


'''simple docstring'''






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
 a_					:    str          =    "pt"
elif is_tf_available():
 a_					:    int          =    "tf"
else:
 a_					:    Union[str, Any]          =    "jax"




class  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	ByTaTokenizer
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							int:
      super().setUp()
      _a			      =							ByTaTokenizer()
      tokenizer.save_pretrained(self.tmpdirname						)
 @cached_property
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return ByTaTokenizer.from_pretrained('google/byt5-small'						)
 def 							__UpperCAmelCase		( self      ,				**__magic_name__						)					->							ByTaTokenizer:
      return self.tokenizer_class.from_pretrained(self.tmpdirname      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=False      ,				__magic_name__=20      ,				__magic_name__=5						)					->							Tuple[str, list]:
      # XXX The default common tokenizer tests assume that every ID is decodable on its own.
      # This assumption is invalid for ByT5 because single bytes might not be
      # valid utf-8 (byte 128 for instance).
      # Here we're overriding the smallest possible method to provide
      # a clean sequence without making the same assumption.

      _a			      =							[]
      for i in range(len(__magic_name__						)						):
           try:
                _a			      =							tokenizer.decode([i]      ,				clean_up_tokenization_spaces=__magic_name__						)
           except UnicodeDecodeError:
                pass
           toks.append((i, tok)						)

      _a			      =							list(filter(lambda __magic_name__						: re.match(r'^[ a-zA-Z]+$'      ,				t[1]						)      ,				__magic_name__						)						)
      _a			      =							list(filter(lambda __magic_name__						: [t[0]] == tokenizer.encode(t[1]      ,				add_special_tokens=__magic_name__						)      ,				__magic_name__						)						)
      if max_length is not None and len(__magic_name__						) > max_length:
           _a			      =							toks[:max_length]
      if min_length is not None and len(__magic_name__						) < min_length and len(__magic_name__						) > 0:
           while len(__magic_name__						) < min_length:
                _a			      =							toks + toks
        # toks_str = [t[1] for t in toks]
      _a			      =							[t[0] for t in toks]

      # Ensure consistency
      _a			      =							tokenizer.decode(__magic_name__      ,				clean_up_tokenization_spaces=__magic_name__						)
      if " " not in output_txt and len(__magic_name__						) > 1:
           _a			      =							(
               tokenizer.decode([toks_ids[0]]      ,				clean_up_tokenization_spaces=__magic_name__						)
               + ' '
               + tokenizer.decode(toks_ids[1:]      ,				clean_up_tokenization_spaces=__magic_name__						)
           )
      if with_prefix_space:
           _a			      =							' ' + output_txt
      _a			      =							tokenizer.encode(__magic_name__      ,				add_special_tokens=__magic_name__						)
      return output_txt, output_ids
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							self.ta_base_tokenizer
      _a			      =							tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']						)
      _a			      =							tokenizer(['hi', 'I went to the gym', '']						)
      self.assertListEqual(batch_with_eos_added['input_ids']      ,				batch_without_eos_added['input_ids']						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.ta_base_tokenizer
      _a			      =							'Unicode €.'
      _a			      =							tokenizer(__magic_name__						)
      _a			      =							[88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
      self.assertEqual(encoded['input_ids']      ,				__magic_name__						)

      # decoding
      _a			      =							tokenizer.decode(__magic_name__						)
      self.assertEqual(__magic_name__      ,				'Unicode €.</s>'						)

      _a			      =							tokenizer('e è é ê ë'						)
      _a			      =							[1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
      self.assertEqual(encoded['input_ids']      ,				__magic_name__						)
      # decoding
      _a			      =							tokenizer.decode(__magic_name__						)
      self.assertEqual(__magic_name__      ,				'e è é ê ë</s>'						)

