

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch



class 						lowercase_					:



      def __init__(    self   :       Union[str, Any]    ,      A__   :       Union[str, Any]    ,      A__   :       List[str]=14    ,      A__   :       Union[str, Any]=7    ,      A__   :       int=True    ,      A__   :       int=True    ,      A__   :       List[str]=False    ,      A__   :       Optional[Any]=True    ,      A__   :       int=99    ,      A__   :       List[str]=32    ,      A__   :       List[str]=4    ,      A__   :       Optional[Any]=4    ,      A__   :       str=[[["global", "local"], 2]]    ,      A__   :       List[str]=37    ,      A__   :       Any="gelu"    ,      A__   :       List[Any]=0.1    ,      A__   :       str=0.1    ,      A__   :       Optional[Any]=512    ,      A__   :       Optional[Any]=7    ,      A__   :       Union[str, Any]=0.02    ,      )					->  int:
        _snake_case		=							parent
        _snake_case		=							batch_size
        _snake_case		=							seq_length
        _snake_case		=							is_training
        _snake_case		=							use_input_mask
        _snake_case		=							use_token_type_ids
        _snake_case		=							use_labels
        _snake_case		=							vocab_size
        _snake_case		=							hidden_size
        _snake_case		=							num_hidden_layers
        _snake_case		=							num_attention_heads
        _snake_case		=							attention_types
        _snake_case		=							intermediate_size
        _snake_case		=							hidden_act
        _snake_case		=							hidden_dropout_prob
        _snake_case		=							attention_probs_dropout_prob
        _snake_case		=							max_position_embeddings
        _snake_case		=							window_size
        _snake_case		=							initializer_range
        _snake_case		=							None
        _snake_case		=							vocab_size - 1
        _snake_case		=							vocab_size - 1
        _snake_case		=							vocab_size - 1



      def 		UpperCamelCase_	(    self   :       Any	)					->  List[Any]:
        _snake_case		=							ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size	)

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

        _snake_case		=							GPTNeoConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_layers=self.num_hidden_layers    ,      num_heads=self.num_attention_heads    ,      max_position_embeddings=self.max_position_embeddings    ,      use_cache=A__    ,      bos_token_id=self.bos_token_id    ,      eos_token_id=self.eos_token_id    ,      pad_token_id=self.pad_token_id    ,      window_size=self.window_size    ,      attention_types=self.attention_types    ,      )

        return (config, input_ids, input_mask)



      def 		UpperCamelCase_	(    self   :       str	)					->  Tuple:
        _snake_case		=							self.prepare_config_and_inputs()
        _snake_case,  _snake_case,  _snake_case		=							config_and_inputs
        _snake_case		=							{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict



      def 		UpperCamelCase_	(    self   :       Tuple    ,      A__   :       Tuple    ,      A__   :       Any    ,      A__   :       Dict    ,      A__   :       str	)					->  str:
        _snake_case		=							20
        _snake_case		=							model_class_name(A__	)

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

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

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

        _snake_case		=							model(A__	)

        _snake_case		=							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 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       Dict    ,      A__   :       Dict    ,      A__   :       List[Any]    ,      A__   :       Union[str, Any]	)					->  int:
        _snake_case		=							20
        _snake_case		=							model_class_name(A__	)

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

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

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

        _snake_case		=							model(A__    ,      attention_mask=A__	)

        _snake_case		=							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_					(						__lowercase     ,							__lowercase     ,							unittest.TestCase  ):
      UpperCamelCase_    :	Union[str, Any]		   =       (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      UpperCamelCase_    :	List[str]		   =       (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Optional[Any]:
        _snake_case		=							FlaxGPTNeoModelTester(self	)



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Union[str, Any]:
        for model_class_name in self.all_model_classes:
          _snake_case,  _snake_case,  _snake_case		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(A__    ,      A__    ,      A__    ,      A__	)



      def 		UpperCamelCase_	(    self   :       Dict	)					->  int:
        for model_class_name in self.all_model_classes:
          _snake_case,  _snake_case,  _snake_case		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              A__    ,      A__    ,      A__    ,      A__	)



      @slow
      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Union[str, Any]:
        _snake_case		=							GPTaTokenizer.from_pretrained('''gpt2'''    ,      pad_token='''<|endoftext|>'''    ,      padding_side='''left'''	)
        _snake_case		=							tokenizer(['''Hello this is a long string''', '''Hey''']    ,      return_tensors='''np'''    ,      padding=A__    ,      truncation=A__	)

        _snake_case		=							FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''	)
        _snake_case		=							False
        _snake_case		=							model.config.eos_token_id

        _snake_case		=							jax.jit(model.generate	)

        _snake_case		=							jit_generate(
            inputs['''input_ids''']    ,      attention_mask=inputs['''attention_mask''']    ,      pad_token_id=tokenizer.pad_token_id	).sequences

