



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()
_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	"""Hello world! cécé herlolip"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  bool  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						FairseqRobertaModel.from_pretrained(__lowerCamelCase  )
 roberta.eval()  # disable dropout
 __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.encoder.sentence_encoder
 __SCREAMING_SNAKE_CASE							:     str							=						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:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
 print("Our BERT config:"		,						__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     List[str]							=						RobertaForSequenceClassification(__lowerCamelCase  ) if classification_head else RobertaForMaskedLM(__lowerCamelCase  )
 model.eval()

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

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

  # intermediate
  __SCREAMING_SNAKE_CASE							:     BertIntermediate							=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta_layer.fca.weight
  __SCREAMING_SNAKE_CASE							:     Dict							=						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							:     str							=						roberta_layer.fca.weight
  __SCREAMING_SNAKE_CASE							:     List[str]							=						roberta_layer.fca.bias
  __SCREAMING_SNAKE_CASE							:     Tuple							=						roberta_layer.final_layer_norm.weight
  __SCREAMING_SNAKE_CASE							:     str							=						roberta_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.classification_heads["mnli"].dense.weight
  __SCREAMING_SNAKE_CASE							:     Dict							=						roberta.model.classification_heads["mnli"].dense.bias
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						roberta.model.classification_heads["mnli"].out_proj.weight
  __SCREAMING_SNAKE_CASE							:     Tuple							=						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							:     Dict							=						roberta.model.encoder.lm_head.dense.bias
  __SCREAMING_SNAKE_CASE							:     Tuple							=						roberta.model.encoder.lm_head.layer_norm.weight
  __SCREAMING_SNAKE_CASE							:     Any							=						roberta.model.encoder.lm_head.layer_norm.bias
  __SCREAMING_SNAKE_CASE							:     Tuple							=						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(__lowerCamelCase  ).unsqueeze(0  )  # batch of size 1

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

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


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











import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


__A       =get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Optional[int]						=      SpeechTaTokenizer(a_					)
					tokenizer.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Union[str, Any]						=      {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1e-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__UpperCAmelCase       :	Tuple						=      os.path.join(self.tmpdirname			,			a_					)
					with open(self.feature_extraction_file			,			'''w'''			,			encoding='''utf-8'''					) as fp:
							fp.write(json.dumps(a_					) + '''\n'''					)




			def 					snake_case__	(  self				:					Any			,			**a_				:					Tuple					):
					'''simple docstring'''





					return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					Dict			,			**a_				:					str					):
					'''simple docstring'''





					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_tokenizer()
					__UpperCAmelCase       :	str						=      self.get_feature_extractor()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					processor.save_pretrained(self.tmpdirname					)
					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(self.tmpdirname					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor.to_json_string()					)
					self.assertIsInstance(processor.feature_extractor			,			a_					)




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      SpeechTaProcessor(tokenizer=self.get_tokenizer()			,			feature_extractor=self.get_feature_extractor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.feature_extractor			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Tuple						=      feature_extractor(audio=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	List[str]						=      processor(audio=a_			,			return_tensors='''np'''					)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2					)




			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Dict						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Dict						=      feature_extractor(audio_target=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	str						=      processor(audio_target=a_			,			return_tensors='''np'''					)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[int]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	List[Any]						=      processor(text=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	Optional[Any]						=      processor(text_target=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	int						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Optional[Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	Optional[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_feature_extractor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					self.assertListEqual(
					    processor.model_input_names			,			feature_extractor.model_input_names			,			msg='''`processor` and `feature_extractor` model input names do not match'''			,			)





