





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__  			="""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__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase=None,							__lowerCamelCase=None,							__lowerCamelCase=None,							__lowerCamelCase=None,							__lowerCamelCase=None,							__lowerCamelCase=None,							):


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




       def      UpperCamelCase_      (   self				)      ->  Union[str, Any]:
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,    self.vocab_size				)     ,    3     ,    self.vocab_size				)
            _SCREAMING_SNAKE_CASE							:						List[str]         =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,    dtype=np.intaa				))     ,    -1				)

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

            _SCREAMING_SNAKE_CASE							:						int         =				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=__SCREAMING_SNAKE_CASE     ,    )
            _SCREAMING_SNAKE_CASE							:						int         =				prepare_blenderbot_inputs_dict(__SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE				)
            return config, inputs_dict




       def      UpperCamelCase_      (   self				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE							:						str         =				self.prepare_config_and_inputs()
            return config, inputs_dict




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						str         =				2_0
            _SCREAMING_SNAKE_CASE							:						int         =				model_class_name(__SCREAMING_SNAKE_CASE				)

            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				model.encode(inputs_dict["input_ids"]				)

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

            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				model.init_cache(decoder_input_ids.shape[0]     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						List[str]         =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,    dtype="i4"				)

            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]     ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,    )
            _SCREAMING_SNAKE_CASE							:						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     ,    )

            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,    dtype="i4"				)
            _SCREAMING_SNAKE_CASE							:						Dict         =				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     ,    )

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

            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				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				)      ->  Union[str, Any]:
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				2_0
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				model_class_name(__SCREAMING_SNAKE_CASE				)

            _SCREAMING_SNAKE_CASE							:						List[Any]         =				model.encode(inputs_dict["input_ids"]				)

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

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

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

            _SCREAMING_SNAKE_CASE							:						int         =				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     ,    )
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,    dtype="i4"				)
            _SCREAMING_SNAKE_CASE							:						Tuple         =				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     ,    )

            _SCREAMING_SNAKE_CASE							:						Dict         =				model.decode(__SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    decoder_attention_mask=__SCREAMING_SNAKE_CASE				)

            _SCREAMING_SNAKE_CASE							:						Tuple         =				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			      =     9_9




       def      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE							:						Any         =				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     ,    )

            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				input_ids.shape[0]
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				BlenderbotConfig(
                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				)      ->  Dict:
            _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				self._get_config_and_data()
            _SCREAMING_SNAKE_CASE							:						int         =				FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						Dict         =				lm_model(input_ids=__SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						Any         =				(batch_size, input_ids.shape[1], config.vocab_size)
            self.assertEqual(outputs["logits"].shape     ,    __SCREAMING_SNAKE_CASE				)




       def      UpperCamelCase_      (   self				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						Dict         =				BlenderbotConfig(
                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     ,    )
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				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				)
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]     ,    dtype=np.intaa				)
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				lm_model(input_ids=__SCREAMING_SNAKE_CASE     ,    decoder_input_ids=__SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						Dict         =				(*summary.shape, config.vocab_size)
            self.assertEqual(outputs["logits"].shape     ,    __SCREAMING_SNAKE_CASE				)







       def      UpperCamelCase_      (   self				)      ->  int:
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				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				)
            _SCREAMING_SNAKE_CASE							:						str         =				shift_tokens_right(__SCREAMING_SNAKE_CASE     ,    1     ,    2				)
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				np.equal(__SCREAMING_SNAKE_CASE     ,    1				).astype(np.floataa				).sum()
            _SCREAMING_SNAKE_CASE							:						Union[str, 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__(			a_	,  unittest.TestCase	,  a_     ):






       '''simple docstring'''


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




       def      UpperCamelCase_      (   self				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				FlaxBlenderbotModelTester(self				)




       def      UpperCamelCase_      (   self				)      ->  List[Any]:
            _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE							:						Any         =				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      UpperCamelCase_      (   self				)      ->  Tuple:
            _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE							:						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      UpperCamelCase_      (   self				)      ->  str:
            _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                      _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE				)
                      _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				model_class(__SCREAMING_SNAKE_CASE				)

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

                      with self.subTest("JIT Enabled"				):
                           _SCREAMING_SNAKE_CASE							:						List[Any]         =				encode_jitted(**__SCREAMING_SNAKE_CASE				).to_tuple()

                      with self.subTest("JIT Disabled"				):
                           with jax.disable_jit():
                                _SCREAMING_SNAKE_CASE							:						Dict         =				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      UpperCamelCase_      (   self				)      ->  Union[str, Any]:
            _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE							:						int         =				self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                      _SCREAMING_SNAKE_CASE							:						Tuple         =				model_class(__SCREAMING_SNAKE_CASE				)
                      _SCREAMING_SNAKE_CASE							:						List[str]         =				model.encode(inputs_dict["input_ids"]     ,    inputs_dict["attention_mask"]				)

                      _SCREAMING_SNAKE_CASE							:						List[str]         =				{
                          "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=__SCREAMING_SNAKE_CASE     ,    decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,    encoder_outputs=__SCREAMING_SNAKE_CASE     ,    )

                      with self.subTest("JIT Enabled"				):
                           _SCREAMING_SNAKE_CASE							:						List[Any]         =				decode_jitted(**__SCREAMING_SNAKE_CASE				).to_tuple()

                      with self.subTest("JIT Disabled"				):
                           with jax.disable_jit():
                                _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				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      UpperCamelCase_      (   self				)      ->  Union[str, Any]:
            for model_class_name in self.all_model_classes:
                 _SCREAMING_SNAKE_CASE							:						Optional[int]         =				model_class_name.from_pretrained("facebook/blenderbot-400M-distill"				)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 _SCREAMING_SNAKE_CASE							:						List[Any]         =				np.ones((1, 1)				) * model.config.eos_token_id
                 _SCREAMING_SNAKE_CASE							:						Tuple         =				model(__SCREAMING_SNAKE_CASE				)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE				)







       @unittest.skipUnless(jax_device != "cpu"     ,    "3B test too slow on CPU."				)
       @slow
       def      UpperCamelCase_      (   self				)      ->  Union[str, Any]:
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				{"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
            _SCREAMING_SNAKE_CASE							:						List[str]         =				{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

            _SCREAMING_SNAKE_CASE							:						str         =				FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"     ,    from_pt=__SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"				)

            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				["Sam"]
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				tokenizer(__SCREAMING_SNAKE_CASE     ,    return_tensors="jax"				)

            _SCREAMING_SNAKE_CASE							:						List[str]         =				model.generate(**__SCREAMING_SNAKE_CASE     ,    **__SCREAMING_SNAKE_CASE				)
            _SCREAMING_SNAKE_CASE							:						Any         =				"Sam is a great name. It means \"sun\" in Gaelic."

            _SCREAMING_SNAKE_CASE							:						str         =				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,    **__SCREAMING_SNAKE_CASE				)
            assert generated_txt[0].strip() == tgt_text
def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return x if y == 0 else greatest_common_divisor(_A				,					x % y       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (x * y) // greatest_common_divisor(_A				,					_A       )



def 	__A		(      _A = 20       ):



 """simple docstring"""


 __a							=	1
 for i in range(1				,					n + 1       ):
  __a							=	lcm(_A				,					_A       )
 return g


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


