






"""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 _lowerCAmelCase	(  __lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Tuple ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Dict   ):

						'''simple docstring'''






						with open(__lowerCamelCase   ) as metadata_file:
												__magic_name__            =     json.load(__lowerCamelCase   )
						__magic_name__            =     LukeConfig(use_entity_aware_attention=__lowerCamelCase ,						**metadata["model_config"]   )

						# Load in the weights from the checkpoint_path
						__magic_name__            =     torch.load(__lowerCamelCase ,						map_location="cpu"   )

						# Load the entity vocab file
						__magic_name__            =     load_entity_vocab(__lowerCamelCase   )

						__magic_name__            =     RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]   )

						# Add special tokens to the token vocabulary for downstream tasks
						__magic_name__            =     AddedToken("<ent>" ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase   )
						__magic_name__            =     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 ,						LukeTokenizer.vocab_files_names["entity_vocab_file"]   ) ,						"w"   ) as f:
												json.dump(__lowerCamelCase ,						__lowerCamelCase   )

						__magic_name__            =     LukeTokenizer.from_pretrained(__lowerCamelCase   )

						# Initialize the embeddings of the special tokens
						__magic_name__            =     state_dict["embeddings.word_embeddings.weight"]
						__magic_name__            =     word_emb[tokenizer.convert_tokens_to_ids(["@"]   )[0]].unsqueeze(0   )
						__magic_name__            =     word_emb[tokenizer.convert_tokens_to_ids(["#"]   )[0]].unsqueeze(0   )
						__magic_name__            =     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"]:
																		__magic_name__            =     f'''encoder.layer.{layer_index}.attention.self.'''
																		__magic_name__            =     state_dict[prefix + matrix_name]
																		__magic_name__            =     state_dict[prefix + matrix_name]
																		__magic_name__            =     state_dict[prefix + matrix_name]

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

						__magic_name__            =     LukeModel(config=__lowerCamelCase   ).eval()

						__magic_name__      ,    __magic_name__            =     model.load_state_dict(__lowerCamelCase ,						strict=__lowerCamelCase   )
						if not (len(__lowerCamelCase   ) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(f'''Missing keys {", ".join(__lowerCamelCase   )}. 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
						__magic_name__            =     LukeTokenizer.from_pretrained(__lowerCamelCase ,						task="entity_classification"   )

						__magic_name__            =     (
						    "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 ."
						)
						__magic_name__            =     (3_9, 4_2)
						__magic_name__            =     tokenizer(__lowerCamelCase ,						entity_spans=[span] ,						add_prefix_space=__lowerCamelCase ,						return_tensors="pt"   )

						__magic_name__            =     model(**__lowerCamelCase   )

						# Verify word hidden states
						if model_size == "large":
												__magic_name__            =     torch.Size((1, 4_2, 1_0_2_4)   )
												__magic_name__            =     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
												__magic_name__            =     torch.Size((1, 4_2, 7_6_8)   )
												__magic_name__            =     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] ,						__lowerCamelCase ,						atol=1E-4   ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												__magic_name__            =     torch.Size((1, 1, 1_0_2_4)   )
												__magic_name__            =     torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]   )
						else:  # base
												__magic_name__            =     torch.Size((1, 1, 7_6_8)   )
												__magic_name__            =     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] ,						__lowerCamelCase ,						atol=1E-4   ):
												raise ValueError

						# Finally, save our PyTorch model and tokenizer
						print("Saving PyTorch model to {}".format(__lowerCamelCase   )   )
						model.save_pretrained(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:List[str]   ):

						'''simple docstring'''






						__magic_name__            =     {}
						with open(__lowerCamelCase ,						"r" ,						encoding="utf-8"   ) as f:
												for index, line in enumerate(__lowerCamelCase   ):
																		__magic_name__      ,    __magic_name__            =     line.rstrip().split("\t"   )
																		__magic_name__            =     index

						return entity_vocab


if __name__ == "__main__":
			lowercase    			=       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.'''
			)
			lowercase    			=       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 unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				KandinskyVaaPriorPipeline
				UpperCAmelCase__        =				['''prompt''']
				UpperCAmelCase__        =				['''prompt''', '''negative_prompt''']
				UpperCAmelCase__        =				[
				    '''num_images_per_prompt''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				UpperCAmelCase__        =				False







				@property
				def     _snake_case					(							self							:						Dict	)				->					Dict:
										return 3_2







				@property
				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										return 3_2







