





'''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__							:							int ='''platform'''

    import jax
    import jax.numpy as jnp

    from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
        FlaxBlenderbotSmallForConditionalGeneration,
        FlaxBlenderbotSmallModel,
        shift_tokens_right,
    )
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  Optional[int]				,  __lowercase						:  str=None				,  __lowercase						:  List[Any]=None				,  __lowercase						:  Dict=None				,  __lowercase						:  int=None				,  __lowercase						:  Union[str, Any]=None				,  __lowercase						:  Dict=None				,  ) ->      List[Any]:



       """simple docstring"""






       if attention_mask is None:
              __UpperCamelCase       =  np.where(input_ids != config.pad_token_id				,  1				,  0     )
       if decoder_attention_mask is None:
              __UpperCamelCase       =  np.where(decoder_input_ids != config.pad_token_id				,  1				,  0     )
       if head_mask is None:
              __UpperCamelCase       =  np.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              __UpperCamelCase       =  np.ones((config.decoder_layers, config.decoder_attention_heads)     )
       if cross_attn_head_mask is None:
              __UpperCamelCase       =  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      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Optional[int]							,	__A       :							List[Any]							,	__A       :							List[str]=1_3							,	__A       :							str=7							,	__A       :							Any=True							,	__A       :							Optional[Any]=False							,	__A       :							Optional[int]=9_9							,	__A       :							str=1_6							,	__A       :							List[str]=2							,	__A       :							str=4							,	__A       :							str=4							,	__A       :							Optional[Any]="gelu"							,	__A       :							Dict=0.1							,	__A       :							Optional[Any]=0.1							,	__A       :							Tuple=3_2							,	__A       :							Dict=2							,	__A       :							Tuple=1							,	__A       :							List[str]=0							,	__A       :							Optional[int]=0.02							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  is_training
        __UpperCamelCase       =  use_labels
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  eos_token_id
        __UpperCamelCase       =  pad_token_id
        __UpperCamelCase       =  bos_token_id
        __UpperCamelCase       =  initializer_range






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,	self.vocab_size	)							,	3							,	self.vocab_size	)
        __UpperCamelCase       =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,	dtype=np.intaa	))							,	-1	)

        __UpperCamelCase       =  shift_tokens_right(__A							,	1							,	2	)

        __UpperCamelCase       =  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=__A							,	)
        __UpperCamelCase       =  prepare_blenderbot_inputs_dict(__A							,	__A							,	__A	)
        return config, inputs_dict






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.prepare_config_and_inputs()
        return config, inputs_dict






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Dict							,	__A       :							Tuple							,	__A       :							Optional[int]	):
        __UpperCamelCase       =  2_0
        __UpperCamelCase       =  model_class_name(__A	)

        __UpperCamelCase       =  model.encode(inputs_dict['input_ids']	)

        __UpperCamelCase       ,						__UpperCamelCase       =  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __UpperCamelCase       =  model.init_cache(decoder_input_ids.shape[0]							,	__A							,	__A	)
        __UpperCamelCase       =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,	dtype='i4'	)

        __UpperCamelCase       =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]							,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,	)
        __UpperCamelCase       =  model.decode(
            decoder_input_ids[:, :-1]							,	__A							,	decoder_attention_mask=__A							,	past_key_values=__A							,	decoder_position_ids=__A							,	)

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

        __UpperCamelCase       =  model.decode(__A							,	__A	)

        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							List[Any]							,	__A       :							Union[str, Any]							,	__A       :							Any	):
        __UpperCamelCase       =  2_0
        __UpperCamelCase       =  model_class_name(__A	)

        __UpperCamelCase       =  model.encode(inputs_dict['input_ids']	)

        __UpperCamelCase       ,						__UpperCamelCase       =  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

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

        __UpperCamelCase       =  model.init_cache(decoder_input_ids.shape[0]							,	__A							,	__A	)
        __UpperCamelCase       =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]							,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,	)

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

        __UpperCamelCase       =  model.decode(__A							,	__A							,	decoder_attention_mask=__A	)

        __UpperCamelCase       =  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      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=99






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  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       =  input_ids.shape[0]
        __UpperCamelCase       =  BlenderbotSmallConfig(
            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        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  self._get_config_and_data()
        __UpperCamelCase       =  FlaxBlenderbotSmallForConditionalGeneration(__A	)
        __UpperCamelCase       =  lm_model(input_ids=__A	)
        __UpperCamelCase       =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['logits'].shape							,	__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  BlenderbotSmallConfig(
            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       =  FlaxBlenderbotSmallForConditionalGeneration(__A	)
        __UpperCamelCase       =  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       =  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,	dtype=np.intaa	)
        __UpperCamelCase       =  lm_model(input_ids=__A							,	decoder_input_ids=__A	)
        __UpperCamelCase       =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['logits'].shape							,	__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  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       =  shift_tokens_right(__A							,	1							,	2	)
        __UpperCamelCase       =  np.equal(__A							,	1	).astype(np.floataa	).sum()
        __UpperCamelCase       =  np.equal(__A							,	1	).astype(np.floataa	).sum()
        self.assertEqual(shifted.shape							,	input_ids.shape	)
        self.assertEqual(__A							,	n_pad_before - 1	)
        self.assertTrue(np.equal(shifted[:, 0]							,	2	).all()	)






@require_flax
class      snake_case     (    __lowerCamelCase			,       unittest.TestCase			,       __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=True
 SCREAMING_SNAKE_CASE_	:					Tuple					=(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  FlaxBlenderbotSmallModelTester(self	)






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(__A							,	__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  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(__A							,	__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__	):
                      __UpperCamelCase       =  self._prepare_for_class(__A							,	__A	)
                      __UpperCamelCase       =  model_class(__A	)

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

                      with self.subTest('JIT Enabled'	):
                             __UpperCamelCase       =  encode_jitted(**__A	).to_tuple()

                      with self.subTest('JIT Disabled'	):
                             with jax.disable_jit():
                                    __UpperCamelCase       =  encode_jitted(**__A	).to_tuple()

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






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__	):
                      __UpperCamelCase       =  model_class(__A	)
                      __UpperCamelCase       =  model.encode(inputs_dict['input_ids']							,	inputs_dict['attention_mask']	)

                      __UpperCamelCase       =  {
                          '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       :							List[Any]							,	__A       :							Tuple							,	__A       :							List[Any]	):
                             return model.decode(
                                 decoder_input_ids=__A							,	decoder_attention_mask=__A							,	encoder_outputs=__A							,	)

                      with self.subTest('JIT Enabled'	):
                             __UpperCamelCase       =  decode_jitted(**__A	).to_tuple()

                      with self.subTest('JIT Disabled'	):
                             with jax.disable_jit():
                                    __UpperCamelCase       =  decode_jitted(**__A	).to_tuple()

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






 @slow
 def        _lowerCamelCase					(       self       :							str	):
        for model_class_name in self.all_model_classes:
               __UpperCamelCase       =  model_class_name.from_pretrained('facebook/blenderbot_small-90M'	)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __UpperCamelCase       =  np.ones((1, 1)	) * model.config.eos_token_id
               __UpperCamelCase       =  model(__A	)
               self.assertIsNotNone(__A	)







'''simple docstring'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
