


'''simple docstring'''





from __future__ import annotations

import random
import string







class    lowerCamelCase__     :





     def __init__(self	,  UpperCAmelCase = None )  ->     Union[str, Any]:
      _lowercase							   =passcode or self.__passcode_creator()
      _lowercase							   =self.__make_key_list()
      _lowercase							   =self.__make_shift_key()





     def __str__(self )  ->     str:
      return "".join(self.__passcode )





     def   __A      (self	,  UpperCAmelCase )  ->     Dict:






      for i in range(1	,  len(UpperCAmelCase )	,  2 ):
       iterlist[i] *= -1
      return iterlist





     def   __A      (self )  ->     Union[str, Any]:
      _lowercase							   =string.ascii_letters + string.digits
      _lowercase							   =[random.choice(UpperCAmelCase ) for _ in range(random.randint(1_0	,  2_0 ) )]
      return password





     def   __A      (self )  ->     str:
      _lowercase							   =(
          string.ascii_letters + string.digits + string.punctuation + " \t\n"
      )

      _lowercase							   =[]

      # creates points known as breakpoints to break the key_list_options at those
      # points and pivot each substring
      _lowercase							   =sorted(set(self.__passcode ) )
      _lowercase							   =[]

      # algorithm for creating a new shuffled list, keys_l, out of key_list_options
      for i in key_list_options:
       temp_list.extend(UpperCAmelCase )

       # checking breakpoints at which to pivot temporary sublist and add it into
       # keys_l
       if i in breakpoints or i == key_list_options[-1]:
        keys_l.extend(temp_list[::-1] )
        temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
      return keys_l





     def   __A      (self )  ->     Optional[Any]:
      _lowercase							   =sum(self.__neg_pos([ord(UpperCAmelCase ) for x in self.__passcode] ) )
      return num if num > 0 else len(self.__passcode )





     def   __A      (self	,  UpperCAmelCase )  ->     Dict:
      _lowercase							   =""

      # decoding shift like Caesar cipher algorithm implementing negative shift or
      # reverse shift or left shift
      for i in encoded_message:
       _lowercase							   =self.__key_list.index(UpperCAmelCase )
       decoded_message += self.__key_list[
           (position - self.__shift_key) % -len(self.__key_list )
       ]

      return decoded_message





     def   __A      (self	,  UpperCAmelCase )  ->     List[str]:
      _lowercase							   =""

      # encoding shift like Caesar cipher algorithm implementing positive shift or
      # forward shift or right shift
      for i in plaintext:
       _lowercase							   =self.__key_list.index(UpperCAmelCase )
       encoded_message += self.__key_list[
           (position + self.__shift_key) % len(self.__key_list )
       ]

      return encoded_message



def        UpperCAmelCase_      (							__snake_case = "Hello, this is a modified Caesar cipher"		)  ->    str:







 """simple docstring"""


 _lowercase							   =ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(__lowerCAmelCase		)		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch







class    lowerCamelCase__     :





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase=1_4	,  UpperCAmelCase=7	,  UpperCAmelCase=True	,  UpperCAmelCase=True	,  UpperCAmelCase=False	,  UpperCAmelCase=True	,  UpperCAmelCase=9_9	,  UpperCAmelCase=3_2	,  UpperCAmelCase=5	,  UpperCAmelCase=4	,  UpperCAmelCase=3_7	,  UpperCAmelCase="gelu"	,  UpperCAmelCase=0.1	,  UpperCAmelCase=0.1	,  UpperCAmelCase=5_1_2	,  UpperCAmelCase=0.02	,  )  ->     Any:
      _lowercase							   =parent
      _lowercase							   =batch_size
      _lowercase							   =seq_length
      _lowercase							   =is_training
      _lowercase							   =use_input_mask
      _lowercase							   =use_token_type_ids
      _lowercase							   =use_labels
      _lowercase							   =vocab_size
      _lowercase							   =hidden_size
      _lowercase							   =num_hidden_layers
      _lowercase							   =num_attention_heads
      _lowercase							   =intermediate_size
      _lowercase							   =hidden_act
      _lowercase							   =hidden_dropout_prob
      _lowercase							   =attention_probs_dropout_prob
      _lowercase							   =max_position_embeddings
      _lowercase							   =initializer_range
      _lowercase							   =None
      _lowercase							   =vocab_size - 1
      _lowercase							   =vocab_size - 1
      _lowercase							   =vocab_size - 1





     def   __A      (self )  ->     int:
      _lowercase							   =ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

