
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()
lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			"""Hello world! cécé herlolip"""


def 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		FairseqRobertaModel.from_pretrained(snake_case							)
  roberta.eval()  # disable dropout
  __SCREAMING_SNAKE_CASE      :	Dict					=		roberta.model.encoder.sentence_encoder
  __SCREAMING_SNAKE_CASE      :	str					=		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=514       ,			type_vocab_size=1       ,			layer_norm_eps=1E-5       ,			)
  if classification_head:
    __SCREAMING_SNAKE_CASE      :	Tuple					=		roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

  print('''Our RoBERTa config:'''       ,			snake_case							)

  __SCREAMING_SNAKE_CASE      :	Any					=		XLMRobertaXLForSequenceClassification(snake_case							) if classification_head else XLMRobertaXLForMaskedLM(snake_case							)
  model.eval()

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

  __SCREAMING_SNAKE_CASE      :	Dict					=		roberta_sent_encoder.layer_norm.weight
  __SCREAMING_SNAKE_CASE      :	Tuple					=		roberta_sent_encoder.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]

    __SCREAMING_SNAKE_CASE      :	RobertaAttention					=		layer.attention
    __SCREAMING_SNAKE_CASE      :	Dict					=		roberta_layer.self_attn_layer_norm.weight
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		roberta_layer.self_attn_layer_norm.bias

    # 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[Any]					=		roberta_layer.self_attn.q_proj.weight
    __SCREAMING_SNAKE_CASE      :	Any					=		roberta_layer.self_attn.q_proj.bias
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		roberta_layer.self_attn.k_proj.weight
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		roberta_layer.self_attn.k_proj.bias
    __SCREAMING_SNAKE_CASE      :	str					=		roberta_layer.self_attn.v_proj.weight
    __SCREAMING_SNAKE_CASE      :	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      :	List[Any]					=		roberta_layer.self_attn.out_proj.weight
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    __SCREAMING_SNAKE_CASE      :	Tuple					=		roberta_layer.final_layer_norm.weight
    __SCREAMING_SNAKE_CASE      :	Dict					=		roberta_layer.final_layer_norm.bias

    # intermediate
    __SCREAMING_SNAKE_CASE      :	BertIntermediate					=		layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    __SCREAMING_SNAKE_CASE      :	str					=		roberta_layer.fca.weight
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		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      :	int					=		roberta_layer.fca.weight
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		roberta_layer.fca.bias
    # end of layer

  if classification_head:
    __SCREAMING_SNAKE_CASE      :	Any					=		roberta.model.classification_heads['''mnli'''].dense.weight
    __SCREAMING_SNAKE_CASE      :	int					=		roberta.model.classification_heads['''mnli'''].dense.bias
    __SCREAMING_SNAKE_CASE      :	List[str]					=		roberta.model.classification_heads['''mnli'''].out_proj.weight
    __SCREAMING_SNAKE_CASE      :	str					=		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      :	int					=		roberta.model.encoder.lm_head.dense.bias
    __SCREAMING_SNAKE_CASE      :	int					=		roberta.model.encoder.lm_head.layer_norm.weight
    __SCREAMING_SNAKE_CASE      :	int					=		roberta.model.encoder.lm_head.layer_norm.bias
    __SCREAMING_SNAKE_CASE      :	Dict					=		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(snake_case							).unsqueeze(0							)  # batch of size 1

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

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


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








"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase							=  {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				SqueezeBertTokenizer

					def __init__(			self  :      Tuple     ,		__snake_case  :      str=None     ,		__snake_case  :      Optional[Any]=None     ,		__snake_case  :      str=True     ,		__snake_case  :      str="[UNK]"     ,		__snake_case  :      int="[SEP]"     ,		__snake_case  :      int="[PAD]"     ,		__snake_case  :      Any="[CLS]"     ,		__snake_case  :      List[Any]="[MASK]"     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Dict=None     ,		**__snake_case  :      str     ,		)			->							List[Any]:
											super().__init__(
											    __snake_case     ,		tokenizer_file=__snake_case     ,		do_lower_case=__snake_case     ,		unk_token=__snake_case     ,		sep_token=__snake_case     ,		pad_token=__snake_case     ,		cls_token=__snake_case     ,		mask_token=__snake_case     ,		tokenize_chinese_chars=__snake_case     ,		strip_accents=__snake_case     ,		**__snake_case     ,		)

											__magic_name__:	List[Any]							=       json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
											if (
											    normalizer_state.get("""lowercase"""     ,		__snake_case					) != do_lower_case
											    or normalizer_state.get("""strip_accents"""     ,		__snake_case					) != strip_accents
											    or normalizer_state.get("""handle_chinese_chars"""     ,		__snake_case					) != tokenize_chinese_chars
											):
																	__magic_name__:	Dict							=       getattr(__snake_case     ,		normalizer_state.pop("""type"""					)					)
																	__magic_name__:	Optional[Any]							=       do_lower_case
																	__magic_name__:	Any							=       strip_accents
																	__magic_name__:	Any							=       tokenize_chinese_chars
																	__magic_name__:	Union[str, Any]							=       normalizer_class(**__snake_case					)

											__magic_name__:	Optional[Any]							=       do_lower_case

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      int=None					)			->							Optional[int]:
											__magic_name__:	int							=       [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   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None					)			->							List[int]:
											__magic_name__:	Dict							=       [self.sep_token_id]
											__magic_name__:	List[str]							=       [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   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Optional[str] = None					)			->							Tuple[str]:
											__magic_name__:	Any							=       self._tokenizer.model.save(__snake_case     ,		name=__snake_case					)
											return tuple(__snake_case					)



