



"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel







@require_tf
class    __lowercase     :







						"""simple docstring"""







						_A		:							int							       =		BlenderbotConfig
						_A		:							str							       =		{}
						_A		:							List[Any]							       =		"""gelu"""







						def __init__(self   ,		lowercase__   ,		lowercase__=13   ,		lowercase__=7   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=99   ,		lowercase__=32   ,		lowercase__=2   ,		lowercase__=4   ,		lowercase__=37   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=20   ,		lowercase__=2   ,		lowercase__=1   ,		lowercase__=0   ,		):
											snake_case_					:		List[str] 					=					parent
											snake_case_					:		int 					=					batch_size
											snake_case_					:		Dict 					=					seq_length
											snake_case_					:		Optional[Any] 					=					is_training
											snake_case_					:		Any 					=					use_labels
											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		Any 					=					hidden_size
											snake_case_					:		Union[str, Any] 					=					num_hidden_layers
											snake_case_					:		Optional[int] 					=					num_attention_heads
											snake_case_					:		List[str] 					=					intermediate_size

											snake_case_					:		Any 					=					hidden_dropout_prob
											snake_case_					:		List[Any] 					=					attention_probs_dropout_prob
											snake_case_					:		Dict 					=					max_position_embeddings
											snake_case_					:		Optional[Any] 					=					eos_token_id
											snake_case_					:		Optional[Any] 					=					pad_token_id
											snake_case_					:		Dict 					=					bos_token_id







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					ids_tensor([self.batch_size, self.seq_length - 1]   ,		self.vocab_size       )
											snake_case_					:		List[Any] 					=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )   ,		1       )
											snake_case_					:		Optional[int] 					=					tf.concat([input_ids, eos_tensor]   ,		axis=1       )

											snake_case_					:		Any 					=					ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size       )

											snake_case_					:		int 					=					self.config_cls(
											    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_ids=[2]   ,		bos_token_id=self.bos_token_id   ,		pad_token_id=self.pad_token_id   ,		decoder_start_token_id=self.pad_token_id   ,		**self.config_updates   ,		)
											snake_case_					:		List[str] 					=					prepare_blenderbot_inputs_dict(lowercase__   ,		lowercase__   ,		lowercase__       )
											return config, inputs_dict







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					TFBlenderbotModel(config=lowercase__       ).get_decoder()
											snake_case_					:		Dict 					=					inputs_dict["""input_ids"""]

											snake_case_					:		Optional[Any] 					=					input_ids[:1, :]
											snake_case_					:		Dict 					=					inputs_dict["""attention_mask"""][:1, :]
											snake_case_					:		Dict 					=					inputs_dict["""head_mask"""]
											snake_case_					:		Dict 					=					1

											# first forward pass
											snake_case_					:		List[str] 					=					model(lowercase__   ,		attention_mask=lowercase__   ,		head_mask=lowercase__   ,		use_cache=lowercase__       )

											snake_case_					:		Any 					=					outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											snake_case_					:		List[Any] 					=					ids_tensor((self.batch_size, 3)   ,		config.vocab_size       )
											snake_case_					:		Optional[Any] 					=					tf.cast(ids_tensor((self.batch_size, 3)   ,		2       )   ,		tf.inta       )

											# append to next input_ids and
											snake_case_					:		Optional[int] 					=					tf.concat([input_ids, next_tokens]   ,		axis=-1       )
											snake_case_					:		Optional[int] 					=					tf.concat([attention_mask, next_attn_mask]   ,		axis=-1       )

											snake_case_					:		List[Any] 					=					model(lowercase__   ,		attention_mask=lowercase__       )[0]
											snake_case_					:		int 					=					model(lowercase__   ,		attention_mask=lowercase__   ,		past_key_values=lowercase__       )[0]

											self.parent.assertEqual(next_tokens.shape[1]   ,		output_from_past.shape[1]       )

											# select random slice
											snake_case_					:		str 					=					int(ids_tensor((1,)   ,		output_from_past.shape[-1]       )       )
											snake_case_					:		List[Any] 					=					output_from_no_past[:, -3:, random_slice_idx]
											snake_case_					:		str 					=					output_from_past[:, :, random_slice_idx]

											# test that outputs are equal for slice
											tf.debugging.assert_near(lowercase__   ,		lowercase__   ,		rtol=1e-3       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  List[str]=None				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=None				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=None				,    SCREAMING_SNAKE_CASE__ :  List[str]=None				,    SCREAMING_SNAKE_CASE__ :  Optional[int]=None				,    ):



					"""simple docstring"""





					if attention_mask is None:
										snake_case_					:		List[Any] 					=					tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__				,    config.pad_token_id			)				,    tf.inta			)
					if decoder_attention_mask is None:
										snake_case_					:		Any 					=					tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape				,    dtype=tf.inta			),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,    config.pad_token_id			)				,    tf.inta			),
										    ]				,    axis=-1				,    )
					if head_mask is None:
										snake_case_					:		Dict 					=					tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
					if decoder_head_mask is None:
										snake_case_					:		List[Any] 					=					tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
					if cross_attn_head_mask is None:
										snake_case_					:		List[Any] 					=					tf.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": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}







@require_tf
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							int							       =		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
						_A		:							Optional[Any]							       =		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
						_A		:							Optional[Any]							       =		(
						    {
						        """conversational""": TFBlenderbotForConditionalGeneration,
						        """feature-extraction""": TFBlenderbotModel,
						        """summarization""": TFBlenderbotForConditionalGeneration,
						        """text2text-generation""": TFBlenderbotForConditionalGeneration,
						        """translation""": TFBlenderbotForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_A		:							str							       =		True
						_A		:							Optional[Any]							       =		False
						_A		:							Any							       =		False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					TFBlenderbotModelTester(self       )
											snake_case_					:		str 					=					ConfigTester(self   ,		config_class=lowercase__       )







						def 						__UpperCamelCase							(self       ):
											self.config_tester.run_common_tests()