      _lowercase							   =None
      if self.use_input_mask:
       _lowercase							   =random_attention_mask([self.batch_size, self.seq_length] )

      _lowercase							   =GPTaConfig(
          vocab_size=self.vocab_size	,  n_embd=self.hidden_size	,  n_layer=self.num_hidden_layers	,  n_head=self.num_attention_heads	,  n_positions=self.max_position_embeddings	,  use_cache=UpperCAmelCase	,  bos_token_id=self.bos_token_id	,  eos_token_id=self.eos_token_id	,  pad_token_id=self.pad_token_id	,  )

      return (config, input_ids, input_mask)





     def   __A      (self )  ->     List[Any]:
      _lowercase							   =self.prepare_config_and_inputs()
      _lowercase   ,					_lowercase   ,					_lowercase							   =config_and_inputs
      _lowercase							   ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict





     def   __A      (self )  ->     Tuple:
      _lowercase   ,					_lowercase   ,					_lowercase							   =self.prepare_config_and_inputs()

      _lowercase							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      _lowercase							   =ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Dict:
      _lowercase							   =2_0
      _lowercase							   =model_class_name(UpperCAmelCase )

      _lowercase							   =model.init_cache(input_ids.shape[0]	,  UpperCAmelCase )
      _lowercase							   =jnp.ones((input_ids.shape[0], max_decoder_length)	,  dtype='''i4''' )

      _lowercase							   =jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
      _lowercase							   =model(
          input_ids[:, :-1]	,  attention_mask=UpperCAmelCase	,  past_key_values=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,  dtype='''i4''' )
      _lowercase							   =model(
          input_ids[:, -1:]	,  attention_mask=UpperCAmelCase	,  past_key_values=outputs_cache.past_key_values	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =model(UpperCAmelCase )

      _lowercase							   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1e-3	,  msg=f"Max diff is {diff}" )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     List[str]:
      _lowercase							   =2_0
      _lowercase							   =model_class_name(UpperCAmelCase )

      _lowercase							   =jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]	,  axis=-1	,  )

      _lowercase							   =model.init_cache(input_ids.shape[0]	,  UpperCAmelCase )
      _lowercase							   =jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

      _lowercase							   =model(
          input_ids[:, :-1]	,  attention_mask=UpperCAmelCase	,  past_key_values=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )
      _lowercase							   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,  dtype='''i4''' )
      _lowercase							   =model(
          input_ids[:, -1:]	,  past_key_values=outputs_cache.past_key_values	,  attention_mask=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =model(UpperCAmelCase	,  attention_mask=UpperCAmelCase )

      _lowercase							   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1e-3	,  msg=f"Max diff is {diff}" )







@require_flax
class    lowerCamelCase__     ( lowerCAmelCase     ,							lowerCAmelCase     ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__       =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE__       =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()





     def   __A      (self )  ->     str:
      _lowercase							   =FlaxGPTaModelTester(self )





     def   __A      (self )  ->     Optional[int]:
      for model_class_name in self.all_model_classes:
       _lowercase   ,					_lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self )  ->     Dict:
      for model_class_name in self.all_model_classes:
       _lowercase   ,					_lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     @slow
     def   __A      (self )  ->     Dict:
      _lowercase							   =GPTaTokenizer.from_pretrained('''gpt2'''	,  pad_token='''</s>'''	,  padding_side='''left''' )
      _lowercase							   =tokenizer(['''Hello this is a long string''', '''Hey''']	,  return_tensors='''np'''	,  padding=UpperCAmelCase	,  truncation=UpperCAmelCase )

