

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__       (    _a							,	_a							,	_a							,	_a							,	_a):
	# Load configuration defined in the metadata file
	with open(UpperCAmelCase_) as metadata_file:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				json.load(UpperCAmelCase_)
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				LukeConfig(use_entity_aware_attention=UpperCAmelCase_							,	**metadata["model_config"])

	# Load in the weights from the checkpoint_path
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.load(UpperCAmelCase_							,	map_location="cpu")

	# Load the entity vocab file
	SCREAMING_SNAKE_CASE      :					Tuple    =				load_entity_vocab(UpperCAmelCase_)

	SCREAMING_SNAKE_CASE      :					int    =				RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])

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

	SCREAMING_SNAKE_CASE      :					List[str]    =				LukeTokenizer.from_pretrained(UpperCAmelCase_)

	# Initialize the embeddings of the special tokens
	SCREAMING_SNAKE_CASE      :					Any    =				state_dict['embeddings.word_embeddings.weight']
	SCREAMING_SNAKE_CASE      :					Any    =				word_emb[tokenizer.convert_tokens_to_ids(["@"])[0]].unsqueeze(0)
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				word_emb[tokenizer.convert_tokens_to_ids(["#"])[0]].unsqueeze(0)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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"]:
			SCREAMING_SNAKE_CASE      :					Tuple    =				f"encoder.layer.{layer_index}.attention.self."
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				state_dict[prefix + matrix_name]
			SCREAMING_SNAKE_CASE      :					List[Any]    =				state_dict[prefix + matrix_name]
			SCREAMING_SNAKE_CASE      :					List[str]    =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				state_dict['entity_embeddings.entity_embeddings.weight']
	SCREAMING_SNAKE_CASE      :					str    =				entity_emb[entity_vocab['[MASK]']]

	SCREAMING_SNAKE_CASE      :					Any    =				LukeModel(config=UpperCAmelCase_).eval()

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				model.load_state_dict(UpperCAmelCase_							,	strict=UpperCAmelCase_)
	if not (len(UpperCAmelCase_) == 1 and missing_keys[0] == "embeddings.position_ids"):
		raise ValueError(f"Missing keys {', '.join(UpperCAmelCase_)}. 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
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				LukeTokenizer.from_pretrained(UpperCAmelCase_							,	task="entity_classification")

	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(
	    '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 .'
	)
	SCREAMING_SNAKE_CASE      :					Dict    =				(39, 42)
	SCREAMING_SNAKE_CASE      :					Any    =				tokenizer(UpperCAmelCase_							,	entity_spans=[span]							,	add_prefix_space=UpperCAmelCase_							,	return_tensors="pt")

	SCREAMING_SNAKE_CASE      :					List[Any]    =				model(**UpperCAmelCase_)

	# Verify word hidden states
	if model_size == "large":
		SCREAMING_SNAKE_CASE      :					int    =				torch.Size((1, 42, 1024))
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.tensor(
		    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
	else:  # base
		SCREAMING_SNAKE_CASE      :					Any    =				torch.Size((1, 42, 768))
		SCREAMING_SNAKE_CASE      :					Any    =				torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

	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]							,	UpperCAmelCase_							,	atol=1E-4):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		SCREAMING_SNAKE_CASE      :					Dict    =				torch.Size((1, 1, 1024))
		SCREAMING_SNAKE_CASE      :					Dict    =				torch.tensor([[0.0466, -0.0106, -0.0179]])
	else:  # base
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.Size((1, 1, 768))
		SCREAMING_SNAKE_CASE      :					int    =				torch.tensor([[0.1457, 0.1044, 0.0174]])

	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]							,	UpperCAmelCase_							,	atol=1E-4):
		raise ValueError

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

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				{}
	with open(UpperCAmelCase_							,	"r"							,	encoding="utf-8") as f:
		for index, line in enumerate(UpperCAmelCase_):
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				line.rstrip().split("\t")
			SCREAMING_SNAKE_CASE      :					Tuple    =				index

	return entity_vocab


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 gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp





@slow
@require_flax
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE_	) for s in shape]	)}.npy'







    def       lowercase_				(							self	)				->     Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 4, 64, 64)			,     SCREAMING_SNAKE_CASE_=False	)				->     str:
         __lowerCamelCase   :	List[str]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Tuple								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return image







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="CompVis/stable-diffusion-v1-4"	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		'bf16' if fpaa else None

         __lowerCamelCase				,       __lowerCamelCase   :	str								=		FlaxUNetaDConditionModel.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     subfolder='unet'			,     dtype=SCREAMING_SNAKE_CASE_			,     revision=SCREAMING_SNAKE_CASE_	)
         return model, params







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 77, 7_68)			,     SCREAMING_SNAKE_CASE_=False	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return hidden_states







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
            [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
            [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
            [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Any								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
            [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
            [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
            [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     shape=(4, 4, 96, 96)			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     shape=(4, 77, 10_24)			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Optional[int]								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)






