






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__a   :							Any						= logging.get_logger(__name__)


__a   :							Dict						= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a   :							List[Any]						= {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__a   :							Tuple						= {
    "allenai/led-base-16384": 16384,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					LEDTokenizer
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      Union[str, Any]=None			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      List[str]="replace"			,						UpperCamelCase_							:      int="<s>"			,						UpperCamelCase_							:      Dict="</s>"			,						UpperCamelCase_							:      List[str]="</s>"			,						UpperCamelCase_							:      Dict="<s>"			,						UpperCamelCase_							:      Optional[int]="<unk>"			,						UpperCamelCase_							:      List[Any]="<pad>"			,						UpperCamelCase_							:      List[str]="<mask>"			,						UpperCamelCase_							:      Optional[int]=False			,						UpperCamelCase_							:      List[Any]=True			,						**UpperCamelCase_							:      List[Any]			,						):


         """simple docstring"""



         super().__init__(
             UpperCamelCase_			,						UpperCamelCase_			,						tokenizer_file=UpperCamelCase_			,						errors=UpperCamelCase_			,						bos_token=UpperCamelCase_			,						eos_token=UpperCamelCase_			,						sep_token=UpperCamelCase_			,						cls_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						add_prefix_space=UpperCamelCase_			,						trim_offsets=UpperCamelCase_			,						**UpperCamelCase_			,						)

         __A       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
             __A       =						getattr(UpperCamelCase_			,						pre_tok_state.pop("""type"""					)					)
             __A       =						add_prefix_space
             __A       =						pre_tok_class(**UpperCamelCase_					)

         __A       =						add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         __A       =						"""post_processor"""
         __A       =						getattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)
         if tokenizer_component_instance:
             __A       =						json.loads(tokenizer_component_instance.__getstate__()					)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __A       =						tuple(state["""sep"""]					)
             if "cls" in state:
                 __A       =						tuple(state["""cls"""]					)

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

             if changes_to_apply:
                 __A       =						getattr(UpperCamelCase_			,						state.pop("""type"""					)					)
                 __A       =						component_class(**UpperCamelCase_					)
                 setattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)




     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         if self._mask_token is None:
             if self.verbose:
                 logger.error("""Using mask_token, but it is not set yet."""					)
             return None
         return str(self._mask_token					)




     @mask_token.setter
     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						*UpperCamelCase_							:      int			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                 """to use it with pretokenized inputs."""					)

         return super()._batch_encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict			,						*UpperCamelCase_							:      Union[str, Any]			,						**UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                 """to use it with pretokenized inputs."""					)

         return super()._encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Dict=None					):


         """simple docstring"""



         __A       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep					) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Union[Dict[str, EncodedInput], BatchEncoding]			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						):


         """simple docstring"""



         __A       =						super()._pad(
             encoded_inputs=UpperCamelCase_			,						max_length=UpperCamelCase_			,						padding_strategy=UpperCamelCase_			,						pad_to_multiple_of=UpperCamelCase_			,						return_attention_mask=UpperCamelCase_			,						)

         # Load from model defaults
         if return_attention_mask is None:
             __A       =						"""attention_mask""" in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
             __A       =						encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             __A       =						len(encoded_inputs["""global_attention_mask"""]					) != len(UpperCamelCase_					)

             if needs_to_be_padded:
                 __A       =						len(UpperCamelCase_					) - len(encoded_inputs["""global_attention_mask"""]					)

                 if self.padding_side == "right":
                     # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                     __A       =						(
                         encoded_inputs["""global_attention_mask"""] + [-1] * difference
                     )
                 elif self.padding_side == "left":
                     __A       =						[-1] * difference + encoded_inputs[
                         """global_attention_mask"""
                     ]
                 else:
                     raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

         return encoded_inputs












import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
  raise Exception("requires fairseq >= 1.0.0a")

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

__a   :							Tuple						= "Hello world! cécé herlolip"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      bool     )    ->   List[Any]:





    """simple docstring"""

