'''simple docstring'''




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roformer.modeling_flax_roformer import (
     FlaxRoFormerForMaskedLM,
     FlaxRoFormerForMultipleChoice,
     FlaxRoFormerForQuestionAnswering,
     FlaxRoFormerForSequenceClassification,
     FlaxRoFormerForTokenClassification,
     FlaxRoFormerModel,
 )


class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=13       ,				_a=7       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=99       ,				_a=32       ,				_a=5       ,				_a=4       ,				_a=37       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=16       ,				_a=2       ,				_a=0.02       ,				_a=4       ,				)    ->     List[str]:
        lowercase_					:       Tuple     				=					parent
        lowercase_					:       List[str]     				=					batch_size
        lowercase_					:       int     				=					seq_length
        lowercase_					:       List[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_attention_mask
        lowercase_					:       Optional[Any]     				=					use_token_type_ids
        lowercase_					:       Union[str, Any]     				=					use_labels
        lowercase_					:       str     				=					vocab_size
        lowercase_					:       List[str]     				=					hidden_size
        lowercase_					:       List[str]     				=					num_hidden_layers
        lowercase_					:       int     				=					num_attention_heads
        lowercase_					:       Dict     				=					intermediate_size
        lowercase_					:       Union[str, Any]     				=					hidden_act
        lowercase_					:       List[str]     				=					hidden_dropout_prob
        lowercase_					:       Any     				=					attention_probs_dropout_prob
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       str     				=					type_sequence_label_size
        lowercase_					:       Dict     				=					initializer_range
        lowercase_					:       str     				=					num_choices
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

        lowercase_					:       Tuple     				=					None
        if self.use_attention_mask:
             lowercase_					:       Union[str, Any]     				=					random_attention_mask([self.batch_size, self.seq_length]				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_token_type_ids:
             lowercase_					:       str     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size				)

        lowercase_					:       Union[str, Any]     				=					RoFormerConfig(
            vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				is_decoder=_lowercase       ,				initializer_range=self.initializer_range       ,				)

        return config, input_ids, token_type_ids, attention_mask





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.prepare_config_and_inputs()
        lowercase_					:       List[Any]     				=					config_and_inputs
        lowercase_					:       Tuple     				=					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict


@require_flax
class       UpperCAmelCase__  (       SCREAMING_SNAKE_CASE__					,     unittest.TestCase			):






   """simple docstring"""
   A							:       int     =    True

   A							:       Union[str, Any]     =    (
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Dict     				=					FlaxRoFormerModelTester(self				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:

        for model_class_name in self.all_model_classes:
             lowercase_					:       Tuple     				=					model_class_name.from_pretrained('junnyu/roformer_chinese_small'       ,				from_pt=_lowercase				)
             lowercase_					:       List[str]     				=					model(np.ones((1, 1)				)				)
             self.assertIsNotNone(_lowercase				)


@require_flax
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   @slow
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'				)
        lowercase_					:       Tuple     				=					jnp.array([[0, 1, 2, 3, 4, 5]]				)
        lowercase_					:       Dict     				=					model(_lowercase				)[0]

        lowercase_					:       Optional[int]     				=					50_000

        lowercase_					:       Union[str, Any]     				=					(1, 6, vocab_size)
        self.assertEqual(output.shape       ,				_lowercase				)

        lowercase_					:       Dict     				=					jnp.array(
            [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]				)

        self.assertTrue(jnp.allclose(output[:, :3, :3]       ,				_lowercase       ,				atol=1e-4				)				)





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




