



"""simple docstring"""





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
  lowerCamelCase       	=						"""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        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      ):
      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 				lowercase__       :







       '''simple docstring'''


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


             '''simple docstring'''




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


             '''simple docstring'''




             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(_UpperCAmelCase		,    1		,    2   )

             UpperCAmelCase_      			=							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=_UpperCAmelCase		,    )
             UpperCAmelCase_      			=							prepare_blenderbot_inputs_dict(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             return config, inputs_dict



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


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.prepare_config_and_inputs()
             return config, inputs_dict



       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  Dict   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							20
             UpperCAmelCase_      			=							model_class_name(_UpperCAmelCase   )

             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]		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             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]		,    _UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase		,    past_key_values=_UpperCAmelCase		,    decoder_position_ids=_UpperCAmelCase		,    )

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

             UpperCAmelCase_      			=							model.decode(_UpperCAmelCase		,    _UpperCAmelCase   )

             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 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Dict   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							20
             UpperCAmelCase_      			=							model_class_name(_UpperCAmelCase   )

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

             UpperCAmelCase_      			=							model.decode(_UpperCAmelCase		,    _UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase   )

             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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					99



       def 					lowercase__    (							self      :  Tuple   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							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		,    )

             UpperCAmelCase_      			=							input_ids.shape[0]
             UpperCAmelCase_      			=							BlenderbotConfig(
                 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      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							self._get_config_and_data()
             UpperCAmelCase_      			=							FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase   )
             UpperCAmelCase_      			=							lm_model(input_ids=_UpperCAmelCase   )
             UpperCAmelCase_      			=							(batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["logits"].shape		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  int   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							BlenderbotConfig(
                 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		,    )
             UpperCAmelCase_      			=							FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase   )
             UpperCAmelCase_      			=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,    dtype=np.intaa   )
             UpperCAmelCase_      			=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,    dtype=np.intaa   )
             UpperCAmelCase_      			=							lm_model(input_ids=_UpperCAmelCase		,    decoder_input_ids=_UpperCAmelCase   )
             UpperCAmelCase_      			=							(*summary.shape, config.vocab_size)
             self.assertEqual(outputs["logits"].shape		,    _UpperCAmelCase   )







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


             '''simple docstring'''




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



@require_flax
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase    , SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					True
       UpperCamelCase						=					(
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase						=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



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


             '''simple docstring'''




             UpperCAmelCase_      			=							FlaxBlenderbotModelTester(self   )



       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )



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


             '''simple docstring'''




             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(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )



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


             '''simple docstring'''




             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(_UpperCAmelCase		,    _UpperCAmelCase   )
                         UpperCAmelCase_      			=							model_class(_UpperCAmelCase   )

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

                         with self.subTest("JIT Enabled"   ):
                               UpperCAmelCase_      			=							encode_jitted(**_UpperCAmelCase   ).to_tuple()

                         with self.subTest("JIT Disabled"   ):
                               with jax.disable_jit():
                                     UpperCAmelCase_      			=							encode_jitted(**_UpperCAmelCase   ).to_tuple()

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



       def 					lowercase__    (							self      :  Optional[Any]   )			->						int:


             '''simple docstring'''




             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(_UpperCAmelCase   )
                         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(_UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  List[Any]   ):
                               return model.decode(
                                   decoder_input_ids=_UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase		,    encoder_outputs=_UpperCAmelCase		,    )

                         with self.subTest("JIT Enabled"   ):
                               UpperCAmelCase_      			=							decode_jitted(**_UpperCAmelCase   ).to_tuple()

                         with self.subTest("JIT Disabled"   ):
                               with jax.disable_jit():
                                     UpperCAmelCase_      			=							decode_jitted(**_UpperCAmelCase   ).to_tuple()

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



       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						List[str]:


             '''simple docstring'''




             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_      			=							model_class_name.from_pretrained("facebook/blenderbot-400M-distill"   )
                   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                   UpperCAmelCase_      			=							np.ones((1, 1)   ) * model.config.eos_token_id
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )
                   self.assertIsNotNone(_UpperCAmelCase   )







       @unittest.skipUnless(jax_device != "cpu"		,    "3B test too slow on CPU."   )
       @slow
       def 					lowercase__    (							self      :  Union[str, Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
             UpperCAmelCase_      			=							{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

             UpperCAmelCase_      			=							FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,    from_pt=_UpperCAmelCase   )
             UpperCAmelCase_      			=							BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )

             UpperCAmelCase_      			=							["Sam"]
             UpperCAmelCase_      			=							tokenizer(_UpperCAmelCase		,    return_tensors="jax"   )

             UpperCAmelCase_      			=							model.generate(**_UpperCAmelCase		,    **_UpperCAmelCase   )
             UpperCAmelCase_      			=							"Sam is a great name. It means \"sun\" in Gaelic."

             UpperCAmelCase_      			=							tokenizer.batch_decode(_UpperCAmelCase		,    **_UpperCAmelCase   )
             assert generated_txt[0].strip() == tgt_text









"""simple docstring"""





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					None
       UpperCamelCase						=					None



lowerCamelCase       	=						namedtuple("""CoinsDistribResult""", """moves excess""")
def        a__     (				lowerCAmelCase__				):

      if root is None:
            return 0

      # Validation
      def count_nodes(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left				) + count_nodes(node.right				) + 1

      def count_coins(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_coins(node.left				) + count_coins(node.right				) + node.data

      if count_nodes(lowerCAmelCase__				) != count_coins(lowerCAmelCase__				):
            raise ValueError("The nodes number should be same as the number of coins"				)

      # Main calculation
      def get_distrib(lowerCAmelCase__				) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0     ,      1				)

            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.left				)
            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.right				)

            UpperCAmelCase_      			=							1 - left_distrib_excess
            UpperCAmelCase_      			=							1 - right_distrib_excess

            UpperCAmelCase_      			=							(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowerCAmelCase__				)
                + abs(lowerCAmelCase__				)
            )
            UpperCAmelCase_      			=							node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowerCAmelCase__     ,      lowerCAmelCase__				)

      return get_distrib(lowerCAmelCase__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




