



'''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     UpperCamelCase_				:
	lowercase    =				BlenderbotConfig
	lowercase    =				{}
	lowercase    =				'gelu'





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

								UpperCAmelCase  :	str							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Tuple							  =	max_position_embeddings
								UpperCAmelCase  :	Tuple							  =	eos_token_id
								UpperCAmelCase  :	Any							  =	pad_token_id
								UpperCAmelCase  :	Dict							  =	bos_token_id





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

								UpperCAmelCase  :	Tuple							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size					)

								UpperCAmelCase  :	List[str]							  =	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	,	)
								UpperCAmelCase  :	int							  =	prepare_blenderbot_inputs_dict(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_					)
								return config, inputs_dict






	def 				_lowercase(						self	,	A	,	A					)    ->    Dict:
								UpperCAmelCase  :	List[Any]							  =	TFBlenderbotModel(config=UpperCamelCase_					).get_decoder()
								UpperCAmelCase  :	List[Any]							  =	inputs_dict["""input_ids"""]

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

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

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

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

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

								UpperCAmelCase  :	Union[str, Any]							  =	model(UpperCamelCase_	,	attention_mask=UpperCamelCase_					)[0]
								UpperCAmelCase  :	Dict							  =	model(UpperCamelCase_	,	attention_mask=UpperCamelCase_	,	past_key_values=UpperCamelCase_					)[0]

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

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

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




def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       )   ->    Optional[int]:



							if attention_mask is None:
														UpperCAmelCase  :	Optional[int]							  =	tf.cast(tf.math.not_equal(A__			,       config.pad_token_id	)			,       tf.inta	)
							if decoder_attention_mask is None:
														UpperCAmelCase  :	str							  =	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:
														UpperCAmelCase  :	Union[str, Any]							  =	tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														UpperCAmelCase  :	List[str]							  =	tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														UpperCAmelCase  :	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     UpperCamelCase_				(							__lowerCamelCase   ,  __lowerCamelCase   ,  unittest.TestCase    ):
	lowercase    =				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
	lowercase    =				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
	lowercase    =				(
	    {
	        'conversational': TFBlenderbotForConditionalGeneration,
	        'feature-extraction': TFBlenderbotModel,
	        'summarization': TFBlenderbotForConditionalGeneration,
	        'text2text-generation': TFBlenderbotForConditionalGeneration,
	        'translation': TFBlenderbotForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowercase    =				True
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Dict							  =	TFBlenderbotModelTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=UpperCamelCase_					)





	def 				_lowercase(						self					)    ->    List[Any]:
								self.config_tester.run_common_tests()






	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_					)




@require_tokenizers
@require_tf
class     UpperCamelCase_				(							unittest.TestCase    ):
	lowercase    =				['My friends are cool but they eat too many carbs.']
	lowercase    =				'facebook/blenderbot-400M-distill'





	@cached_property
	def 				_lowercase(						self					)    ->    Optional[int]:
								return BlenderbotTokenizer.from_pretrained(self.model_name					)





	@cached_property
	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Dict							  =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
								return model






	@slow
	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Tuple							  =	self.tokenizer(self.src_text	,	return_tensors="""tf"""					)
								UpperCAmelCase  :	Optional[Any]							  =	self.model.generate(
								    model_inputs.input_ids	,	)
								UpperCAmelCase  :	Tuple							  =	self.tokenizer.batch_decode(generated_ids.numpy()	,	skip_special_tokens=UpperCamelCase_					)[0]
								assert (
								    generated_words
								    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
								)









'''simple docstring'''




a     :    Tuple      						=					[sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	str							  =	0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
														number //= 1_0_0_0_0_0

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a     :    list[bool | None]      						=					[None] * 1_0_0_0_0_0_0_0
a     :    Optional[Any]      						=					True
a     :    List[Any]      						=					False



def    __lowerCamelCase						(      _lowercase	)   ->    bool:

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							UpperCAmelCase  :	List[str]							  =	chain(next_number(_lowercase	)	)
							UpperCAmelCase  :	Tuple							  =	number_chain

							while number < 1_0_0_0_0_0_0_0:
														UpperCAmelCase  :	List[str]							  =	number_chain
														number *= 1_0

							return number_chain



def    __lowerCamelCase						(      _lowercase = 1_0_0_0_0_0_0_0	)   ->    int:
							for i in range(1			,       _lowercase	):
														if CHAINS[i] is None:
																					chain(i + 1	)

							return CHAINS[:number].count(_lowercase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'''{solution() = }''')

