
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__lowerCamelCase				=			logging.get_logger(__name__)






class     UpperCAmelCase						:



   def __init__(self						:   int       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Tuple     							= question_encoder
          snake_case			:  Dict     							= generator
          snake_case			:  Any     							= self.question_encoder



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Dict					)						->      Dict:


          '''simple docstring'''


          if os.path.isfile(snake_case__					):
                 raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""					)
          os.makedirs(snake_case__       ,		exist_ok=snake_case__					)
          snake_case			:  List[str]     							= os.path.join(snake_case__       ,		"question_encoder_tokenizer"					)
          snake_case			:  List[str]     							= os.path.join(snake_case__       ,		"generator_tokenizer"					)
          self.question_encoder.save_pretrained(snake_case__					)
          self.generator.save_pretrained(snake_case__					)



   @classmethod
   def 							_SCREAMING_SNAKE_CASE  (cls						:   int       ,		snake_case__						:   List[Any]       ,		**snake_case__						:   Tuple					)						->      Union[str, Any]:


          '''simple docstring'''


          from ..auto.tokenization_auto import AutoTokenizer

          snake_case			:  Union[str, Any]     							= kwargs.pop("config"       ,		snake_case__					)

          if config is None:
                 snake_case			:  Dict     							= RagConfig.from_pretrained(snake_case__					)

          snake_case			:  Optional[Any]     							= AutoTokenizer.from_pretrained(
              snake_case__       ,		config=config.question_encoder       ,		subfolder="question_encoder_tokenizer"					)
          snake_case			:  str     							= AutoTokenizer.from_pretrained(
              snake_case__       ,		config=config.generator       ,		subfolder="generator_tokenizer"					)
          return cls(question_encoder=snake_case__       ,		generator=snake_case__					)



   def __call__(self						:   Dict       ,		*snake_case__						:   Dict       ,		**snake_case__						:   Optional[int]					)						->      str:


          '''simple docstring'''


          return self.current_tokenizer(*snake_case__       ,		**snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		*snake_case__						:   str       ,		**snake_case__						:   Dict					)						->      Tuple:


          '''simple docstring'''


          return self.generator.batch_decode(*snake_case__       ,		**snake_case__					)



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


          '''simple docstring'''


          return self.generator.decode(*snake_case__       ,		**snake_case__					)



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


          '''simple docstring'''


          snake_case			:  str     							= self.question_encoder



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


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.generator



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   List[str]       ,		snake_case__						:   Optional[List[str]] = None       ,		snake_case__						:   Optional[int] = None       ,		snake_case__						:   Optional[int] = None       ,		snake_case__						:   str = "longest"       ,		snake_case__						:   str = None       ,		snake_case__						:   bool = True       ,		**snake_case__						:   Optional[int]       ,		)						->      BatchEncoding:


          '''simple docstring'''


          warnings.warn(
              "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
              "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
              "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
              "details"       ,		snake_case__       ,		)
          if max_length is None:
                 snake_case			:  List[str]     							= self.current_tokenizer.model_max_length
          snake_case			:  Union[str, Any]     							= self(
              snake_case__       ,		add_special_tokens=snake_case__       ,		return_tensors=snake_case__       ,		max_length=snake_case__       ,		padding=snake_case__       ,		truncation=snake_case__       ,		**snake_case__       ,		)
          if tgt_texts is None:
                 return model_inputs
          # Process tgt_texts
          if max_target_length is None:
                 snake_case			:  Union[str, Any]     							= self.current_tokenizer.model_max_length
          snake_case			:  Tuple     							= self(
              text_target=snake_case__       ,		add_special_tokens=snake_case__       ,		return_tensors=snake_case__       ,		padding=snake_case__       ,		max_length=snake_case__       ,		truncation=snake_case__       ,		**snake_case__       ,		)
          snake_case			:  Tuple     							= labels["input_ids"]
          return model_inputs







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()





