def 		snake_case__  (		lowercase       ,							lowercase				):
   if not isinstance(lowercase       ,							lowercase				):
      raise TypeError("plaintext must be a string"				)
   if not isinstance(lowercase       ,							lowercase				):
      raise TypeError("key must be a string"				)

   if not plaintext:
      raise ValueError("plaintext is empty"				)
   if not key:
      raise ValueError("key is empty"				)

   key += plaintext
   lowerCAmelCase_:       str         =  plaintext.lower()
   lowerCAmelCase_:       int         =  key.lower()
   lowerCAmelCase_:       List[str]         =  0
   lowerCAmelCase_:       List[str]         =  0
   lowerCAmelCase_:       Optional[int]         =  ""
   while plaintext_iterator < len(lowercase				):
      if (
          ord(plaintext[plaintext_iterator]				) < 97
          or ord(plaintext[plaintext_iterator]				) > 122
      ):
         ciphertext += plaintext[plaintext_iterator]
         plaintext_iterator += 1
      elif ord(key[key_iterator]				) < 97 or ord(key[key_iterator]				) > 122:
         key_iterator += 1
      else:
         ciphertext += chr(
             (
                 (ord(plaintext[plaintext_iterator]				) - 97 + ord(key[key_iterator]				))
                 - 97
             )
             % 26
             + 97				)
         key_iterator += 1
         plaintext_iterator += 1
   return ciphertext
def 		snake_case__  (		lowercase       ,							lowercase				):
   if not isinstance(lowercase       ,							lowercase				):
      raise TypeError("ciphertext must be a string"				)
   if not isinstance(lowercase       ,							lowercase				):
      raise TypeError("key must be a string"				)

   if not ciphertext:
      raise ValueError("ciphertext is empty"				)
   if not key:
      raise ValueError("key is empty"				)

   lowerCAmelCase_:       Optional[Any]         =  key.lower()
   lowerCAmelCase_:       List[str]         =  0
   lowerCAmelCase_:       List[str]         =  0
   lowerCAmelCase_:       Dict         =  ""
   while ciphertext_iterator < len(lowercase				):
      if (
          ord(ciphertext[ciphertext_iterator]				) < 97
          or ord(ciphertext[ciphertext_iterator]				) > 122
      ):
         plaintext += ciphertext[ciphertext_iterator]
      else:
         plaintext += chr(
             (ord(ciphertext[ciphertext_iterator]				) - ord(key[key_iterator]				)) % 26
             + 97				)
         key += chr(
             (ord(ciphertext[ciphertext_iterator]				) - ord(key[key_iterator]				)) % 26
             + 97				)
         key_iterator += 1
      ciphertext_iterator += 1
   return plaintext


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 a     :			List[Any]					    =						int(input("""Type 1 to encrypt or 2 to decrypt:"""))
 if operation == 1:
  a     :			List[Any]					    =						input("""Typeplaintext to be encrypted:\n""")
  a     :			Optional[int]					    =						input("""Type the key:\n""")
  print(encrypt(plaintext, key))
 elif operation == 2:
  a     :			List[str]					    =						input("""Type the ciphertext to be decrypted:\n""")
  a     :			List[Any]					    =						input("""Type the key:\n""")
  print(decrypt(ciphertext, key))
 decrypt("""jsqqs avvwo""", """coffee""")
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 				_lowercase						:






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
       lowerCAmelCase_:       int         =  parent
       lowerCAmelCase_:       Tuple         =  batch_size
       lowerCAmelCase_:       int         =  seq_length
       lowerCAmelCase_:       Any         =  is_training
       lowerCAmelCase_:       Any         =  use_input_mask
       lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
       lowerCAmelCase_:       str         =  use_labels
       lowerCAmelCase_:       Tuple         =  vocab_size
       lowerCAmelCase_:       List[Any]         =  hidden_size
       lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
       lowerCAmelCase_:       List[str]         =  num_attention_heads
       lowerCAmelCase_:       int         =  intermediate_size
       lowerCAmelCase_:       Tuple         =  hidden_act
       lowerCAmelCase_:       Dict         =  hidden_dropout_prob
       lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
       lowerCAmelCase_:       Any         =  max_position_embeddings
       lowerCAmelCase_:       Dict         =  initializer_range
       lowerCAmelCase_:       List[Any]         =  None
       lowerCAmelCase_:       List[Any]         =  vocab_size - 1
       lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
       lowerCAmelCase_:       int         =  vocab_size - 1




    def     _a						(						self   ):
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )

       lowerCAmelCase_:       Any         =  None
       if self.use_input_mask:
          lowerCAmelCase_:       Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]   )

       lowerCAmelCase_:       Union[str, Any]         =  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=lowerCamelCase__					,	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   ):
       lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
       lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict




    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()

       lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       lowerCAmelCase_:       Tuple         =  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					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Optional[Any]         =  20
       lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )

       lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
       lowerCAmelCase_:       Optional[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Optional[int]         =  model(
           input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  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					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Union[str, Any]         =  20
       lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )

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

       lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       List[Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       lowerCAmelCase_:       List[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
       lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Dict         =  model(
           input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  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 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    @slow
    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
       lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
       lowerCAmelCase_:       List[str]         =  False
       lowerCAmelCase_:       int         =  model.config.eos_token_id

       lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )

       lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences

       lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )

       lowerCAmelCase_:       Dict         =  [
           "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(lowerCamelCase__					,	lowerCamelCase__   )




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  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
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       Dict         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Union[str, Any]         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       List[str]         =  0
                lowerCAmelCase_:       int         =  1
                lowerCAmelCase_:       List[Any]         =  0
                lowerCAmelCase_:       List[Any]         =  1
             lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
             lowerCAmelCase_:       Optional[int]         =  fx_state

             with torch.no_grad():
                lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       List[str]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )

             lowerCAmelCase_:       List[Any]         =  fx_model_loaded(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(
                 len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output_loaded, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output_loaded[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  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
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
             lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       List[Any]         =  1
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       Optional[int]         =  1

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

             with torch.no_grad():
                lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       Union[str, Any]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )

             with torch.no_grad():
                lowerCAmelCase_:       int         =  pt_model_loaded(**lowerCamelCase__   ).to_tuple()

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




    @slow
    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
          lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
          self.assertIsNotNone(lowerCamelCase__   )