





"""simple docstring"""






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       __A (     a_  :List[str]				,    a_  :Optional[Any]				,    a_  :List[str]				,    a_  :Tuple				,    a_  :str)						->						Optional[Any]:
				# Load configuration defined in the metadata file
				with open(a_) as metadata_file:
								__a	:  List[Any]         =					json.load(a_)
				__a	:  Optional[Any]         =					LukeConfig(use_entity_aware_attention=a_				,    **metadata['''model_config'''])

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

				# Load the entity vocab file
				__a	:  Tuple         =					load_original_entity_vocab(a_)
				# add an entry for [MASK2]
				__a	:  str         =					max(entity_vocab.values()) + 1
				config.entity_vocab_size += 1

				__a	:  Optional[int]         =					XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])

				# Add special tokens to the token vocabulary for downstream tasks
				__a	:  Tuple         =					AddedToken('''<ent>'''				,    lstrip=a_				,    rstrip=a_)
				__a	:  Union[str, Any]         =					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:
								__a	:  int         =					json.load(a_)
				__a	:  List[str]         =					'''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_)

				__a	:  Any         =					MLukeTokenizer.from_pretrained(a_)

				# Initialize the embeddings of the special tokens
				__a	:  Optional[int]         =					tokenizer.convert_tokens_to_ids(['''@'''])[0]
				__a	:  str         =					tokenizer.convert_tokens_to_ids(['''#'''])[0]

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

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

				__a	:  int         =					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''')
				__a	:  int         =					OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith('''lm_head''') or key.startswith('''entity_predictions''')):
												__a	:  Optional[int]         =					state_dict[key]
								else:
												__a	:  Optional[int]         =					state_dict[key]

				__a    ,  __a	:  List[str]         =					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
				__a	:  List[str]         =					MLukeTokenizer.from_pretrained(a_				,    task='''entity_classification''')

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

				__a	:  Optional[int]         =					model(**a_)

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								__a	:  List[str]         =					torch.Size((1, 33, 7_68))
								__a	:  Optional[int]         =					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
								__a	:  Any         =					torch.Size((1, 1, 7_68))
								__a	:  List[Any]         =					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
				__a	:  int         =					MLukeTokenizer.from_pretrained(a_)
				__a	:  Optional[int]         =					'''Tokyo is the capital of <mask>.'''
				__a	:  Optional[int]         =					(24, 30)
				__a	:  List[str]         =					tokenizer(a_				,    entity_spans=[span]				,    return_tensors='''pt''')

				__a	:  int         =					model(**a_)

				__a	:  Any         =					encoding['''input_ids'''][0].tolist()
				__a	:  Union[str, Any]         =					input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''))
				__a	:  Dict         =					outputs.logits[0][mask_position_id].argmax(dim=-1)
				assert "Japan" == tokenizer.decode(a_)

				__a	:  Any         =					outputs.entity_logits[0][0].argmax().item()
				__a	:  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(a_))
				model.save_pretrained(a_)

def       __A (     a_  :str)						->						str:
				__a	:  Tuple         =					['''[MASK]''', '''[PAD]''', '''[UNK]''']

				__a	:  str         =					[json.loads(a_) for line in open(a_)]

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






"""simple docstring"""






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort




class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0       ):
									__a	:  Tuple         =					floats_tensor((1, 3, 128, 128)		,     rng=random.Random(_UpperCAmelCase       )       )
									__a	:  Any         =					np.random.RandomState(_UpperCAmelCase       )
									__a	:  Any         =					{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''strength''': 0.7_5,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Dict         =					self.get_dummy_inputs()
									__a	:  Any         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Optional[int]         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 128, 128, 3)
									__a	:  List[Any]         =					np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]       )
									assert np.abs(image_slice - expected_slice       ).max() < 1e-1







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Tuple         =					PNDMScheduler.from_config(pipe.scheduler.config		,     skip_prk_steps=_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Optional[int]         =					self.get_dummy_inputs()
									__a	:  Optional[Any]         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Dict         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[int]         =					np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Any         =					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									# warmup pass to apply optimizations
									__a	:  Any         =					pipe(**self.get_dummy_inputs()       )

									__a	:  List[str]         =					self.get_dummy_inputs()
									__a	:  Tuple         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Tuple         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__a	:  int         =					np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Any         =					EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[Any]         =					np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Any         =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[int]         =					np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  str         =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Optional[int]         =					self.get_dummy_inputs()
									__a	:  Optional[Any]         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Union[str, Any]         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[Any]         =					np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]       )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					@property
					def 	_lowerCamelCase						(  self       ):
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					ort.SessionOptions()
									__a	:  Any         =					False
									return options







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/img2img/sketch-mountains-input.jpg'''       )
									__a	:  Tuple         =					init_image.resize((768, 512)       )
									# using the PNDM scheduler by default
									__a	:  List[str]         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    '''CompVis/stable-diffusion-v1-4'''		,     revision='''onnx'''		,     safety_checker=_UpperCAmelCase		,     feature_extractor=_UpperCAmelCase		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Tuple         =					'''A fantasy landscape, trending on artstation'''

									__a	:  Tuple         =					np.random.RandomState(0       )
									__a	:  int         =					pipe(
									    prompt=_UpperCAmelCase		,     image=_UpperCAmelCase		,     strength=0.7_5		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_UpperCAmelCase		,     output_type='''np'''		,     )
									__a	:  List[Any]         =					output.images
									__a	:  int         =					images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									__a	:  Any         =					np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]       )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2e-2







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/img2img/sketch-mountains-input.jpg'''       )
									__a	:  Tuple         =					init_image.resize((768, 512)       )
									__a	:  str         =					LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''		,     subfolder='''scheduler'''		,     revision='''onnx'''       )
									__a	:  Optional[int]         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''		,     revision='''onnx'''		,     scheduler=_UpperCAmelCase		,     safety_checker=_UpperCAmelCase		,     feature_extractor=_UpperCAmelCase		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  List[str]         =					'''A fantasy landscape, trending on artstation'''

									__a	:  str         =					np.random.RandomState(0       )
									__a	:  str         =					pipe(
									    prompt=_UpperCAmelCase		,     image=_UpperCAmelCase		,     strength=0.7_5		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_UpperCAmelCase		,     output_type='''np'''		,     )
									__a	:  Dict         =					output.images
									__a	:  List[Any]         =					images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									__a	:  Dict         =					np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]       )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2e-2