





'''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 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_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},
            }
        }
        _SCREAMING_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(A       ) ,		A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(transpose(A       ) ,		x.transpose()       )       )

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


    @require_torch
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		transpose(A       ).numpy()       )       )

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


    @require_tf
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		transpose(A       ).numpy()       )       )

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


    @require_flax
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		np.asarray(transpose(A       )       )       )       )

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


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		np.reshape(A ,		(4, 3)       )       )       )

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


    @require_torch
    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		reshape(A ,		(4, 3)       ).numpy()       )       )

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


    @require_tf
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		reshape(A ,		(4, 3)       ).numpy()       )       )

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


    @require_flax
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		np.asarray(reshape(A ,		(4, 3)       )       )       )       )

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


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		np.squeeze(A       )       )       )

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


    @require_torch
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		squeeze(A       ).numpy()       )       )

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


    @require_tf
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		squeeze(A       ).numpy()       )       )

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


    @require_flax
    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		np.asarray(squeeze(A       )       )       )       )

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


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		np.expand_dims(A ,		axis=1       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		expand_dims(A ,		axis=1       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		expand_dims(A ,		axis=1       ).numpy()       )       )





    @require_flax
    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		np.asarray(expand_dims(A ,		axis=1       )       )       )       )










'''simple docstring'''


import math
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int ):
   lowercase_			:Dict    			= []
   lowercase_			:List[Any]    			= 2
   lowercase_			:Optional[Any]    			= int(math.sqrt(__lowerCamelCase ) )  # Size of every segment
   lowercase_			:Optional[Any]    			= [True] * (end + 1)
   lowercase_			:Dict    			= []

   while start <= end:
      if temp[start] is True:
         in_prime.append(__lowerCamelCase )
         for i in range(start * start	,end + 1	,__lowerCamelCase ):
            lowercase_			:List[str]    			= False
      start += 1
   prime += in_prime

   lowercase_			:Dict    			= end + 1
   lowercase_			:Dict    			= min(2 * end	,__lowerCamelCase )

   while low <= n:
      lowercase_			:Any    			= [True] * (high - low + 1)
      for each in in_prime:
         lowercase_			:List[Any]    			= math.floor(low / each ) * each
         if t < low:
            t += each

         for j in range(__lowerCamelCase	,high + 1	,__lowerCamelCase ):
            lowercase_			:str    			= False

      for j in range(len(__lowerCamelCase ) ):
         if temp[j] is True:
            prime.append(j + low )

      lowercase_			:Dict    			= high + 1
      lowercase_			:Dict    			= min(high + end	,__lowerCamelCase )

   return prime


print(sieve(10**6))


