code: 
  
  
  '''simple docstring'''
  
  
  
  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()
  A__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
  					__lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
  					roberta.eval()  # disable dropout
  					__lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
  					__lowerCamelCase     :			Optional[Any]              =	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=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
  					if classification_head:
  										__lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
  					print('Our BERT config:'   ,					UpperCAmelCase_							)
  
  					__lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
  					model.eval()
  
  					# Now let's copy all the weights.
  					# Embeddings
  					__lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
  					__lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
  					__lowerCamelCase     :			Tuple              =	torch.zeros_like(
  					    model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
  					__lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
  					__lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias
  
  					for i in range(config.num_hidden_layers							):
  										# Encoder: start of layer
  										__lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
  										__lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]
  
  										# self attention
  										__lowerCamelCase     :			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)							)
  										)
  
  										__lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
  										__lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
  										__lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
  										__lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
  										__lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
  										__lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias
  
  										# self-attention output
  										__lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
  										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  										__lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
  										__lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
  										__lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
  										__lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias
  
  										# intermediate
  										__lowerCamelCase     :			BertIntermediate              =	layer.intermediate
  										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  										__lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
  										__lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias
  
  										# output
  										__lowerCamelCase     :			BertOutput              =	layer.output
  										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  										__lowerCamelCase     :			str              =	roberta_layer.fca.weight
  										__lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
  										__lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
  										__lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
  										# end of layer
  
  					if classification_head:
  										__lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
  										__lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
  										__lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
  										__lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
  					else:
  										# LM Head
  										__lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
  										__lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
  										__lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
  										__lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
  										__lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
  										__lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
  					# Let's check that we get the same results.
  					__lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
  					__lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
  					if classification_head:
  										__lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
  					else:
  										__lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
  					print(our_output.shape   ,					their_output.shape							)
  					__lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
  					print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
  					__lowerCamelCase     :			List[str]              =	torch.allclose(UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=1e-3							)
  					print('Do both models output the same tensors?'   ,					'🔥' if success else '💩'							)
  					if not success:
  										raise Exception('Something went wRoNg'							)
  
  					pathlib.Path(UpperCAmelCase_							).mkdir(parents=UpperCAmelCase_   ,					exist_ok=UpperCAmelCase_							)
  					print(F'Saving model to {pytorch_dump_folder_path}'							)
  					model.save_pretrained(UpperCAmelCase_							)
  
  
  if __name__ == "__main__":
  						A__	:      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."""
  						)
  						A__	:      Optional[Any]				=						parser.parse_args()
  						convert_roberta_checkpoint_to_pytorch(
  						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  						)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  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      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Any							  =    BartTokenizer
      lowerCamelCase						:      Optional[Any]							  =    BartTokenizerFast
      lowerCamelCase						:      Tuple							  =    True
      lowerCamelCase						:      Optional[Any]							  =    filter_roberta_detectors
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           super().setUp()
           __lowerCamelCase     :			List[Any]              =	[
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           __lowerCamelCase     :			Optional[Any]              =	dict(zip(SCREAMING_SNAKE_CASE_    ,  range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
           __lowerCamelCase     :			Union[str, Any]              =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           __lowerCamelCase     :			int              =	{'unk_token': '<unk>'}
  
           __lowerCamelCase     :			int              =	os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES['vocab_file']			)
           __lowerCamelCase     :			str              =	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(SCREAMING_SNAKE_CASE_			) + '\n'			)
           with open(self.merges_file    ,  'w'    ,  encoding='utf-8'			) as fp:
                fp.write('\n'.join(SCREAMING_SNAKE_CASE_			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           kwargs.update(self.special_tokens_map			)
           return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  int:
           kwargs.update(self.special_tokens_map			)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           return "lower newer", "lower newer"
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  str:
           return BartTokenizer.from_pretrained('facebook/bart-large'			)
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  Any:
           return BartTokenizerFast.from_pretrained('facebook/bart-large'			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			str              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
           __lowerCamelCase     :			Union[str, Any]              =	[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
  
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  max_length=len(SCREAMING_SNAKE_CASE_			)    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                self.assertEqual((2, 9)    ,  batch.input_ids.shape			)
                self.assertEqual((2, 9)    ,  batch.attention_mask.shape			)
                __lowerCamelCase     :			str              =	batch.input_ids.tolist()[0]
                self.assertListEqual(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                # Test that special tokens are reset
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Optional[int]              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Tuple              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                # check if input_ids are returned and no labels
                self.assertIn('input_ids'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertIn('attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertNotIn('labels'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertNotIn('decoder_attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Any              =	[
               'Summary of the text.',
               'Another summary.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  max_length=32    ,  padding='max_length'    ,  return_tensors='pt'			)
                self.assertEqual(32    ,  targets['input_ids'].shape[1]			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Tuple:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Optional[Any]              =	tokenizer(
                    ['I am a small frog' * 10_24, 'I am a small frog']    ,  padding=SCREAMING_SNAKE_CASE_    ,  truncation=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                self.assertEqual(batch.input_ids.shape    ,  (2, 10_24)			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			Dict              =	['A long paragraph for summarization.']
           __lowerCamelCase     :			str              =	[
               'Summary of the text.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                __lowerCamelCase     :			Optional[Any]              =	inputs['input_ids']
                __lowerCamelCase     :			Dict              =	targets['input_ids']
                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    lowercase_       (							self			)       ->  List[Any]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                     __lowerCamelCase     :			List[str]              =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Optional[int]              =	self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Any              =	'A, <mask> AllenNLP sentence.'
                     __lowerCamelCase     :			Tuple              =	tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Union[str, Any]              =	tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_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     :			int              =	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                     __lowerCamelCase     :			Optional[Any]              =	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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
                     self.assertSequenceEqual(tokens_r['input_ids']    ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
  
                     self.assertSequenceEqual(
                         SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                     self.assertSequenceEqual(
                         SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
  
  
  
  
  
  
  