
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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			{
				    'task_specific_params': {
				        'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
				        'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
				        'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
				    }
				}
				_snake_case		=			{
				    'task_specific_params.summarization.length_penalty': 1.0,
				    'task_specific_params.summarization.max_length': 128,
				    'task_specific_params.summarization.min_length': 12,
				    'task_specific_params.summarization.num_beams': 4,
				    'task_specific_params.summarization_cnn.length_penalty': 2.0,
				    'task_specific_params.summarization_cnn.max_length': 142,
				    'task_specific_params.summarization_cnn.min_length': 56,
				    'task_specific_params.summarization_cnn.num_beams': 4,
				    'task_specific_params.summarization_xsum.length_penalty': 1.0,
				    'task_specific_params.summarization_xsum.max_length': 62,
				    'task_specific_params.summarization_xsum.min_length': 11,
				    'task_specific_params.summarization_xsum.num_beams': 6,
				}

				self.assertEqual(flatten_dict(lowercase	)			,  lowercase	)



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				self.assertTrue(np.allclose(transpose(lowercase	)			,  x.transpose()	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				self.assertTrue(np.allclose(transpose(lowercase			,  axes=(1, 2, 0)	)			,  x.transpose((1, 2, 0)	)	)	)



	@require_torch
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(transpose(lowercase	)			,  transpose(lowercase	).numpy()	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(transpose(lowercase			,  axes=(1, 2, 0)	)			,  transpose(lowercase			,  axes=(1, 2, 0)	).numpy()	)	)



	@require_tf
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(transpose(lowercase	)			,  transpose(lowercase	).numpy()	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(transpose(lowercase			,  axes=(1, 2, 0)	)			,  transpose(lowercase			,  axes=(1, 2, 0)	).numpy()	)	)



	@require_flax
	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(transpose(lowercase	)			,  np.asarray(transpose(lowercase	)	)	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(transpose(lowercase			,  axes=(1, 2, 0)	)			,  np.asarray(transpose(lowercase			,  axes=(1, 2, 0)	)	)	)	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (4, 3)	)			,  np.reshape(lowercase			,  (4, 3)	)	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (12, 5)	)			,  np.reshape(lowercase			,  (12, 5)	)	)	)



	@require_torch
	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (4, 3)	)			,  reshape(lowercase			,  (4, 3)	).numpy()	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (12, 5)	)			,  reshape(lowercase			,  (12, 5)	).numpy()	)	)



	@require_tf
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (4, 3)	)			,  reshape(lowercase			,  (4, 3)	).numpy()	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (12, 5)	)			,  reshape(lowercase			,  (12, 5)	).numpy()	)	)



	@require_flax
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (4, 3)	)			,  np.asarray(reshape(lowercase			,  (4, 3)	)	)	)	)

				_snake_case		=			np.random.randn(3			,  4			,  5	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(reshape(lowercase			,  (12, 5)	)			,  np.asarray(reshape(lowercase			,  (12, 5)	)	)	)	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(1			,  3			,  4	)
				self.assertTrue(np.allclose(squeeze(lowercase	)			,  np.squeeze(lowercase	)	)	)

				_snake_case		=			np.random.randn(1			,  4			,  1			,  5	)
				self.assertTrue(np.allclose(squeeze(lowercase			,  axis=2	)			,  np.squeeze(lowercase			,  axis=2	)	)	)



	@require_torch
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(1			,  3			,  4	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(squeeze(lowercase	)			,  squeeze(lowercase	).numpy()	)	)

				_snake_case		=			np.random.randn(1			,  4			,  1			,  5	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(squeeze(lowercase			,  axis=2	)			,  squeeze(lowercase			,  axis=2	).numpy()	)	)



	@require_tf
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(1			,  3			,  4	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(squeeze(lowercase	)			,  squeeze(lowercase	).numpy()	)	)

				_snake_case		=			np.random.randn(1			,  4			,  1			,  5	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(squeeze(lowercase			,  axis=2	)			,  squeeze(lowercase			,  axis=2	).numpy()	)	)



	@require_flax
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(1			,  3			,  4	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(squeeze(lowercase	)			,  np.asarray(squeeze(lowercase	)	)	)	)

				_snake_case		=			np.random.randn(1			,  4			,  1			,  5	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(squeeze(lowercase			,  axis=2	)			,  np.asarray(squeeze(lowercase			,  axis=2	)	)	)	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				self.assertTrue(np.allclose(expand_dims(lowercase			,  axis=1	)			,  np.expand_dims(lowercase			,  axis=1	)	)	)



	@require_torch
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			torch.tensor(lowercase	)
				self.assertTrue(np.allclose(expand_dims(lowercase			,  axis=1	)			,  expand_dims(lowercase			,  axis=1	).numpy()	)	)



	@require_tf
	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			tf.constant(lowercase	)
				self.assertTrue(np.allclose(expand_dims(lowercase			,  axis=1	)			,  expand_dims(lowercase			,  axis=1	).numpy()	)	)





	@require_flax
	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			np.random.randn(3			,  4	)
				_snake_case		=			jnp.array(lowercase	)
				self.assertTrue(np.allclose(expand_dims(lowercase			,  axis=1	)			,  np.asarray(expand_dims(lowercase			,  axis=1	)	)	)	)

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int | None, int | None, float]:
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			_snake_case		=			(low + high) // 2
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					mid + 1       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_cross_sum(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int, int, float]:
			_snake_case	,					_snake_case		=			float('-inf' ), -1
			_snake_case	,					_snake_case		=			float('-inf' ), -1

			_snake_case		=			0
			for i in range(__lowercase       ,					low - 1       ,					-1 ):
						summ += arr[i]
						if summ > left_sum:
									_snake_case		=			summ
									_snake_case		=			i

			_snake_case		=			0
			for i in range(mid + 1       ,					high + 1 ):
						summ += arr[i]
						if summ > right_sum:
									_snake_case		=			summ
									_snake_case		=			i

			return max_left, max_right, (left_sum + right_sum)






def        a_		(       __lowercase   :	int ) ->  float:
			_snake_case		=			[randint(1       ,					__lowercase ) for _ in range(__lowercase )]
			_snake_case		=			time.time()
			max_subarray(__lowercase       ,					0       ,					input_size - 1 )
			_snake_case		=			time.time()
			return end - start






def        a_		(       ) ->  None:
			_snake_case		=			[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
			_snake_case		=			[time_max_subarray(__lowercase ) for input_size in input_sizes]
			print('No of Inputs\t\tTime Taken' )
			for input_size, runtime in zip(__lowercase       ,					__lowercase ):
						print(__lowercase       ,					'\t\t'       ,					__lowercase )
			plt.plot(__lowercase       ,					__lowercase )
			plt.xlabel('Number of Inputs' )
			plt.ylabel('Time taken in seconds' )
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()