

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							:  Optional[Any]   	=						"""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_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	):
						if attention_mask is None:
												SCREAMING_SNAKE_CASE_: Dict  		=       np.where(input_ids != config.pad_token_id				,	1				,	0    )
						if decoder_attention_mask is None:
												SCREAMING_SNAKE_CASE_: int  		=       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_: Union[str, Any]  		=       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  :     int   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Optional[int]=13   ,						lowerCAmelCase__  :     Optional[Any]=7   ,						lowerCAmelCase__  :     Optional[int]=True   ,						lowerCAmelCase__  :     int=False   ,						lowerCAmelCase__  :     str=99   ,						lowerCAmelCase__  :     List[str]=16   ,						lowerCAmelCase__  :     List[Any]=2   ,						lowerCAmelCase__  :     Any=4   ,						lowerCAmelCase__  :     Dict=4   ,						lowerCAmelCase__  :     Optional[int]="gelu"   ,						lowerCAmelCase__  :     Optional[Any]=0.1   ,						lowerCAmelCase__  :     List[Any]=0.1   ,						lowerCAmelCase__  :     Any=32   ,						lowerCAmelCase__  :     str=2   ,						lowerCAmelCase__  :     str=1   ,						lowerCAmelCase__  :     Dict=0   ,						lowerCAmelCase__  :     Optional[int]=0.02   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       parent
									SCREAMING_SNAKE_CASE_: List[str]  		=       batch_size
									SCREAMING_SNAKE_CASE_: str  		=       seq_length
									SCREAMING_SNAKE_CASE_: int  		=       is_training
									SCREAMING_SNAKE_CASE_: List[str]  		=       use_labels
									SCREAMING_SNAKE_CASE_: List[Any]  		=       vocab_size
									SCREAMING_SNAKE_CASE_: Any  		=       hidden_size
									SCREAMING_SNAKE_CASE_: str  		=       num_hidden_layers
									SCREAMING_SNAKE_CASE_: Tuple  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: List[str]  		=       intermediate_size
									SCREAMING_SNAKE_CASE_: int  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Dict  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       max_position_embeddings
									SCREAMING_SNAKE_CASE_: str  		=       eos_token_id
									SCREAMING_SNAKE_CASE_: List[str]  		=       pad_token_id
									SCREAMING_SNAKE_CASE_: List[str]  		=       bos_token_id
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       initializer_range



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

									SCREAMING_SNAKE_CASE_: str  		=       shift_tokens_right(lowerCAmelCase__   ,						1   ,						2)

									SCREAMING_SNAKE_CASE_: int  		=       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=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       prepare_blenderbot_inputs_dict(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									return config, inputs_dict



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.prepare_config_and_inputs()
									return config, inputs_dict



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       20
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       model.encode(inputs_dict["input_ids"])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.init_cache(decoder_input_ids.shape[0]   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,						dtype="i4")

									SCREAMING_SNAKE_CASE_: Any  		=       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]   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						past_key_values=lowerCAmelCase__   ,						decoder_position_ids=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,						dtype="i4")
									SCREAMING_SNAKE_CASE_: List[str]  		=       model.decode(
									    decoder_input_ids[:, -1:]   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						past_key_values=outputs_cache.past_key_values   ,						decoder_position_ids=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Tuple  		=       model.decode(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: 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}")




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: List[str]  		=       20
									SCREAMING_SNAKE_CASE_: int  		=       model_class_name(lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       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_: Dict  		=       model.init_cache(decoder_input_ids.shape[0]   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: 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_: List[Any]  		=       model.decode(
									    decoder_input_ids[:, :-1]   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						past_key_values=lowerCAmelCase__   ,						decoder_position_ids=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,						dtype="i4")
									SCREAMING_SNAKE_CASE_: str  		=       model.decode(
									    decoder_input_ids[:, -1:]   ,						lowerCAmelCase__   ,						past_key_values=outputs_cache.past_key_values   ,						decoder_attention_mask=lowerCAmelCase__   ,						decoder_position_ids=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: List[str]  		=       model.decode(lowerCAmelCase__   ,						lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     99



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       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   ,						)

									SCREAMING_SNAKE_CASE_: Tuple  		=       input_ids.shape[0]
									SCREAMING_SNAKE_CASE_: List[str]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._get_config_and_data()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       lm_model(input_ids=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["logits"].shape   ,						lowerCAmelCase__)



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




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,						dtype=np.intaa)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       shift_tokens_right(lowerCAmelCase__   ,						1   ,						2)
									SCREAMING_SNAKE_CASE_: int  		=       np.equal(lowerCAmelCase__   ,						1).astype(np.floataa).sum()
									SCREAMING_SNAKE_CASE_: Dict  		=       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   __lowercase				( UpperCAmelCase_		,       unittest.TestCase		,       UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     True
			_UpperCAmelCase   :							str										=     (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			_UpperCAmelCase   :							Optional[Any]										=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxBlenderbotSmallModelTester(self)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       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(lowerCAmelCase__   ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)

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

																					with self.subTest("JIT Enabled"):
																											SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       encode_jitted(**lowerCAmelCase__).to_tuple()

																					with self.subTest("JIT Disabled"):
																											with jax.disable_jit():
																																	SCREAMING_SNAKE_CASE_: str  		=       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        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       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_: int  		=       model_class(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.encode(inputs_dict["input_ids"]   ,						inputs_dict["attention_mask"])

																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {
																					    "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__  :     str   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Any):
																											return model.decode(
																											    decoder_input_ids=lowerCAmelCase__   ,						decoder_attention_mask=lowerCAmelCase__   ,						encoder_outputs=lowerCAmelCase__   ,						)

																					with self.subTest("JIT Enabled"):
																											SCREAMING_SNAKE_CASE_: Optional[Any]  		=       decode_jitted(**lowerCAmelCase__).to_tuple()

																					with self.subTest("JIT Disabled"):
																											with jax.disable_jit():
																																	SCREAMING_SNAKE_CASE_: List[Any]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class_name.from_pretrained("facebook/blenderbot_small-90M")
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.ones((1, 1)) * model.config.eos_token_id
															SCREAMING_SNAKE_CASE_: Any  		=       model(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)










'''simple docstring'''



from math import isqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int							)     ->		bool:
     return all(number % divisor != 0 for divisor in range(2      ,    isqrt(UpperCAmelCase_							) + 1							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int = 10**6							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	Optional[int]								=		1
     __lowerCamelCase   :	List[str]								=		7
     while prime_candidate < max_prime:
          primes_count += is_prime(UpperCAmelCase_							)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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






