

'''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 lowerCamelCase			:



			'''simple docstring'''






			__snake_case        =	BlenderbotConfig
			__snake_case        =	{}
			__snake_case        =	'gelu'


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





									'''simple docstring'''
									A__					:       Optional[int]     =parent
									A__					:       int     =batch_size
									A__					:       Union[str, Any]     =seq_length
									A__					:       Optional[Any]     =is_training
									A__					:       Any     =use_labels
									A__					:       Union[str, Any]     =vocab_size
									A__					:       Any     =hidden_size
									A__					:       Optional[int]     =num_hidden_layers
									A__					:       Tuple     =num_attention_heads
									A__					:       Tuple     =intermediate_size

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


			def lowercase__						(				self	:						Dict			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size			)
									A__					:       Tuple     =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)		,      1			)
									A__					:       Optional[int]     =tf.concat([input_ids, eos_tensor]		,      axis=1			)

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

									A__					:       Union[str, Any]     =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__					:       Any     =prepare_blenderbot_inputs_dict(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)
									return config, inputs_dict



			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						Optional[int]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[Any]     =TFBlenderbotModel(config=lowerCAmelCase_			).get_decoder()
									A__					:       Union[str, Any]     =inputs_dict["""input_ids"""]

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

									# first forward pass
									A__					:       str     =model(lowerCAmelCase_		,      attention_mask=lowerCAmelCase_		,      head_mask=lowerCAmelCase_		,      use_cache=lowerCAmelCase_			)

									A__   ,						A__					:       Optional[Any]     =outputs.to_tuple()

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

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

									A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      attention_mask=lowerCAmelCase_			)[0]
									A__					:       List[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
									A__					:       Tuple     =int(ids_tensor((1,)		,      output_from_past.shape[-1]			)			)
									A__					:       Optional[int]     =output_from_no_past[:, -3:, random_slice_idx]
									A__					:       Optional[Any]     =output_from_past[:, :, random_slice_idx]

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


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






						"""simple docstring"""





						if attention_mask is None:
												A__					:       List[str]     =tf.cast(tf.math.not_equal(__snake_case,		config.pad_token_id	),		tf.inta	)
						if decoder_attention_mask is None:
												A__					:       Dict     =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__					:       Dict     =tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
						if decoder_head_mask is None:
												A__					:       Any     =tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
						if cross_attn_head_mask is None:
												A__					:       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 lowerCamelCase			(  lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
			__snake_case        =	(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
			__snake_case        =	(
			    {
			        'conversational': TFBlenderbotForConditionalGeneration,
			        'feature-extraction': TFBlenderbotModel,
			        'summarization': TFBlenderbotForConditionalGeneration,
			        'text2text-generation': TFBlenderbotForConditionalGeneration,
			        'translation': TFBlenderbotForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__snake_case        =	True
			__snake_case        =	False
			__snake_case        =	False


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Tuple     =TFBlenderbotModelTester(self			)
									A__					:       int     =ConfigTester(self		,      config_class=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		Tuple:





									'''simple docstring'''
									self.config_tester.run_common_tests()



			def lowercase__						(				self	:						Optional[Any]			)      ->		str:





									'''simple docstring'''
									A__					:       int     =self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_			)



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



			'''simple docstring'''






			__snake_case        =	['My friends are cool but they eat too many carbs.']
			__snake_case        =	'facebook/blenderbot-400M-distill'


			@cached_property
			def lowercase__						(				self	:						str			)      ->		Any:





									'''simple docstring'''
									return BlenderbotTokenizer.from_pretrained(self.model_name			)


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





									'''simple docstring'''
									A__					:       str     =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
									return model



			@slow
			def lowercase__						(				self	:						Optional[Any]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.tokenizer(self.src_text		,      return_tensors="""tf"""			)
									A__					:       Dict     =self.model.generate(
									    model_inputs.input_ids		,      )
									A__					:       List[Any]     =self.tokenizer.batch_decode(generated_ids.numpy()		,      skip_special_tokens=lowerCAmelCase_			)[0]
									assert (
									    generated_words
									    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
									)




'''simple docstring'''



from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 						__lowerCamelCase	(       __snake_case					:		int	)	->							int:






						"""simple docstring"""





						A__					:       List[Any]     =prime_factors(__snake_case	)
						if is_square_free(__snake_case	):
												return -1 if len(__snake_case	) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

