



'''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
   _UpperCamelCase											=						'platform'

   import jax
   import jax.numpy as jnp

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       ,)      ->      Tuple:
  if attention_mask is None:
    __lowerCamelCase       :       Optional[Any]           = np.where(input_ids != config.pad_token_id       ,1       ,0       )
  if decoder_attention_mask is None:
    __lowerCamelCase       :       Union[str, Any]           = np.where(decoder_input_ids != config.pad_token_id       ,1       ,0       )
  if head_mask is None:
    __lowerCamelCase       :       Any           = np.ones((config.encoder_layers, config.encoder_attention_heads)       )
  if decoder_head_mask is None:
    __lowerCamelCase       :       List[Any]           = np.ones((config.decoder_layers, config.decoder_attention_heads)       )
  if cross_attn_head_mask is None:
    __lowerCamelCase       :       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      :	List[str]			,	_a      :	str			,	_a      :	List[str]=13			,	_a      :	Dict=7			,	_a      :	int=True			,	_a      :	Dict=False			,	_a      :	Tuple=99			,	_a      :	List[str]=16			,	_a      :	int=2			,	_a      :	Any=4			,	_a      :	Optional[int]=4			,	_a      :	Any="gelu"			,	_a      :	str=0.1			,	_a      :	Any=0.1			,	_a      :	List[Any]=32			,	_a      :	Tuple=2			,	_a      :	str=1			,	_a      :	Dict=0			,	_a      :	List[str]=0.02			,	)					->					int:
    __lowerCamelCase       :       Dict           = parent
    __lowerCamelCase       :       str           = batch_size
    __lowerCamelCase       :       Optional[int]           = seq_length
    __lowerCamelCase       :       int           = is_training
    __lowerCamelCase       :       List[str]           = use_labels
    __lowerCamelCase       :       List[Any]           = vocab_size
    __lowerCamelCase       :       Optional[int]           = hidden_size
    __lowerCamelCase       :       Union[str, Any]           = num_hidden_layers
    __lowerCamelCase       :       Optional[Any]           = num_attention_heads
    __lowerCamelCase       :       List[str]           = intermediate_size
    __lowerCamelCase       :       str           = hidden_act
    __lowerCamelCase       :       Union[str, Any]           = hidden_dropout_prob
    __lowerCamelCase       :       Union[str, Any]           = attention_probs_dropout_prob
    __lowerCamelCase       :       Tuple           = max_position_embeddings
    __lowerCamelCase       :       Tuple           = eos_token_id
    __lowerCamelCase       :       Tuple           = pad_token_id
    __lowerCamelCase       :       List[Any]           = bos_token_id
    __lowerCamelCase       :       int           = initializer_range




  def 						_lowercase     ( self      :	Dict   )					->					int:
    __lowerCamelCase       :       Optional[int]           = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,	self.vocab_size   )			,	3			,	self.vocab_size   )
    __lowerCamelCase       :       str           = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,	dtype=np.intaa   ))			,	-1   )

    __lowerCamelCase       :       Dict           = shift_tokens_right(_a			,	1			,	2   )

    __lowerCamelCase       :       Optional[Any]           = 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			,	)
    __lowerCamelCase       :       List[str]           = prepare_blenderbot_inputs_dict(_a			,	_a			,	_a   )
    return config, inputs_dict




  def 						_lowercase     ( self      :	List[str]   )					->					str:
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = self.prepare_config_and_inputs()
    return config, inputs_dict




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Optional[Any]			,	_a      :	List[str]			,	_a      :	List[str]   )					->					List[str]:
    __lowerCamelCase       :       List[str]           = 20
    __lowerCamelCase       :       List[str]           = model_class_name(_a   )

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

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

    __lowerCamelCase       :       Dict           = model.init_cache(decoder_input_ids.shape[0]			,	_a			,	_a   )
    __lowerCamelCase       :       Optional[Any]           = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,	dtype='i4'   )

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

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

    __lowerCamelCase       :       List[Any]           = model.decode(_a			,	_a   )

    __lowerCamelCase       :       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}'   )




  def 						_lowercase     ( self      :	int			,	_a      :	Optional[Any]			,	_a      :	Tuple			,	_a      :	List[Any]   )					->					Optional[Any]:
    __lowerCamelCase       :       Tuple           = 20
    __lowerCamelCase       :       int           = model_class_name(_a   )

    __lowerCamelCase       :       str           = model.encode(inputs_dict['input_ids']   )

    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = (
        inputs_dict['decoder_input_ids'],
        inputs_dict['decoder_attention_mask'],
    )