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*lowercase__       )







@require_tokenizers
@require_tf
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						_A		:							Dict							       =		["""My friends are cool but they eat too many carbs."""]
						_A		:							int							       =		"""facebook/blenderbot-400M-distill"""







						@cached_property
						def 						__UpperCamelCase							(self       ):
											return BlenderbotTokenizer.from_pretrained(self.model_name       )







						@cached_property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
											return model







						@slow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					self.tokenizer(self.src_text   ,		return_tensors="""tf"""       )
											snake_case_					:		Tuple 					=					self.model.generate(
											    model_inputs.input_ids   ,		)
											snake_case_					:		List[str] 					=					self.tokenizer.batch_decode(generated_ids.numpy()   ,		skip_special_tokens=lowercase__       )[0]
											assert (
											    generated_words
											    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
											)










"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					num - 1
					snake_case_					:		List[str] 					=					0

					while s % 2 == 0:
										snake_case_					:		str 					=					s // 2
										t += 1

					for _ in range(5			):
										snake_case_					:		List[Any] 					=					random.randrange(2				,    num - 1			)
										snake_case_					:		Dict 					=					pow(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										if v != 1:
															snake_case_					:		int 					=					0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									snake_case_					:		str 					=					i + 1
																									snake_case_					:		int 					=					(v**2) % num
					return True







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					if num < 2:
										return False

					snake_case_					:		Dict 					=					[
					    2,
					    3,
					    5,
					    7,
					    1_1,
					    1_3,
					    1_7,
					    1_9,
					    2_3,
					    2_9,
					    3_1,
					    3_7,
					    4_1,
					    4_3,
					    4_7,
					    5_3,
					    5_9,
					    6_1,
					    6_7,
					    7_1,
					    7_3,
					    7_9,
					    8_3,
					    8_9,
					    9_7,
					    1_0_1,
					    1_0_3,
					    1_0_7,
					    1_0_9,
					    1_1_3,
					    1_2_7,
					    1_3_1,
					    1_3_7,
					    1_3_9,
					    1_4_9,
					    1_5_1,
					    1_5_7,
					    1_6_3,
					    1_6_7,
					    1_7_3,
					    1_7_9,
					    1_8_1,
					    1_9_1,
					    1_9_3,
					    1_9_7,
					    1_9_9,
					    2_1_1,
					    2_2_3,
					    2_2_7,
					    2_2_9,
					    2_3_3,
					    2_3_9,
					    2_4_1,
					    2_5_1,
					    2_5_7,
					    2_6_3,
					    2_6_9,
					    2_7_1,
					    2_7_7,
					    2_8_1,
					    2_8_3,
					    2_9_3,
					    3_0_7,
					    3_1_1,
					    3_1_3,
					    3_1_7,
					    3_3_1,
					    3_3_7,
					    3_4_7,
					    3_4_9,
					    3_5_3,
					    3_5_9,
					    3_6_7,
					    3_7_3,
					    3_7_9,
					    3_8_3,
					    3_8_9,
					    3_9_7,
					    4_0_1,
					    4_0_9,
					    4_1_9,
					    4_2_1,
					    4_3_1,
					    4_3_3,
					    4_3_9,
					    4_4_3,
					    4_4_9,
					    4_5_7,
					    4_6_1,
					    4_6_3,
					    4_6_7,
					    4_7_9,
					    4_8_7,
					    4_9_1,
					    4_9_9,
					    5_0_3,
					    5_0_9,
					    5_2_1,
					    5_2_3,
					    5_4_1,
					    5_4_7,
					    5_5_7,
					    5_6_3,
					    5_6_9,
					    5_7_1,
					    5_7_7,
					    5_8_7,
					    5_9_3,
					    5_9_9,
					    6_0_1,
					    6_0_7,
					    6_1_3,
					    6_1_7,
					    6_1_9,
					    6_3_1,
					    6_4_1,
					    6_4_3,
					    6_4_7,
					    6_5_3,
					    6_5_9,
					    6_6_1,
					    6_7_3,
					    6_7_7,
					    6_8_3,
					    6_9_1,
					    7_0_1,
					    7_0_9,
					    7_1_9,
					    7_2_7,
					    7_3_3,
					    7_3_9,
					    7_4_3,
					    7_5_1,
					    7_5_7,
					    7_6_1,
					    7_6_9,
					    7_7_3,
					    7_8_7,
					    7_9_7,
					    8_0_9,
					    8_1_1,
					    8_2_1,
					    8_2_3,
					    8_2_7,
					    8_2_9,
					    8_3_9,
					    8_5_3,
					    8_5_7,
					    8_5_9,
					    8_6_3,
					    8_7_7,
					    8_8_1,
					    8_8_3,
					    8_8_7,
					    9_0_7,
					    9_1_1,
					    9_1_9,
					    9_2_9,
					    9_3_7,
					    9_4_1,
					    9_4_7,
					    9_5_3,
					    9_6_7,
					    9_7_1,
					    9_7_7,
					    9_8_3,
					    9_9_1,
					    9_9_7,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int = 1_0_2_4			):



					"""simple docstring"""





					while True:
										snake_case_					:		Tuple 					=					random.randrange(2 ** (keysize - 1)				,    2 ** (keysize)			)
										if is_prime_low_num(SCREAMING_SNAKE_CASE__			):
															return num


if __name__ == "__main__":
			a_			   =					generate_large_prime()
			print(('''Prime number:''', num))
			print(('''is_prime_low_num:''', is_prime_low_num(num)))




