






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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_						:						Any						      =					'platform'

   import jax
   import jax.numpy as jnp

   from transformers import BlenderbotTokenizer
   from transformers.models.blenderbot.modeling_flax_blenderbot import (
       FlaxBlenderbotForConditionalGeneration,
       FlaxBlenderbotModel,
       shift_tokens_right,
   )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       )			->							List[Any]:
  if attention_mask is None:
    UpperCamelCase_:   Optional[Any]        =							np.where(input_ids != config.pad_token_id      ,       1      ,       0    )
  if decoder_attention_mask is None:
    UpperCamelCase_:   Dict        =							np.where(decoder_input_ids != config.pad_token_id      ,       1      ,       0    )
  if head_mask is None:
    UpperCamelCase_:   Any        =							np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    UpperCamelCase_:   int        =							np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    UpperCamelCase_:   List[str]        =							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 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase=1_3    ,     _lowerCamelCase=7    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase=9_9    ,     _lowerCamelCase=1_6    ,     _lowerCamelCase=2    ,     _lowerCamelCase=4    ,     _lowerCamelCase=4    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=3_2    ,     _lowerCamelCase=2    ,     _lowerCamelCase=1    ,     _lowerCamelCase=0    ,     _lowerCamelCase=0.0_2    ,     ):
         UpperCamelCase_:   Any        =							parent
         UpperCamelCase_:   List[Any]        =							batch_size
         UpperCamelCase_:   List[Any]        =							seq_length
         UpperCamelCase_:   Optional[int]        =							is_training
         UpperCamelCase_:   Optional[int]        =							use_labels
         UpperCamelCase_:   List[str]        =							vocab_size
         UpperCamelCase_:   Any        =							hidden_size
         UpperCamelCase_:   Tuple        =							num_hidden_layers
         UpperCamelCase_:   Dict        =							num_attention_heads
         UpperCamelCase_:   Union[str, Any]        =							intermediate_size
         UpperCamelCase_:   Optional[Any]        =							hidden_act
         UpperCamelCase_:   Tuple        =							hidden_dropout_prob
         UpperCamelCase_:   Tuple        =							attention_probs_dropout_prob
         UpperCamelCase_:   List[Any]        =							max_position_embeddings
         UpperCamelCase_:   str        =							eos_token_id
         UpperCamelCase_:   Optional[Any]        =							pad_token_id
         UpperCamelCase_:   List[str]        =							bos_token_id
         UpperCamelCase_:   List[str]        =							initializer_range





       def      _a       (   self				):
         UpperCamelCase_:   Any        =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,     self.vocab_size				)    ,     3    ,     self.vocab_size				)
         UpperCamelCase_:   Union[str, Any]        =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,     dtype=np.intaa				))    ,     -1				)

         UpperCamelCase_:   Dict        =							shift_tokens_right(_lowerCamelCase    ,     1    ,     2				)

         UpperCamelCase_:   Optional[int]        =							BlenderbotConfig(
             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_:   Union[str, Any]        =							prepare_blenderbot_inputs_dict(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         return config, inputs_dict





       def      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							self.prepare_config_and_inputs()
         return config, inputs_dict





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							2_0
         UpperCamelCase_:   Tuple        =							model_class_name(_lowerCamelCase				)

         UpperCamelCase_:   Union[str, Any]        =							model.encode(inputs_dict['input_ids']				)

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

         UpperCamelCase_:   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_:   int        =							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]    ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,     )
         UpperCamelCase_:   Optional[int]        =							model.decode(
             decoder_input_ids[:, :-1]    ,     _lowerCamelCase    ,     decoder_attention_mask=_lowerCamelCase    ,     past_key_values=_lowerCamelCase    ,     decoder_position_ids=_lowerCamelCase    ,     )

         UpperCamelCase_:   Tuple        =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,     dtype='i4'				)
         UpperCamelCase_:   Any        =							model.decode(
             decoder_input_ids[:, -1:]    ,     _lowerCamelCase    ,     decoder_attention_mask=_lowerCamelCase    ,     past_key_values=outputs_cache.past_key_values    ,     decoder_position_ids=_lowerCamelCase    ,     )

         UpperCamelCase_:   List[Any]        =							model.decode(_lowerCamelCase    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[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      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Tuple        =							2_0
         UpperCamelCase_:   List[str]        =							model_class_name(_lowerCamelCase				)

         UpperCamelCase_:   Union[str, Any]        =							model.encode(inputs_dict['input_ids']				)

         UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         UpperCamelCase_:   Optional[Any]        =							jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
             ]    ,     axis=-1    ,     )

         UpperCamelCase_:   Dict        =							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_:   int        =							model.decode(
             decoder_input_ids[:, :-1]    ,     _lowerCamelCase    ,     decoder_attention_mask=_lowerCamelCase    ,     past_key_values=_lowerCamelCase    ,     decoder_position_ids=_lowerCamelCase    ,     )
         UpperCamelCase_:   Union[str, Any]        =							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[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}'''				)







@require_flax
class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       a							:						Any										=99





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							np.array(
             [
                 [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                 [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                 [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                 [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                 [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                 [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                 [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                 [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                 [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                 [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                 [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                 [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                 [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
             ]    ,     dtype=np.intaa    ,     )