        _snake_case		=							tokenizer.batch_decode(A__    ,      skip_special_tokens=A__	)

        _snake_case		=							[
            '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
            '''Hey, I\'m a little late to the party. I\'m going to''',
        ]

        self.assertListEqual(A__    ,      A__	)



      @is_pt_flax_cross_test
      def 		UpperCamelCase_	(    self   :       int	)					->  Union[str, Any]:
        _snake_case,  _snake_case		=							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
            _snake_case		=							self._prepare_for_class(A__    ,      A__	)
            _snake_case		=							{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

            _snake_case		=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,      A__	)
            _snake_case		=							fx_state

            with torch.no_grad():
              _snake_case		=							pt_model(**A__	).to_tuple()

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

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

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



      @is_pt_flax_cross_test
      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Any:
        _snake_case,  _snake_case		=							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
            _snake_case		=							self._prepare_for_class(A__    ,      A__	)
            _snake_case		=							{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

            _snake_case		=							pt_model_class(A__	).eval()
            _snake_case		=							model_class(A__    ,      dtype=jnp.floataa	)

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

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

            with torch.no_grad():
              _snake_case		=							pt_model(**A__	).to_tuple()

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

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

            with torch.no_grad():
              _snake_case		=							pt_model_loaded(**A__	).to_tuple()

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







      @slow
      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  str:
        for model_class_name in self.all_model_classes:
          _snake_case		=							model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''	)
          _snake_case		=							model(np.ones((1, 1)	)	)
          self.assertIsNotNone(A__	)




import random
import sys

from . import cryptomath_module as cryptomath

__A    	=			(
    r''' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'''
    r'''abcdefghijklmnopqrstuvwxyz{|}~'''
)



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			None:



  """simple docstring"""






  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          '''The affine cipher becomes weak when key '''
          '''A is set to 1. Choose different key'''  )
    if key_b == 0:
      sys.exit(
          '''The affine cipher becomes weak when key '''
          '''B is set to 0. Choose different key'''  )
  if key_a < 0 or key_b < 0 or key_b > len(_UpperCamelCase  ) - 1:
    sys.exit(
        '''Key A must be greater than 0 and key B must '''
        F"""be between 0 and {len(_UpperCamelCase  ) - 1}."""  )
  if cryptomath.gcd(_UpperCamelCase				, len(_UpperCamelCase  )  ) != 1:
    sys.exit(
        F"""Key A {key_a} and the symbol set size {len(_UpperCamelCase  )} """
        '''are not relatively prime. Choose a different key.'''  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=							divmod(_UpperCamelCase				, len(_UpperCamelCase  )  )
  check_keys(_UpperCamelCase				, _UpperCamelCase				, '''encrypt'''  )
  _snake_case		=							''''''
  for symbol in message:
    if symbol in SYMBOLS:
      _snake_case		=							SYMBOLS.find(_UpperCamelCase  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_UpperCamelCase  )]
    else:
      cipher_text += symbol
  return cipher_text



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=							divmod(_UpperCamelCase				, len(_UpperCamelCase  )  )
  check_keys(_UpperCamelCase				, _UpperCamelCase				, '''decrypt'''  )
  _snake_case		=							''''''
  _snake_case		=							cryptomath.find_mod_inverse(_UpperCamelCase				, len(_UpperCamelCase  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      _snake_case		=							SYMBOLS.find(_UpperCamelCase  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_UpperCamelCase  )
      ]
    else:
      plain_text += symbol
  return plain_text



def    snake_case_()							->			int:



  """simple docstring"""






  while True:
    _snake_case		=							random.randint(2				, len(_UpperCamelCase  )  )
    _snake_case		=							random.randint(2				, len(_UpperCamelCase  )  )
    if cryptomath.gcd(_UpperCamelCase				, len(_UpperCamelCase  )  ) == 1 and key_b % len(_UpperCamelCase  ) != 0:
      return key_b * len(_UpperCamelCase  ) + key_b



def    snake_case_()							->			None:



  """simple docstring"""






  _snake_case		=							input('''Enter message: '''  ).strip()
  _snake_case		=							int(input('''Enter key [2000 - 9000]: '''  ).strip()  )
  _snake_case		=							input('''Encrypt/Decrypt [E/D]: '''  ).strip().lower()

  if mode.startswith('''e'''  ):
    _snake_case		=							'''encrypt'''
    _snake_case		=							encrypt_message(_UpperCamelCase				, _UpperCamelCase  )
  elif mode.startswith('''d'''  ):
    _snake_case		=							'''decrypt'''
    _snake_case		=							decrypt_message(_UpperCamelCase				, _UpperCamelCase  )
  print(F"""\n{mode.title()}ed text: \n{translated}"""  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()

