






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class    _UpperCAmelCase			:




   """simple docstring"""

   snake_case    				=						PegasusConfig
   snake_case    				=						{}
   snake_case    				=						'''gelu'''

   def __init__(		self :	Tuple      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Optional[Any]=13      ,      __UpperCAmelCase :	int=7      ,      __UpperCAmelCase :	List[str]=True      ,      __UpperCAmelCase :	str=False      ,      __UpperCAmelCase :	Union[str, Any]=99      ,      __UpperCAmelCase :	Tuple=32      ,      __UpperCAmelCase :	Tuple=2      ,      __UpperCAmelCase :	int=4      ,      __UpperCAmelCase :	Tuple=37      ,      __UpperCAmelCase :	Optional[int]=0.1      ,      __UpperCAmelCase :	Optional[int]=0.1      ,      __UpperCAmelCase :	List[str]=40      ,      __UpperCAmelCase :	Tuple=2      ,      __UpperCAmelCase :	Optional[int]=1      ,      __UpperCAmelCase :	Any=0      ,      ):





         '''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 						lowerCAmelCase (		self :	List[Any]		):





         '''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_pegasus_inputs_dict(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)
         return config, inputs_dict

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	Optional[int]      ,      __UpperCAmelCase :	int		):





         '''simple docstring'''





         _A			     =	TFPegasusModel(config=__UpperCAmelCase		).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(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase      ,      head_mask=__UpperCAmelCase      ,      use_cache=__UpperCAmelCase		)

         _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(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase		)[0]
         _A			     =	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
         _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(__UpperCAmelCase      ,      __UpperCAmelCase      ,      rtol=1E-3		)





def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  )       ->						Union[str, Any]:

      '''simple docstring'''


      if attention_mask is None:
            _A			     =	tf.cast(tf.math.not_equal(__lowercase   ,  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    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   snake_case    				=						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   snake_case    				=						(
       {
           '''conversational''': TFPegasusForConditionalGeneration,
           '''feature-extraction''': TFPegasusModel,
           '''summarization''': TFPegasusForConditionalGeneration,
           '''text2text-generation''': TFPegasusForConditionalGeneration,
           '''translation''': TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   snake_case    				=						True
   snake_case    				=						False
   snake_case    				=						False

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A			     =	TFPegasusModelTester(self		)
         _A			     =	ConfigTester(self      ,      config_class=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         _A			     =	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase		)





@require_sentencepiece
@require_tokenizers
@require_tf
class    _UpperCAmelCase			(     unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						[
       ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
       ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
   ]
   snake_case    				=						[
       '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
       ''' reduce the risk of wildfires.''',
       '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
   ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
   snake_case    				=						'''google/pegasus-xsum'''

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





         '''simple docstring'''





         return AutoTokenizer.from_pretrained(self.model_name		)

   @cached_property
   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	List[Any]      ,      **__UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	self.translate_src_text(**__UpperCAmelCase		)
         assert self.expected_text == generated_words

   def 						lowerCAmelCase (		self :	Dict      ,      **__UpperCAmelCase :	Optional[int]		):





         '''simple docstring'''





         _A			     =	self.tokenizer(self.src_text      ,      **__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      return_tensors="tf"		)
         _A			     =	self.model.generate(
             model_inputs.input_ids      ,      attention_mask=model_inputs.attention_mask      ,      num_beams=2      ,      use_cache=__UpperCAmelCase      ,      )
         _A			     =	self.tokenizer.batch_decode(generated_ids.numpy()      ,      skip_special_tokens=__UpperCAmelCase		)
         return generated_words

   @slow
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         self._assert_generated_batch_equal_expected()








import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =data
     a__  =[0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]



   @staticmethod
   def  _UpperCAmelCase (				lowercase_,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     return ((n << b) | (n >> (32 - b))) & 0Xffffffff



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =b'''\x80''' + b'''\x00''' * (63 - (len(self.data       ) + 8) % 64)
     a__  =self.data + padding + struct.pack('''>Q''',    8 * len(self.data       )       )
     return padded_data



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     return [
         self.padded_data[i : i + 64] for i in range(0,    len(self.padded_data       ),    64       )
     ]



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[Any]:






     """simple docstring"""






     a__  =list(struct.unpack('''>16L''',    lowercase_       )       ) + [0] * 64
     for i in range(16,    80       ):
       a__  =self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),    1       )
     return w



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.padding()
     a__  =self.split_blocks()
     for block in self.blocks:
       a__  =self.expand_block(lowercase_       )
       a__,      a__,      a__,      a__,      a__  =self.h
       for i in range(0,    80       ):
         if 0 <= i < 20:
           a__  =(b & c) | ((~b) & d)
           a__  =0X5a827999
         elif 20 <= i < 40:
           a__  =b ^ c ^ d
           a__  =0X6ed9eba1
         elif 40 <= i < 60:
           a__  =(b & c) | (b & d) | (c & d)
           a__  =0X8f1bbcdc
         elif 60 <= i < 80:
           a__  =b ^ c ^ d
           a__  =0Xca62c1d6
         a__,      a__,      a__,      a__,      a__  =(
             self.rotate(lowercase_,    5       ) + f + e + k + expanded_block[i] & 0Xffffffff,
             a,
             self.rotate(lowercase_,    30       ),
             c,
             d,
         )
       a__  =(
           self.h[0] + a & 0Xffffffff,
           self.h[1] + b & 0Xffffffff,
           self.h[2] + c & 0Xffffffff,
           self.h[3] + d & 0Xffffffff,
           self.h[4] + e & 0Xffffffff,
       )
     return ("{:08x}" * 5).format(*self.h       )





def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =b'''Test String'''
  assert SHAaHash(_A							).final_hash() == hashlib.shaa(_A							).hexdigest()  # noqa: S324




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =argparse.ArgumentParser(description='''Process some strings or files'''							)
  parser.add_argument(
      '''--string'''	,      dest='''input_string'''	,      default='''Hello World!! Welcome to Cryptography'''	,      help='''Hash the string'''	,      )
  parser.add_argument('''--file'''	,      dest='''input_file'''	,      help='''Hash contents of a file'''							)
  a__  =parser.parse_args()
  a__  =args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file	,      '''rb'''							) as f:
      a__  =f.read()
  else:
    a__  =bytes(_A	,      '''utf-8'''							)
  print(SHAaHash(_A							).final_hash()							)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod()
