




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class   A(						_a							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						MvpTokenizer
 UpperCamelCase						       =						MvpTokenizerFast
 UpperCamelCase						       =						True
 UpperCamelCase						       =						filter_roberta_detectors

 def     a__			(    self :  Union[str, Any]  )       ->     Tuple:

       """simple docstring"""






       super().setUp()
       lowerCamelCase_							=   [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       lowerCamelCase_							=   dict(zip(snake_case_						,							range(len(snake_case_  )  )  )  )
       lowerCamelCase_							=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       lowerCamelCase_							=   {"""unk_token""": """<unk>"""}

       lowerCamelCase_							=   os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['vocab_file']  )
       lowerCamelCase_							=   os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file						,							'w'						,							encoding='utf-8'  ) as fp:
             fp.write(json.dumps(snake_case_  ) + '\n'  )
       with open(self.merges_file						,							'w'						,							encoding='utf-8'  ) as fp:
             fp.write('\n'.join(snake_case_  )  )

 def     a__			(    self :  Optional[Any]						,							**A_ :  Union[str, Any]  )       ->     Optional[Any]:

       """simple docstring"""






       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,							**snake_case_  )

 def     a__			(    self :  List[str]						,							**A_ :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       kwargs.update(self.special_tokens_map  )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,							**snake_case_  )

 def     a__			(    self :  List[Any]						,							A_ :  List[str]  )       ->     Dict:

       """simple docstring"""






       return "lower newer", "lower newer"

 @cached_property
 def     a__			(    self :  int  )       ->     Tuple:

       """simple docstring"""






       return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )

 @cached_property
 def     a__			(    self :  Tuple  )       ->     Tuple:

       """simple docstring"""






       return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )

 @require_torch
 def     a__			(    self :  Any  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       lowerCamelCase_							=   [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase_							=   tokenizer(snake_case_						,							max_length=len(snake_case_  )						,							padding=snake_case_						,							return_tensors='pt'  )
             self.assertIsInstance(snake_case_						,							snake_case_  )

             self.assertEqual((2, 9)						,							batch.input_ids.shape  )
             self.assertEqual((2, 9)						,							batch.attention_mask.shape  )
             lowerCamelCase_							=   batch.input_ids.tolist()[0]
             self.assertListEqual(snake_case_						,							snake_case_  )
             # Test that special tokens are reset

 @require_torch
 def     a__			(    self :  Dict  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase_							=   tokenizer(snake_case_						,							padding=snake_case_						,							return_tensors='pt'  )
             # check if input_ids are returned and no labels
             self.assertIn('input_ids'						,							snake_case_  )
             self.assertIn('attention_mask'						,							snake_case_  )
             self.assertNotIn('labels'						,							snake_case_  )
             self.assertNotIn('decoder_attention_mask'						,							snake_case_  )

 @require_torch
 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   [
           """Summary of the text.""",
           """Another summary.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase_							=   tokenizer(text_target=snake_case_						,							max_length=32						,							padding='max_length'						,							return_tensors='pt'  )
             self.assertEqual(32						,							targets['input_ids'].shape[1]  )

 @require_torch
 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase_							=   tokenizer(
                 ['I am a small frog' * 1024, 'I am a small frog']						,							padding=snake_case_						,							truncation=snake_case_						,							return_tensors='pt'  )
             self.assertIsInstance(snake_case_						,							snake_case_  )
             self.assertEqual(batch.input_ids.shape						,							(2, 1024)  )

 @require_torch
 def     a__			(    self :  str  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   ["""A long paragraph for summarization."""]
       lowerCamelCase_							=   [
           """Summary of the text.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase_							=   tokenizer(snake_case_						,							text_target=snake_case_						,							return_tensors='pt'  )
             lowerCamelCase_							=   inputs["""input_ids"""]
             lowerCamelCase_							=   inputs["""labels"""]
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )

 def     a__			(    self :  int  )       ->     List[Any]:

       """simple docstring"""






       pass





 def     a__			(    self :  str  )       ->     Any:

       """simple docstring"""






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                   lowerCamelCase_							=   self.rust_tokenizer_class.from_pretrained(snake_case_						,							**snake_case_  )
                   lowerCamelCase_							=   self.tokenizer_class.from_pretrained(snake_case_						,							**snake_case_  )
                   lowerCamelCase_							=   """A, <mask> AllenNLP sentence."""
                   lowerCamelCase_							=   tokenizer_r.encode_plus(snake_case_						,							add_special_tokens=snake_case_						,							return_token_type_ids=snake_case_  )
                   lowerCamelCase_							=   tokenizer_p.encode_plus(snake_case_						,							add_special_tokens=snake_case_						,							return_token_type_ids=snake_case_  )

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['token_type_ids']  )						,							sum(tokens_p['token_type_ids']  )  )

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  )						,							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  )						,							)

                   lowerCamelCase_							=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                   lowerCamelCase_							=   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['input_ids']						,							[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
                   self.assertSequenceEqual(tokens_r['input_ids']						,							[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

                   self.assertSequenceEqual(
                       snake_case_						,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                   self.assertSequenceEqual(
                       snake_case_						,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )









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()
lowerCamelCase    :		Tuple			   =       logging.get_logger(__name__)

lowerCamelCase    :		Union[str, Any]			   =       "Hello world! cécé herlolip"

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	bool					):


      '''simple docstring'''



      lowerCamelCase_							=   FairseqRobertaModel.from_pretrained(lowercase					)
      roberta.eval()  # disable dropout
      lowerCamelCase_							=   roberta.model.encoder.sentence_encoder
      lowerCamelCase_							=   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_14     ,							type_vocab_size=1     ,							layer_norm_eps=1e-5     ,							)
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our RoBERTa config:'     ,							lowercase					)

      lowerCamelCase_							=   XLMRobertaXLForSequenceClassification(lowercase					) if classification_head else XLMRobertaXLForMaskedLM(lowercase					)
      model.eval()

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

      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.weight
      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.bias

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

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

            # self attention
            lowerCamelCase_							=   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)					)
            )

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

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

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

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

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

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

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

      lowerCamelCase_							=   model(lowercase					)[0]
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'](roberta.extract_features(lowercase					)					)
      else:
            lowerCamelCase_							=   roberta.model(lowercase					)[0]
      print(our_output.shape     ,							their_output.shape					)
      lowerCamelCase_							=   torch.max(torch.abs(our_output - their_output					)					).item()
      print(f"""max_absolute_diff = {max_absolute_diff}"""					)  # ~ 1e-7
      lowerCamelCase_							=   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__":
 lowerCamelCase    :		Optional[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."
 )
 lowerCamelCase    :		List[Any]			   =       parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



