



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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
      raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	"""Hello world! cécé herlolip"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  bool  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						FairseqRobertaModel.from_pretrained(__lowerCamelCase  )
 roberta.eval()  # disable dropout
 __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.encoder.sentence_encoder
 __SCREAMING_SNAKE_CASE							:     str							=						RobertaConfig(
     vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings		,						hidden_size=roberta.args.encoder_embed_dim		,						num_hidden_layers=roberta.args.encoder_layers		,						num_attention_heads=roberta.args.encoder_attention_heads		,						intermediate_size=roberta.args.encoder_ffn_embed_dim		,						max_position_embeddings=514		,						type_vocab_size=1		,						layer_norm_eps=1E-5		,						)
 if classification_head:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
 print("Our BERT config:"		,						__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     List[str]							=						RobertaForSequenceClassification(__lowerCamelCase  ) if classification_head else RobertaForMaskedLM(__lowerCamelCase  )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __SCREAMING_SNAKE_CASE							:     str							=						roberta_sent_encoder.embed_tokens.weight
 __SCREAMING_SNAKE_CASE							:     Dict							=						roberta_sent_encoder.embed_positions.weight
 __SCREAMING_SNAKE_CASE							:     str							=						torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
 __SCREAMING_SNAKE_CASE							:     List[str]							=						roberta_sent_encoder.emb_layer_norm.weight
 __SCREAMING_SNAKE_CASE							:     int							=						roberta_sent_encoder.emb_layer_norm.bias

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

  # self attention
  __SCREAMING_SNAKE_CASE							:     BertSelfAttention							=						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)  )
  )

  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						roberta_layer.self_attn.q_proj.weight
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta_layer.self_attn.q_proj.bias
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						roberta_layer.self_attn.k_proj.weight
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						roberta_layer.self_attn.k_proj.bias
  __SCREAMING_SNAKE_CASE							:     str							=						roberta_layer.self_attn.v_proj.weight
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						roberta_layer.self_attn.v_proj.bias

  # self-attention output
  __SCREAMING_SNAKE_CASE							:     BertSelfOutput							=						layer.attention.output
  assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						roberta_layer.self_attn.out_proj.weight
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						roberta_layer.self_attn.out_proj.bias
  __SCREAMING_SNAKE_CASE							:     int							=						roberta_layer.self_attn_layer_norm.weight
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						roberta_layer.self_attn_layer_norm.bias

  # intermediate
  __SCREAMING_SNAKE_CASE							:     BertIntermediate							=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta_layer.fca.weight
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta_layer.fca.bias

  # output
  __SCREAMING_SNAKE_CASE							:     BertOutput							=						layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  __SCREAMING_SNAKE_CASE							:     str							=						roberta_layer.fca.weight
  __SCREAMING_SNAKE_CASE							:     List[str]							=						roberta_layer.fca.bias
  __SCREAMING_SNAKE_CASE							:     Tuple							=						roberta_layer.final_layer_norm.weight
  __SCREAMING_SNAKE_CASE							:     str							=						roberta_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.classification_heads["mnli"].dense.weight
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta.model.classification_heads["mnli"].dense.bias
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						roberta.model.classification_heads["mnli"].out_proj.weight
  __SCREAMING_SNAKE_CASE							:     Tuple							=						roberta.model.classification_heads["mnli"].out_proj.bias
 else:
  # LM Head
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						roberta.model.encoder.lm_head.dense.weight
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta.model.encoder.lm_head.dense.bias
  __SCREAMING_SNAKE_CASE							:     Tuple							=						roberta.model.encoder.lm_head.layer_norm.weight
  __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.encoder.lm_head.layer_norm.bias
  __SCREAMING_SNAKE_CASE							:     Tuple							=						roberta.model.encoder.lm_head.weight
  __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 __SCREAMING_SNAKE_CASE							:     torch.Tensor							=						roberta.encode(__lowerCamelCase  ).unsqueeze(0  )  # batch of size 1

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

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


if __name__ == "__main__":
      _lowerCamelCase         =	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."""
      )
      _lowerCamelCase         =	parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[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				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





