

'''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   (		SCREAMING_SNAKE_CASE_							,unittest.TestCase  ):
						__SCREAMING_SNAKE_CASE     :					List[Any]  =      HerbertTokenizer
						__SCREAMING_SNAKE_CASE     :					Optional[int]  =      HerbertTokenizerFast
						__SCREAMING_SNAKE_CASE     :					Union[str, Any]  =      True

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->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:
								_lowercase							=     f_data.read().replace("""\n\n"""    ,			"""\n""").strip()

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

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

						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						List[Any])						->Optional[Any]:

							"""simple docstring"""



							_lowercase							=     """lower newer"""
							_lowercase							=     """lower newer"""
							return input_text, output_text

						def 	_UpperCAmelCase							(		self	:						List[Any])						->Optional[int]:

							"""simple docstring"""



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

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

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

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

							"""simple docstring"""



							if not self.test_rust_tokenizer:
								return

							_lowercase							=     self.get_tokenizer()
							_lowercase							=     self.get_rust_tokenizer()

							_lowercase							=     """lower,newer"""

							_lowercase							=     tokenizer.tokenize(__UpperCAmelCase)
							_lowercase							=     rust_tokenizer.tokenize(__UpperCAmelCase)
							self.assertListEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

							_lowercase							=     tokenizer.encode(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
							_lowercase							=     rust_tokenizer.encode(__UpperCAmelCase    ,			add_special_tokens=__UpperCAmelCase)
							self.assertListEqual(__UpperCAmelCase    ,			__UpperCAmelCase)

							_lowercase							=     self.get_rust_tokenizer()
							_lowercase							=     tokenizer.encode(__UpperCAmelCase)
							_lowercase							=     rust_tokenizer.encode(__UpperCAmelCase)
							self.assertListEqual(__UpperCAmelCase    ,			__UpperCAmelCase)


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

							"""simple docstring"""



							_lowercase							=     self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased""")

							_lowercase							=     tokenizer.encode("""konstruowanie sekwencji"""    ,			add_special_tokens=__UpperCAmelCase)
							_lowercase							=     tokenizer.encode("""konstruowanie wielu sekwencji"""    ,			add_special_tokens=__UpperCAmelCase)

							_lowercase							=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
							_lowercase							=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase    ,			__UpperCAmelCase)

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






"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


if __name__ == "__main__":
      UpperCamelCase_						   =   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)