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,
    )










from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)

__snake_case						:Any      		=     {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''switch_transformers'''
       UpperCamelCase__  :    Optional[Any]											=						['''past_key_values''']
       UpperCamelCase__  :    Optional[Any]											=						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=32_128     ,		__SCREAMING_SNAKE_CASE							:     int=768     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     List[str]=64     ,		__SCREAMING_SNAKE_CASE							:     int=12     ,		__SCREAMING_SNAKE_CASE							:     Any=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=12     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=3     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.01     ,		__SCREAMING_SNAKE_CASE							:     Dict="float32"     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=32     ,		__SCREAMING_SNAKE_CASE							:     int=128     ,		__SCREAMING_SNAKE_CASE							:     Any=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=1E-6     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     int=0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           __a						       =				vocab_size
           __a						       =				d_model
           __a						       =				d_kv
           __a						       =				d_ff

           __a						       =				num_sparse_encoder_layers

           __a						       =				num_layers
           __a						       =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __a						       =				num_sparse_decoder_layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_encoder_layers > 0:
               __a						       =				self.num_layers // self.num_sparse_encoder_layers
           else:
               __a						       =				self.num_layers  # HACK: this will create 0 sparse layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_decoder_layers > 0:
               __a						       =				self.num_decoder_layers // self.num_sparse_decoder_layers
           else:
               __a						       =				self.num_decoder_layers  # HACK: this will create 0 sparse layers

           __a						       =				num_heads
           __a						       =				num_experts
           __a						       =				expert_capacity
           __a						       =				router_bias
           __a						       =				router_jitter_noise
           if router_dtype not in ["float32", "float16", "bfloat16"]:
               raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
           __a						       =				router_dtype

           __a						       =				router_ignore_padding_tokens
           __a						       =				relative_attention_num_buckets
           __a						       =				relative_attention_max_distance

           __a						       =				dropout_rate
           __a						       =				layer_norm_epsilon
           __a						       =				initializer_factor
           __a						       =				feed_forward_proj
           __a						       =				use_cache
           __a						       =				add_router_probs

           __a						       =				router_z_loss_coef
           __a						       =				router_aux_loss_coef

           __a						       =				self.feed_forward_proj.split('''-''')
           __a						       =				act_info[-1]
           __a						       =				act_info[0] == '''gated'''

           if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
               raise ValueError(
                   F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                   '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                   '''\'gated-gelu\' or \'relu\'''')

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
               __a						       =				'''gelu_new'''

           super().__init__(
               pad_token_id=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE     ,		is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





