
from __future__ import annotations

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







class      lowerCamelCase		:





     def __init__(self      :    int				,    _A      :    int   )						->						None:
         snake_case      =					-1
         snake_case      =					-1
         snake_case      =					[-1] * length
         snake_case      =					None
         snake_case      =					None





     def __repr__(self      :    Any   )						->						str:
         return f'Node(min_value={self.minn} max_value={self.maxx})'



def 						lowercase_			(   A__		)							->							Node | None:
    """simple docstring"""

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

    snake_case      =					[]
    snake_case      =					[]

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



def 						lowercase_			(   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
    snake_case      =					(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 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__		)							->							int:
    """simple docstring"""

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



def 						lowercase_			(   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]
    snake_case      =					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 						lowercase_			(   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
    snake_case      =					range_counting(
        node.left ,		(node.map_left[start - 1] if start else 0) ,		node.map_left[end] - 1 ,		A__ ,		A__ ,		)
    snake_case      =					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()



import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch







class      lowerCamelCase		(  unittest.TestCase  ):





     @patch("sys.argv"				,    ["fakeprogrampath", "env"]   )
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Dict:
         # test transformers-cli env
         import transformers.commands.transformers_cli

         with CaptureStd() as cs:
             transformers.commands.transformers_cli.main()
         self.assertIn("Python version"				,    cs.out   )
         self.assertIn("Platform"				,    cs.out   )
         self.assertIn("Using distributed or parallel set-up in script?"				,    cs.out   )





     @is_pt_tf_cross_test
     @patch(
         "sys.argv"				,    ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
     def 		UpperCAmelCase(self      :    Any   )						->						int:
         import transformers.commands.transformers_cli

         shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"				,    ignore_errors=_A   )  # cleans potential past runs
         transformers.commands.transformers_cli.main()

         # The original repo has no TF weights -- if they exist, they were created by the CLI
         self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )





     @require_torch
     @patch("sys.argv"				,    ["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
     def 		UpperCAmelCase(self      :    Dict   )						->						List[Any]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )





     @require_torch
     @patch(
         "sys.argv"				,    [
             "fakeprogrampath",
             "download",
             "hf-internal-testing/test_dynamic_model_with_tokenizer",
             "--trust-remote-code",
             "--cache-dir",
             "/tmp",
         ]				,    )
     def 		UpperCAmelCase(self      :    Any   )						->						List[str]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
         self.assertTrue(
             os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )

