





def        __lowerCamelCase (							__a       :int = 3   ,					__a       :int = 7   ,					__a       :int = 1_0_0_0_0_0_0   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       1

		for current_denominator in range(1   ,					limit + 1   ):
				A__  				=       current_denominator * numerator // denominator
				if current_denominator % denominator == 0:
						current_numerator -= 1
				if current_numerator * max_denominator > current_denominator * max_numerator:
						A__  				=       current_numerator
						A__  				=       current_denominator
		return max_numerator


if __name__ == "__main__":
					print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))










from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel


@require_tf
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[Any]          =   BlenderbotSmallConfig
			__lowerCamelCase     :	Optional[Any]          =   {}
			__lowerCamelCase     :	List[Any]          =   '''gelu'''






			def __init__(      self       :   Dict							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   List[str]=13							,     __lowerCAmelCase       :   List[Any]=7							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   List[Any]=False							,     __lowerCAmelCase       :   Union[str, Any]=99							,     __lowerCAmelCase       :   Union[str, Any]=32							,     __lowerCAmelCase       :   Any=2							,     __lowerCAmelCase       :   Optional[Any]=4							,     __lowerCAmelCase       :   Tuple=37							,     __lowerCAmelCase       :   List[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   List[str]=20							,     __lowerCAmelCase       :   Union[str, Any]=2							,     __lowerCAmelCase       :   Dict=1							,     __lowerCAmelCase       :   int=0							,     )     ->     Any:







					"""simple docstring"""






					A__  				=       parent
					A__  				=       batch_size
					A__  				=       seq_length
					A__  				=       is_training
					A__  				=       use_labels
					A__  				=       vocab_size
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       intermediate_size

					A__  				=       hidden_dropout_prob
					A__  				=       attention_probs_dropout_prob
					A__  				=       max_position_embeddings
					A__  				=       eos_token_id
					A__  				=       pad_token_id
					A__  				=       bos_token_id






			def        a_	(      self       :   Optional[Any]       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size       )
					A__  				=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )							,     1       )
					A__  				=       tf.concat([input_ids, eos_tensor]							,     axis=1       )

					A__  				=       ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size       )

					A__  				=       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							,     )
					A__  				=       prepare_blenderbot_small_inputs_dict(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )
					return config, inputs_dict







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







					"""simple docstring"""






					A__  				=       TFBlenderbotSmallModel(config=__lowerCAmelCase       ).get_decoder()
					A__  				=       inputs_dict["""input_ids"""]

					A__  				=       input_ids[:1, :]
					A__  				=       inputs_dict["""attention_mask"""][:1, :]
					A__  				=       inputs_dict["""head_mask"""]
					A__  				=       1

					# first forward pass
					A__  				=       model(__lowerCAmelCase							,     attention_mask=__lowerCAmelCase							,     head_mask=__lowerCAmelCase							,     use_cache=__lowerCAmelCase       )

					A__				,      A__  				=       outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					A__  				=       ids_tensor((self.batch_size, 3)							,     config.vocab_size       )
					A__  				=       tf.cast(ids_tensor((self.batch_size, 3)							,     2       )							,     tf.inta       )

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

					A__  				=       model(__lowerCAmelCase							,     attention_mask=__lowerCAmelCase       )[0]
					A__  				=       model(__lowerCAmelCase							,     attention_mask=__lowerCAmelCase							,     past_key_values=__lowerCAmelCase       )[0]

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

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

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







def        __lowerCamelCase (							__a       :Dict   ,					__a       :Tuple   ,					__a       :List[Any]   ,					__a       :List[str]=None   ,					__a       :List[Any]=None   ,					__a       :Optional[Any]=None   ,					__a       :List[str]=None   ,					__a       :int=None   ,					)		->    Optional[Any]:






		"""simple docstring"""

		if attention_mask is None:
				A__  				=       tf.cast(tf.math.not_equal(__a   ,					config.pad_token_id   )   ,					tf.inta   )
		if decoder_attention_mask is None:
				A__  				=       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:
				A__  				=       tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
		if decoder_head_mask is None:
				A__  				=       tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
		if cross_attn_head_mask is None:
				A__  				=       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   A      (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					unittest.TestCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   (
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			__lowerCamelCase     :	List[Any]          =   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			__lowerCamelCase     :	Tuple          =   (
			    {
			        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
			        '''feature-extraction''': TFBlenderbotSmallModel,
			        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
			        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
			        '''translation''': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__lowerCamelCase     :	Dict          =   True
			__lowerCamelCase     :	Optional[Any]          =   False
			__lowerCamelCase     :	Tuple          =   False






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







					"""simple docstring"""






					A__  				=       TFBlenderbotSmallModelTester(self       )
					A__  				=       ConfigTester(self							,     config_class=__lowerCAmelCase       )






			def        a_	(      self       :   List[str]       )     ->     int:







					"""simple docstring"""






					self.config_tester.run_common_tests()







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







					"""simple docstring"""






					A__  				=       self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase       )


@require_tokenizers
@require_tf
class   A      (unittest.TestCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[str]          =   [
			    '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
			    '''  i\'m going to throw up.\nand why is that?'''
			]
			__lowerCamelCase     :	Optional[int]          =   '''facebook/blenderbot_small-90M'''






			@cached_property
			def        a_	(      self       :   Optional[int]       )     ->     List[str]:







					"""simple docstring"""






					return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""       )






			@cached_property
			def        a_	(      self       :   List[str]       )     ->     List[str]:







					"""simple docstring"""






					A__  				=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
					return model







			@slow
			def        a_	(      self       :   int       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       self.tokenizer(self.src_text							,     return_tensors="""tf"""       )
					A__  				=       self.model.generate(
					    model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     use_cache=__lowerCAmelCase							,     )
					A__  				=       self.tokenizer.batch_decode(generated_ids.numpy()							,     skip_special_tokens=__lowerCAmelCase       )[0]
					assert generated_words in (
					    "i don't know. i just feel like i'm going to throw up. it's not fun.",
					    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
					    "i'm not sure. i just feel like i've been in a bad situation.",
					)



