





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
      lowercase_          =		"""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       __UpperCamelCase  (_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		,						)    ->       Optional[int]:
  if attention_mask is None:
    lowercase__           =  np.where(input_ids != config.pad_token_id		,						1		,						0		)
  if decoder_attention_mask is None:
    lowercase__           =  np.where(decoder_input_ids != config.pad_token_id		,						1		,						0		)
  if head_mask is None:
    lowercase__           =  np.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    lowercase__           =  np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    lowercase__           =  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     SCREAMING_SNAKE_CASE       :


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





     """simple docstring"""







     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  seq_length
     lowercase__           =  is_training
     lowercase__           =  use_labels
     lowercase__           =  vocab_size
     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  num_attention_heads
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_act
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  attention_probs_dropout_prob
     lowercase__           =  max_position_embeddings
     lowercase__           =  eos_token_id
     lowercase__           =  pad_token_id
     lowercase__           =  bos_token_id
     lowercase__           =  initializer_range


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







     lowercase__           =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,					self.vocab_size	)      ,					3      ,					self.vocab_size	)
     lowercase__           =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,					dtype=np.intaa	))      ,					-1	)

     lowercase__           =  shift_tokens_right(_UpperCAmelCase      ,					1      ,					2	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Tuple:





     """simple docstring"""







     lowercase__           =  self.prepare_config_and_inputs()
     return config, inputs_dict


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Dict	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  20
     lowercase__           =  model_class_name(_UpperCAmelCase	)

     lowercase__           =  model.encode(inputs_dict['input_ids']	)

     lowercase__           =  (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     lowercase__           =  model.init_cache(decoder_input_ids.shape[0]      ,					_UpperCAmelCase      ,					_UpperCAmelCase	)
     lowercase__           =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,					dtype='i4'	)

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

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

     lowercase__           =  model.decode(_UpperCAmelCase      ,					_UpperCAmelCase	)

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    Dict      ,					a      :    Optional[int]      ,					a      :    Optional[Any]	)->       int:





     """simple docstring"""







     lowercase__           =  20
     lowercase__           =  model_class_name(_UpperCAmelCase	)

     lowercase__           =  model.encode(inputs_dict['input_ids']	)

     lowercase__           =  (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

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

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

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

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

     lowercase__           =  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     SCREAMING_SNAKE_CASE       (unittest.TestCase				):
   _UpperCamelCase   :		Tuple			 =	99


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  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      ,					)

     lowercase__           =  input_ids.shape[0]
     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[Any]:





     """simple docstring"""







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


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





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,					dtype=np.intaa	)
     lowercase__           =  shift_tokens_right(_UpperCAmelCase      ,					1      ,					2	)
     lowercase__           =  np.equal(_UpperCAmelCase      ,					1	).astype(np.floataa	).sum()
     lowercase__           =  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     SCREAMING_SNAKE_CASE       (_UpperCAmelCase      ,     unittest.TestCase      ,     _UpperCAmelCase				):
   _UpperCamelCase   :		List[Any]			 =	True
   _UpperCamelCase   :		List[Any]			 =	(
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   _UpperCamelCase   :		Optional[int]			 =	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  FlaxBlenderbotModelTester(self	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Dict:





     """simple docstring"""







     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Tuple:





     """simple docstring"""







     lowercase__           =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
         lowercase__           =  self._prepare_for_class(_UpperCAmelCase      ,					_UpperCAmelCase	)
         lowercase__           =  model_class(_UpperCAmelCase	)

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

         with self.subTest('JIT Enabled'	):
           lowercase__           =  encode_jitted(**_UpperCAmelCase	).to_tuple()

         with self.subTest('JIT Disabled'	):
           with jax.disable_jit():
             lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       int:





     """simple docstring"""







     lowercase__           =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
         lowercase__           =  model_class(_UpperCAmelCase	)
         lowercase__           =  model.encode(inputs_dict['input_ids']      ,					inputs_dict['attention_mask']	)

         lowercase__           =  {
             '''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      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Any	):
           return model.decode(
               decoder_input_ids=_UpperCAmelCase      ,					decoder_attention_mask=_UpperCAmelCase      ,					encoder_outputs=_UpperCAmelCase      ,					)

         with self.subTest('JIT Enabled'	):
           lowercase__           =  decode_jitted(**_UpperCAmelCase	).to_tuple()

         with self.subTest('JIT Disabled'	):
           with jax.disable_jit():
             lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Optional[int]:





     """simple docstring"""







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


   @unittest.skipUnless(jax_device != 'cpu'      ,					'3B test too slow on CPU.'	)
   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
     lowercase__           =  {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

     lowercase__           =  ['''Sam''']
     lowercase__           =  tokenizer(_UpperCAmelCase      ,					return_tensors='jax'	)

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

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









def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       int:

  if exponent == 1:
    return base
  if exponent % 2 == 0:
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						exponent // 2		,						_SCREAMING_SNAKE_CASE		) % modulo_value
    return (x * x) % modulo_value
  else:
    return (base * _modexpt(_SCREAMING_SNAKE_CASE		,						exponent - 1		,						_SCREAMING_SNAKE_CASE		)) % modulo_value




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = 1777		,						_SCREAMING_SNAKE_CASE = 1855		,						_SCREAMING_SNAKE_CASE = 8		)    ->       int:
  lowercase__           =  base
  for _ in range(1		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						10**digits		)

  return result


if __name__ == "__main__":
      print(f'''{solution() = }''')


