


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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			):
  if attention_mask is None:
    A_      :				Union[str, Any]		     =					np.where(input_ids != config.pad_token_id ,			1 ,			0       )
  if decoder_attention_mask is None:
    A_      :				Optional[int]		     =					np.where(decoder_input_ids != config.pad_token_id ,			1 ,			0       )
  if head_mask is None:
    A_      :				List[str]		     =					np.ones((config.encoder_layers, config.encoder_attention_heads)       )
  if decoder_head_mask is None:
    A_      :				Optional[Any]		     =					np.ones((config.decoder_layers, config.decoder_attention_heads)       )
  if cross_attn_head_mask is None:
    A_      :				Dict		     =					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				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=7				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=99				,       _SCREAMING_SNAKE_CASE=16				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       )->str:




         '''simple docstring'''





         A_      :				int		     =					parent
         A_      :				Optional[Any]		     =					batch_size
         A_      :				Any		     =					seq_length
         A_      :				Optional[int]		     =					is_training
         A_      :				List[Any]		     =					use_labels
         A_      :				List[str]		     =					vocab_size
         A_      :				Any		     =					hidden_size
         A_      :				str		     =					num_hidden_layers
         A_      :				Tuple		     =					num_attention_heads
         A_      :				Optional[int]		     =					intermediate_size
         A_      :				List[str]		     =					hidden_act
         A_      :				Union[str, Any]		     =					hidden_dropout_prob
         A_      :				List[str]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					max_position_embeddings
         A_      :				Tuple		     =					eos_token_id
         A_      :				Dict		     =					pad_token_id
         A_      :				Any		     =					bos_token_id
         A_      :				int		     =					initializer_range



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





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

         A_      :				str		     =					shift_tokens_right(_SCREAMING_SNAKE_CASE				,       1				,       2		)

         A_      :				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=_SCREAMING_SNAKE_CASE				,       )
         A_      :				List[Any]		     =					prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         return config, inputs_dict



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				str		     =					self.prepare_config_and_inputs()
         return config, inputs_dict



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Dict:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					20
         A_      :				List[Any]		     =					model_class_name(_SCREAMING_SNAKE_CASE		)

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

         A_     ,       A_      :				Tuple		     =					(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

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

         A_      :				Tuple		     =					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]				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       past_key_values=_SCREAMING_SNAKE_CASE				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )

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

         A_      :				str		     =					model.decode(_SCREAMING_SNAKE_CASE				,       _SCREAMING_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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					20
         A_      :				str		     =					model_class_name(_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					model.encode(inputs_dict['''input_ids''']		)

         A_     ,       A_      :				Tuple		     =					(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

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

         A_      :				Any		     =					model.init_cache(decoder_input_ids.shape[0]				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )

         A_      :				Dict		     =					model.decode(
             decoder_input_ids[:, :-1]				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       past_key_values=_SCREAMING_SNAKE_CASE				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )
         A_      :				Tuple		     =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='''i4'''		)
         A_      :				Optional[int]		     =					model.decode(
             decoder_input_ids[:, -1:]				,       _SCREAMING_SNAKE_CASE				,       past_key_values=outputs_cache.past_key_values				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )

         A_      :				Optional[Any]		     =					model.decode(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					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"""
       snake_case      =    99



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Optional[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				,       )

         A_      :				int		     =					input_ids.shape[0]
         A_      :				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        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_     ,       A_     ,       A_      :				List[str]		     =					self._get_config_and_data()
         A_      :				Optional[Any]		     =					FlaxBlenderbotSmallForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					lm_model(input_ids=_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				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				,       )
         A_      :				Union[str, Any]		     =					FlaxBlenderbotSmallForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,       dtype=np.intaa		)
         A_      :				int		     =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,       dtype=np.intaa		)
         A_      :				Optional[int]		     =					lm_model(input_ids=_SCREAMING_SNAKE_CASE				,       decoder_input_ids=_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape				,       _SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





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






@require_flax
class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase					,							UpperCamelCase			):




       """simple docstring"""
       snake_case      =    True
       snake_case      =    (
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       snake_case      =    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				int		     =					FlaxBlenderbotSmallModelTester(self		)



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_     ,       A_      :				Optional[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(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_     ,       A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__		):
             A_      :				Optional[Any]		     =					self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
             A_      :				Any		     =					model_class(_SCREAMING_SNAKE_CASE		)

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

             with self.subTest('''JIT Enabled'''		):
               A_      :				Tuple		     =					encode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

             with self.subTest('''JIT Disabled'''		):
               with jax.disable_jit():
                 A_      :				Optional[int]		     =					encode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

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



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       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_      :				List[str]		     =					model_class(_SCREAMING_SNAKE_CASE		)
             A_      :				int		     =					model.encode(inputs_dict['''input_ids''']				,       inputs_dict['''attention_mask''']		)

             A_      :				int		     =					{
                 '''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(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
               return model.decode(
                   decoder_input_ids=_SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       encoder_outputs=_SCREAMING_SNAKE_CASE				,       )

             with self.subTest('''JIT Enabled'''		):
               A_      :				str		     =					decode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

             with self.subTest('''JIT Disabled'''		):
               with jax.disable_jit():
                 A_      :				List[str]		     =					decode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

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







       @slow
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         for model_class_name in self.all_model_classes:
           A_      :				List[str]		     =					model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''		)
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           A_      :				int		     =					np.ones((1, 1)		) * model.config.eos_token_id
           A_      :				Dict		     =					model(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)








def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




