

															snake_case_				=							{'image_embeds': image_embeds, 'hint': hint}
															snake_case_				=							self.unet(
															    sample=a       ,			timestep=a       ,			encoder_hidden_states=a       ,			added_cond_kwargs=a       ,			return_dict=a       ,			)[0]

															if do_classifier_free_guidance:
																						snake_case_			,		snake_case_				=							noise_pred.split(latents.shape[1]       ,			dim=1					)
																						snake_case_			,		snake_case_				=							noise_pred.chunk(2					)
																						snake_case_			,		snake_case_				=							variance_pred.chunk(2					)
																						snake_case_				=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						snake_case_				=							torch.cat([noise_pred, variance_pred_text]       ,			dim=1					)

															if not (
															    hasattr(self.scheduler.config       ,			'variance_type'					)
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																						snake_case_			,		snake_case_				=							noise_pred.split(latents.shape[1]       ,			dim=1					)

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_				=							self.scheduler.step(
															    a       ,			a       ,			a       ,			generator=a       ,			)[0]
								# post-processing
								snake_case_				=							self.movq.decode(a       ,			force_not_quantize=a					)['sample']

								if output_type not in ["pt", "np", "pil"]:
															raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''					)

								if output_type in ["np", "pil"]:
															snake_case_				=							image * 0.5 + 0.5
															snake_case_				=							image.clamp(0       ,			1					)
															snake_case_				=							image.cpu().permute(0       ,			2       ,			3       ,			1					).float().numpy()

								if output_type == "pil":
															snake_case_				=							self.numpy_to_pil(a					)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a					)





