"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     if not head:
          return True
     # split the list to two parts
     __snake_case		,	__snake_case				=							head.next, head
     while fast and fast.next:
          __snake_case				=							fast.next.next
          __snake_case				=							slow.next
     __snake_case				=							slow.next
     __snake_case				=							None  # Don't forget here! But forget still works!
     # reverse the second part
     __snake_case				=							None
     while second:
          __snake_case				=							second.next
          __snake_case				=							node
          __snake_case				=							second
          __snake_case				=							nxt
     # compare two parts
     # second part has the same or one less node
     while node:
          if node.val != head.val:
               return False
          __snake_case				=							node.next
          __snake_case				=							head.next
     return True





def    SCREAMING_SNAKE_CASE       (     snake_case):
     if not head or not head.next:
          return True

     # 1. Get the midpoint (slow)
     __snake_case				=							__snake_case				=							__snake_case				=							head
     while fast and fast.next:
          __snake_case		,	__snake_case				=							fast.next.next, slow.next

     # 2. Push the second half into the stack
     __snake_case				=							[slow.val]
     while slow.next:
          __snake_case				=							slow.next
          stack.append(slow.val)

     # 3. Comparison
     while stack:
          if stack.pop() != cur.val:
               return False
          __snake_case				=							cur.next

     return True





def    SCREAMING_SNAKE_CASE       (     snake_case):
     if not head or not head.next:
          return True
     __snake_case				=							{}
     __snake_case				=							0
     while head:
          if head.val in d:
               d[head.val].append(snake_case)
          else:
               __snake_case				=							[pos]
          __snake_case				=							head.next
          pos += 1
     __snake_case				=							pos - 1
     __snake_case				=							0
     for v in d.values():
          if len(snake_case) % 2 != 0:
               middle += 1
          else:
               __snake_case				=							0
               for i in range(0,      len(snake_case)):
                    if v[i] + v[len(snake_case) - 1 - step] != checksum:
                         return False
                    step += 1
          if middle > 1:
               return False
     return True
"""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      _A	(						unittest.TestCase					):




    """simple docstring"""




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

         __snake_case				=							None
         if self.use_attention_mask:
              __snake_case				=							random_attention_mask([self.batch_size, self.seq_length]    )

         __snake_case				=							None
         if self.use_token_type_ids:
              __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size    )

         __snake_case				=							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=A_	,      initializer_range=self.initializer_range	,      )

         return config, input_ids, token_type_ids, attention_mask







    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							self.prepare_config_and_inputs()
         __snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							config_and_inputs
         __snake_case				=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					True

    UpperCamelCase_	:					List[str]							=					(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerModelTester(self    )







    @slow
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''	,      from_pt=A_    )
              __snake_case				=							model(np.ones((1, 1)    )    )
              self.assertIsNotNone(A_    )






@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @slow
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
         __snake_case				=							jnp.array([[0, 1, 2, 3, 4, 5]]    )
         __snake_case				=							model(A_    )[0]

         __snake_case				=							50_000

         __snake_case				=							(1, 6, vocab_size)
         self.assertEqual(output.shape	,      A_    )

         __snake_case				=							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]	,      A_	,      atol=1E-4    )    )