



"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

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

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A:  Tuple      				=						'platform'

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )

def  _snake_case		(		UpperCamelCase		:						int     ,						UpperCamelCase		:						List[Any]     ,						UpperCamelCase		:						Optional[int]=None     ,						UpperCamelCase		:						str=None     ,						UpperCamelCase		:						Optional[int]=None     ,						UpperCamelCase		:						Optional[int]=None     ,						UpperCamelCase		:						str=None     ,						UpperCamelCase		:						Optional[Any]=None     ,						):
 if attention_mask is None:
  UpperCAmelCase      :   Any      =      np.where(input_ids != config.pad_token_id     ,						1     ,						0		)
 if decoder_attention_mask is None:
  UpperCAmelCase      :   Union[str, Any]      =      np.where(decoder_input_ids != config.pad_token_id     ,						1     ,						0		)
 if head_mask is None:
  UpperCAmelCase      :   Optional[int]      =      np.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  UpperCAmelCase      :   Union[str, Any]      =      np.ones((config.decoder_layers, config.decoder_attention_heads)		)
 if cross_attn_head_mask is None:
  UpperCAmelCase      :   Optional[int]      =      np.ones((config.decoder_layers, config.decoder_attention_heads)		)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }



class  SCREAMING_SNAKE_CASE__    :

   def __init__(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=13 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	)    -> int:





    '''simple docstring'''




    UpperCAmelCase      :   Dict      =      parent
    UpperCAmelCase      :   str      =      batch_size
    UpperCAmelCase      :   Optional[Any]      =      seq_length
    UpperCAmelCase      :   List[str]      =      is_training
    UpperCAmelCase      :   str      =      use_labels
    UpperCAmelCase      :   Optional[Any]      =      vocab_size
    UpperCAmelCase      :   Union[str, Any]      =      hidden_size
    UpperCAmelCase      :   List[str]      =      num_hidden_layers
    UpperCAmelCase      :   Tuple      =      num_attention_heads
    UpperCAmelCase      :   List[Any]      =      intermediate_size
    UpperCAmelCase      :   Optional[int]      =      hidden_act
    UpperCAmelCase      :   Optional[int]      =      hidden_dropout_prob
    UpperCAmelCase      :   Union[str, Any]      =      attention_probs_dropout_prob
    UpperCAmelCase      :   Dict      =      max_position_embeddings
    UpperCAmelCase      :   Tuple      =      eos_token_id
    UpperCAmelCase      :   Optional[Any]      =      pad_token_id
    UpperCAmelCase      :   Dict      =      bos_token_id
    UpperCAmelCase      :   Tuple      =      initializer_range

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





    '''simple docstring'''




    UpperCAmelCase      :   List[str]      =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,	self.vocab_size		) ,	3 ,	self.vocab_size		)
    UpperCAmelCase      :   List[str]      =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,	dtype=np.intaa		)) ,	-1		)

    UpperCAmelCase      :   int      =      shift_tokens_right(lowerCamelCase__ ,	1 ,	2		)

    UpperCAmelCase      :   str      =      BlenderbotSmallConfig(
        vocab_size=self.vocab_size ,	d_model=self.hidden_size ,	encoder_layers=self.num_hidden_layers ,	decoder_layers=self.num_hidden_layers ,	encoder_attention_heads=self.num_attention_heads ,	decoder_attention_heads=self.num_attention_heads ,	encoder_ffn_dim=self.intermediate_size ,	decoder_ffn_dim=self.intermediate_size ,	dropout=self.hidden_dropout_prob ,	attention_dropout=self.attention_probs_dropout_prob ,	max_position_embeddings=self.max_position_embeddings ,	eos_token_id=self.eos_token_id ,	bos_token_id=self.bos_token_id ,	pad_token_id=self.pad_token_id ,	initializer_range=self.initializer_range ,	use_cache=lowerCamelCase__ ,	)
    UpperCAmelCase      :   List[Any]      =      prepare_blenderbot_inputs_dict(lowerCamelCase__ ,	lowerCamelCase__ ,	lowerCamelCase__		)
    return config, inputs_dict