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

    __lowerCamelCase       :       Any           = model.init_cache(decoder_input_ids.shape[0]			,	_a			,	_a   )
    __lowerCamelCase       :       List[Any]           = jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]			,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,	)

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

    __lowerCamelCase       :       List[str]           = model.decode(_a			,	_a			,	decoder_attention_mask=_a   )

    __lowerCamelCase       :       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 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  a_												=99




  def 						_lowercase     ( self      :	List[str]   )					->					str:
    __lowerCamelCase       :       Union[str, Any]           = 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			,	)

    __lowerCamelCase       :       Optional[Any]           = input_ids.shape[0]
    __lowerCamelCase       :       Optional[Any]           = 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 						_lowercase     ( self      :	str   )					->					Optional[int]:
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Any           = self._get_config_and_data()
    __lowerCamelCase       :       Dict           = FlaxBlenderbotSmallForConditionalGeneration(_a   )
    __lowerCamelCase       :       List[str]           = lm_model(input_ids=_a   )
    __lowerCamelCase       :       int           = (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['logits'].shape			,	_a   )




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




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,	dtype=np.intaa   )
    __lowerCamelCase       :       List[Any]           = shift_tokens_right(_a			,	1			,	2   )
    __lowerCamelCase       :       Any           = np.equal(_a			,	1   ).astype(np.floataa   ).sum()
    __lowerCamelCase       :       Dict           = 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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase   ,   SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=True
  a_												=(
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  a_												=(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




  def 						_lowercase     ( self      :	Any   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = FlaxBlenderbotSmallModelTester(self   )




  def 						_lowercase     ( self      :	Any   )					->					List[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = 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 						_lowercase     ( self      :	str   )					->					Optional[int]:
    __lowerCamelCase       ,__lowerCamelCase       :       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(_a			,	_a			,	_a   )




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__   ):
        __lowerCamelCase       :       Any           = self._prepare_for_class(_a			,	_a   )
        __lowerCamelCase       :       int           = model_class(_a   )

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

        with self.subTest('JIT Enabled'   ):
          __lowerCamelCase       :       List[Any]           = encode_jitted(**_a   ).to_tuple()

        with self.subTest('JIT Disabled'   ):
          with jax.disable_jit():
            __lowerCamelCase       :       Tuple           = 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 						_lowercase     ( self      :	Any   )					->					List[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCamelCase       :       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      :	Optional[Any]			,	_a      :	Union[str, Any]			,	_a      :	Optional[Any]   ):
          return model.decode(
              decoder_input_ids=_a			,	decoder_attention_mask=_a			,	encoder_outputs=_a			,	)

        with self.subTest('JIT Enabled'   ):
          __lowerCamelCase       :       Optional[Any]           = decode_jitted(**_a   ).to_tuple()

        with self.subTest('JIT Disabled'   ):
          with jax.disable_jit():
            __lowerCamelCase       :       Optional[int]           = 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 						_lowercase     ( self      :	str   )					->					int:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase       :       Optional[int]           = model_class_name.from_pretrained('facebook/blenderbot_small-90M'   )
      # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
      __lowerCamelCase       :       Union[str, Any]           = np.ones((1, 1)   ) * model.config.eos_token_id
      __lowerCamelCase       :       Tuple           = model(_a   )
      self.assertIsNotNone(_a   )











'''simple docstring'''





def        a_     (							_lowerCAmelCase       )      ->      str:
  if not all(char in '01' for char in bin_string       ):
    raise ValueError('Non-binary value was passed to the function'       )
  if not bin_string:
    raise ValueError('Empty string was passed to the function'       )
  __lowerCamelCase       :       int           = ''
  while len(_lowerCAmelCase       ) % 3 != 0:
    __lowerCamelCase       :       str           = '0' + bin_string
  __lowerCamelCase       :       Union[str, Any]           = [
      bin_string[index : index + 3]
      for index in range(len(_lowerCAmelCase       )       )
      if index % 3 == 0
  ]
  for bin_group in bin_string_in_3_list:
    __lowerCamelCase       :       Tuple           = 0
    for index, val in enumerate(_lowerCAmelCase       ):
      oct_val += int(2 ** (2 - index) * int(_lowerCAmelCase       )       )
    oct_string += str(_lowerCAmelCase       )
  return oct_string


if __name__ == "__main__":
   from doctest import testmod

   testmod()






