


'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 		lowerCAmelCase__	(	__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase		):
			lowerCAmelCase       :		Dict				      =   StableDiffusionInstructPixaPixPipeline
			lowerCAmelCase       :		Tuple				      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
			lowerCAmelCase       :		List[Any]				      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCAmelCase       :		Tuple				      =   IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCAmelCase       :		List[str]				      =   IMAGE_TO_IMAGE_IMAGE_PARAMS




			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Dict:



					'''simple docstring'''







					torch.manual_seed(0       )
					_UpperCAmelCase						:				Any	    =					UNetaDConditionModel(
					    block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=8		,     out_channels=4		,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,     cross_attention_dim=32		,     )
					_UpperCAmelCase						:				Union[str, Any]	    =					PNDMScheduler(skip_prk_steps=__A       )
					torch.manual_seed(0       )
					_UpperCAmelCase						:				Union[str, Any]	    =					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       )
					_UpperCAmelCase						:				List[str]	    =					CLIPTextConfig(
					    bos_token_id=0		,     eos_token_id=2		,     hidden_size=32		,     intermediate_size=37		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=10_00		,     )
					_UpperCAmelCase						:				Dict	    =					CLIPTextModel(__A       )
					_UpperCAmelCase						:				str	    =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

					_UpperCAmelCase						:				Any	    =					{
					    "unet": unet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Dict		,     lowerCamelCase__     :   Any=0       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					floats_tensor((1, 3, 32, 32)		,     rng=random.Random(__A       )       ).to(__A       )
					_UpperCAmelCase						:				List[str]	    =					image.cpu().permute(0		,     2		,     3		,     1       )[0]
					_UpperCAmelCase						:				List[str]	    =					Image.fromarray(np.uinta(__A       )       ).convert("RGB"       )
					if str(__A       ).startswith("mps"       ):
							_UpperCAmelCase						:				Union[str, Any]	    =					torch.manual_seed(__A       )
					else:
							_UpperCAmelCase						:				Dict	    =					torch.Generator(device=__A       ).manual_seed(__A       )
					_UpperCAmelCase						:				Union[str, Any]	    =					{
					    "prompt": "A painting of a squirrel eating a burger",
					    "image": image,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "image_guidance_scale": 1,
					    "output_type": "numpy",
					}
					return inputs




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



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					"cpu"  # ensure determinism for the device-dependent torch.Generator
					_UpperCAmelCase						:				List[Any]	    =					self.get_dummy_components()
					_UpperCAmelCase						:				Any	    =					StableDiffusionInstructPixaPixPipeline(**__A       )
					_UpperCAmelCase						:				List[str]	    =					sd_pipe.to(__A       )
					sd_pipe.set_progress_bar_config(disable=__A       )

					_UpperCAmelCase						:				List[str]	    =					self.get_dummy_inputs(__A       )
					_UpperCAmelCase						:				Dict	    =					sd_pipe(**__A       ).images
					_UpperCAmelCase						:				int	    =					image[0, -3:, -3:, -1]
					assert image.shape == (1, 32, 32, 3)
					_UpperCAmelCase						:				Union[str, Any]	    =					np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]       )

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




			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					"cpu"  # ensure determinism for the device-dependent torch.Generator
					_UpperCAmelCase						:				Optional[Any]	    =					self.get_dummy_components()
					_UpperCAmelCase						:				int	    =					StableDiffusionInstructPixaPixPipeline(**__A       )
					_UpperCAmelCase						:				str	    =					sd_pipe.to(__A       )
					sd_pipe.set_progress_bar_config(disable=__A       )

					_UpperCAmelCase						:				Any	    =					self.get_dummy_inputs(__A       )
					_UpperCAmelCase						:				str	    =					"french fries"
					_UpperCAmelCase						:				List[Any]	    =					sd_pipe(**__A		,     negative_prompt=__A       )
					_UpperCAmelCase						:				List[Any]	    =					output.images
					_UpperCAmelCase						:				List[str]	    =					image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCAmelCase						:				List[str]	    =					np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]       )

