




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
 lowerCAmelCase_						=			'''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(    __magic_name__			,		__magic_name__			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None			,		)			->							Optional[Any]:





     '''simple docstring'''


     if attention_mask is None:
          lowercase :       List[str]       =			np.where(input_ids != config.pad_token_id			,		1			,		0   )
     if decoder_attention_mask is None:
          lowercase :       List[Any]       =			np.where(decoder_input_ids != config.pad_token_id			,		1			,		0   )
     if head_mask is None:
          lowercase :       int       =			np.ones((config.encoder_layers, config.encoder_attention_heads)   )
     if decoder_head_mask is None:
          lowercase :       int       =			np.ones((config.decoder_layers, config.decoder_attention_heads)   )
     if cross_attn_head_mask is None:
          lowercase :       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 						_A :






  def __init__(  self		:     Any				,						_A		:     Optional[int]				,						_A		:     Tuple=13				,						_A		:     Tuple=7				,						_A		:     int=True				,						_A		:     Optional[Any]=False				,						_A		:     List[str]=99				,						_A		:     List[str]=16				,						_A		:     Optional[int]=2				,						_A		:     Dict=4				,						_A		:     List[str]=4				,						_A		:     Any="gelu"				,						_A		:     Optional[int]=0.1				,						_A		:     Optional[int]=0.1				,						_A		:     List[Any]=32				,						_A		:     Optional[int]=2				,						_A		:     Optional[Any]=1				,						_A		:     int=0				,						_A		:     str=0.02				,						)		->   Any:






       """simple docstring"""







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






  def  __a      (  self		:     Tuple				)		->   List[str]:






       """simple docstring"""







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

       lowercase :       Tuple       =			shift_tokens_right(SCREAMING_SNAKE_CASE_				,						1				,						2				)

       lowercase :       int       =			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=SCREAMING_SNAKE_CASE_				,						)
       lowercase :       Union[str, Any]       =			prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)
       return config, inputs_dict






  def  __a      (  self		:     Dict				)		->   Tuple:






       """simple docstring"""







       lowercase :       Optional[int]       =			self.prepare_config_and_inputs()
       return config, inputs_dict






  def  __a      (  self		:     Any				,						_A		:     Any				,						_A		:     Any				,						_A		:     Optional[Any]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			20
       lowercase :       Tuple       =			model_class_name(SCREAMING_SNAKE_CASE_				)

       lowercase :       Any       =			model.encode(inputs_dict['''input_ids''']				)

       lowercase :       Any       =			(
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

       lowercase :       Dict       =			model.init_cache(decoder_input_ids.shape[0]				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)
       lowercase :       int       =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,						dtype='''i4'''				)

       lowercase :       List[str]       =			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)
       lowercase :       Union[str, Any]       =			model.decode(
           decoder_input_ids[:, :-1]				,						SCREAMING_SNAKE_CASE_				,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				,						past_key_values=SCREAMING_SNAKE_CASE_				,						decoder_position_ids=SCREAMING_SNAKE_CASE_				,						)

       lowercase :       Optional[int]       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype='''i4'''				)
       lowercase :       Any       =			model.decode(
           decoder_input_ids[:, -1:]				,						SCREAMING_SNAKE_CASE_				,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				,						past_key_values=outputs_cache.past_key_values				,						decoder_position_ids=SCREAMING_SNAKE_CASE_				,						)

       lowercase :       str       =			model.decode(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)

       lowercase :       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		:     Optional[Any]				,						_A		:     Optional[int]				,						_A		:     Any				,						_A		:     Dict				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       List[Any]       =			20
       lowercase :       int       =			model_class_name(SCREAMING_SNAKE_CASE_				)

       lowercase :       int       =			model.encode(inputs_dict['''input_ids''']				)

       lowercase :       Dict       =			(
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

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

       lowercase :       Optional[Any]       =			model.init_cache(decoder_input_ids.shape[0]				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)
       lowercase :       Tuple       =			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)

       lowercase :       Optional[Any]       =			model.decode(
           decoder_input_ids[:, :-1]				,						SCREAMING_SNAKE_CASE_				,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				,						past_key_values=SCREAMING_SNAKE_CASE_				,						decoder_position_ids=SCREAMING_SNAKE_CASE_				,						)
       lowercase :       Tuple       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype='''i4'''				)
       lowercase :       List[str]       =			model.decode(
           decoder_input_ids[:, -1:]				,						SCREAMING_SNAKE_CASE_				,						past_key_values=outputs_cache.past_key_values				,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				,						decoder_position_ids=SCREAMING_SNAKE_CASE_				,						)

       lowercase :       Any       =			model.decode(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				)

       lowercase :       Optional[int]       =			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 						_A (  unittest.TestCase				):
  _UpperCamelCase       :      int			=      9_9






  def  __a      (  self		:     Dict				)		->   List[Any]:






