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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

    # Load in the weights from the checkpoint_path
    __UpperCamelCase     :Optional[Any]			  =		torch.load(SCREAMING_SNAKE_CASE  ,			map_location='''cpu'''	)['''module''']

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

    __UpperCamelCase     :Any			  =		XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']	)

    # Add special tokens to the token vocabulary for downstream tasks
    __UpperCamelCase     :Optional[int]			  =		AddedToken('''<ent>'''  ,			lstrip=SCREAMING_SNAKE_CASE  ,			rstrip=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		AddedToken('''<ent2>'''  ,			lstrip=SCREAMING_SNAKE_CASE  ,			rstrip=SCREAMING_SNAKE_CASE	)
    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(SCREAMING_SNAKE_CASE	)
    with open(os.path.join(SCREAMING_SNAKE_CASE  ,			'''tokenizer_config.json'''	)  ,			'''r'''	) as f:
        __UpperCamelCase     :Any			  =		json.load(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		'''MLukeTokenizer'''
    with open(os.path.join(SCREAMING_SNAKE_CASE  ,			'''tokenizer_config.json'''	)  ,			'''w'''	) as f:
        json.dump(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Optional[int]			  =		MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)

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

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

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

    __UpperCamelCase     :str			  =		LukeForMaskedLM(config=SCREAMING_SNAKE_CASE	).eval()

    state_dict.pop('''entity_predictions.decoder.weight'''	)
    state_dict.pop('''lm_head.decoder.weight'''	)
    state_dict.pop('''lm_head.decoder.bias'''	)
    __UpperCamelCase     :Any			  =		OrderedDict()
    for key, value in state_dict.items():
        if not (key.startswith('''lm_head'''	) or key.startswith('''entity_predictions'''	)):
            __UpperCamelCase     :Union[str, Any]			  =		state_dict[key]
        else:
            __UpperCamelCase     :Optional[int]			  =		state_dict[key]

    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		model.load_state_dict(SCREAMING_SNAKE_CASE  ,			strict=SCREAMING_SNAKE_CASE	)

    if set(SCREAMING_SNAKE_CASE	) != {"luke.embeddings.position_ids"}:
        raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""	)
    if set(SCREAMING_SNAKE_CASE	) != {
        "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     :List[str]			  =		MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE  ,			task='''entity_classification'''	)

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

    __UpperCamelCase     :int			  =		model(**SCREAMING_SNAKE_CASE	)

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        __UpperCamelCase     :Optional[int]			  =		torch.Size((1, 33, 768)	)
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]	)

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

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        __UpperCamelCase     :Union[str, Any]			  =		torch.Size((1, 1, 768)	)
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]	)

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

    # Verify masked word/entity prediction
    __UpperCamelCase     :Optional[Any]			  =		MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[int]			  =		'''Tokyo is the capital of <mask>.'''
    __UpperCamelCase     :Any			  =		(24, 30)
    __UpperCamelCase     :Optional[int]			  =		tokenizer(SCREAMING_SNAKE_CASE  ,			entity_spans=[span]  ,			return_tensors='''pt'''	)

    __UpperCamelCase     :Tuple			  =		model(**SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :int			  =		outputs.entity_logits[0][0].argmax().item()
    __UpperCamelCase     :Union[str, 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(SCREAMING_SNAKE_CASE	)	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		['''[MASK]''', '''[PAD]''', '''[UNK]''']

    __UpperCamelCase     :List[Any]			  =		[json.loads(SCREAMING_SNAKE_CASE	) for line in open(SCREAMING_SNAKE_CASE	)]

    __UpperCamelCase     :int			  =		{}
    for entry in data:
        __UpperCamelCase     :int			  =		entry['''id''']
        for entity_name, language in entry["entities"]:
            if entity_name in SPECIAL_TOKENS:
                __UpperCamelCase     :Optional[int]			  =		entity_id
                break
            __UpperCamelCase     :Tuple			  =		f"""{language}:{entity_name}"""
            __UpperCamelCase     :Union[str, Any]			  =		entity_id
    return new_mapping


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,
    )













import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(lowerCamelCase,  '''tf_padding'''  )  )
								self.parent.assertTrue(hasattr(lowerCamelCase,  '''depth_multiplier'''  )  )



class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    List[str],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]=13,  lowerCamelCase						:    List[str]=3,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    Union[str, Any]=0.25,  lowerCamelCase						:    int=8,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    Optional[int]=1_024,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    Optional[int]="relu6",  lowerCamelCase						:    Optional[int]=0.1,  lowerCamelCase						:    Optional[Any]=0.02,  lowerCamelCase						:    List[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    Dict=10,  lowerCamelCase						:    Optional[int]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			num_channels
								lowercase__    =			image_size
								lowercase__    =			depth_multiplier
								lowercase__    =			min_depth
								lowercase__    =			tf_padding
								lowercase__    =			int(last_hidden_size * depth_multiplier  )
								lowercase__    =			output_stride
								lowercase__    =			hidden_act
								lowercase__    =			classifier_dropout_prob
								lowercase__    =			use_labels
								lowercase__    =			is_training
								lowercase__    =			num_labels
								lowercase__    =			initializer_range
								lowercase__    =			scope




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size, self.image_size, self.image_size],  self.num_labels  )

								lowercase__    =			self.get_config()

								return config, pixel_values, labels, pixel_labels




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								return MobileNetVaConfig(
								    num_channels=self.num_channels,  image_size=self.image_size,  depth_multiplier=self.depth_multiplier,  min_depth=self.min_depth,  tf_padding=self.tf_padding,  hidden_act=self.hidden_act,  classifier_dropout_prob=self.classifier_dropout_prob,  initializer_range=self.initializer_range,  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			MobileNetVaModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape,  (
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    ),  )




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    List[str],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			MobileNetVaForImageClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()
								lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__    =			config_and_inputs
								lowercase__    =			{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
				lowercase__											=       (
				    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			MobileNetVaModelTester(self  )
								lowercase__    =			MobileNetVaConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''  )
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''MobileNetV1 does not output attentions'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__    =			model_class(lowerCamelCase  )
												lowercase__    =			inspect.signature(model.forward  )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowercase__    =			[*signature.parameters.keys()]

												lowercase__    =			['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase						:    str,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    List[str]  ):
												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.eval()

												with torch.no_grad():
																lowercase__    =			model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )

												lowercase__    =			outputs.hidden_states

												lowercase__    =			26
												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__    =			True
												check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowercase__    =			True

												check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )




				@slow
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			MobileNetVaModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )






def   a			(						):






				'''simple docstring'''
				lowercase__    =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image




@require_torch
@require_vision
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				@cached_property
				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								return (
								    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''  ) if is_vision_available() else None
								)




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''  ).to(lowerCamelCase  )

								lowercase__    =			self.default_image_processor
								lowercase__    =			prepare_img()
								lowercase__    =			image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__    =			model(**lowerCamelCase  )

								# verify the logits
								lowercase__    =			torch.Size((1, 1_001)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )

								lowercase__    =			torch.tensor([-4.1739, -1.1233, 3.1205]  ).to(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4  )  )




