




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				):







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				{
              "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},
              }
          }
          a     =				{
              "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       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				np.random.randn(3  ,		4			)
          self.assertTrue(np.allclose(transpose(A			)  ,		x.transpose()			)			)

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







     @require_torch
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



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

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







     @require_tf
     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



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

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







     @require_flax
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				np.random.randn(3  ,		4			)
          self.assertTrue(np.allclose(reshape(A  ,		(4, 3)			)  ,		np.reshape(A  ,		(4, 3)			)			)			)

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







     @require_torch
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



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

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







     @require_tf
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



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

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







     @require_flax
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				np.random.randn(1  ,		3  ,		4			)
          self.assertTrue(np.allclose(squeeze(A			)  ,		np.squeeze(A			)			)			)

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







     @require_torch
     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



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

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







     @require_tf
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



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

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







     @require_flax
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



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

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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



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







     @require_torch
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



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







     @require_tf
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



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







     @require_flax
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



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







from math import isqrt

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     return all(number % divisor != 0 for divisor in range(2		, isqrt(__UpperCamelCase) + 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 10**6)      ->					int:
     a     =				0
     a     =				1
     a     =				7
     while prime_candidate < max_prime:
          primes_count += is_prime(__UpperCamelCase)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


if __name__ == "__main__":
   print(F'{solution() = }')

