



'''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
    UpperCamelCase_       :  Tuple					=  """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 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase=None   ,					_lowercase=None   ,					_lowercase=None   ,					_lowercase=None   ,					_lowercase=None   ,					_lowercase=None   ,					):

       """simple docstring"""

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




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

             a__								= shift_tokens_right(__lowerCamelCase			,1			,2   )

             a__								= 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=__lowerCamelCase			,)
             a__								= prepare_blenderbot_inputs_dict(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase   )
             return config, inputs_dict




      def lowerCAmelCase_					(       self							:     Any   ):
             a__						,					a__								= self.prepare_config_and_inputs()
             return config, inputs_dict




      def lowerCAmelCase_					(       self							:     Optional[int]			,a__							:     Optional[Any]			,a__							:     List[Any]			,a__							:     Any   ):
             a__								= 20
             a__								= model_class_name(__lowerCamelCase   )

             a__								= model.encode(inputs_dict["input_ids"]   )

             a__						,					a__								= (
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

             a__								= model.init_cache(decoder_input_ids.shape[0]			,__lowerCamelCase			,__lowerCamelCase   )
             a__								= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,dtype="i4"   )

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

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

             a__								= model.decode(__lowerCamelCase			,__lowerCamelCase   )

             a__								= 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							:     Tuple			,a__							:     List[Any]			,a__							:     Any			,a__							:     str   ):
             a__								= 20
             a__								= model_class_name(__lowerCamelCase   )

             a__								= model.encode(inputs_dict["input_ids"]   )

             a__						,					a__								= (
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

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

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

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

             a__								= model.decode(__lowerCamelCase			,__lowerCamelCase			,decoder_attention_mask=__lowerCamelCase   )

             a__								= 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"""
      UpperCamelCase__			 =     99




      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= 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			,)

             a__								= input_ids.shape[0]
             a__								= 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 lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__						,					a__						,					a__								= self._get_config_and_data()
             a__								= FlaxBlenderbotForConditionalGeneration(__lowerCamelCase   )
             a__								= lm_model(input_ids=__lowerCamelCase   )
             a__								= (batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["logits"].shape			,__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= 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			,)
             a__								= FlaxBlenderbotForConditionalGeneration(__lowerCamelCase   )
             a__								= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,dtype=np.intaa   )
             a__								= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,dtype=np.intaa   )
             a__								= lm_model(input_ids=__lowerCamelCase			,decoder_input_ids=__lowerCamelCase   )
             a__								= (*summary.shape, config.vocab_size)
             self.assertEqual(outputs["logits"].shape			,__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             a__								= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,dtype=np.intaa   )
             a__								= shift_tokens_right(__lowerCamelCase			,1			,2   )
             a__								= np.equal(__lowerCamelCase			,1   ).astype(np.floataa   ).sum()
             a__								= np.equal(__lowerCamelCase			,1   ).astype(np.floataa   ).sum()
             self.assertEqual(shifted.shape			,input_ids.shape   )
             self.assertEqual(__lowerCamelCase			,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"""
      UpperCamelCase__			 =     True
      UpperCamelCase__			 =     (
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      UpperCamelCase__			 =     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= FlaxBlenderbotModelTester(self   )




      def lowerCAmelCase_					(       self							:     List[Any]   ):
             a__						,					a__								= self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     Optional[int]   ):
             a__						,					a__								= 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(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase   )




      def lowerCAmelCase_					(       self							:     Any   ):
             a__						,					a__								= self.model_tester.prepare_config_and_inputs_for_common()

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

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

                           with self.subTest("JIT Enabled"   ):
                                  a__								= encode_jitted(**__lowerCamelCase   ).to_tuple()

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

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




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             a__						,					a__								= self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__   ):
                           a__								= model_class(__lowerCamelCase   )
                           a__								= model.encode(inputs_dict["input_ids"]			,inputs_dict["attention_mask"]   )

                           a__								= {
                               "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[int]			,a__							:     Union[str, Any]			,a__							:     Optional[int]   ):
                                  return model.decode(
                                      decoder_input_ids=__lowerCamelCase			,decoder_attention_mask=__lowerCamelCase			,encoder_outputs=__lowerCamelCase			,)

                           with self.subTest("JIT Enabled"   ):
                                  a__								= decode_jitted(**__lowerCamelCase   ).to_tuple()

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

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




      @slow
      def lowerCAmelCase_					(       self							:     Dict   ):
             for model_class_name in self.all_model_classes:
                    a__								= model_class_name.from_pretrained("facebook/blenderbot-400M-distill"   )
                    # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                    a__								= np.ones((1, 1)   ) * model.config.eos_token_id
                    a__								= model(__lowerCamelCase   )
                    self.assertIsNotNone(__lowerCamelCase   )




      @unittest.skipUnless(jax_device != "cpu"			,"3B test too slow on CPU."   )
      @slow
      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
             a__								= {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

             a__								= ["Sam"]
             a__								= tokenizer(__lowerCamelCase			,return_tensors="jax"   )

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

             a__								= tokenizer.batch_decode(__lowerCamelCase			,**__lowerCamelCase   )
             assert generated_txt[0].strip() == tgt_text





"""simple docstring"""






from copy import deepcopy



class     UpperCAmelCase    :



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	list[int] | None = None							,       __lowerCamelCase    :	int | None = None				):



     """simple docstring"""

     if arr is None and size is not None:
       _snake_case   	=      size
       _snake_case   	=      [0] * size
     elif arr is not None:
       self.init(__lowerCamelCase				)
     else:
       raise ValueError('''Either arr or size must be specified'''				)



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	list[int]				):



     """simple docstring"""
     _snake_case   	=      len(__lowerCamelCase				)
     _snake_case   	=      deepcopy(__lowerCamelCase				)
     for i in range(1							,       self.size				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         self.tree[j] += self.tree[i]



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.tree[:]
     for i in range(self.size - 1							,       0							,       -1				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         arr[j] -= arr[i]
     return arr



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index + (index & (-index))



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index - (index & (-index))



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if index == 0:
       self.tree[0] += value
       return
     while index < self.size:
       self.tree[index] += value
       _snake_case   	=      self.next_(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     self.add(__lowerCamelCase							,       value - self.get(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if right == 0:
       return 0
     _snake_case   	=      self.tree[0]
     right -= 1  # make right inclusive
     while right > 0:
       result += self.tree[right]
       _snake_case   	=      self.prev(__lowerCamelCase				)
     return result



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.prefix(__lowerCamelCase				) - self.prefix(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.query(__lowerCamelCase							,       index + 1				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     value -= self.tree[0]
     if value < 0:
       return -1

     _snake_case   	=      1  # Largest power of 2 <= size
     while j * 2 < self.size:
       j *= 2

     _snake_case   	=      0

     while j > 0:
       if i + j < self.size and self.tree[i + j] <= value:
         value -= self.tree[i + j]
         i += j
       j //= 2
     return i


if __name__ == "__main__":
     import doctest

     doctest.testmod()





