
'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





class 					A							(				__snake_case       ,     unittest.TestCase		):
     __magic_name__          =						CanineTokenizer
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()
         A			:  Tuple   =		CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname      )





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         return CanineTokenizer.from_pretrained('''google/canine-s'''      )





     def 							__lowerCAmelCase				(				self		,     **SCREAMING_SNAKE_CASE      )					->							CanineTokenizer:






         """simple docstring"""






         A			:  Optional[int]   =		self.tokenizer_class.from_pretrained(self.tmpdirname		,     **SCREAMING_SNAKE_CASE      )
         A			:  Dict   =		1024
         return tokenizer





     @require_torch
     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Dict   =		self.canine_tokenizer
         A			:  List[str]   =		['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
         # fmt: off
         A			:  int   =		[57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
         # fmt: on
         A			:  Tuple   =		tokenizer(SCREAMING_SNAKE_CASE		,     padding=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         A			:  Optional[Any]   =		list(batch.input_ids.numpy()[0]      )

         self.assertListEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         self.assertEqual((2, 39)		,     batch.input_ids.shape      )
         self.assertEqual((2, 39)		,     batch.attention_mask.shape      )





     @require_torch
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  int   =		self.canine_tokenizer
         A			:  Any   =		['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
         A			:  List[Any]   =		tokenizer(SCREAMING_SNAKE_CASE		,     padding=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      )
         # check if input_ids, attention_mask and token_type_ids are returned
         self.assertIn('''input_ids'''		,     SCREAMING_SNAKE_CASE      )
         self.assertIn('''attention_mask'''		,     SCREAMING_SNAKE_CASE      )
         self.assertIn('''token_type_ids'''		,     SCREAMING_SNAKE_CASE      )





     @require_torch
     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  Optional[int]   =		self.canine_tokenizer
         A			:  Dict   =		[
             '''What\'s the weater?''',
             '''It\'s about 25 degrees.''',
         ]
         A			:  Optional[int]   =		tokenizer(
             text_target=SCREAMING_SNAKE_CASE		,     max_length=32		,     padding='''max_length'''		,     truncation=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      )
         self.assertEqual(32		,     targets['''input_ids'''].shape[1]      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  List[Any]   =		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			:  Optional[int]   =		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			:  Any   =		tempfile.mkdtemp()

                 A			:  str   =		''' He is very happy, UNwant\u00E9d,running'''
                 A			:  str   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE      )

                 A			:  Optional[Any]   =		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE      )
                 A			:  Union[str, Any]   =		after_tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 self.assertListEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

                 shutil.rmtree(SCREAMING_SNAKE_CASE      )

         A			:  Union[str, Any]   =		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			:  str   =		tempfile.mkdtemp()

                 A			:  Union[str, Any]   =		''' He is very happy, UNwant\u00E9d,running'''

                 A			:  Optional[Any]   =		tokenizer.additional_special_tokens

                 # We can add a new special token for Canine as follows:
                 A			:  Union[str, Any]   =		chr(0XE_007      )
                 additional_special_tokens.append(SCREAMING_SNAKE_CASE      )
                 tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}      )
                 A			:  str   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE      )

                 A			:  Optional[Any]   =		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE      )
                 A			:  List[str]   =		after_tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 self.assertListEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
                 self.assertIn(SCREAMING_SNAKE_CASE		,     after_tokenizer.additional_special_tokens      )
                 self.assertEqual(after_tokenizer.model_max_length		,     42      )

                 A			:  str   =		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE		,     model_max_length=43      )
                 self.assertEqual(tokenizer.model_max_length		,     43      )

                 shutil.rmtree(SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         A			:  Tuple   =		self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE      )
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'      ):
                 A,    A			:  Optional[int]   =		self.get_clean_sequence(SCREAMING_SNAKE_CASE      )

                 # a special token for Canine can be defined as follows:
                 A			:  Any   =		0XE_005
                 A			:  List[str]   =		chr(SCREAMING_SNAKE_CASE      )

                 tokenizer.add_special_tokens({'''cls_token''': special_token}      )
                 A			:  Union[str, Any]   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     1      )

                 A			:  List[Any]   =		tokenizer.decode(ids + encoded_special_token		,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
                 A			:  List[Any]   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )

                 A			:  Any   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 A			:  List[str]   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 self.assertEqual(SCREAMING_SNAKE_CASE		,     input_encoded + special_token_id      )

                 A			:  Optional[int]   =		tokenizer.decode(SCREAMING_SNAKE_CASE		,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
                 self.assertTrue(special_token not in decoded      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE      )
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'      ):
                 A			:  Union[str, Any]   =		chr(0XE_005      )
                 A			:  Optional[int]   =		chr(0XE_006      )

                 # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                 tokenizer.add_tokens([SPECIAL_TOKEN_1]		,     special_tokens=SCREAMING_SNAKE_CASE      )
                 # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                 # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                 tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}      )

                 A			:  Tuple   =		tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
                 A			:  Tuple   =		tokenizer.tokenize(SCREAMING_SNAKE_CASE      )

                 self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     1      )
                 self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     1      )
                 self.assertEqual(token_a[0]		,     SCREAMING_SNAKE_CASE      )
                 self.assertEqual(token_a[0]		,     SCREAMING_SNAKE_CASE      )





     @require_tokenizers
     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  int   =		self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE      )
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'      ):
                 # a special token for Canine can be defined as follows:
                 A			:  str   =		0XE_006
                 A			:  Optional[int]   =		chr(SCREAMING_SNAKE_CASE      )

                 A			:  Tuple   =		AddedToken(SCREAMING_SNAKE_CASE		,     lstrip=SCREAMING_SNAKE_CASE      )
                 tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}      )

                 with tempfile.TemporaryDirectory() as tmp_dir_name:
                     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE      )
                     tokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Optional[Any]   =		[]
         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(SCREAMING_SNAKE_CASE      )

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

                 with open(os.path.join(SCREAMING_SNAKE_CASE		,     '''tokenizer_config.json'''      )		,     encoding='''utf-8'''      ) as json_file:
                     A			:  List[Any]   =		json.load(SCREAMING_SNAKE_CASE      )

                 # a special token for Canine can be defined as follows:
                 A			:  int   =		0XE_006
                 A			:  Optional[int]   =		chr(SCREAMING_SNAKE_CASE      )

                 A			:  Union[str, Any]   =		[new_token_a]
                 A			:  str   =		[new_token_a]

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

                 # 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			:  int   =		tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE		,     extra_ids=0      )
                 self.assertIn(SCREAMING_SNAKE_CASE		,     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(
                     [new_token_a]		,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                         tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]      )      )		,     )

                 A			:  Tuple   =		0XE_007
                 A			:  List[str]   =		chr(SCREAMING_SNAKE_CASE      )
                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 A			:  Optional[Any]   =		[AddedToken(SCREAMING_SNAKE_CASE		,     lstrip=SCREAMING_SNAKE_CASE      )]
                 A			:  int   =		tokenizer_class.from_pretrained(
                     SCREAMING_SNAKE_CASE		,     additional_special_tokens=SCREAMING_SNAKE_CASE		,     extra_ids=0      )

                 self.assertIn(SCREAMING_SNAKE_CASE		,     tokenizer.additional_special_tokens      )
                 # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                 self.assertEqual(
                     [new_token_a]		,     tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]      )      )      )





     @require_tokenizers
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Dict   =		self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE      )
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'      ):
                 A			:  Any   =		'''hello world'''
                 if self.space_between_special_tokens:
                     A			:  Dict   =		'''[CLS] hello world [SEP]'''
                 else:
                     A			:  Union[str, Any]   =		input
                 A			:  List[Any]   =		tokenizer.encode(SCREAMING_SNAKE_CASE		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
                 A			:  Tuple   =		tokenizer.decode(SCREAMING_SNAKE_CASE		,     spaces_between_special_tokens=self.space_between_special_tokens      )
                 self.assertIn(SCREAMING_SNAKE_CASE		,     [output, output.lower()]      )





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






         """simple docstring"""






         A			:  int   =		self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'      ):
                 A			:  int   =		[
                     '''bos_token''',
                     '''eos_token''',
                     '''unk_token''',
                     '''sep_token''',
                     '''pad_token''',
                     '''cls_token''',
                     '''mask_token''',
                 ]

         A			:  str   =		'''a'''
         A			:  Any   =		ord(SCREAMING_SNAKE_CASE      )

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

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

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

         A			:  Tuple   =		0XE_006
         A			:  Tuple   =		chr(SCREAMING_SNAKE_CASE      )
         setattr(SCREAMING_SNAKE_CASE		,     '''additional_special_tokens_ids'''		,     [additional_special_token_id]      )
         self.assertListEqual(getattr(SCREAMING_SNAKE_CASE		,     '''additional_special_tokens'''      )		,     [additional_special_token]      )
         self.assertListEqual(getattr(SCREAMING_SNAKE_CASE		,     '''additional_special_tokens_ids'''      )		,     [additional_special_token_id]      )





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass






'''simple docstring'''




from random import randint, random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__ = False						,							snake_case__ = False						,							snake_case__ = 5						,							):




    '''simple docstring'''



    A			:  List[str]   =		[[-1] * number_of_cells]  # Create a highway without any car
    A			:  Optional[Any]   =		0
    A			:  Tuple   =		max(snake_case__						,							0		)
    while i < number_of_cells:
        A			:  Optional[int]   =		(
            randint(0						,							snake_case__		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1						,							max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    A			:  str   =		highway_now[car_index + 1 :]
    for cell in range(len(snake_case__		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(snake_case__						,							-1		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  int   =		len(snake_case__		)
    # Beforce calculations, the highway is empty
    A			:  int   =		[-1] * number_of_cells

    for car_index in range(snake_case__		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:  Optional[Any]   =		min(highway_now[car_index] + 1						,							snake_case__		)
            # Number of empty cell before the next car
            A			:  List[str]   =		get_distance(snake_case__						,							snake_case__		) - 1
            # We can't have the car causing an accident
            A			:  Union[str, Any]   =		min(next_highway[car_index]						,							snake_case__		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:  List[str]   =		max(next_highway[car_index] - 1						,							0		)
    return next_highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		len(highway[0]		)

    for i in range(snake_case__		):
        A			:  int   =		update(highway[i]						,							snake_case__						,							snake_case__		)
        A			:  Any   =		[-1] * number_of_cells

        for car_index in range(snake_case__		):
            A			:  Dict   =		next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:  Dict   =		(car_index + speed) % number_of_cells
                # Commit the change of position
                A			:  List[str]   =		speed
        highway.append(snake_case__		)

    return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