      # encode/decode, but with `encode` instead of `__call__`
      self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'						)						)      ,				'e è é ê ë</s>'						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							self.ta_base_tokenizer
      _a			      =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
      # fmt: off
      _a			      =							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
      # fmt: on
      _a			      =							tokenizer(__magic_name__      ,				padding=__magic_name__      ,				return_tensors=__magic_name__						)
      self.assertIsInstance(__magic_name__      ,				__magic_name__						)

      if FRAMEWORK != "jax":
           _a			      =							list(batch.input_ids.numpy()[0]						)
      else:
           _a			      =							list(batch.input_ids.tolist()[0]						)

      self.assertListEqual(__magic_name__      ,				__magic_name__						)

      self.assertEqual((2, 37)      ,				batch.input_ids.shape						)
      self.assertEqual((2, 37)      ,				batch.attention_mask.shape						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.ta_base_tokenizer
      _a			      =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
      _a			      =							tokenizer(__magic_name__      ,				padding=__magic_name__      ,				return_tensors=__magic_name__						)
      # check if input_ids are returned and no decoder_input_ids
      self.assertIn('input_ids'      ,				__magic_name__						)
      self.assertIn('attention_mask'      ,				__magic_name__						)
      self.assertNotIn('decoder_input_ids'      ,				__magic_name__						)
      self.assertNotIn('decoder_attention_mask'      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							self.ta_base_tokenizer
      _a			      =							[
          'Summary of the text.',
          'Another summary.',
      ]
      _a			      =							tokenizer(
          text_target=__magic_name__      ,				max_length=32      ,				padding='max_length'      ,				truncation=__magic_name__      ,				return_tensors=__magic_name__						)
      self.assertEqual(32      ,				targets['input_ids'].shape[1]						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							self.ta_base_tokenizer
      _a			      =							['A long paragraph for summarization. </s>']
      _a			      =							['Summary of the text. </s>']
      # fmt: off
      _a			      =							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
      _a			      =							[86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
      # fmt: on

      _a			      =							tokenizer(__magic_name__      ,				text_target=__magic_name__						)

      self.assertEqual(__magic_name__      ,				batch['input_ids'][0]						)
      self.assertEqual(__magic_name__      ,				batch['labels'][0]						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      # safety check on max_len default value so we are sure the test works
      _a			      =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'						):
                self.assertNotEqual(tokenizer.model_max_length      ,				42						)

        # Now let's start the test
      _a			      =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'						):
                # Isolate this from the other tests because we save additional tokens/etc
                _a			      =							tempfile.mkdtemp()

                _a			      =							' He is very happy, UNwant\u00E9d,running'
                _a			      =							tokenizer.encode(__magic_name__      ,				add_special_tokens=__magic_name__						)
                tokenizer.save_pretrained(__magic_name__						)

                _a			      =							tokenizer.__class__.from_pretrained(__magic_name__						)
                _a			      =							after_tokenizer.encode(__magic_name__      ,				add_special_tokens=__magic_name__						)
                self.assertListEqual(__magic_name__      ,				__magic_name__						)

                shutil.rmtree(__magic_name__						)

      _a			      =							self.get_tokenizers(model_max_length=42						)
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'						):
                # Isolate this from the other tests because we save additional tokens/etc
                _a			      =							tempfile.mkdtemp()

                _a			      =							' He is very happy, UNwant\u00E9d,running'
                tokenizer.add_tokens(['bim', 'bambam']						)
                _a			      =							tokenizer.additional_special_tokens
                additional_special_tokens.append('new_additional_special_token'						)
                tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}						)
                _a			      =							tokenizer.encode(__magic_name__      ,				add_special_tokens=__magic_name__						)
                tokenizer.save_pretrained(__magic_name__						)

                _a			      =							tokenizer.__class__.from_pretrained(__magic_name__						)
                _a			      =							after_tokenizer.encode(__magic_name__      ,				add_special_tokens=__magic_name__						)
                self.assertListEqual(__magic_name__      ,				__magic_name__						)
                self.assertIn('new_additional_special_token'      ,				after_tokenizer.additional_special_tokens						)
                self.assertEqual(after_tokenizer.model_max_length      ,				42						)

                _a			      =							tokenizer.__class__.from_pretrained(__magic_name__      ,				model_max_length=43						)
                self.assertEqual(tokenizer.model_max_length      ,				43						)

                shutil.rmtree(__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							[]
      if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

      if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

      for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(__magic_name__						)

                with open(os.path.join(__magic_name__      ,				'special_tokens_map.json'						)      ,				encoding='utf-8'						) as json_file:
                     _a			      =							json.load(__magic_name__						)

                with open(os.path.join(__magic_name__      ,				'tokenizer_config.json'						)      ,				encoding='utf-8'						) as json_file:
                     _a			      =							json.load(__magic_name__						)

