






"""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 					lowerCamelCase__					(       _lowerCamelCase    ,     unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			HerbertTokenizer
    SCREAMING_SNAKE_CASE		=			HerbertTokenizerFast
    SCREAMING_SNAKE_CASE		=			True






    def 				_UpperCamelCase							( self					):
          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:
                UpperCAmelCase							=       f_data.read().replace("""\n\n"""	,"""\n"""					).strip()

          UpperCAmelCase							=       [
              """<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>""",
          ]
          UpperCAmelCase							=       dict(zip(A__	,range(len(A__					)					)					)					)
          UpperCAmelCase							=       ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

          UpperCAmelCase							=       os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES["""vocab_file"""]					)
          UpperCAmelCase							=       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 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       """lower newer"""
          UpperCAmelCase							=       """lower newer"""
          return input_text, output_text






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.tokenizer_class(vocab_file=self.vocab_file	,merges_file=self.merges_file					)

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

          UpperCAmelCase							=       tokens + ["""<unk>"""]
          UpperCAmelCase							=       [16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)	,A__					)






    def 				_UpperCamelCase							( self					):
          if not self.test_rust_tokenizer:
                return

          UpperCAmelCase							=       self.get_tokenizer()
          UpperCAmelCase							=       self.get_rust_tokenizer()

          UpperCAmelCase							=       """lower,newer"""

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

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

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






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased"""					)

          UpperCAmelCase							=       tokenizer.encode("""konstruowanie sekwencji"""	,add_special_tokens=A__					)
          UpperCAmelCase							=       tokenizer.encode("""konstruowanie wielu sekwencji"""	,add_special_tokens=A__					)

          UpperCAmelCase							=       tokenizer.build_inputs_with_special_tokens(A__					)
          UpperCAmelCase							=       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

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