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




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					"cpu"  # ensure determinism for the device-dependent torch.Generator
					_UpperCAmelCase						:				Optional[Any]	    =					self.get_dummy_components()
					_UpperCAmelCase						:				Optional[Any]	    =					StableDiffusionInstructPixaPixPipeline(**__A       )
					_UpperCAmelCase						:				Optional[int]	    =					sd_pipe.to(__A       )
					sd_pipe.set_progress_bar_config(disable=__A       )

					_UpperCAmelCase						:				List[Any]	    =					self.get_dummy_inputs(__A       )
					_UpperCAmelCase						:				List[str]	    =					[inputs["prompt"]] * 2

					_UpperCAmelCase						:				Optional[int]	    =					np.array(inputs["image"]       ).astype(np.floataa       ) / 2_5_5.0
					_UpperCAmelCase						:				Tuple	    =					torch.from_numpy(__A       ).unsqueeze(0       ).to(__A       )
					_UpperCAmelCase						:				List[Any]	    =					image / 2 + 0.5
					_UpperCAmelCase						:				Optional[int]	    =					image.permute(0		,     3		,     1		,     2       )
					_UpperCAmelCase						:				List[str]	    =					image.repeat(2		,     1		,     1		,     1       )

					_UpperCAmelCase						:				int	    =					sd_pipe(**__A       ).images
					_UpperCAmelCase						:				Dict	    =					image[-1, -3:, -3:, -1]

					assert image.shape == (2, 32, 32, 3)
					_UpperCAmelCase						:				Union[str, Any]	    =					np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]       )

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




			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					"cpu"  # ensure determinism for the device-dependent torch.Generator
					_UpperCAmelCase						:				Any	    =					self.get_dummy_components()
					_UpperCAmelCase						:				Optional[int]	    =					EulerAncestralDiscreteScheduler(
					    beta_start=0.0_0_0_8_5		,     beta_end=0.0_1_2		,     beta_schedule="scaled_linear"       )
					_UpperCAmelCase						:				int	    =					StableDiffusionInstructPixaPixPipeline(**__A       )
					_UpperCAmelCase						:				int	    =					sd_pipe.to(__A       )
					sd_pipe.set_progress_bar_config(disable=__A       )

					_UpperCAmelCase						:				List[str]	    =					self.get_dummy_inputs(__A       )
					_UpperCAmelCase						:				List[Any]	    =					sd_pipe(**__A       ).images
					_UpperCAmelCase						:				List[str]	    =					image[0, -3:, -3:, -1]

					_UpperCAmelCase						:				Optional[int]	    =					[round(__A		,     4       ) for x in image_slice.flatten().tolist()]
					print(",".join([str(__A       ) for x in slice]       )       )

					assert image.shape == (1, 32, 32, 3)
					_UpperCAmelCase						:				int	    =					np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]       )

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




			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''simple docstring'''







					super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






			def 							lowerCAmelCase__			(						self     :   str       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					self.get_dummy_components()
					_UpperCAmelCase						:				List[str]	    =					StableDiffusionInstructPixaPixPipeline(**__A       )
					_UpperCAmelCase						:				int	    =					VaeImageProcessor(do_resize=__A		,     do_normalize=__A       )
					_UpperCAmelCase						:				str	    =					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )

					_UpperCAmelCase						:				Optional[int]	    =					pipe(**self.get_dummy_inputs_by_type(__A		,     input_image_type="pt"       )       )[0]

					_UpperCAmelCase						:				Any	    =					components["vae"]
					_UpperCAmelCase						:				List[Any]	    =					self.get_dummy_inputs_by_type(__A		,     input_image_type="pt"       )

					for image_param in self.image_latents_params:
							if image_param in inputs.keys():
									_UpperCAmelCase						:				List[Any]	    =					vae.encode(inputs[image_param]       ).latent_dist.mode()

					_UpperCAmelCase						:				int	    =					pipe(**__A       )[0]

					_UpperCAmelCase						:				int	    =					np.abs(out - out_latents_inputs       ).max()
					self.assertLess(__A		,     1E-4		,     "passing latents as image input generate different result from passing image"       )