                _a			      =							[f'<extra_id_{i}>' for i in range(1_25						)]

                _a			      =							added_tokens_extra_ids + [
                    'an_additional_special_token'
                ]
                _a			      =							added_tokens_extra_ids + [
                    'an_additional_special_token'
                ]

                with open(os.path.join(__magic_name__      ,				'special_tokens_map.json'						)      ,				'w'      ,				encoding='utf-8'						) as outfile:
                     json.dump(__magic_name__      ,				__magic_name__						)
                with open(os.path.join(__magic_name__      ,				'tokenizer_config.json'						)      ,				'w'      ,				encoding='utf-8'						) as outfile:
                     json.dump(__magic_name__      ,				__magic_name__						)

                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                # "special_tokens_map.json" files
                _a			      =							tokenizer_class.from_pretrained(
                    __magic_name__      ,				)
                self.assertIn(
                    'an_additional_special_token'      ,				tokenizer_without_change_in_init.additional_special_tokens						)
                # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                self.assertEqual(
                    ['an_additional_special_token']      ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']						)						)      ,				)

                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                _a			      =							added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'      ,				lstrip=__magic_name__						)]
                _a			      =							tokenizer_class.from_pretrained(
                    __magic_name__      ,				additional_special_tokens=__magic_name__      ,				)

                self.assertIn('a_new_additional_special_token'      ,				tokenizer.additional_special_tokens						)
                self.assertEqual(
                    ['a_new_additional_special_token']      ,				tokenizer.convert_ids_to_tokens(
                        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']						)						)      ,				)
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a			      =							[]
      if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

      if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

      for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(__magic_name__						)

                _a			      =							tokenizer_class.from_pretrained(__magic_name__						)

                self.assertTrue(tokenizer.decode([2_55]						) == ''						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      pass
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      pass
 def 							__UpperCAmelCase		( self						)					->							Any:
      pass
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      pass
 def 							__UpperCAmelCase		( self						)					->							Any:
      # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
      # and special added tokens as tokens
      _a			      =							self.get_tokenizers(fast=__magic_name__      ,				do_lower_case=__magic_name__						)
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'						):
                _a			      =							['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                _a			      =							tokenizer.convert_tokens_to_string(__magic_name__						)

                self.assertIsInstance(__magic_name__      ,				__magic_name__						)




 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'						):
                _a			      =							[
                    'bos_token',
                    'eos_token',
                    'unk_token',
                    'sep_token',
                    'pad_token',
                    'cls_token',
                    'mask_token',
                ]

                _a			      =							0
                _a			      =							tokenizer.convert_ids_to_tokens(
                    __magic_name__      ,				skip_special_tokens=__magic_name__						)

                for attr in attributes_list:
                     setattr(__magic_name__      ,				attr + '_id'      ,				__magic_name__						)
                     self.assertEqual(getattr(__magic_name__      ,				__magic_name__						)      ,				__magic_name__						)
                     self.assertEqual(getattr(__magic_name__      ,				attr + '_id'						)      ,				__magic_name__						)

                     setattr(__magic_name__      ,				attr + '_id'      ,				__magic_name__						)
                     self.assertEqual(getattr(__magic_name__      ,				__magic_name__						)      ,				__magic_name__						)
                     self.assertEqual(getattr(__magic_name__      ,				attr + '_id'						)      ,				__magic_name__						)

                setattr(__magic_name__      ,				'additional_special_tokens_ids'      ,				[]						)
                self.assertListEqual(getattr(__magic_name__      ,				'additional_special_tokens'						)      ,				[]						)
                self.assertListEqual(getattr(__magic_name__      ,				'additional_special_tokens_ids'						)      ,				[]						)

                setattr(__magic_name__      ,				'additional_special_tokens_ids'      ,				[token_id_to_test_setters]						)
                self.assertListEqual(getattr(__magic_name__      ,				'additional_special_tokens'						)      ,				[token_to_test_setters]						)
                self.assertListEqual(getattr(__magic_name__      ,				'additional_special_tokens_ids'						)      ,				[token_id_to_test_setters]						)





'''simple docstring'''






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'		)
     torch.save(model.state_dict()				,						lowerCAmelCase__		)


if __name__ == "__main__":
 a_					:    Optional[int]          =    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."
 )
 a_					:    Optional[int]          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