         UpperCamelCase_:   str        =							input_ids.shape[0]
         UpperCamelCase_:   str        =							BlenderbotConfig(
             vocab_size=self.vocab_size    ,     d_model=2_4    ,     encoder_layers=2    ,     decoder_layers=2    ,     encoder_attention_heads=2    ,     decoder_attention_heads=2    ,     encoder_ffn_dim=3_2    ,     decoder_ffn_dim=3_2    ,     max_position_embeddings=4_8    ,     eos_token_id=2    ,     pad_token_id=1    ,     bos_token_id=0    ,     )
         return config, input_ids, batch_size





       def      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   str        =							self._get_config_and_data()
         UpperCamelCase_:   Optional[Any]        =							FlaxBlenderbotForConditionalGeneration(_lowerCamelCase				)
         UpperCamelCase_:   Any        =							lm_model(input_ids=_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							BlenderbotConfig(
             vocab_size=self.vocab_size    ,     d_model=1_4    ,     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=4_8    ,     )
         UpperCamelCase_:   Optional[int]        =							FlaxBlenderbotForConditionalGeneration(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]    ,     dtype=np.intaa				)
         UpperCamelCase_:   Tuple        =							np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,     dtype=np.intaa				)
         UpperCamelCase_:   Tuple        =							lm_model(input_ids=_lowerCamelCase    ,     decoder_input_ids=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['logits'].shape    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]    ,     dtype=np.intaa				)
         UpperCamelCase_:   Tuple        =							shift_tokens_right(_lowerCamelCase    ,     1    ,     2				)
         UpperCamelCase_:   Union[str, Any]        =							np.equal(_lowerCamelCase    ,     1				).astype(np.floataa				).sum()
         UpperCamelCase_:   Optional[int]        =							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 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase				,      UpperCAmelCase_   ):





       """simple docstring"""







       a							:						Optional[Any]										=True
       a							:						Union[str, Any]										=(
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       a							:						Optional[int]										=(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							FlaxBlenderbotModelTester(self				)





       def      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_:   str        =							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      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_:   List[Any]        =							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      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_:   Union[str, 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_:   Dict        =							self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase				)
             UpperCamelCase_:   Union[str, Any]        =							model_class(_lowerCamelCase				)

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

             with self.subTest('JIT Enabled'				):
               UpperCamelCase_:   Optional[int]        =							encode_jitted(**_lowerCamelCase				).to_tuple()

             with self.subTest('JIT Disabled'				):
               with jax.disable_jit():
                 UpperCamelCase_:   Dict        =							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      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							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_:   Dict        =							model.encode(inputs_dict['input_ids']    ,     inputs_dict['attention_mask']				)

             UpperCamelCase_:   List[str]        =							{
                 '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(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
               return model.decode(
                   decoder_input_ids=_lowerCamelCase    ,     decoder_attention_mask=_lowerCamelCase    ,     encoder_outputs=_lowerCamelCase    ,     )

             with self.subTest('JIT Enabled'				):
               UpperCamelCase_:   List[Any]        =							decode_jitted(**_lowerCamelCase				).to_tuple()

             with self.subTest('JIT Disabled'				):
               with jax.disable_jit():
                 UpperCamelCase_:   Dict        =							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      _a       (   self				):
         for model_class_name in self.all_model_classes:
           UpperCamelCase_:   Dict        =							model_class_name.from_pretrained('facebook/blenderbot-400M-distill'				)
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           UpperCamelCase_:   Optional[Any]        =							np.ones((1, 1)				) * model.config.eos_token_id
           UpperCamelCase_:   List[Any]        =							model(_lowerCamelCase				)
           self.assertIsNotNone(_lowerCamelCase				)





       @unittest.skipUnless(jax_device != 'cpu'    ,     '3B test too slow on CPU.'				)
       @slow
       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							{'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
         UpperCamelCase_:   List[Any]        =							{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

         UpperCamelCase_:   Tuple        =							FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'    ,     from_pt=_lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'				)

         UpperCamelCase_:   int        =							['Sam']
         UpperCamelCase_:   str        =							tokenizer(_lowerCamelCase    ,     return_tensors='jax'				)

         UpperCamelCase_:   Union[str, Any]        =							model.generate(**_lowerCamelCase    ,     **_lowerCamelCase				)
         UpperCamelCase_:   Any        =							'Sam is a great name. It means "sun" in Gaelic.'

         UpperCamelCase_:   Any        =							tokenizer.batch_decode(_lowerCamelCase    ,     **_lowerCamelCase				)
         assert generated_txt[0].strip() == tgt_text

lowerCamelCase__     :		dict[tuple[int, int, int], int]        = {}

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if late == 3 or absent == 2:
    return 0

  # if we have no days left, and have not failed any other rules,
  # we have a prize string
  if days == 0:
    return 1

  # No easy solution, so now we need to do the recursive calculation

  # First, check if the combination is already in the cache, and
  # if yes, return the stored value from there since we already
  # know the number of possible prize strings from this point on
  lowercase__	: Tuple     		=   (days, absent, late)
  if key in cache:
    return cache[key]

  # now we calculate the three possible ways that can unfold from
  # this point on, depending on our attendance today

  # 1) if we are late (but not absent), the "absent" counter stays as
  # it is, but the "late" counter increases by one
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

  # 3) if we are on time, this resets the "late" counter and keeps the
  # absent counter
  lowercase__	: Dict     		=   _calculate(days - 1			,			lowercase_			,			0 )

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


if __name__ == "__main__":
     print(solution())






