





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        snake_case_    (		unittest.TestCase						):






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       {
       """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},
       }
   }
   __lowerCAmelCase					:	Optional[int]      =       {
       """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(_snake_case      )       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       np.random.randn(3       ,  4      )
   self.assertTrue(np.allclose(transpose(_snake_case      )       ,  x.transpose()      )      )

   __lowerCAmelCase					:	Optional[int]      =       np.random.randn(3       ,  4       ,  5      )
   self.assertTrue(np.allclose(transpose(_snake_case       ,  axes=(1, 2, 0)      )       ,  x.transpose((1, 2, 0)      )      )      )






  @require_torch
  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	List[str]      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(transpose(_snake_case      )       ,  transpose(_snake_case      ).numpy()      )      )

   __lowerCAmelCase					:	Tuple      =       np.random.randn(3       ,  4       ,  5      )
   __lowerCAmelCase					:	Union[str, Any]      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(transpose(_snake_case       ,  axes=(1, 2, 0)      )       ,  transpose(_snake_case       ,  axes=(1, 2, 0)      ).numpy()      )      )






  @require_tf
  def 				UpperCAmelCase__						(							self			:					List[str]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	str      =       tf.constant(_snake_case      )
   self.assertTrue(np.allclose(transpose(_snake_case      )       ,  transpose(_snake_case      ).numpy()      )      )

   __lowerCAmelCase					:	Tuple      =       np.random.randn(3       ,  4       ,  5      )
   __lowerCAmelCase					:	List[str]      =       tf.constant(_snake_case      )
   self.assertTrue(np.allclose(transpose(_snake_case       ,  axes=(1, 2, 0)      )       ,  transpose(_snake_case       ,  axes=(1, 2, 0)      ).numpy()      )      )






  @require_flax
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	List[str]      =       jnp.array(_snake_case      )
   self.assertTrue(np.allclose(transpose(_snake_case      )       ,  np.asarray(transpose(_snake_case      )      )      )      )

   __lowerCAmelCase					:	Optional[int]      =       np.random.randn(3       ,  4       ,  5      )
   __lowerCAmelCase					:	int      =       jnp.array(_snake_case      )
   self.assertTrue(np.allclose(transpose(_snake_case       ,  axes=(1, 2, 0)      )       ,  np.asarray(transpose(_snake_case       ,  axes=(1, 2, 0)      )      )      )      )






  def 				UpperCAmelCase__						(							self			:					Dict      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       np.random.randn(3       ,  4      )
   self.assertTrue(np.allclose(reshape(_snake_case       ,  (4, 3)      )       ,  np.reshape(_snake_case       ,  (4, 3)      )      )      )

   __lowerCAmelCase					:	Optional[Any]      =       np.random.randn(3       ,  4       ,  5      )
   self.assertTrue(np.allclose(reshape(_snake_case       ,  (12, 5)      )       ,  np.reshape(_snake_case       ,  (12, 5)      )      )      )






  @require_torch
  def 				UpperCAmelCase__						(							self			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	Optional[int]      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(reshape(_snake_case       ,  (4, 3)      )       ,  reshape(_snake_case       ,  (4, 3)      ).numpy()      )      )

   __lowerCAmelCase					:	Union[str, Any]      =       np.random.randn(3       ,  4       ,  5      )
   __lowerCAmelCase					:	Union[str, Any]      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(reshape(_snake_case       ,  (12, 5)      )       ,  reshape(_snake_case       ,  (12, 5)      ).numpy()      )      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	List[Any]      =       tf.constant(_snake_case      )
   self.assertTrue(np.allclose(reshape(_snake_case       ,  (4, 3)      )       ,  reshape(_snake_case       ,  (4, 3)      ).numpy()      )      )

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






  @require_flax
  def 				UpperCAmelCase__						(							self			:					Dict      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	Optional[int]      =       jnp.array(_snake_case      )
   self.assertTrue(np.allclose(reshape(_snake_case       ,  (4, 3)      )       ,  np.asarray(reshape(_snake_case       ,  (4, 3)      )      )      )      )

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






  def 				UpperCAmelCase__						(							self			:					Any      )->List[Any]:






   '''simple docstring'''

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

   __lowerCAmelCase					:	List[Any]      =       np.random.randn(1       ,  4       ,  1       ,  5      )
   self.assertTrue(np.allclose(squeeze(_snake_case       ,  axis=2      )       ,  np.squeeze(_snake_case       ,  axis=2      )      )      )






  @require_torch
  def 				UpperCAmelCase__						(							self			:					str      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       np.random.randn(1       ,  3       ,  4      )
   __lowerCAmelCase					:	Union[str, Any]      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(squeeze(_snake_case      )       ,  squeeze(_snake_case      ).numpy()      )      )

   __lowerCAmelCase					:	str      =       np.random.randn(1       ,  4       ,  1       ,  5      )
   __lowerCAmelCase					:	List[str]      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(squeeze(_snake_case       ,  axis=2      )       ,  squeeze(_snake_case       ,  axis=2      ).numpy()      )      )






  @require_tf
  def 				UpperCAmelCase__						(							self			:					str      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       np.random.randn(1       ,  3       ,  4      )
   __lowerCAmelCase					:	Optional[Any]      =       tf.constant(_snake_case      )
   self.assertTrue(np.allclose(squeeze(_snake_case      )       ,  squeeze(_snake_case      ).numpy()      )      )

   __lowerCAmelCase					:	List[Any]      =       np.random.randn(1       ,  4       ,  1       ,  5      )
   __lowerCAmelCase					:	Dict      =       tf.constant(_snake_case      )
   self.assertTrue(np.allclose(squeeze(_snake_case       ,  axis=2      )       ,  squeeze(_snake_case       ,  axis=2      ).numpy()      )      )






  @require_flax
  def 				UpperCAmelCase__						(							self			:					Dict      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       np.random.randn(1       ,  3       ,  4      )
   __lowerCAmelCase					:	Tuple      =       jnp.array(_snake_case      )
   self.assertTrue(np.allclose(squeeze(_snake_case      )       ,  np.asarray(squeeze(_snake_case      )      )      )      )

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






  def 				UpperCAmelCase__						(							self			:					str      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       np.random.randn(3       ,  4      )
   self.assertTrue(np.allclose(expand_dims(_snake_case       ,  axis=1      )       ,  np.expand_dims(_snake_case       ,  axis=1      )      )      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	str      =       torch.tensor(_snake_case      )
   self.assertTrue(np.allclose(expand_dims(_snake_case       ,  axis=1      )       ,  expand_dims(_snake_case       ,  axis=1      ).numpy()      )      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	Union[str, Any]      =       tf.constant(_snake_case      )
   self.assertTrue(np.allclose(expand_dims(_snake_case       ,  axis=1      )       ,  expand_dims(_snake_case       ,  axis=1      ).numpy()      )      )






  @require_flax
  def 				UpperCAmelCase__						(							self			:					int      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       np.random.randn(3       ,  4      )
   __lowerCAmelCase					:	Tuple      =       jnp.array(_snake_case      )
   self.assertTrue(np.allclose(expand_dims(_snake_case       ,  axis=1      )       ,  np.asarray(expand_dims(_snake_case       ,  axis=1      )      )      )      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()