


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__       							=      {
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__       							=      ['GPT2TokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__       							=      [
          'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPT2DoubleHeadsModel',
          'GPT2ForQuestionAnswering',
          'GPT2ForSequenceClassification',
          'GPT2ForTokenClassification',
          'GPT2LMHeadModel',
          'GPT2Model',
          'GPT2PreTrainedModel',
          'load_tf_weights_in_gpt2',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__       							=      [
          'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFGPT2DoubleHeadsModel',
          'TFGPT2ForSequenceClassification',
          'TFGPT2LMHeadModel',
          'TFGPT2MainLayer',
          'TFGPT2Model',
          'TFGPT2PreTrainedModel',
      ]

try:
      if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__       							=      ['TFGPT2Tokenizer']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__       							=      ['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_fast import GPTaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpta import (
                GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTaDoubleHeadsModel,
                GPTaForQuestionAnswering,
                GPTaForSequenceClassification,
                GPTaForTokenClassification,
                GPTaLMHeadModel,
                GPTaModel,
                GPTaPreTrainedModel,
                load_tf_weights_in_gpta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gpta import (
                TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGPTaDoubleHeadsModel,
                TFGPTaForSequenceClassification,
                TFGPTaLMHeadModel,
                TFGPTaMainLayer,
                TFGPTaModel,
                TFGPTaPreTrainedModel,
            )

      try:
            if not is_keras_nlp_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_tf import TFGPTaTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
      import sys

      UpperCamelCase__       							=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




from __future__ import annotations

UpperCamelCase__       							=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class   A							:


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		int	)		->       None:
         """simple docstring"""



         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						[-1] * length
         UpperCAmelCase__			=						None
         UpperCAmelCase__			=						None





   def __repr__(self  :		Union[str, Any]	)		->       str:
         """simple docstring"""



         return f"""Node(min_value={self.minn} max_value={self.maxx})"""




def      lowerCAmelCase_				(  __A							)  ->       Node | None:







      '''simple docstring'''


      UpperCAmelCase__			=						Node(len(__A							)							)
      UpperCAmelCase__	,   UpperCAmelCase__			=						min(__A							), max(__A							)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      UpperCAmelCase__			=						(root.minn + root.maxx) // 2

      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						[]

      for index, num in enumerate(__A							):
            if num <= pivot:
                  left_arr.append(__A							)
            else:
                  right_arr.append(__A							)
            UpperCAmelCase__			=						len(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      return root




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      UpperCAmelCase__			=						(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left,						__A,						node.map_left[index] - 1							)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right,						__A,						index - node.map_left[index]							)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if start > end:
            return 0
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						__A							)
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						start - 1							)
      return rank_till_end - rank_before_start




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      UpperCAmelCase__			=						node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left,						__A,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						)
      else:
            return quantile(
                node.right,						index - num_elements_in_left_tree,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      UpperCAmelCase__			=						range_counting(
          node.left,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						__A,						__A,						)
      UpperCAmelCase__			=						range_counting(
          node.right,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						__A,						__A,						)
      return left + right


if __name__ == "__main__":
      import doctest

      doctest.testmod()