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





    '''simple docstring'''




    UpperCAmelCase      :   List[Any]      =      self.prepare_config_and_inputs()
    return config, inputs_dict

   def       SCREAMING_SNAKE_CASE	(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE		)    -> str:





    '''simple docstring'''




    UpperCAmelCase      :   Optional[Any]      =      20
    UpperCAmelCase      :   Optional[Any]      =      model_class_name(lowerCamelCase__		)

    UpperCAmelCase      :   Optional[Any]      =      model.encode(inputs_dict["""input_ids"""]		)

    UpperCAmelCase      :   Optional[int]      =      (
        inputs_dict['decoder_input_ids'],
        inputs_dict['decoder_attention_mask'],
    )

    UpperCAmelCase      :   List[Any]      =      model.init_cache(decoder_input_ids.shape[0] ,	lowerCamelCase__ ,	lowerCamelCase__		)
    UpperCAmelCase      :   List[str]      =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,	dtype="""i4"""		)

    UpperCAmelCase      :   Optional[Any]      =      jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :] ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,	)
    UpperCAmelCase      :   Tuple      =      model.decode(
        decoder_input_ids[:, :-1] ,	lowerCamelCase__ ,	decoder_attention_mask=lowerCamelCase__ ,	past_key_values=lowerCamelCase__ ,	decoder_position_ids=lowerCamelCase__ ,	)

    UpperCAmelCase      :   Optional[Any]      =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,	dtype="""i4"""		)
    UpperCAmelCase      :   Dict      =      model.decode(
        decoder_input_ids[:, -1:] ,	lowerCamelCase__ ,	decoder_attention_mask=lowerCamelCase__ ,	past_key_values=outputs_cache.past_key_values ,	decoder_position_ids=lowerCamelCase__ ,	)

    UpperCAmelCase      :   str      =      model.decode(lowerCamelCase__ ,	lowerCamelCase__		)

    UpperCAmelCase      :   Union[str, Any]      =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
    self.parent.assertTrue(diff < 1E-3 ,	msg=F"Max diff is {diff}"		)




   def       SCREAMING_SNAKE_CASE	(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE		)    -> List[Any]:





    '''simple docstring'''




    UpperCAmelCase      :   List[Any]      =      20
    UpperCAmelCase      :   Optional[int]      =      model_class_name(lowerCamelCase__		)

    UpperCAmelCase      :   int      =      model.encode(inputs_dict["""input_ids"""]		)

    UpperCAmelCase      :   List[Any]      =      (
        inputs_dict['decoder_input_ids'],
        inputs_dict['decoder_attention_mask'],
    )

    UpperCAmelCase      :   int      =      jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
        ] ,	axis=-1 ,	)

    UpperCAmelCase      :   Optional[Any]      =      model.init_cache(decoder_input_ids.shape[0] ,	lowerCamelCase__ ,	lowerCamelCase__		)
    UpperCAmelCase      :   str      =      jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :] ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,	)

    UpperCAmelCase      :   List[str]      =      model.decode(
        decoder_input_ids[:, :-1] ,	lowerCamelCase__ ,	decoder_attention_mask=lowerCamelCase__ ,	past_key_values=lowerCamelCase__ ,	decoder_position_ids=lowerCamelCase__ ,	)
    UpperCAmelCase      :   Dict      =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,	dtype="""i4"""		)
    UpperCAmelCase      :   Any      =      model.decode(
        decoder_input_ids[:, -1:] ,	lowerCamelCase__ ,	past_key_values=outputs_cache.past_key_values ,	decoder_attention_mask=lowerCamelCase__ ,	decoder_position_ids=lowerCamelCase__ ,	)

    UpperCAmelCase      :   Optional[int]      =      model.decode(lowerCamelCase__ ,	lowerCamelCase__ ,	decoder_attention_mask=lowerCamelCase__		)

