



"""simple docstring"""





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
  lowerCamelCase  			=		"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
      FlaxBlenderbotSmallForConditionalGeneration,
      FlaxBlenderbotSmallModel,
      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     :    List[str]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Optional[Any]=13						,							_UpperCAmelCase     :    List[Any]=7						,							_UpperCAmelCase     :    List[str]=True						,							_UpperCAmelCase     :    Optional[int]=False						,							_UpperCAmelCase     :    str=99						,							_UpperCAmelCase     :    List[str]=16						,							_UpperCAmelCase     :    Any=2						,							_UpperCAmelCase     :    Optional[Any]=4						,							_UpperCAmelCase     :    List[Any]=4						,							_UpperCAmelCase     :    str="gelu"						,							_UpperCAmelCase     :    Optional[Any]=0.1						,							_UpperCAmelCase     :    List[Any]=0.1						,							_UpperCAmelCase     :    Any=32						,							_UpperCAmelCase     :    int=2						,							_UpperCAmelCase     :    str=1						,							_UpperCAmelCase     :    int=0						,							_UpperCAmelCase     :    int=0.02						,							)					->		Optional[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     :    List[str]       )					->		int:


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



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


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    int       )					->		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_     					=				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     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Optional[int]       )					->		List[str]:


             '''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     :    Dict       )					->		Dict:


             '''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_     					=				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 	lowercase__							(				self     :    int       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				self._get_config_and_data()
             UpperCAmelCase_     					=				FlaxBlenderbotSmallForConditionalGeneration(_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     :    List[Any]       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				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						,							)
             UpperCAmelCase_     					=				FlaxBlenderbotSmallForConditionalGeneration(_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     :    Any       )					->		Tuple:


             '''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		=	(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase		=	(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



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


             '''simple docstring'''




             UpperCAmelCase_     					=				FlaxBlenderbotSmallModelTester(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     :    int       )					->		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     :    int       )					->		Tuple:


             '''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     :    int						,							_UpperCAmelCase     :    Optional[int]=None						,							**_UpperCAmelCase     :    Tuple       ):
                               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     :    int       )					->		Optional[Any]:


             '''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     :    Tuple						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Tuple       ):
                               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     :    str       )					->		Tuple:


             '''simple docstring'''




             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_     					=				model_class_name.from_pretrained("facebook/blenderbot_small-90M"       )
                   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                   UpperCAmelCase_     					=				np.ones((1, 1)       ) * model.config.eos_token_id
                   UpperCAmelCase_     					=				model(_UpperCAmelCase       )
                   self.assertIsNotNone(_UpperCAmelCase       )









"""simple docstring"""






# Function to print upper half of diamond (pyramid)


def      _lowercase							(    __snake_case      )		->  Dict:
			for i in range(0   ,__snake_case      ):
						for _ in range(0   ,n - i - 1      ):  # printing spaces
									print(" "   ,end=""      )
						for _ in range(0   ,i + 1      ):  # printing stars
									print("* "   ,end=""      )
						print()


def      _lowercase							(    __snake_case      )		->  Tuple:
			for i in range(__snake_case   ,0   ,-1      ):
						for _ in range(__snake_case   ,0   ,-1      ):  # printing stars
									print("* "   ,end=""      )
						print()
						for _ in range(n - i + 1   ,0   ,-1      ):  # printing spaces
									print(" "   ,end=""      )


def      _lowercase							(    __snake_case      )		->  Any:
			if n <= 0:
						print("       ...       ....        nothing printing :("      )
						return
			floyd(__snake_case      )  # upper half
			reverse_floyd(__snake_case      )  # lower half


if __name__ == "__main__":
	print(R'| /\ | |- |  |-  |--| |\  /| |-')
	print(R'|/  \| |- |_ |_  |__| | \/ | |_')
	__snake_case				:  Tuple =       1
	while K:
		__snake_case				:  List[str] =       int(input('enter the number and , and see the magic : '))
		print()
		pretty_print(user_number)
		__snake_case				:  Optional[Any] =       int(input('press 0 to exit... and 1 to continue...'))

	print('Good Bye...')