       """simple docstring"""







       lowercase :       Dict       =			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				,						)

       lowercase :       List[str]       =			input_ids.shape[0]
       lowercase :       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  __a      (  self		:     Union[str, Any]				)		->   Tuple:






       """simple docstring"""







       lowercase :       Tuple       =			self._get_config_and_data()
       lowercase :       Union[str, Any]       =			FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE_				)
       lowercase :       str       =			lm_model(input_ids=SCREAMING_SNAKE_CASE_				)
       lowercase :       Dict       =			(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape				,						SCREAMING_SNAKE_CASE_				)






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






       """simple docstring"""







       lowercase :       List[Any]       =			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				,						)
       lowercase :       Tuple       =			FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE_				)
       lowercase :       int       =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,						dtype=np.intaa				)
       lowercase :       Dict       =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,						dtype=np.intaa				)
       lowercase :       Dict       =			lm_model(input_ids=SCREAMING_SNAKE_CASE_				,						decoder_input_ids=SCREAMING_SNAKE_CASE_				)
       lowercase :       Dict       =			(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape				,						SCREAMING_SNAKE_CASE_				)






  def  __a      (  self		:     Optional[int]				)		->   str:






       """simple docstring"""







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


@require_flax
class 						_A (  _UpperCAmelCase      ,       unittest.TestCase      ,       _UpperCAmelCase				):
  _UpperCamelCase       :      Union[str, Any]			=      True
  _UpperCamelCase       :      List[str]			=      (
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  _UpperCamelCase       :      int			=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






  def  __a      (  self		:     Optional[int]				)		->   List[str]:






       """simple docstring"""







       lowercase :       List[str]       =			FlaxBlenderbotSmallModelTester(self				)






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






       """simple docstring"""







       lowercase :       List[Any]       =			self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)






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






       """simple docstring"""







       lowercase :       int       =			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(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)






  def  __a      (  self		:     Tuple				)		->   Dict:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
                 lowercase :       str       =			self._prepare_for_class(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				)
                 lowercase :       Any       =			model_class(SCREAMING_SNAKE_CASE_				)

                 @jax.jit
                 def encode_jitted(_A		:     int				,						_A		:     str=None				,						**_A		:     List[str]				):
                      return model.encode(input_ids=SCREAMING_SNAKE_CASE_				,						attention_mask=SCREAMING_SNAKE_CASE_				)

                 with self.subTest('''JIT Enabled'''				):
                      lowercase :       Union[str, Any]       =			encode_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

                 with self.subTest('''JIT Disabled'''				):
                      with jax.disable_jit():
                           lowercase :       Optional[Any]       =			encode_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

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






  def  __a      (  self		:     Union[str, Any]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       str       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
                 lowercase :       List[Any]       =			model_class(SCREAMING_SNAKE_CASE_				)
                 lowercase :       Any       =			model.encode(inputs_dict['''input_ids''']				,						inputs_dict['''attention_mask''']				)

                 lowercase :       Union[str, Any]       =			{
                     """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(_A		:     Any				,						_A		:     List[Any]				,						_A		:     List[str]				):
                      return model.decode(
                          decoder_input_ids=SCREAMING_SNAKE_CASE_				,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				,						encoder_outputs=SCREAMING_SNAKE_CASE_				,						)

                 with self.subTest('''JIT Enabled'''				):
                      lowercase :       Dict       =			decode_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

                 with self.subTest('''JIT Disabled'''				):
                      with jax.disable_jit():
                           lowercase :       List[Any]       =			decode_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

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






  @slow
  def  __a      (  self		:     int				)		->   Any:






       """simple docstring"""







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





import math






def  snake_case(    __magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

     return exponent == int(__magic_name__   )






def  snake_case(    __magic_name__ = 1 / 1_23_45   )			->							int:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			0
     lowercase :       str       =			0

     lowercase :       Optional[int]       =			3
     while True:
          lowercase :       Any       =			(integer**2 - 1) / 4
          # if candidate is an integer, then there is a partition for k
          if partition_candidate == int(__magic_name__   ):
               lowercase :       Any       =			int(__magic_name__   )
               total_partitions += 1
               if check_partition_perfect(__magic_name__   ):
                    perfect_partitions += 1
          if perfect_partitions > 0:
               if perfect_partitions / total_partitions < max_proportion:
                    return int(__magic_name__   )
          integer += 1


if __name__ == "__main__":
 print(f'''{solution() = }''')