






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__a   :							int						= get_tests_dir("fixtures/test_sentencepiece.model")
__a   :							str						= get_tests_dir("fixtures/test_sentencepiece_bpe.model")

__a   :							Optional[int]						= "pt" if is_torch_available() else "tf"


@require_sentencepiece
@require_tokenizers
class    __lowercase   (						lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					CamembertTokenizer
     SCREAMING_SNAKE_CASE     =					CamembertTokenizerFast
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					True




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         super().setUp()

         # We have a SentencePiece fixture for testing
         __A       =						CamembertTokenizer(UpperCamelCase_					)
         tokenizer.save_pretrained(self.tmpdirname					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						"""<pad>"""
         __A       =						1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_					)			,						UpperCamelCase_					)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_					)			,						UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						list(self.get_tokenizer().get_vocab().keys()					)

         self.assertEqual(vocab_keys[0]			,						"""<s>NOTUSED"""					)
         self.assertEqual(vocab_keys[1]			,						"""<pad>"""					)
         self.assertEqual(vocab_keys[-1]			,						"""<mask>"""					)
         self.assertEqual(len(UpperCamelCase_					)			,						1_004					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size			,						1_005					)




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						CamembertTokenizer(UpperCamelCase_					)
         tokenizer.save_pretrained(self.tmpdirname					)
         __A       =						CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

         __A       =						"""I was born in 92000, and this is falsé."""

         __A       =						tokenizer.encode(UpperCamelCase_					)
         __A       =						rust_tokenizer.encode(UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						tokenizer.encode(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_					)
         __A       =						rust_tokenizer.encode(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)

         # <unk> tokens are not the same for `rust` than for `slow`.
         # Because spm gives back raw token instead of `unk` in EncodeAsPieces
         # tokens = tokenizer.tokenize(sequence)
         __A       =						tokenizer.convert_ids_to_tokens(UpperCamelCase_					)
         __A       =						rust_tokenizer.tokenize(UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)




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


         """simple docstring"""



         if not self.test_rust_tokenizer:
             return

         __A       =						self.get_tokenizer()
         __A       =						self.get_rust_tokenizer()

         __A       =						"""I was born in 92000, and this is falsé."""

         __A       =						tokenizer.tokenize(UpperCamelCase_					)
         __A       =						rust_tokenizer.tokenize(UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						tokenizer.encode(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_					)
         __A       =						rust_tokenizer.encode(UpperCamelCase_			,						add_special_tokens=UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						self.get_rust_tokenizer()
         __A       =						tokenizer.encode(UpperCamelCase_					)
         __A       =						rust_tokenizer.encode(UpperCamelCase_					)
         self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_					)





     @slow
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						{"""input_ids""": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         # camembert is a french model. So we also use french texts.
         __A       =						[
             """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
             """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
             """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
             """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
             """telles que la traduction et la synthèse de texte.""",
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=UpperCamelCase_			,						model_name="""camembert-base"""			,						revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""			,						sequences=UpperCamelCase_			,						)












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
  )




