



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()
__snake_case	:				Optional[Any]  					=			logging.get_logger(__name__)

__snake_case	:				int  					=			'Hello world! cécé herlolip'



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   FairseqRobertaModel.from_pretrained(__a      )
     roberta.eval()  # disable dropout
     A__				=   roberta.model.encoder.sentence_encoder
     A__				=   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:
          A__				=   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''		,							__a      )

     A__				=   RobertaForSequenceClassification(__a      ) if classification_head else RobertaForMaskedLM(__a      )
     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.emb_layer_norm.weight
     A__				=   roberta_sent_encoder.emb_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]

          # 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
          A__				=   roberta_layer.self_attn_layer_norm.weight
          A__				=   roberta_layer.self_attn_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
          A__				=   roberta_layer.final_layer_norm.weight
          A__				=   roberta_layer.final_layer_norm.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(__a      ).unsqueeze(0      )  # batch of size 1

     A__				=   model(__a      )[0]
     if classification_head:
          A__				=   roberta.model.classification_heads['''mnli'''](roberta.extract_features(__a      )      )
     else:
          A__				=   roberta.model(__a      )[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(__a		,							__a		,							atol=1e-3      )
     print('''Do both models output the same tensors?'''		,							'''🔥''' if success else '''💩'''      )
     if not success:
          raise Exception('''Something went wRoNg'''      )

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


if __name__ == "__main__":
 __snake_case	:				Optional[int]  					=			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.'
 )
 __snake_case	:				Union[str, Any]  					=			parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )








"""simple docstring"""







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Dict				   =HerbertTokenizer
     _lowerCamelCase					: Optional[int]				   =HerbertTokenizerFast
     _lowerCamelCase					: Optional[Any]				   =True





     def 						A__  (					self :							Dict ):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''	,      encoding='''utf-8''' ) as f_data:
               A__				=   f_data.read().replace('''\n\n'''	,      '''\n''' ).strip()

          A__				=   [
              '''<s>''',
              '''</s>''',
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''w</w>''',
              '''r</w>''',
              '''t</w>''',
              '''lo''',
              '''low''',
              '''er</w>''',
              '''low</w>''',
              '''lowest</w>''',
              '''newer</w>''',
              '''wider</w>''',
              ''',</w>''',
              '''<unk>''',
          ]
          A__				=   dict(zip(_lowerCamelCase	,      range(len(_lowerCamelCase ) ) ) )
          A__				=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

          A__				=   os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file'''] )
          A__				=   os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''merges_file'''] )
          with open(self.vocab_file	,      '''w''' ) as fp:
               fp.write(json.dumps(_lowerCamelCase ) )
          with open(self.merges_file	,      '''w''' ) as fp:
               fp.write('''\n'''.join(_lowerCamelCase ) )





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          A__				=   '''lower newer'''
          A__				=   '''lower newer'''
          return input_text, output_text





     def 						A__  (					self :							int ):
          A__				=   self.tokenizer_class(vocab_file=self.vocab_file	,      merges_file=self.merges_file )

          A__				=   '''lower'''
          A__				=   ['''low''', '''er</w>''']
          A__				=   tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokens + ['''<unk>''']
          A__				=   [1_6, 1_7, 2_3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )	,      _lowerCamelCase )





     def 						A__  (					self :							Optional[int] ):
          if not self.test_rust_tokenizer:
               return

          A__				=   self.get_tokenizer()
          A__				=   self.get_rust_tokenizer()

          A__				=   '''lower,newer'''

          A__				=   tokenizer.tokenize(_lowerCamelCase )
          A__				=   rust_tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   self.get_rust_tokenizer()
          A__				=   tokenizer.encode(_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )





     @slow
     def 						A__  (					self :							Any ):
          A__				=   self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased''' )

          A__				=   tokenizer.encode('''konstruowanie sekwencji'''	,      add_special_tokens=_lowerCamelCase )
          A__				=   tokenizer.encode('''konstruowanie wielu sekwencji'''	,      add_special_tokens=_lowerCamelCase )

          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,      _lowerCamelCase )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]