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




			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''simple docstring'''







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   int=0       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					torch.manual_seed(__A       )
					_UpperCAmelCase						:				Any	    =					load_image(
					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"       )
					_UpperCAmelCase						:				List[Any]	    =					{
					    "prompt": "turn him into a cyborg",
					    "image": image,
					    "generator": generator,
					    "num_inference_steps": 3,
					    "guidance_scale": 7.5,
					    "image_guidance_scale": 1.0,
					    "output_type": "numpy",
					}
					return inputs




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"		,     safety_checker=__A       )
					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )
					pipe.enable_attention_slicing()

					_UpperCAmelCase						:				Optional[int]	    =					self.get_inputs()
					_UpperCAmelCase						:				Tuple	    =					pipe(**__A       ).images
					_UpperCAmelCase						:				Tuple	    =					image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 5_12, 5_12, 3)
					_UpperCAmelCase						:				int	    =					np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]       )

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




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"		,     safety_checker=__A       )
					_UpperCAmelCase						:				Tuple	    =					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )
					pipe.enable_attention_slicing()

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

					assert image.shape == (1, 5_12, 5_12, 3)
					_UpperCAmelCase						:				Tuple	    =					np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]       )

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




			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"		,     safety_checker=__A       )
					_UpperCAmelCase						:				List[Any]	    =					DDIMScheduler.from_config(pipe.scheduler.config       )
					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )
					pipe.enable_attention_slicing()

					_UpperCAmelCase						:				str	    =					self.get_inputs()
					_UpperCAmelCase						:				str	    =					pipe(**__A       ).images
					_UpperCAmelCase						:				List[str]	    =					image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 5_12, 5_12, 3)
					_UpperCAmelCase						:				List[Any]	    =					np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]       )

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




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					0

					def callback_fn(lowerCamelCase__     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   torch.FloatTensor       ) -> None:
							_UpperCAmelCase						:				List[str]	    =					True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
									_UpperCAmelCase						:				Optional[Any]	    =					latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									_UpperCAmelCase						:				str	    =					latents[0, -3:, -3:, -1]
									_UpperCAmelCase						:				Any	    =					np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]       )

									assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
							elif step == 2:
									_UpperCAmelCase						:				Any	    =					latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									_UpperCAmelCase						:				List[str]	    =					latents[0, -3:, -3:, -1]
									_UpperCAmelCase						:				Any	    =					np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]       )

									assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

					_UpperCAmelCase						:				Optional[int]	    =					False

					_UpperCAmelCase						:				Optional[Any]	    =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"		,     safety_checker=__A		,     torch_dtype=torch.floataa       )
					_UpperCAmelCase						:				Any	    =					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )
					pipe.enable_attention_slicing()

					_UpperCAmelCase						:				str	    =					self.get_inputs()
					pipe(**__A		,     callback=__A		,     callback_steps=1       )
					assert callback_fn.has_been_called
					assert number_of_steps == 3




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Optional[int]:



					'''simple docstring'''







					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					_UpperCAmelCase						:				Dict	    =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    "timbrooks/instruct-pix2pix"		,     safety_checker=__A		,     torch_dtype=torch.floataa       )
					_UpperCAmelCase						:				Tuple	    =					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )
					pipe.enable_attention_slicing(1       )
					pipe.enable_sequential_cpu_offload()

					_UpperCAmelCase						:				List[str]	    =					self.get_inputs()
					_UpperCAmelCase						:				int	    =					pipe(**__A       )

					_UpperCAmelCase						:				List[Any]	    =					torch.cuda.max_memory_allocated()
					# make sure that less than 2.2 GB is allocated
					assert mem_bytes < 2.2 * 10**9






			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.get_inputs()
					# resize to resolution that is divisible by 8 but not 16 or 32
					_UpperCAmelCase						:				Optional[int]	    =					inputs["image"].resize((5_04, 5_04)       )

					_UpperCAmelCase						:				List[Any]	    =					"timbrooks/instruct-pix2pix"
					_UpperCAmelCase						:				int	    =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    __A		,     safety_checker=__A		,     )
					pipe.to(__A       )
					pipe.set_progress_bar_config(disable=__A       )
					pipe.enable_attention_slicing()