    UpperCAmelCase      :   List[str]      =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
    self.parent.assertTrue(diff < 1E-3 ,	msg=F"Max diff is {diff}"		)



@require_flax
class  SCREAMING_SNAKE_CASE__    (   unittest.TestCase					):
   __lowerCAmelCase    :  Any		=		99

   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[str]:





    '''simple docstring'''




    UpperCAmelCase      :   int      =      np.array(
        [
            [71, 82, 18, 33, 46, 91, 2],
            [68, 34, 26, 58, 30, 82, 2],
            [5, 97, 17, 39, 94, 40, 2],
            [76, 83, 94, 25, 70, 78, 2],
            [87, 59, 41, 35, 48, 66, 2],
            [55, 13, 16, 58, 5, 2, 1],  # note padding
            [64, 27, 31, 51, 12, 75, 2],
            [52, 64, 86, 17, 83, 39, 2],
            [48, 61, 9, 24, 71, 82, 2],
            [26, 1, 60, 48, 22, 13, 2],
            [21, 5, 62, 28, 14, 76, 2],
            [45, 98, 37, 86, 59, 48, 2],
            [70, 70, 50, 9, 28, 0, 2],
        ] ,	dtype=np.intaa ,	)

    UpperCAmelCase      :   List[Any]      =      input_ids.shape[0]
    UpperCAmelCase      :   int      =      BlenderbotSmallConfig(
        vocab_size=self.vocab_size ,	d_model=24 ,	encoder_layers=2 ,	decoder_layers=2 ,	encoder_attention_heads=2 ,	decoder_attention_heads=2 ,	encoder_ffn_dim=32 ,	decoder_ffn_dim=32 ,	max_position_embeddings=48 ,	eos_token_id=2 ,	pad_token_id=1 ,	bos_token_id=0 ,	)
    return config, input_ids, batch_size

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





    '''simple docstring'''




    UpperCAmelCase      :   Dict      =      self._get_config_and_data()
    UpperCAmelCase      :   Optional[Any]      =      FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__		)
    UpperCAmelCase      :   Optional[Any]      =      lm_model(input_ids=lowerCamelCase__		)
    UpperCAmelCase      :   str      =      (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape ,	lowerCamelCase__		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Tuple:





    '''simple docstring'''




    UpperCAmelCase      :   str      =      BlenderbotSmallConfig(
        vocab_size=self.vocab_size ,	d_model=14 ,	encoder_layers=2 ,	decoder_layers=2 ,	encoder_attention_heads=2 ,	decoder_attention_heads=2 ,	encoder_ffn_dim=8 ,	decoder_ffn_dim=8 ,	max_position_embeddings=48 ,	)
    UpperCAmelCase      :   Optional[int]      =      FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__		)
    UpperCAmelCase      :   int      =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,	dtype=np.intaa		)
    UpperCAmelCase      :   Tuple      =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,	dtype=np.intaa		)
    UpperCAmelCase      :   Optional[Any]      =      lm_model(input_ids=lowerCamelCase__ ,	decoder_input_ids=lowerCamelCase__		)
    UpperCAmelCase      :   Optional[Any]      =      (*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape ,	lowerCamelCase__		)




   def       SCREAMING_SNAKE_CASE	(  self		)    -> int:





    '''simple docstring'''




    UpperCAmelCase      :   List[Any]      =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,	dtype=np.intaa		)
    UpperCAmelCase      :   str      =      shift_tokens_right(lowerCamelCase__ ,	1 ,	2		)
    UpperCAmelCase      :   Dict      =      np.equal(lowerCamelCase__ ,	1		).astype(np.floataa		).sum()
    UpperCAmelCase      :   Union[str, Any]      =      np.equal(lowerCamelCase__ ,	1		).astype(np.floataa		).sum()
    self.assertEqual(shifted.shape ,	input_ids.shape		)
    self.assertEqual(lowerCamelCase__ ,	n_pad_before - 1		)
    self.assertTrue(np.equal(shifted[:, 0] ,	2		).all()		)



