




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'''







 lowercase_							       =       BlenderbotConfig
 lowercase_							       =       {}
 lowercase_							       =       "gelu"



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

       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     eos_token_id
       SCREAMING_SNAKE_CASE_      		=     pad_token_id
       SCREAMING_SNAKE_CASE_      		=     bos_token_id



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size		)
       SCREAMING_SNAKE_CASE_      		=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)							,							1		)
       SCREAMING_SNAKE_CASE_      		=     tf.concat([input_ids, eos_tensor]							,							axis=1		)

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

       SCREAMING_SNAKE_CASE_      		=     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_      		=     prepare_blenderbot_inputs_dict(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       return config, inputs_dict




 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     TFBlenderbotModel(config=_lowerCAmelCase		).get_decoder()
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE_      		=     input_ids[:1, :]
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE_      		=     1

       # first forward pass
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							use_cache=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     outputs.to_tuple()

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

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

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     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_      		=     int(ids_tensor((1,)							,							output_from_past.shape[-1]		)		)
       SCREAMING_SNAKE_CASE_      		=     output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE_      		=     output_from_past[:, :, random_slice_idx]

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


def    UpperCAmelCase_				( __UpperCAmelCase		:      int     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     __UpperCAmelCase		:      List[Any]=None     ,     __UpperCAmelCase		:      str=None     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     __UpperCAmelCase		:      Dict=None     ,     )   ->					str:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.cast(tf.math.not_equal(__UpperCAmelCase     ,     config.pad_token_id				)     ,     tf.inta				)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     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_      		=     tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     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_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 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  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     TFBlenderbotModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str		):
       self.config_tester.run_common_tests()




 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     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'''







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



 @cached_property
 def  lowerCAmelCase_    (				self    :						List[str]		):
       return BlenderbotTokenizer.from_pretrained(self.model_name		)



 @cached_property
 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
       return model




 @slow
 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer(self.src_text							,							return_tensors='tf'		)
       SCREAMING_SNAKE_CASE_      		=     self.model.generate(
           model_inputs.input_ids							,							)
       SCREAMING_SNAKE_CASE_      		=     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?"
       )

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCamelCase_						:     List[Any]	       =							3



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			int:






						'''simple docstring'''

						print("Generating primitive root of p"  )
						while True:
												_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )
												if pow(_UpperCAmelCase ,			2 ,			_UpperCAmelCase  ) == 1:
																		continue
												if pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) == 1:
																		continue
												return g



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			tuple[tuple[int, int, int, int], tuple[int, int]]:






						'''simple docstring'''

						print("Generating prime p..."  )
						_lowercase       :	Optional[int]           =   rabin_miller.generate_large_prime(_UpperCAmelCase  )  # select large prime number.
						_lowercase       :	Dict           =   primitive_root(_UpperCAmelCase  )  # one primitive root on modulo p.
						_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )  # private_key -> have to be greater than 2 for safety.
						_lowercase       :	Tuple           =   cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) ,			_UpperCAmelCase  )

						_lowercase       :	List[Any]           =   (key_size, e_a, e_a, p)
						_lowercase       :	Union[str, Any]           =   (key_size, d)

						return public_key, private_key



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			None:






						'''simple docstring'''

						if os.path.exists(f"""{name}_pubkey.txt"""  ) or os.path.exists(f"""{name}_privkey.txt"""  ):
												print("\nWARNING:"  )
												print(
												    f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_lowercase      ,    _lowercase       :	Optional[Any]           =   generate_key(_UpperCAmelCase  )
						print(f"""\nWriting public key to file {name}_pubkey.txt..."""  )
						with open(f"""{name}_pubkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""  )

						print(f"""Writing private key to file {name}_privkey.txt..."""  )
						with open(f"""{name}_privkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{private_key[0]},{private_key[1]}"""  )



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						print("Making key files..."  )
						make_key_files("elgamal" ,			2048  )
						print("Key files generation successful"  )


if __name__ == "__main__":
							main()