				@property
				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										return self.time_input_dim







				@property
				def     _snake_case					(							self							:						List[Any]	)				->					str:
										return self.time_input_dim * 4







				@property
				def     _snake_case					(							self							:						List[str]	)				->					List[Any]:
										return 1_0_0







				@property
				def     _snake_case					(							self							:						List[str]	)				->					Tuple:
										__magic_name__            =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer







				@property
				def     _snake_case					(							self							:						Optional[int]	)				->					Optional[int]:
										torch.manual_seed(0	)
										__magic_name__            =     CLIPTextConfig(
										    bos_token_id=0						,    eos_token_id=2						,    hidden_size=self.text_embedder_hidden_size						,    projection_dim=self.text_embedder_hidden_size						,    intermediate_size=3_7						,    layer_norm_eps=1e-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1_0_0_0						,    )
										return CLIPTextModelWithProjection(__lowerCamelCase	)







				@property
				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										torch.manual_seed(0	)

										__magic_name__            =     {
										    "num_attention_heads": 2,
										    "attention_head_dim": 1_2,
										    "embedding_dim": self.text_embedder_hidden_size,
										    "num_layers": 1,
										}

										__magic_name__            =     PriorTransformer(**__lowerCamelCase	)
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										__magic_name__            =     nn.Parameter(torch.ones(model.clip_std.shape	)	)
										return model







				@property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										torch.manual_seed(0	)
										__magic_name__            =     CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size						,    image_size=2_2_4						,    projection_dim=self.text_embedder_hidden_size						,    intermediate_size=3_7						,    num_attention_heads=4						,    num_channels=3						,    num_hidden_layers=5						,    patch_size=1_4						,    )

										__magic_name__            =     CLIPVisionModelWithProjection(__lowerCamelCase	)
										return model







				@property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										__magic_name__            =     CLIPImageProcessor(
										    crop_size=2_2_4						,    do_center_crop=__lowerCamelCase						,    do_normalize=__lowerCamelCase						,    do_resize=__lowerCamelCase						,    image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,    image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,    resample=3						,    size=2_2_4						,    )

										return image_processor







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     self.dummy_prior
										__magic_name__            =     self.dummy_image_encoder
										__magic_name__            =     self.dummy_text_encoder
										__magic_name__            =     self.dummy_tokenizer
										__magic_name__            =     self.dummy_image_processor

										__magic_name__            =     UnCLIPScheduler(
										    variance_type="fixed_small_log"						,    prediction_type="sample"						,    num_train_timesteps=1_0_0_0						,    clip_sample=__lowerCamelCase						,    clip_sample_range=10.0						,    )

										__magic_name__            =     {
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "scheduler": scheduler,
										    "image_processor": image_processor,
										}

										return components







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						str=0	)				->					Optional[int]:
										if str(__lowerCamelCase	).startswith("mps"	):
																__magic_name__            =     torch.manual_seed(__lowerCamelCase	)
										else:
																__magic_name__            =     torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
										__magic_name__            =     {
										    "prompt": "horse",
										    "generator": generator,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[Any]:
										__magic_name__            =     "cpu"

										__magic_name__            =     self.get_dummy_components()

										__magic_name__            =     self.pipeline_class(**__lowerCamelCase	)
										__magic_name__            =     pipe.to(__lowerCamelCase	)

										pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     pipe(**self.get_dummy_inputs(__lowerCamelCase	)	)
										__magic_name__            =     output.image_embeds

										__magic_name__            =     pipe(
										    **self.get_dummy_inputs(__lowerCamelCase	)						,    return_dict=__lowerCamelCase						,    )[0]

										__magic_name__            =     image[0, -1_0:]
										__magic_name__            =     image_from_tuple[0, -1_0:]

										assert image.shape == (1, 3_2)

										__magic_name__            =     np.array(
										    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







				@skip_mps
				def     _snake_case					(							self							:						Dict	)				->					List[str]:
										__magic_name__            =     torch_device == "cpu"
										__magic_name__            =     True
										__magic_name__            =     False

										self._test_inference_batch_single_identical(
										    test_max_difference=__lowerCamelCase						,    relax_max_difference=__lowerCamelCase						,    test_mean_pixel_difference=__lowerCamelCase						,    )







				@skip_mps
				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										__magic_name__            =     torch_device == "cpu"
										__magic_name__            =     False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=__lowerCamelCase						,    test_mean_pixel_difference=__lowerCamelCase						,    )



