





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )






class   snake_case_							(						unittest.TestCase		):






  def __init__(				self					:	str  ,    _snake_case					:	Any  ,    _snake_case					:	Union[str, Any]=13  ,    _snake_case					:	List[str]=7  ,    _snake_case					:	Tuple=True  ,    _snake_case					:	Any=True  ,    _snake_case					:	Dict=True  ,    _snake_case					:	Union[str, Any]=True  ,    _snake_case					:	Tuple=99  ,    _snake_case					:	Dict=32  ,    _snake_case					:	Dict=5  ,    _snake_case					:	Optional[Any]=4  ,    _snake_case					:	Union[str, Any]=37  ,    _snake_case					:	Tuple="gelu"  ,    _snake_case					:	Union[str, Any]=0.1  ,    _snake_case					:	int=0.1  ,    _snake_case					:	Dict=512  ,    _snake_case					:	Optional[Any]=16  ,    _snake_case					:	int=2  ,    _snake_case					:	Optional[int]=0.02  ,    _snake_case					:	int=4  ,    )->Dict:






   '''simple docstring'''

   __lowerCAmelCase	:			int         =  parent
   __lowerCAmelCase	:			Optional[Any]         =  batch_size
   __lowerCAmelCase	:			Union[str, Any]         =  seq_length
   __lowerCAmelCase	:			Any         =  is_training
   __lowerCAmelCase	:			List[str]         =  use_attention_mask
   __lowerCAmelCase	:			List[Any]         =  use_token_type_ids
   __lowerCAmelCase	:			int         =  use_labels
   __lowerCAmelCase	:			int         =  vocab_size
   __lowerCAmelCase	:			int         =  hidden_size
   __lowerCAmelCase	:			List[str]         =  num_hidden_layers
   __lowerCAmelCase	:			Dict         =  num_attention_heads
   __lowerCAmelCase	:			Tuple         =  intermediate_size
   __lowerCAmelCase	:			Tuple         =  hidden_act
   __lowerCAmelCase	:			Union[str, Any]         =  hidden_dropout_prob
   __lowerCAmelCase	:			Optional[int]         =  attention_probs_dropout_prob
   __lowerCAmelCase	:			Union[str, Any]         =  max_position_embeddings
   __lowerCAmelCase	:			List[str]         =  type_vocab_size
   __lowerCAmelCase	:			Tuple         =  type_sequence_label_size
   __lowerCAmelCase	:			List[str]         =  initializer_range
   __lowerCAmelCase	:			Optional[int]         =  num_choices






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






   '''simple docstring'''

   __lowerCAmelCase	:			List[str]         =  ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size     )

   __lowerCAmelCase	:			List[str]         =  None
   if self.use_attention_mask:
    __lowerCAmelCase	:			Optional[int]         =  random_attention_mask([self.batch_size, self.seq_length]     )

   __lowerCAmelCase	:			List[str]         =  None
   if self.use_token_type_ids:
    __lowerCAmelCase	:			Dict         =  ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size     )

   __lowerCAmelCase	:			List[Any]         =  RobertaPreLayerNormConfig(
       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=__lowerCamelCase  ,    initializer_range=self.initializer_range  ,    )

   return config, input_ids, token_type_ids, attention_mask






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






   '''simple docstring'''

   __lowerCAmelCase	:			Union[str, Any]         =  self.prepare_config_and_inputs()
   __lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	:			Dict         =  config_and_inputs
   __lowerCAmelCase	:			Any         =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
   return config, inputs_dict






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






   '''simple docstring'''

   __lowerCAmelCase	:			Dict         =  self.prepare_config_and_inputs()
   __lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	:			Union[str, Any]         =  config_and_inputs

   __lowerCAmelCase	:			str         =  True
   __lowerCAmelCase	:			str         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
   __lowerCAmelCase	:			List[Any]         =  ids_tensor([self.batch_size, self.seq_length]  ,    vocab_size=2     )

   return (
       config,
       input_ids,
       token_type_ids,
       encoder_hidden_states,
       encoder_attention_mask,
   )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class   snake_case_							(						UpperCAmelCase_	,unittest.TestCase		):
  A_     				=       True

  A_     				=       (
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






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






   '''simple docstring'''

   __lowerCAmelCase	:			Union[str, Any]         =  FlaxRobertaPreLayerNormModelTester(self     )






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






   '''simple docstring'''

   for model_class_name in self.all_model_classes:
    __lowerCAmelCase	:			List[Any]         =  model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,    from_pt=__lowerCamelCase     )
    __lowerCAmelCase	:			str         =  model(np.ones((1, 1)     )     )
    self.assertIsNotNone(__lowerCamelCase     )






@require_flax
class   snake_case_							(						unittest.TestCase		):






  @slow
  def  UpperCAmelCase__		(				self					:	Any     )->Any:






   '''simple docstring'''

   __lowerCAmelCase	:			int         =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,    from_pt=__lowerCamelCase     )

   __lowerCAmelCase	:			Tuple         =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,    dtype=jnp.intaa     )
   __lowerCAmelCase	:			List[Any]         =  model(__lowerCamelCase     )[0]
   __lowerCAmelCase	:			List[Any]         =  [1, 11, 50265]
   self.assertEqual(list(output.shape     )  ,    __lowerCamelCase     )
   # compare the actual values for a slice.
   __lowerCAmelCase	:			Optional[int]         =  np.array(
       [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]  ,    dtype=np.floataa     )
   self.assertTrue(np.allclose(output[:, :3, :3]  ,    __lowerCamelCase  ,    atol=1E-4     )     )






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






   '''simple docstring'''

   __lowerCAmelCase	:			str         =  FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,    from_pt=__lowerCamelCase     )

   __lowerCAmelCase	:			Dict         =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,    dtype=jnp.intaa     )
   __lowerCAmelCase	:			Dict         =  model(__lowerCamelCase     )[0]
   # compare the actual values for a slice.
   __lowerCAmelCase	:			Optional[int]         =  np.array(
       [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]  ,    dtype=np.floataa     )
   self.assertTrue(np.allclose(output[:, :3, :3]  ,    __lowerCamelCase  ,    atol=1E-4     )     )






from __future__ import annotations



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[tuple[int, int]]:
		__lowercase						,      __lowercase											=     position
		__lowercase											=     [
		    (y + 1, x + 2),
		    (y - 1, x + 2),
		    (y + 1, x - 2),
		    (y - 1, x - 2),
		    (y + 2, x + 1),
		    (y + 2, x - 1),
		    (y - 2, x + 1),
		    (y - 2, x - 1),
		]
		__lowercase											=     []

		for position in positions:
				__lowercase						,      __lowercase											=     position
				if 0 <= y_test < n and 0 <= x_test < n:
						permissible_positions.append(snake_case							)

		return permissible_positions



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    bool:
		return not any(elem == 0 for row in board for elem in row							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:

		if is_complete(snake_case							):
				return True

		for position in get_valid_pos(snake_case						,   len(snake_case							)							):
				__lowercase						,      __lowercase											=     position

				if board[y][x] == 0:
						__lowercase											=     curr + 1
						if open_knight_tour_helper(snake_case						,   snake_case						,   curr + 1							):
								return True
						__lowercase											=     0

		return False



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    list[list[int]]:
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		for i in range(snake_case							):
				for j in range(snake_case							):
						__lowercase											=     1
						if open_knight_tour_helper(snake_case						,   (i, j)						,   1							):
								return board
						__lowercase											=     0

		__lowercase											=     F"Open Kight Tour cannot be performed on a board of size {n}"
		raise ValueError(snake_case							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