      _lowercase							   =FlaxGPTaLMHeadModel.from_pretrained('''gpt2''' )
      _lowercase							   =False
      _lowercase							   =model.config.eos_token_id

      _lowercase							   =jax.jit(model.generate )

      _lowercase							   =jit_generate(inputs['''input_ids''']	,  attention_mask=inputs['''attention_mask'''] ).sequences

      _lowercase							   =tokenizer.batch_decode(UpperCAmelCase	,  skip_special_tokens=UpperCAmelCase )

      _lowercase							   =[
          '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
          '''Hey, I\'m not sure if I\'m going to be able to do''',
      ]

      self.assertListEqual(UpperCAmelCase	,  UpperCAmelCase )





     @is_pt_flax_cross_test
     def   __A      (self )  ->     Optional[Any]:
      _lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        # prepare inputs
        _lowercase							   =self._prepare_for_class(UpperCAmelCase	,  UpperCAmelCase )
        _lowercase							   ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        _lowercase							   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
        _lowercase							   =getattr(UpperCAmelCase	,  UpperCAmelCase )

        _lowercase   ,					_lowercase							   =pt_inputs['''input_ids'''].shape
        _lowercase							   =np.random.randint(0	,  seq_length - 1	,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(UpperCAmelCase ):
         _lowercase							   =0
         _lowercase							   =1
         _lowercase							   =0
         _lowercase							   =1
        _lowercase							   =pt_model_class(UpperCAmelCase ).eval()
        _lowercase							   =model_class(UpperCAmelCase	,  dtype=jnp.floataa )

        _lowercase							   =convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,  UpperCAmelCase )
        _lowercase							   =fx_state

        with torch.no_grad():
         _lowercase							   =pt_model(**UpperCAmelCase ).to_tuple()

        _lowercase							   =fx_model(**UpperCAmelCase ).to_tuple()
        self.assertEqual(len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(UpperCAmelCase )
         _lowercase							   =model_class.from_pretrained(UpperCAmelCase	,  from_pt=UpperCAmelCase )

        _lowercase							   =fx_model_loaded(**UpperCAmelCase ).to_tuple()
        self.assertEqual(
            len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output_loaded, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output_loaded[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )





     @is_pt_flax_cross_test
     def   __A      (self )  ->     Optional[Any]:
      _lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        # prepare inputs
        _lowercase							   =self._prepare_for_class(UpperCAmelCase	,  UpperCAmelCase )
        _lowercase							   ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        _lowercase							   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
        _lowercase							   =getattr(UpperCAmelCase	,  UpperCAmelCase )

        _lowercase							   =pt_model_class(UpperCAmelCase ).eval()
        _lowercase							   =model_class(UpperCAmelCase	,  dtype=jnp.floataa )

        _lowercase							   =load_flax_weights_in_pytorch_model(UpperCAmelCase	,  fx_model.params )
        _lowercase   ,					_lowercase							   =pt_inputs['''input_ids'''].shape
        _lowercase							   =np.random.randint(0	,  seq_length - 1	,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(UpperCAmelCase ):
         _lowercase							   =0
         _lowercase							   =1
         _lowercase							   =0
         _lowercase							   =1

        # make sure weights are tied in PyTorch
        pt_model.tie_weights()

        with torch.no_grad():
         _lowercase							   =pt_model(**UpperCAmelCase ).to_tuple()

        _lowercase							   =fx_model(**UpperCAmelCase ).to_tuple()
        self.assertEqual(len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(UpperCAmelCase )
         _lowercase							   =pt_model_class.from_pretrained(UpperCAmelCase	,  from_flax=UpperCAmelCase )

        with torch.no_grad():
         _lowercase							   =pt_model_loaded(**UpperCAmelCase ).to_tuple()

        self.assertEqual(
            len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )





     @slow
     def   __A      (self )  ->     Union[str, Any]:
      for model_class_name in self.all_model_classes:
       _lowercase							   =model_class_name.from_pretrained('''gpt2'''	,  from_pt=UpperCAmelCase )
       _lowercase							   =model(np.ones((1, 1) ) )
       self.assertIsNotNone(UpperCAmelCase )

