
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	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  Any:
		'''simple docstring'''

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

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

		# Load the entity vocab file
		UpperCamelCase          =					load_original_entity_vocab(_lowerCAmelCase			)
		# add an entry for [MASK2]
		UpperCamelCase          =					max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

		UpperCamelCase          =					XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']			)

		# Add special tokens to the token vocabulary for downstream tasks
		UpperCamelCase          =					AddedToken('<ent>'   ,	lstrip=_lowerCAmelCase   ,	rstrip=_lowerCAmelCase			)
		UpperCamelCase          =					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   ,	'tokenizer_config.json'			)   ,	'r'			) as f:
				UpperCamelCase          =					json.load(_lowerCAmelCase			)
		UpperCamelCase          =					"""MLukeTokenizer"""
		with open(os.path.join(_lowerCAmelCase   ,	'tokenizer_config.json'			)   ,	'w'			) as f:
				json.dump(_lowerCAmelCase   ,	_lowerCAmelCase			)

		with open(os.path.join(_lowerCAmelCase   ,	MLukeTokenizer.vocab_files_names['entity_vocab_file']			)   ,	'w'			) as f:
				json.dump(_lowerCAmelCase   ,	_lowerCAmelCase			)

		UpperCamelCase          =					MLukeTokenizer.from_pretrained(_lowerCAmelCase			)

		# Initialize the embeddings of the special tokens
		UpperCamelCase          =					tokenizer.convert_tokens_to_ids(['@']			)[0]
		UpperCamelCase          =					tokenizer.convert_tokens_to_ids(['#']			)[0]

		UpperCamelCase          =					state_dict["""embeddings.word_embeddings.weight"""]
		UpperCamelCase          =					word_emb[ent_init_index].unsqueeze(0			)
		UpperCamelCase          =					word_emb[enta_init_index].unsqueeze(0			)
		UpperCamelCase          =					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"]:
				UpperCamelCase          =					state_dict[bias_name]
				UpperCamelCase          =					decoder_bias[ent_init_index].unsqueeze(0			)
				UpperCamelCase          =					decoder_bias[enta_init_index].unsqueeze(0			)
				UpperCamelCase          =					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"]:
						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]"""]].unsqueeze(0			)
		UpperCamelCase          =					torch.cat([entity_emb, entity_mask_emb]			)
		# add [MASK2] for 'entity_predictions.bias'
		UpperCamelCase          =					state_dict["""entity_predictions.bias"""]
		UpperCamelCase          =					entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0			)
		UpperCamelCase          =					torch.cat([entity_prediction_bias, entity_mask_bias]			)

		UpperCamelCase          =					LukeForMaskedLM(config=_lowerCAmelCase			).eval()

		state_dict.pop('entity_predictions.decoder.weight'			)
		state_dict.pop('lm_head.decoder.weight'			)
		state_dict.pop('lm_head.decoder.bias'			)
		UpperCamelCase          =					OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith('lm_head'			) or key.startswith('entity_predictions'			)):
						UpperCamelCase          =					state_dict[key]
				else:
						UpperCamelCase          =					state_dict[key]

		UpperCamelCase          =					model.load_state_dict(_lowerCAmelCase   ,	strict=_lowerCAmelCase			)

		if set(_lowerCAmelCase			) != {"luke.embeddings.position_ids"}:
				raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''			)
		if set(_lowerCAmelCase			) != {
		    "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
		UpperCamelCase          =					MLukeTokenizer.from_pretrained(_lowerCAmelCase   ,	task='entity_classification'			)

		UpperCamelCase          =					"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
		UpperCamelCase          =					(0, 9)
		UpperCamelCase          =					tokenizer(_lowerCAmelCase   ,	entity_spans=[span]   ,	return_tensors='pt'			)

		UpperCamelCase          =					model(**_lowerCAmelCase			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				UpperCamelCase          =					torch.Size((1, 33, 768)			)
				UpperCamelCase          =					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]   ,	_lowerCAmelCase   ,	atol=1e-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				UpperCamelCase          =					torch.Size((1, 1, 768)			)
				UpperCamelCase          =					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]   ,	_lowerCAmelCase   ,	atol=1e-4			):
				raise ValueError

		# Verify masked word/entity prediction
		UpperCamelCase          =					MLukeTokenizer.from_pretrained(_lowerCAmelCase			)
		UpperCamelCase          =					"""Tokyo is the capital of <mask>."""
		UpperCamelCase          =					(24, 30)
		UpperCamelCase          =					tokenizer(_lowerCAmelCase   ,	entity_spans=[span]   ,	return_tensors='pt'			)

		UpperCamelCase          =					model(**_lowerCAmelCase			)

		UpperCamelCase          =					encoding["""input_ids"""][0].tolist()
		UpperCamelCase          =					input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'			)			)
		UpperCamelCase          =					outputs.logits[0][mask_position_id].argmax(dim=-1			)
		assert "Japan" == tokenizer.decode(_lowerCAmelCase			)

		UpperCamelCase          =					outputs.entity_logits[0][0].argmax().item()
		UpperCamelCase          =					[
		    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(_lowerCAmelCase			)			)
		model.save_pretrained(_lowerCAmelCase			)


def 				A	( lowercase			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					["""[MASK]""", """[PAD]""", """[UNK]"""]

		UpperCamelCase          =					[json.loads(_lowerCAmelCase			) for line in open(_lowerCAmelCase			)]

		UpperCamelCase          =					{}
		for entry in data:
				UpperCamelCase          =					entry["""id"""]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								UpperCamelCase          =					entity_id
								break
						UpperCamelCase          =					f'''{language}:{entity_name}'''
						UpperCamelCase          =					entity_id
		return new_mapping


if __name__ == "__main__":
					_UpperCAmelCase :			Dict              =	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."
					)
					_UpperCAmelCase :			int              =	parser.parse_args()
					convert_luke_checkpoint(
					    args.checkpoint_path,
					    args.metadata_path,
					    args.entity_vocab_path,
					    args.pytorch_dump_folder_path,
					    args.model_size,
					)


import os
import sys
import unittest


_UpperCAmelCase :			str              =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

				# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
				# self.assertEqual(backend_with_underscore, "tensorflow_text")

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers'	)

				# double_backend_with_underscore = find_backend(
				#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
				# )
				# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

				UpperCamelCase          =					find_backend(
				    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers_and_onnx'	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('UNet2DModel'    ,				objects['torch']	)
				self.assertIn('FlaxUNet2DConditionModel'    ,				objects['flax']	)
				self.assertIn('StableDiffusionPipeline'    ,				objects['torch_and_transformers']	)
				self.assertIn('FlaxStableDiffusionPipeline'    ,				objects['flax_and_transformers']	)
				self.assertIn('LMSDiscreteScheduler'    ,				objects['torch_and_scipy']	)
				self.assertIn('OnnxStableDiffusionPipeline'    ,				objects['torch_and_transformers_and_onnx']	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

				UpperCamelCase          =					create_dummy_object('function'    ,				'\'torch\''	)
				self.assertEqual(
				    A_    ,				'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

				UpperCamelCase          =					'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
