



'''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 __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Dict										=	HerbertTokenizer
 __lowerCamelCase   :	Dict										=	HerbertTokenizerFast
 __lowerCamelCase   :	Optional[int]										=	True



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


      """simple docstring"""



      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(A			,       range(len(A	)	)	)	)
      _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(A	)	)
      with open(self.merges_file			,       '''w'''	) as fp:
           fp.write('''\n'''.join(A	)	)



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


      """simple docstring"""



      _a     =  '''lower newer'''
      _a     =  '''lower newer'''
      return input_text, output_text



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


      """simple docstring"""



      _a     =  self.tokenizer_class(vocab_file=self.vocab_file			,       merges_file=self.merges_file	)

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

      _a     =  tokens + ['''<unk>''']
      _a     =  [16, 17, 23]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A	)			,       A	)



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


      """simple docstring"""



      if not self.test_rust_tokenizer:
           return

      _a     =  self.get_tokenizer()
      _a     =  self.get_rust_tokenizer()

      _a     =  '''lower,newer'''

      _a     =  tokenizer.tokenize(A	)
      _a     =  rust_tokenizer.tokenize(A	)
      self.assertListEqual(A			,       A	)

      _a     =  tokenizer.encode(A			,       add_special_tokens=A	)
      _a     =  rust_tokenizer.encode(A			,       add_special_tokens=A	)
      self.assertListEqual(A			,       A	)

      _a     =  self.get_rust_tokenizer()
      _a     =  tokenizer.encode(A	)
      _a     =  rust_tokenizer.encode(A	)
      self.assertListEqual(A			,       A	)



 @slow
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''	)

      _a     =  tokenizer.encode('''konstruowanie sekwencji'''			,       add_special_tokens=A	)
      _a     =  tokenizer.encode('''konstruowanie wielu sekwencji'''			,       add_special_tokens=A	)

      _a     =  tokenizer.build_inputs_with_special_tokens(A	)
      _a     =  tokenizer.build_inputs_with_special_tokens(A			,       A	)

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








'''simple docstring'''





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
lowercase_			=  logging.get_logger(__name__)

lowercase_			=  "Hello world! cécé herlolip"




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a     =  os.path.split(__A)
     _a     =  DataaVecTextModel.from_pretrained(
         __A  ,     checkpoint_file=__A)
     dataavec.eval()  # disable dropout
     _a     =  dataavec.models[0]
     _a     =  dataavec_model.encoder.sentence_encoder
     _a     =  DataaVecTextConfig(
         vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,     hidden_size=dataavec_model.args.encoder_embed_dim  ,     num_hidden_layers=dataavec_model.args.encoder_layers  ,     num_attention_heads=dataavec_model.args.encoder_attention_heads  ,     intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,     max_position_embeddings=514  ,     type_vocab_size=1  ,     layer_norm_eps=1e-5  ,     )
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''  ,     __A)

     _a     =  DataaVecTextForSequenceClassification(__A) if classification_head else DataaVecTextForMaskedLM(__A)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     _a     =  dataavec_sent_encoder.embed_tokens.weight
     _a     =  dataavec_sent_encoder.embed_positions.weight
     _a     =  torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
     _a     =  dataavec_sent_encoder.layernorm_embedding.weight
     _a     =  dataavec_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers):
          # Encoder: start of layer
          _a     =  model.dataavec_text.encoder.layer[i]
          _a     =  dataavec_sent_encoder.layers[i]

          # self attention
          _a     =  layer.attention.self
          assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )

          _a     =  dataavec_layer.self_attn.q_proj.weight
          _a     =  dataavec_layer.self_attn.q_proj.bias
          _a     =  dataavec_layer.self_attn.k_proj.weight
          _a     =  dataavec_layer.self_attn.k_proj.bias
          _a     =  dataavec_layer.self_attn.v_proj.weight
          _a     =  dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          _a     =  layer.attention.output
          assert (
              self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
          ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
          _a     =  dataavec_layer.self_attn.out_proj.weight
          _a     =  dataavec_layer.self_attn.out_proj.bias
          _a     =  dataavec_layer.self_attn_layer_norm.weight
          _a     =  dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _a     =  layer.intermediate
          assert (
              intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias

          # output
          _a     =  layer.output
          assert (
              bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias
          _a     =  dataavec_layer.final_layer_norm.weight
          _a     =  dataavec_layer.final_layer_norm.bias
          # end of layer

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

     # Let's check that we get the same results.
     _a     =  dataavec.encode(__A).unsqueeze(0)  # batch of size 1

     _a     =  model(__A)[0]
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__A))
     else:
          _a     =  dataavec_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__":
  lowercase_			=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--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."
  )
  lowercase_			=  parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



