



'''simple docstring'''




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 						_UpperCamelCase  :


			'''simple docstring'''

			_A      :		int			    =		BlenderbotSmallConfig
			_A      :		int			    =		{}
			_A      :		Dict			    =		'''gelu'''

			def __init__(							self     : Tuple							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : Tuple=1_3							,     lowerCAmelCase__     : int=7							,     lowerCAmelCase__     : Optional[int]=True							,     lowerCAmelCase__     : Dict=False							,     lowerCAmelCase__     : Union[str, Any]=9_9							,     lowerCAmelCase__     : str=3_2							,     lowerCAmelCase__     : int=2							,     lowerCAmelCase__     : str=4							,     lowerCAmelCase__     : List[str]=3_7							,     lowerCAmelCase__     : str=0.1							,     lowerCAmelCase__     : int=0.1							,     lowerCAmelCase__     : Tuple=2_0							,     lowerCAmelCase__     : Dict=2							,     lowerCAmelCase__     : str=1							,     lowerCAmelCase__     : List[Any]=0							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		parent
					__SCREAMING_SNAKE_CASE       :		str           =		batch_size
					__SCREAMING_SNAKE_CASE       :		List[str]           =		seq_length
					__SCREAMING_SNAKE_CASE       :		str           =		is_training
					__SCREAMING_SNAKE_CASE       :		Any           =		use_labels
					__SCREAMING_SNAKE_CASE       :		Tuple           =		vocab_size
					__SCREAMING_SNAKE_CASE       :		str           =		hidden_size
					__SCREAMING_SNAKE_CASE       :		Any           =		num_hidden_layers
					__SCREAMING_SNAKE_CASE       :		Dict           =		num_attention_heads
					__SCREAMING_SNAKE_CASE       :		Tuple           =		intermediate_size

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		max_position_embeddings
					__SCREAMING_SNAKE_CASE       :		str           =		eos_token_id
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		pad_token_id
					__SCREAMING_SNAKE_CASE       :		Dict           =		bos_token_id

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size						)
					__SCREAMING_SNAKE_CASE       :		int           =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)							,     1						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		tf.concat([input_ids, eos_tensor]							,     axis=1						)

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

					__SCREAMING_SNAKE_CASE       :		Dict           =		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							,     )
					__SCREAMING_SNAKE_CASE       :		int           =		prepare_blenderbot_small_inputs_dict(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
					return config, inputs_dict



			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		TFBlenderbotSmallModel(config=lowerCAmelCase__						).get_decoder()
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		inputs_dict["""input_ids"""]

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		input_ids[:1, :]
					__SCREAMING_SNAKE_CASE       :		Dict           =		inputs_dict["""attention_mask"""][:1, :]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		inputs_dict["""head_mask"""]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		1

					# first forward pass
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     head_mask=lowerCAmelCase__							,     use_cache=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		outputs.to_tuple()

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

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

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__						)[0]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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
					__SCREAMING_SNAKE_CASE       :		Any           =		int(ids_tensor((1,)							,     output_from_past.shape[-1]						)						)
					__SCREAMING_SNAKE_CASE       :		str           =		output_from_no_past[:, -3:, random_slice_idx]
					__SCREAMING_SNAKE_CASE       :		Dict           =		output_from_past[:, :, random_slice_idx]

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


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]       ,      _lowerCamelCase:   Union[str, Any]       ,      _lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   List[str]=None       ,      _lowerCamelCase:   Tuple=None       ,      _lowerCamelCase:   Optional[Any]=None       ,      _lowerCamelCase:   str=None       ,      _lowerCamelCase:   str=None       ,      ):
		if attention_mask is None:
				__SCREAMING_SNAKE_CASE       :		int           =		tf.cast(tf.math.not_equal(_lowerCamelCase       ,      config.pad_token_id					)       ,      tf.inta					)
		if decoder_attention_mask is None:
				__SCREAMING_SNAKE_CASE       :		int           =		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:
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
		if decoder_head_mask is None:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
		if cross_attn_head_mask is None:
				__SCREAMING_SNAKE_CASE       :		Union[str, 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 						_UpperCamelCase  (							lowerCamelCase__    ,					lowerCamelCase__    ,					unittest.TestCase    ):


			'''simple docstring'''

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

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		TFBlenderbotSmallModelTester(self						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ConfigTester(self							,     config_class=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					self.config_tester.run_common_tests()



			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__						)






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


			'''simple docstring'''

			_A      :		Any			    =		[
			    '''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?'''
			]
			_A      :		Tuple			    =		'''facebook/blenderbot_small-90M'''

			@cached_property
			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






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

			@cached_property
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
					return model



			@slow
			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.tokenizer(self.src_text							,     return_tensors="""tf"""						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.model.generate(
					    model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     use_cache=lowerCAmelCase__							,     )
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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.",
					)




'''simple docstring'''




from math import ceil, sqrt


def 				lowerCAmelCase_   (	_lowerCamelCase:   int = 1_00_00_00					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0

		for outer_width in range(3       ,      (limit // 4) + 2					):
				if outer_width**2 > limit:
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		max(ceil(sqrt(outer_width**2 - limit					)					)       ,      1					)
				else:
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		1
				if (outer_width - hole_width_lower_bound) % 2:
						hole_width_lower_bound += 1

				answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

		return answer


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