



"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
     import jax.numpy as jnp

if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				List[Any]								= {
            'task_specific_params': {
                'summarization': {'length_penalty': 1.0, 'max_length': 1_2_8, 'min_length': 1_2, 'num_beams': 4},
                'summarization_cnn': {'length_penalty': 2.0, 'max_length': 1_4_2, 'min_length': 5_6, 'num_beams': 4},
                'summarization_xsum': {'length_penalty': 1.0, 'max_length': 6_2, 'min_length': 1_1, 'num_beams': 6},
            }
        }
        lowerCAmelCase_   :				int								= {
            'task_specific_params.summarization.length_penalty': 1.0,
            'task_specific_params.summarization.max_length': 1_2_8,
            'task_specific_params.summarization.min_length': 1_2,
            'task_specific_params.summarization.num_beams': 4,
            'task_specific_params.summarization_cnn.length_penalty': 2.0,
            'task_specific_params.summarization_cnn.max_length': 1_4_2,
            'task_specific_params.summarization_cnn.min_length': 5_6,
            'task_specific_params.summarization_cnn.num_beams': 4,
            'task_specific_params.summarization_xsum.length_penalty': 1.0,
            'task_specific_params.summarization_xsum.max_length': 6_2,
            'task_specific_params.summarization_xsum.min_length': 1_1,
            'task_specific_params.summarization_xsum.num_beams': 6,
        }

        self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE_       )    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				int								= np.random.randn(3    ,			4       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_       )    ,			x.transpose()       )       )

        lowerCAmelCase_   :				int								= np.random.randn(3    ,			4    ,			5       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       )    ,			x.transpose((1, 2, 0)       )       )       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Any								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				Any								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_       )    ,			transpose(SCREAMING_SNAKE_CASE_       ).numpy()       )       )

        lowerCAmelCase_   :				Optional[int]								= np.random.randn(3    ,			4    ,			5       )
        lowerCAmelCase_   :				Optional[int]								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       )    ,			transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       ).numpy()       )       )



      @require_tf
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				int								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				List[str]								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_       )    ,			transpose(SCREAMING_SNAKE_CASE_       ).numpy()       )       )

        lowerCAmelCase_   :				Any								= np.random.randn(3    ,			4    ,			5       )
        lowerCAmelCase_   :				Optional[Any]								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       )    ,			transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       ).numpy()       )       )



      @require_flax
      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Tuple								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				Optional[int]								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_       )    ,			np.asarray(transpose(SCREAMING_SNAKE_CASE_       )       )       )       )

        lowerCAmelCase_   :				Tuple								= np.random.randn(3    ,			4    ,			5       )
        lowerCAmelCase_   :				List[str]								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       )    ,			np.asarray(transpose(SCREAMING_SNAKE_CASE_    ,			axes=(1, 2, 0)       )       )       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[str]								= np.random.randn(3    ,			4       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       )    ,			np.reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       )       )       )

        lowerCAmelCase_   :				Any								= np.random.randn(3    ,			4    ,			5       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       )    ,			np.reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       )       )       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Any								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				List[Any]								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       )    ,			reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       ).numpy()       )       )

        lowerCAmelCase_   :				Dict								= np.random.randn(3    ,			4    ,			5       )
        lowerCAmelCase_   :				List[str]								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       )    ,			reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       ).numpy()       )       )



      @require_tf
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				Dict								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				Any								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       )    ,			reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       ).numpy()       )       )

        lowerCAmelCase_   :				Tuple								= np.random.randn(3    ,			4    ,			5       )
        lowerCAmelCase_   :				List[Any]								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       )    ,			reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       ).numpy()       )       )



      @require_flax
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[Any]								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				List[str]								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       )    ,			np.asarray(reshape(SCREAMING_SNAKE_CASE_    ,			(4, 3)       )       )       )       )

        lowerCAmelCase_   :				int								= np.random.randn(3    ,			4    ,			5       )
        lowerCAmelCase_   :				Any								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       )    ,			np.asarray(reshape(SCREAMING_SNAKE_CASE_    ,			(1_2, 5)       )       )       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Optional[int]								= np.random.randn(1    ,			3    ,			4       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_       )    ,			np.squeeze(SCREAMING_SNAKE_CASE_       )       )       )

        lowerCAmelCase_   :				Any								= np.random.randn(1    ,			4    ,			1    ,			5       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       )    ,			np.squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       )       )       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				List[Any]								= np.random.randn(1    ,			3    ,			4       )
        lowerCAmelCase_   :				Optional[int]								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_       )    ,			squeeze(SCREAMING_SNAKE_CASE_       ).numpy()       )       )

        lowerCAmelCase_   :				Optional[Any]								= np.random.randn(1    ,			4    ,			1    ,			5       )
        lowerCAmelCase_   :				Optional[int]								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       )    ,			squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       ).numpy()       )       )



      @require_tf
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				int								= np.random.randn(1    ,			3    ,			4       )
        lowerCAmelCase_   :				List[str]								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_       )    ,			squeeze(SCREAMING_SNAKE_CASE_       ).numpy()       )       )

        lowerCAmelCase_   :				Union[str, Any]								= np.random.randn(1    ,			4    ,			1    ,			5       )
        lowerCAmelCase_   :				List[Any]								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       )    ,			squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       ).numpy()       )       )



      @require_flax
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= np.random.randn(1    ,			3    ,			4       )
        lowerCAmelCase_   :				Any								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_       )    ,			np.asarray(squeeze(SCREAMING_SNAKE_CASE_       )       )       )       )

        lowerCAmelCase_   :				int								= np.random.randn(1    ,			4    ,			1    ,			5       )
        lowerCAmelCase_   :				Optional[Any]								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       )    ,			np.asarray(squeeze(SCREAMING_SNAKE_CASE_    ,			axis=2       )       )       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Tuple								= np.random.randn(3    ,			4       )
        self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       )    ,			np.expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       )       )       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				Optional[int]								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				Union[str, Any]								= torch.tensor(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       )    ,			expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       ).numpy()       )       )



      @require_tf
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Tuple								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				Dict								= tf.constant(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       )    ,			expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       ).numpy()       )       )





      @require_flax
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= np.random.randn(3    ,			4       )
        lowerCAmelCase_   :				Dict								= jnp.array(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       )    ,			np.asarray(expand_dims(SCREAMING_SNAKE_CASE_    ,			axis=1       )       )       )       )










"""simple docstring"""







from __future__ import annotations

def 				UpperCamelCase_    (    lowerCAmelCase__      :				list[int]    ,   lowerCAmelCase__      :				int						)						->   bool:

  """simple docstring"""




  if len(lowerCAmelCase__						) == 0:
    return False
  lowerCAmelCase_   :				Union[str, Any]								= len(lowerCAmelCase__						) // 2
  if a_list[midpoint] == item:
    return True
  if item < a_list[midpoint]:
    return binary_search(a_list[:midpoint]    ,   lowerCAmelCase__						)
  else:
    return binary_search(a_list[midpoint + 1 :]    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	input("""Enter numbers separated by comma:\n""").strip()
     lowercase__		:				Optional[int]          =	[int(item.strip()) for item in user_input.split(""",""")]
     lowercase__		:				Optional[Any]          =	int(input("""Enter the number to be found in the list:\n""").strip())
     lowercase__		:				int          =	"""""" if binary_search(sequence, target) else """not """
     print(f'{target} was {not_str}found in {sequence}')





