

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       				=				BlenderbotSmallConfig
			__lowerCamelCase       				=				{}
			__lowerCamelCase       				=				'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					,					)     ->  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    UpperCamelCase						(			self				)     ->  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(lowercase					,					lowercase					,					lowercase				)
							return config, inputs_dict


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__						=	TFBlenderbotSmallModel(config=lowercase				).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(lowercase					,					attention_mask=lowercase					,					head_mask=lowercase					,					use_cache=lowercase				)

							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(lowercase					,					attention_mask=lowercase				)[0]
							A__						=	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
							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(lowercase					,					lowercase					,					rtol=1e-3				)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,				SCREAMING_SNAKE_CASE_:							Optional[int]=None		,				SCREAMING_SNAKE_CASE_:							Optional[int]=None		,				SCREAMING_SNAKE_CASE_:							Dict=None		,				SCREAMING_SNAKE_CASE_:							List[str]=None		,				)	->					List[Any]:




				'''simple docstring'''





				if attention_mask is None:
								A__						=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_		,				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__  (    snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				(
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			__lowerCamelCase       				=				(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			__lowerCamelCase       				=				(
			    {
			        'conversational': TFBlenderbotSmallForConditionalGeneration,
			        'feature-extraction': TFBlenderbotSmallModel,
			        'summarization': TFBlenderbotSmallForConditionalGeneration,
			        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
			        'translation': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__lowerCamelCase       				=				True
			__lowerCamelCase       				=				False
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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

@require_tokenizers
@require_tf
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				[
			    '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       				=				'facebook/blenderbot_small-90M'


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






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


			@slow
			def    UpperCamelCase						(			self				)     ->  int:
							'''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=lowercase					,					)
							A__						=	self.tokenizer.batch_decode(generated_ids.numpy()					,					skip_special_tokens=lowercase				)[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"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in table:
    res += inp[i - 1]
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  return data[1:] + data[0]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in range(len(__snake_case    )    ):
    if a[i] == b[i]:
      res += "0"
    else:
      res += "1"
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       int('0b' + data[0] + data[-1]		,					2    )
  lowercase							    =       int('0b' + data[1:3]		,					2    )
  return bin(s[row][col]    )[2:]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Dict		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       message[:4]
  lowercase							    =       message[4:]
  lowercase							    =       apply_table(__snake_case		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  lowercase							    =       apply_sbox(__snake_case		,					temp[:4]    )  # noqa: E741
  lowercase							    =       apply_sbox(__snake_case		,					temp[4:]    )
  lowercase							    =       '0' * (2 - len(__snake_case    )) + l  # noqa: E741
  lowercase							    =       '0' * (2 - len(__snake_case    )) + r
  lowercase							    =       apply_table(l + r		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  return temp + right


if __name__ == "__main__":
    _UpperCamelCase		:  Dict						=		input('Enter 10 bit key: ')
    _UpperCamelCase		:  int						=		input('Enter 8 bit message: ')

    _UpperCamelCase		:  Optional[int]						=		[6, 3, 7, 4, 8, 5, 1_0, 9]
    _UpperCamelCase		:  Optional[Any]						=		[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
    _UpperCamelCase		:  str						=		[2, 4, 3, 1]
    _UpperCamelCase		:  List[Any]						=		[2, 6, 3, 1, 4, 8, 5, 7]
    _UpperCamelCase		:  List[str]						=		[4, 1, 3, 5, 7, 2, 8, 6]
    _UpperCamelCase		:  int						=		[4, 1, 2, 3, 2, 3, 4, 1]
    _UpperCamelCase		:  Optional[Any]						=		[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    _UpperCamelCase		:  Optional[int]						=		[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    _UpperCamelCase		:  str						=		apply_table(key, paa_table)
    _UpperCamelCase		:  Union[str, Any]						=		temp[:5]
    _UpperCamelCase		:  Dict						=		temp[5:]
    _UpperCamelCase		:  List[Any]						=		left_shift(left)
    _UpperCamelCase		:  str						=		left_shift(right)
    _UpperCamelCase		:  int						=		apply_table(left + right, pa_table)
    _UpperCamelCase		:  Optional[int]						=		left_shift(left)
    _UpperCamelCase		:  Any						=		left_shift(right)
    _UpperCamelCase		:  str						=		left_shift(left)
    _UpperCamelCase		:  Tuple						=		left_shift(right)
    _UpperCamelCase		:  Union[str, Any]						=		apply_table(left + right, pa_table)

    # encryption
    _UpperCamelCase		:  Tuple						=		apply_table(message, IP)
    _UpperCamelCase		:  Any						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  List[str]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    _UpperCamelCase		:  List[Any]						=		apply_table(CT, IP)
    _UpperCamelCase		:  Optional[Any]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  str						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)