@require_flax
class  SCREAMING_SNAKE_CASE__    (   UpperCAmelCase__ ,     unittest.TestCase ,     UpperCAmelCase__					):
   __lowerCAmelCase    :  Optional[Any]		=		True
   __lowerCAmelCase    :  Optional[Any]		=		(
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   __lowerCAmelCase    :  str		=		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase      :   Union[str, Any]      =      FlaxBlenderbotSmallModelTester(self		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Dict:





    '''simple docstring'''




    UpperCAmelCase      :   Any      =      self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(lowerCamelCase__ ,	lowerCamelCase__ ,	lowerCamelCase__		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> int:





    '''simple docstring'''




    UpperCAmelCase      :   Dict      =      self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase__ ,	lowerCamelCase__ ,	lowerCamelCase__		)

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





    '''simple docstring'''




    UpperCAmelCase      :   Any      =      self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__		):
      UpperCAmelCase      :   Optional[Any]      =      self._prepare_for_class(lowerCamelCase__ ,	lowerCamelCase__		)
      UpperCAmelCase      :   Union[str, Any]      =      model_class(lowerCamelCase__		)

      @jax.jit
      def encode_jitted(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE		):
       return model.encode(input_ids=lowerCamelCase__ ,	attention_mask=lowerCamelCase__		)

      with self.subTest("""JIT Enabled"""		):
       UpperCAmelCase      :   Any      =      encode_jitted(**lowerCamelCase__		).to_tuple()

      with self.subTest("""JIT Disabled"""		):
       with jax.disable_jit():
        UpperCAmelCase      :   List[Any]      =      encode_jitted(**lowerCamelCase__		).to_tuple()

      self.assertEqual(len(lowerCamelCase__		) ,	len(lowerCamelCase__		)		)
      for jitted_output, output in zip(lowerCamelCase__ ,	lowerCamelCase__		):
       self.assertEqual(jitted_output.shape ,	output.shape		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> int:





    '''simple docstring'''




    UpperCAmelCase      :   Dict      =      self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__		):
      UpperCAmelCase      :   Tuple      =      model_class(lowerCamelCase__		)
      UpperCAmelCase      :   List[Any]      =      model.encode(inputs_dict["""input_ids"""] ,	inputs_dict["""attention_mask"""]		)

      UpperCAmelCase      :   Optional[int]      =      {
          'decoder_input_ids': inputs_dict['decoder_input_ids'],
          'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
          'encoder_outputs': encoder_outputs,
      }

      @jax.jit
      def decode_jitted(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE		):
       return model.decode(
           decoder_input_ids=lowerCamelCase__ ,	decoder_attention_mask=lowerCamelCase__ ,	encoder_outputs=lowerCamelCase__ ,	)

      with self.subTest("""JIT Enabled"""		):
       UpperCAmelCase      :   Dict      =      decode_jitted(**lowerCamelCase__		).to_tuple()

      with self.subTest("""JIT Disabled"""		):
       with jax.disable_jit():
        UpperCAmelCase      :   List[Any]      =      decode_jitted(**lowerCamelCase__		).to_tuple()

      self.assertEqual(len(lowerCamelCase__		) ,	len(lowerCamelCase__		)		)
      for jitted_output, output in zip(lowerCamelCase__ ,	lowerCamelCase__		):
       self.assertEqual(jitted_output.shape ,	output.shape		)




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





    '''simple docstring'''




    for model_class_name in self.all_model_classes:
     UpperCAmelCase      :   List[Any]      =      model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""		)
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     UpperCAmelCase      :   Optional[Any]      =      np.ones((1, 1)		) * model.config.eos_token_id
     UpperCAmelCase      :   Optional[int]      =      model(lowerCamelCase__		)
     self.assertIsNotNone(lowerCamelCase__		)





'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (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),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


