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__						( lowerCAmelCase__   ,		unittest.TestCase ):
					A     	=							HerbertTokenizer
					A     	=							HerbertTokenizerFast
					A     	=							True

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""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:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   f_data.read().replace("\n\n","\n"   ).strip()

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   [
							    "<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>",
							]
							SCREAMING_SNAKE_CASE_  :		int    				=   dict(zip(__lowerCAmelCase,range(len(__lowerCAmelCase   )   )   )   )
							SCREAMING_SNAKE_CASE_  :		str    				=   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

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

					def     __UpperCamelCase       (   self				:	List[str],_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   "lower newer"
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   "lower newer"
							return input_text, output_text

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.tokenizer_class(vocab_file=self.vocab_file,merges_file=self.merges_file   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   "lower"
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ["low", "er</w>"]
							SCREAMING_SNAKE_CASE_  :		Any    				=   tokenizer.tokenize(__lowerCAmelCase   )
							self.assertListEqual(__lowerCAmelCase,__lowerCAmelCase   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   tokens + ["<unk>"]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   [16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase   ),__lowerCAmelCase   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							if not self.test_rust_tokenizer:
									return

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.get_tokenizer()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.get_rust_tokenizer()

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   "lower,newer"

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   tokenizer.tokenize(__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   rust_tokenizer.tokenize(__lowerCAmelCase   )
							self.assertListEqual(__lowerCAmelCase,__lowerCAmelCase   )

							SCREAMING_SNAKE_CASE_  :		int    				=   tokenizer.encode(__lowerCAmelCase,add_special_tokens=__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   rust_tokenizer.encode(__lowerCAmelCase,add_special_tokens=__lowerCAmelCase   )
							self.assertListEqual(__lowerCAmelCase,__lowerCAmelCase   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   tokenizer.encode(__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   rust_tokenizer.encode(__lowerCAmelCase   )
							self.assertListEqual(__lowerCAmelCase,__lowerCAmelCase   )






					@slow
					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"   )

							SCREAMING_SNAKE_CASE_  :		int    				=   tokenizer.encode("konstruowanie sekwencji",add_special_tokens=__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   tokenizer.encode("konstruowanie wielu sekwencji",add_special_tokens=__lowerCAmelCase   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase,__lowerCAmelCase   )

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







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  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(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT 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.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