    __A       =						FairseqRobertaModel.from_pretrained(__lowercase     )
    roberta.eval()  # disable dropout
    __A       =						roberta.model.encoder.sentence_encoder
    __A       =						XLMRobertaConfig(
        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,					hidden_size=roberta.cfg.model.encoder_embed_dim      ,					num_hidden_layers=roberta.cfg.model.encoder_layers      ,					num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,					max_position_embeddings=5_1_4      ,					type_vocab_size=1      ,					layer_norm_eps=1E-5      ,					)
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

    print("""Our RoBERTa config:"""      ,					__lowercase     )

    __A       =						XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    __A       =						roberta_sent_encoder.embed_tokens.weight
    __A       =						roberta_sent_encoder.embed_positions.weight
    __A       =						torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.

    __A       =						roberta_sent_encoder.layer_norm.weight
    __A       =						roberta_sent_encoder.layer_norm.bias

    for i in range(config.num_hidden_layers     ):
        # Encoder: start of layer
        __A       =						model.roberta.encoder.layer[i]
        __A       =						roberta_sent_encoder.layers[i]

        __A       =						layer.attention
        __A       =						roberta_layer.self_attn_layer_norm.weight
        __A       =						roberta_layer.self_attn_layer_norm.bias

        # self attention
        __A       =						layer.attention.self
        assert (
            roberta_layer.self_attn.k_proj.weight.data.shape
            == roberta_layer.self_attn.q_proj.weight.data.shape
            == roberta_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size)     )
        )

        __A       =						roberta_layer.self_attn.q_proj.weight
        __A       =						roberta_layer.self_attn.q_proj.bias
        __A       =						roberta_layer.self_attn.k_proj.weight
        __A       =						roberta_layer.self_attn.k_proj.bias
        __A       =						roberta_layer.self_attn.v_proj.weight
        __A       =						roberta_layer.self_attn.v_proj.bias

        # self-attention output
        __A       =						layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        __A       =						roberta_layer.self_attn.out_proj.weight
        __A       =						roberta_layer.self_attn.out_proj.bias

        # this one is final layer norm
        __A       =						roberta_layer.final_layer_norm.weight
        __A       =						roberta_layer.final_layer_norm.bias

        # intermediate
        __A       =						layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        __A       =						roberta_layer.fca.weight
        __A       =						roberta_layer.fca.bias

        # output
        __A       =						layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        __A       =						roberta_layer.fca.weight
        __A       =						roberta_layer.fca.bias
        # end of layer

    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].dense.weight
        __A       =						roberta.model.classification_heads["""mnli"""].dense.bias
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.bias
    else:
        # LM Head
        __A       =						roberta.model.encoder.lm_head.dense.weight
        __A       =						roberta.model.encoder.lm_head.dense.bias
        __A       =						roberta.model.encoder.lm_head.layer_norm.weight
        __A       =						roberta.model.encoder.lm_head.layer_norm.bias
        __A       =						roberta.model.encoder.lm_head.weight
        __A       =						roberta.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    __A       =						roberta.encode(__lowercase     ).unsqueeze(0     )  # batch of size 1

    __A       =						model(__lowercase     )[0]
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""](roberta.extract_features(__lowercase     )     )
    else:
        __A       =						roberta.model(__lowercase     )[0]
    print(our_output.shape      ,					their_output.shape     )
    __A       =						torch.max(torch.abs(our_output - their_output     )     ).item()
    print(f"max_absolute_diff = {max_absolute_diff}"     )  # ~ 1e-7
    __A       =						torch.allclose(__lowercase      ,					__lowercase      ,					atol=1E-3     )
    print("""Do both models output the same tensors?"""      ,					"""🔥""" if success else """💩"""     )
    if not success:
        raise Exception("""Something went wRoNg"""     )

    pathlib.Path(__lowercase     ).mkdir(parents=__lowercase      ,					exist_ok=__lowercase     )
    print(f"Saving model to {pytorch_dump_folder_path}"     )
    model.save_pretrained(__lowercase     )


if __name__ == "__main__":
  __a   :							List[Any]						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--classification_head", action="store_true", help="Whether to convert a final classification head."
  )
  __a   :							Dict						= parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )




