
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():
   __lowerCamelCase				=			"""pt"""
elif is_tf_available():
   __lowerCamelCase				=			"""tf"""
else:
   __lowerCamelCase				=			"""jax"""






class     UpperCAmelCase						(		A_			,unittest.TestCase					):
   A__   :						Optional[int]			=	ByTaTokenizer
   A__   :						Tuple			=	False



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      List[str]:


          '''simple docstring'''


          super().setUp()
          snake_case			:  Union[str, Any]     							= ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname					)



   @cached_property
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Tuple:


          '''simple docstring'''


          return ByTaTokenizer.from_pretrained("google/byt5-small"					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		**snake_case__						:   Any					)						->      ByTaTokenizer:


          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname       ,		**snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   List[str]       ,		snake_case__						:   Union[str, Any]=False       ,		snake_case__						:   Optional[Any]=20       ,		snake_case__						:   Optional[int]=5					)						->      Tuple[str, list]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= []
          for i in range(len(snake_case__					)					):
                 try:
                        snake_case			:  List[str]     							= tokenizer.decode([i]       ,		clean_up_tokenization_spaces=snake_case__					)
                 except UnicodeDecodeError:
                        pass
                 toks.append((i, tok)					)

          snake_case			:  List[Any]     							= list(filter(lambda snake_case__					: re.match(R"^[ a-zA-Z]+$"       ,		t[1]					)       ,		snake_case__					)					)
          snake_case			:  Optional[int]     							= list(filter(lambda snake_case__					: [t[0]] == tokenizer.encode(t[1]       ,		add_special_tokens=snake_case__					)       ,		snake_case__					)					)
          if max_length is not None and len(snake_case__					) > max_length:
                 snake_case			:  List[Any]     							= toks[:max_length]
          if min_length is not None and len(snake_case__					) < min_length and len(snake_case__					) > 0:
                 while len(snake_case__					) < min_length:
                        snake_case			:  Dict     							= toks + toks
        # toks_str = [t[1] for t in toks]
          snake_case			:  Dict     							= [t[0] for t in toks]

          # Ensure consistency
          snake_case			:  Tuple     							= tokenizer.decode(snake_case__       ,		clean_up_tokenization_spaces=snake_case__					)
          if " " not in output_txt and len(snake_case__					) > 1:
                 snake_case			:  int     							= (
                     tokenizer.decode([toks_ids[0]]       ,		clean_up_tokenization_spaces=snake_case__					)
                     + " "
                     + tokenizer.decode(toks_ids[1:]       ,		clean_up_tokenization_spaces=snake_case__					)
                 )
          if with_prefix_space:
                 snake_case			:  int     							= " " + output_txt
          snake_case			:  int     							= tokenizer.encode(snake_case__       ,		add_special_tokens=snake_case__					)
          return output_txt, output_ids



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


          '''simple docstring'''


          snake_case			:  Tuple     							= self.ta_base_tokenizer
          snake_case			:  str     							= tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]					)
          snake_case			:  Dict     							= tokenizer(["hi", "I went to the gym", ""]					)
          self.assertListEqual(batch_with_eos_added["input_ids"]       ,		batch_without_eos_added["input_ids"]					)



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


          '''simple docstring'''


          snake_case			:  List[str]     							= self.ta_base_tokenizer
          snake_case			:  List[str]     							= "Unicode €."
          snake_case			:  List[str]     							= tokenizer(snake_case__					)
          snake_case			:  Any     							= [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"]       ,		snake_case__					)

          # decoding
          snake_case			:  Optional[Any]     							= tokenizer.decode(snake_case__					)
          self.assertEqual(snake_case__       ,		"Unicode €.</s>"					)

          snake_case			:  Tuple     							= tokenizer("e è é ê ë"					)
          snake_case			:  List[str]     							= [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"]       ,		snake_case__					)
          # decoding
          snake_case			:  Any     							= tokenizer.decode(snake_case__					)
          self.assertEqual(snake_case__       ,		"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 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= self.ta_base_tokenizer
          snake_case			:  Optional[int]     							= ["A long paragraph for summarization.", "Another paragraph for summarization."]
          # fmt: off
          snake_case			:  Union[str, Any]     							= [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
          snake_case			:  int     							= tokenizer(snake_case__       ,		padding=snake_case__       ,		return_tensors=snake_case__					)
          self.assertIsInstance(snake_case__       ,		snake_case__					)

          if FRAMEWORK != "jax":
                 snake_case			:  Tuple     							= list(batch.input_ids.numpy()[0]					)
          else:
                 snake_case			:  List[Any]     							= list(batch.input_ids.tolist()[0]					)

          self.assertListEqual(snake_case__       ,		snake_case__					)

          self.assertEqual((2, 37)       ,		batch.input_ids.shape					)
          self.assertEqual((2, 37)       ,		batch.attention_mask.shape					)



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


          '''simple docstring'''


          snake_case			:  int     							= self.ta_base_tokenizer
          snake_case			:  Tuple     							= ["A long paragraph for summarization.", "Another paragraph for summarization."]
          snake_case			:  Tuple     							= tokenizer(snake_case__       ,		padding=snake_case__       ,		return_tensors=snake_case__					)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn("input_ids"       ,		snake_case__					)
          self.assertIn("attention_mask"       ,		snake_case__					)
          self.assertNotIn("decoder_input_ids"       ,		snake_case__					)
          self.assertNotIn("decoder_attention_mask"       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      str:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.ta_base_tokenizer
          snake_case			:  Tuple     							= [
              "Summary of the text.",
              "Another summary.",
          ]
          snake_case			:  Any     							= tokenizer(
              text_target=snake_case__       ,		max_length=32       ,		padding="max_length"       ,		truncation=snake_case__       ,		return_tensors=snake_case__					)
          self.assertEqual(32       ,		targets["input_ids"].shape[1]					)



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


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.ta_base_tokenizer
          snake_case			:  Optional[Any]     							= ["A long paragraph for summarization. </s>"]
          snake_case			:  List[str]     							= ["Summary of the text. </s>"]
          # fmt: off
          snake_case			:  str     							= [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]
          snake_case			:  Optional[int]     							= [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

          snake_case			:  List[str]     							= tokenizer(snake_case__       ,		text_target=snake_case__					)

          self.assertEqual(snake_case__       ,		batch["input_ids"][0]					)
          self.assertEqual(snake_case__       ,		batch["labels"][0]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Any:


          '''simple docstring'''


          snake_case			:  Tuple     							= 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
          snake_case			:  Optional[Any]     							= 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
                        snake_case			:  Any     							= tempfile.mkdtemp()

                        snake_case			:  Dict     							= " He is very happy, UNwant\u00E9d,running"
                        snake_case			:  List[str]     							= tokenizer.encode(snake_case__       ,		add_special_tokens=snake_case__					)
                        tokenizer.save_pretrained(snake_case__					)

                        snake_case			:  int     							= tokenizer.__class__.from_pretrained(snake_case__					)
                        snake_case			:  List[Any]     							= after_tokenizer.encode(snake_case__       ,		add_special_tokens=snake_case__					)
                        self.assertListEqual(snake_case__       ,		snake_case__					)

                        shutil.rmtree(snake_case__					)

          snake_case			:  List[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
                        snake_case			:  Any     							= tempfile.mkdtemp()

                        snake_case			:  str     							= " He is very happy, UNwant\u00E9d,running"
                        tokenizer.add_tokens(["bim", "bambam"]					)
                        snake_case			:  int     							= tokenizer.additional_special_tokens
                        additional_special_tokens.append("new_additional_special_token"					)
                        tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}					)
                        snake_case			:  Optional[Any]     							= tokenizer.encode(snake_case__       ,		add_special_tokens=snake_case__					)
                        tokenizer.save_pretrained(snake_case__					)

                        snake_case			:  Any     							= tokenizer.__class__.from_pretrained(snake_case__					)
                        snake_case			:  Tuple     							= after_tokenizer.encode(snake_case__       ,		add_special_tokens=snake_case__					)
                        self.assertListEqual(snake_case__       ,		snake_case__					)
                        self.assertIn("new_additional_special_token"       ,		after_tokenizer.additional_special_tokens					)
                        self.assertEqual(after_tokenizer.model_max_length       ,		42					)

                        snake_case			:  Optional[Any]     							= tokenizer.__class__.from_pretrained(snake_case__       ,		model_max_length=43					)
                        self.assertEqual(tokenizer.model_max_length       ,		43					)

                        shutil.rmtree(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= []
          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(snake_case__					)

                        with open(os.path.join(snake_case__       ,		"special_tokens_map.json"					)       ,		encoding="utf-8"					) as json_file:
                               snake_case			:  Dict     							= json.load(snake_case__					)

                        with open(os.path.join(snake_case__       ,		"tokenizer_config.json"					)       ,		encoding="utf-8"					) as json_file:
                               snake_case			:  Any     							= json.load(snake_case__					)

                        snake_case			:  Optional[int]     							= [f"""<extra_id_{i}>""" for i in range(1_25					)]

                        snake_case			:  int     							= added_tokens_extra_ids + [
                            "an_additional_special_token"
                        ]
                        snake_case			:  List[Any]     							= added_tokens_extra_ids + [
                            "an_additional_special_token"
                        ]

                        with open(os.path.join(snake_case__       ,		"special_tokens_map.json"					)       ,		"w"       ,		encoding="utf-8"					) as outfile:
                               json.dump(snake_case__       ,		snake_case__					)
                        with open(os.path.join(snake_case__       ,		"tokenizer_config.json"					)       ,		"w"       ,		encoding="utf-8"					) as outfile:
                               json.dump(snake_case__       ,		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
                        snake_case			:  Tuple     							= tokenizer_class.from_pretrained(
                            snake_case__       ,		)
                        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
                        snake_case			:  Any     							= added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"       ,		lstrip=snake_case__					)]
                        snake_case			:  List[str]     							= tokenizer_class.from_pretrained(
                            snake_case__       ,		additional_special_tokens=snake_case__       ,		)

                        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 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  List[str]     							= []
          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(snake_case__					)

                        snake_case			:  Union[str, Any]     							= tokenizer_class.from_pretrained(snake_case__					)

                        self.assertTrue(tokenizer.decode([2_55]					) == ""					)



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          pass



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      List[str]:


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.get_tokenizers(fast=snake_case__       ,		do_lower_case=snake_case__					)
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
                        snake_case			:  List[str]     							= ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
                        snake_case			:  Tuple     							= tokenizer.convert_tokens_to_string(snake_case__					)

                        self.assertIsInstance(snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      str:


          '''simple docstring'''


          snake_case			:  Any     							= self.get_tokenizers()
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
                        snake_case			:  List[Any]     							= [
                            "bos_token",
                            "eos_token",
                            "unk_token",
                            "sep_token",
                            "pad_token",
                            "cls_token",
                            "mask_token",
                        ]

                        snake_case			:  Any     							= 0
                        snake_case			:  List[Any]     							= tokenizer.convert_ids_to_tokens(
                            snake_case__       ,		skip_special_tokens=snake_case__					)

                        for attr in attributes_list:
                               setattr(snake_case__       ,		attr + "_id"       ,		snake_case__					)
                               self.assertEqual(getattr(snake_case__       ,		snake_case__					)       ,		snake_case__					)
                               self.assertEqual(getattr(snake_case__       ,		attr + "_id"					)       ,		snake_case__					)

                               setattr(snake_case__       ,		attr + "_id"       ,		snake_case__					)
                               self.assertEqual(getattr(snake_case__       ,		snake_case__					)       ,		snake_case__					)
                               self.assertEqual(getattr(snake_case__       ,		attr + "_id"					)       ,		snake_case__					)

                        setattr(snake_case__       ,		"additional_special_tokens_ids"       ,		[]					)
                        self.assertListEqual(getattr(snake_case__       ,		"additional_special_tokens"					)       ,		[]					)
                        self.assertListEqual(getattr(snake_case__       ,		"additional_special_tokens_ids"					)       ,		[]					)

                        setattr(snake_case__       ,		"additional_special_tokens_ids"       ,		[token_id_to_test_setters]					)
                        self.assertListEqual(getattr(snake_case__       ,		"additional_special_tokens"					)       ,		[token_to_test_setters]					)
                        self.assertListEqual(getattr(snake_case__       ,		"additional_special_tokens_ids"					)       ,		[token_id_to_test_setters]					)







from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__lowerCamelCase				=			TypeVar("""T""")






class     UpperCAmelCase						(		Generic[T]					):



   def __init__(self						:   int       ,		snake_case__						:   list[T]       ,		snake_case__						:   Callable[[T, T], T]					)						->      None:


          '''simple docstring'''


          snake_case			:  Any | T     							= None

          snake_case			:  int     							= len(snake_case__					)
          snake_case			:  list[T]     							= [any_type for _ in range(self.N					)] + arr
          snake_case			:  str     							= fnc
          self.build()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      None:


          '''simple docstring'''


          for p in range(self.N - 1       ,		0       ,		-1					):
                 snake_case			:  int     							= self.fn(self.st[p * 2]       ,		self.st[p * 2 + 1]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   int       ,		snake_case__						:   T					)						->      None:


          '''simple docstring'''


          p += self.N
          snake_case			:  int     							= v
          while p > 1:
                 snake_case			:  List[str]     							= p // 2
                 snake_case			:  Tuple     							= self.fn(self.st[p * 2]       ,		self.st[p * 2 + 1]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   int					)						->      T | None:  # noqa: E741


          '''simple docstring'''


          snake_case							,   snake_case			:  Optional[int]     							= l + self.N, r + self.N

          snake_case			:  T | None     							= None
          while l <= r:
                 if l % 2 == 1:
                        snake_case			:  List[str]     							= self.st[l] if res is None else self.fn(snake_case__       ,		self.st[l]					)
                 if r % 2 == 0:
                        snake_case			:  int     							= self.st[r] if res is None else self.fn(snake_case__       ,		self.st[r]					)
                 snake_case							,   snake_case			:  str     							= (l + 1) // 2, (r - 1) // 2
          return res


if __name__ == "__main__":
   from functools import reduce

   __lowerCamelCase				=			[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

   __lowerCamelCase				=			{
       0: 7,
       1: 2,
       2: 6,
       3: -14,
       4: 5,
       5: 4,
       6: 7,
       7: -10,
       8: 9,
       9: 10,
       10: 12,
       11: 1,
   }

   __lowerCamelCase				=			SegmentTree(test_array, min)
   __lowerCamelCase				=			SegmentTree(test_array, max)
   __lowerCamelCase				=			SegmentTree(test_array, lambda a, b: a + b)


   def      UpperCamelCase   (       ):
          for i in range(len(__lowerCamelCase       )       ):
                 for j in range(__lowerCamelCase							,   len(__lowerCamelCase       )       ):
                        snake_case			:  int     							= reduce(__lowerCamelCase							,   test_array[i : j + 1]       )
                        snake_case			:  Tuple     							= reduce(__lowerCamelCase							,   test_array[i : j + 1]       )
                        snake_case			:  Union[str, Any]     							= reduce(lambda __lowerCamelCase							,   __lowerCamelCase       : a + b							,   test_array[i : j + 1]       )
                        assert min_range == min_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )
                        assert max_range == max_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )
                        assert sum_range == sum_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )

   test_all_segments()

   for index, value in test_updates.items():
      __lowerCamelCase				=			value
      min_segment_tree.update(index, value)
      max_segment_tree.update(index, value)
      sum_segment_tree.update(index, value)
      test_all_segments()