					_UpperCAmelCase						:				List[Any]	    =					pipe(**__A       )
					_UpperCAmelCase						:				str	    =					output.images[0]

					_UpperCAmelCase						:				Any	    =					image[2_55:2_58, 3_83:3_86, -1]

					assert image.shape == (5_04, 5_04, 3)
					_UpperCAmelCase						:				int	    =					np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3










'''simple docstring'''
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Any     ) ->      Optional[Any]:



       """simple docstring"""






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

       # Load in the weights from the checkpoint_path
       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )

       # Load the entity vocab file
       __UpperCamelCase       =  load_entity_vocab(__lowercase     )

       __UpperCamelCase       =  RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

       # Add special tokens to the token vocabulary for downstream tasks
       __UpperCamelCase       =  AddedToken('<ent>'				,  lstrip=__lowercase				,  rstrip=__lowercase     )
       __UpperCamelCase       =  AddedToken('<ent2>'				,  lstrip=__lowercase				,  rstrip=__lowercase     )
       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(__lowercase     )
       with open(os.path.join(__lowercase				,  LukeTokenizer.vocab_files_names['entity_vocab_file']     )				,  'w'     ) as f:
              json.dump(__lowercase				,  __lowercase     )

       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase     )

       # Initialize the embeddings of the special tokens
       __UpperCamelCase       =  state_dict['embeddings.word_embeddings.weight']
       __UpperCamelCase       =  word_emb[tokenizer.convert_tokens_to_ids(['@']     )[0]].unsqueeze(0     )
       __UpperCamelCase       =  word_emb[tokenizer.convert_tokens_to_ids(['#']     )[0]].unsqueeze(0     )
       __UpperCamelCase       =  torch.cat([word_emb, ent_emb, enta_emb]     )

       # 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"]:
                     __UpperCamelCase       =  F'''encoder.layer.{layer_index}.attention.self.'''
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]
                     __UpperCamelCase       =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       __UpperCamelCase       =  state_dict['entity_embeddings.entity_embeddings.weight']
       __UpperCamelCase       =  entity_emb[entity_vocab['[MASK]']]

       __UpperCamelCase       =  LukeModel(config=__lowercase     ).eval()

       __UpperCamelCase       ,						__UpperCamelCase       =  model.load_state_dict(__lowercase				,  strict=__lowercase     )
       if not (len(__lowercase     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
              raise ValueError(F'''Missing keys {', '.join(__lowercase     )}. Expected only missing embeddings.position_ids'''     )
       if not (all(key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ) for key in unexpected_keys     )):
              raise ValueError(
                  'Unexpected keys'
                  F''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ))]     )}'''     )

       # Check outputs
       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase				,  task='entity_classification'     )

       __UpperCamelCase       =  (
           'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
           ' new world number one avoid a humiliating second- round exit at Wimbledon .'
       )
       __UpperCamelCase       =  (39, 42)
       __UpperCamelCase       =  tokenizer(__lowercase				,  entity_spans=[span]				,  add_prefix_space=__lowercase				,  return_tensors='pt'     )

       __UpperCamelCase       =  model(**__lowercase     )

       # Verify word hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 42, 1024)     )
              __UpperCamelCase       =  torch.tensor(
                  [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 42, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]     )

       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]				,  __lowercase				,  atol=1e-4     ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 1, 1024)     )
              __UpperCamelCase       =  torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 1, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]     )

       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]				,  __lowercase				,  atol=1e-4     ):
              raise ValueError

       # Finally, save our PyTorch model and tokenizer
       print('Saving PyTorch model to {}'.format(__lowercase     )     )
       model.save_pretrained(__lowercase     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  {}
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              for index, line in enumerate(__lowercase     ):
                     __UpperCamelCase       ,						__UpperCamelCase       =  line.rstrip().split('\t'     )
                     __UpperCamelCase       =  index

       return entity_vocab


if __name__ == "__main__":
    a__							:							Any =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__							:							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,
    )
