


'''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_      :		str          =		"platform"

 import jax
 import jax.numpy as jnp

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


def    UpperCAmelCase_  (       A		,	A		,	A=None		,	A=None		,	A=None		,	A=None		,	A=None		,	A=None		,	):
   '''simple docstring'''





   if attention_mask is None:
      _a					:  Any						=  np.where(input_ids != config.pad_token_id		,	1		,	0      )
   if decoder_attention_mask is None:
      _a					:  Optional[Any]						=  np.where(decoder_input_ids != config.pad_token_id		,	1		,	0      )
   if head_mask is None:
      _a					:  int						=  np.ones((config.encoder_layers, config.encoder_attention_heads)      )
   if decoder_head_mask is None:
      _a					:  Any						=  np.ones((config.decoder_layers, config.decoder_attention_heads)      )
   if cross_attn_head_mask is None:
      _a					:  List[Any]						=  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			:
 '''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_=0.02		,      )      ->    List[Any]:
    _a					:  Union[str, Any]						=  parent
    _a					:  Tuple						=  batch_size
    _a					:  int						=  seq_length
    _a					:  Optional[int]						=  is_training
    _a					:  str						=  use_labels
    _a					:  Union[str, Any]						=  vocab_size
    _a					:  List[Any]						=  hidden_size
    _a					:  Dict						=  num_hidden_layers
    _a					:  List[str]						=  num_attention_heads
    _a					:  List[Any]						=  intermediate_size
    _a					:  Optional[int]						=  hidden_act
    _a					:  Any						=  hidden_dropout_prob
    _a					:  List[Any]						=  attention_probs_dropout_prob
    _a					:  Union[str, Any]						=  max_position_embeddings
    _a					:  Tuple						=  eos_token_id
    _a					:  int						=  pad_token_id
    _a					:  Any						=  bos_token_id
    _a					:  Tuple						=  initializer_range




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

    _a					:  str						=  shift_tokens_right(snake_case__		,      1		,      2      )

    _a					:  Dict						=  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=snake_case__		,      )
    _a					:  int						=  prepare_blenderbot_inputs_dict(snake_case__		,      snake_case__		,      snake_case__      )
    return config, inputs_dict




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a					:  int						=  self.prepare_config_and_inputs()
    return config, inputs_dict




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Tuple:
    _a					:  str						=  2_0
    _a					:  Optional[Any]						=  model_class_name(snake_case__      )

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

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

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

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

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

    _a					:  Dict						=  model.decode(snake_case__		,      snake_case__      )

    _a					:  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}'''      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Dict:
    _a					:  Dict						=  2_0
    _a					:  Optional[Any]						=  model_class_name(snake_case__      )

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

    _a					:  Tuple						=  (
        inputs_dict["decoder_input_ids"],
        inputs_dict["decoder_attention_mask"],
    )

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

    _a					:  Tuple						=  model.init_cache(decoder_input_ids.shape[0]		,      snake_case__		,      snake_case__      )
    _a					:  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)		,      )

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

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

    _a					:  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 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[Any]							  =		99




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  List[str]						=  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		,      )

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




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Tuple						=  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		,      )
    _a					:  int						=  FlaxBlenderbotSmallForConditionalGeneration(snake_case__      )
    _a					:  Tuple						=  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      )
    _a					:  List[str]						=  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]		,      dtype=np.intaa      )
    _a					:  List[str]						=  lm_model(input_ids=snake_case__		,      decoder_input_ids=snake_case__      )
    _a					:  str						=  (*summary.shape, config.vocab_size)
    self.assertEqual(outputs['logits'].shape		,      snake_case__      )




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







@require_flax
class 					a			(				snake_case__			,							unittest.TestCase			,							snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		True
 __lowerCAmelCase						: str							  =		(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 __lowerCAmelCase						: List[str]							  =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Union[str, Any]						=  FlaxBlenderbotSmallModelTester(self      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Dict						=  self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(snake_case__		,      snake_case__		,      snake_case__      )




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Tuple						=  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(snake_case__		,      snake_case__		,      snake_case__      )




 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  str						=  self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__      ):
          _a					:  str						=  self._prepare_for_class(snake_case__		,      snake_case__      )
          _a					:  Dict						=  model_class(snake_case__      )

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

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

          with self.subTest('JIT Disabled'      ):
             with jax.disable_jit():
                _a					:  int						=  encode_jitted(**snake_case__      ).to_tuple()

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




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  List[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()

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

          _a					:  Tuple						=  {
              "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=snake_case__		,      decoder_attention_mask=snake_case__		,      encoder_outputs=snake_case__		,      )

          with self.subTest('JIT Enabled'      ):
             _a					:  List[str]						=  decode_jitted(**snake_case__      ).to_tuple()

          with self.subTest('JIT Disabled'      ):
             with jax.disable_jit():
                _a					:  Union[str, Any]						=  decode_jitted(**snake_case__      ).to_tuple()

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




 @slow
 def   __UpperCamelCase   (    self      )      ->    Tuple:




    for model_class_name in self.all_model_classes:
       _a					:  Dict						=  model_class_name.from_pretrained('facebook/blenderbot_small-90M'      )
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       _a					:  Tuple						=  np.ones((1, 1)      ) * model.config.eos_token_id
       _a					:  str						=  model(snake_case__      )
       self.assertIsNotNone(snake_case__      )




'''simple docstring'''





def snake_case_     (					lowercase__		):

  if len(lowercase__		) <= 1:
    return [tuple(lowercase__		)]

  UpperCAmelCase__      :					Union[str, Any]				       =							[]

  def generate(lowercase__   ,       lowercase__		):
    UpperCAmelCase__      :					str				       =							[0] * n
    res.append(tuple(lowercase__		)		)

    UpperCAmelCase__      :					Any				       =							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							arr[i], arr[0]
        else:
          UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							arr[i], arr[c[i]]
        res.append(tuple(lowercase__		)		)
        c[i] += 1
        UpperCAmelCase__      :					List[str]				       =							0
      else:
        UpperCAmelCase__      :					Dict				       =							0
        i += 1

  generate(len(lowercase__		)   ,       lowercase__		)
  return res


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       input("""Enter numbers separated by a comma:\n""").strip()
      SCREAMING_SNAKE_CASE     =       [int(item) for item in user_input.split(""",""")]
      print(heaps(arr))



