'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    lowerCAmelCase			:							int     ="platform"

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )







def 						A__  (					__A   , __A   , __A=None   , __A=None   , __A=None   , __A=None   , __A=None   , __A=None   , ):







    '''simple docstring'''
    if attention_mask is None:
        _lowerCamelCase     :		List[str] 						=						np.where(input_ids != config.pad_token_id   , 1   , 0	)
    if decoder_attention_mask is None:
        _lowerCamelCase     :		Optional[int] 						=						np.where(decoder_input_ids != config.pad_token_id   , 1   , 0	)
    if head_mask is None:
        _lowerCamelCase     :		str 						=						np.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        _lowerCamelCase     :		Optional[Any] 						=						np.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        _lowerCamelCase     :		Any 						=						np.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": attention_mask,
    }




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Dict=13    ,  _UpperCamelCase     :				int=7    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				List[str]=False    ,  _UpperCamelCase     :				int=99    ,  _UpperCamelCase     :				Union[str, Any]=16    ,  _UpperCamelCase     :				Any=2    ,  _UpperCamelCase     :				List[Any]=4    ,  _UpperCamelCase     :				List[Any]=4    ,  _UpperCamelCase     :				Dict="gelu"    ,  _UpperCamelCase     :				Union[str, Any]=0.1    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Any=32    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				str=1    ,  _UpperCamelCase     :				Any=0    ,  _UpperCamelCase     :				int=0.0_2    ,  )				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						parent
          _lowerCamelCase     :		Optional[Any] 						=						batch_size
          _lowerCamelCase     :		Union[str, Any] 						=						seq_length
          _lowerCamelCase     :		Union[str, Any] 						=						is_training
          _lowerCamelCase     :		List[str] 						=						use_labels
          _lowerCamelCase     :		Tuple 						=						vocab_size
          _lowerCamelCase     :		List[str] 						=						hidden_size
          _lowerCamelCase     :		List[Any] 						=						num_hidden_layers
          _lowerCamelCase     :		Union[str, Any] 						=						num_attention_heads
          _lowerCamelCase     :		Tuple 						=						intermediate_size
          _lowerCamelCase     :		Any 						=						hidden_act
          _lowerCamelCase     :		List[Any] 						=						hidden_dropout_prob
          _lowerCamelCase     :		List[Any] 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Any 						=						max_position_embeddings
          _lowerCamelCase     :		List[Any] 						=						eos_token_id
          _lowerCamelCase     :		List[Any] 						=						pad_token_id
          _lowerCamelCase     :		List[Any] 						=						bos_token_id
          _lowerCamelCase     :		Tuple 						=						initializer_range



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size)    ,  3    ,  self.vocab_size)
          _lowerCamelCase     :		Optional[int] 						=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,  dtype=np.intaa))    ,  -1)

          _lowerCamelCase     :		List[Any] 						=						shift_tokens_right(_UpperCamelCase    ,  1    ,  2)

          _lowerCamelCase     :		Optional[int] 						=						BlenderbotConfig(
              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_id=self.eos_token_id    ,  bos_token_id=self.bos_token_id    ,  pad_token_id=self.pad_token_id    ,  initializer_range=self.initializer_range    ,  use_cache=_UpperCamelCase    ,  )
          _lowerCamelCase     :		Union[str, Any] 						=						prepare_blenderbot_inputs_dict(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          return config, inputs_dict



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.prepare_config_and_inputs()
          return config, inputs_dict



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[int])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						20
          _lowerCamelCase     :		Tuple 						=						model_class_name(_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						model.encode(inputs_dict["""input_ids"""])

          _lowerCamelCase     :		int 						=						(
              inputs_dict["""decoder_input_ids"""],
              inputs_dict["""decoder_attention_mask"""],
          )

          _lowerCamelCase     :		Optional[int] 						=						model.init_cache(decoder_input_ids.shape[0]    ,  _UpperCamelCase    ,  _UpperCamelCase)
          _lowerCamelCase     :		str 						=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,  dtype="""i4""")

          _lowerCamelCase     :		Any 						=						jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]    ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,  )
          _lowerCamelCase     :		Optional[Any] 						=						model.decode(
              decoder_input_ids[:, :-1]    ,  _UpperCamelCase    ,  decoder_attention_mask=_UpperCamelCase    ,  past_key_values=_UpperCamelCase    ,  decoder_position_ids=_UpperCamelCase    ,  )

          _lowerCamelCase     :		Tuple 						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,  dtype="""i4""")
          _lowerCamelCase     :		int 						=						model.decode(
              decoder_input_ids[:, -1:]    ,  _UpperCamelCase    ,  decoder_attention_mask=_UpperCamelCase    ,  past_key_values=outputs_cache.past_key_values    ,  decoder_position_ids=_UpperCamelCase    ,  )

          _lowerCamelCase     :		Dict 						=						model.decode(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						20
          _lowerCamelCase     :		Tuple 						=						model_class_name(_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						model.encode(inputs_dict["""input_ids"""])

          _lowerCamelCase     :		List[Any] 						=						(
              inputs_dict["""decoder_input_ids"""],
              inputs_dict["""decoder_attention_mask"""],
          )

          _lowerCamelCase     :		List[Any] 						=						jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
              ]    ,  axis=-1    ,  )

          _lowerCamelCase     :		List[Any] 						=						model.init_cache(decoder_input_ids.shape[0]    ,  _UpperCamelCase    ,  _UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]    ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,  )

          _lowerCamelCase     :		Dict 						=						model.decode(
              decoder_input_ids[:, :-1]    ,  _UpperCamelCase    ,  decoder_attention_mask=_UpperCamelCase    ,  past_key_values=_UpperCamelCase    ,  decoder_position_ids=_UpperCamelCase    ,  )
          _lowerCamelCase     :		str 						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,  dtype="""i4""")
          _lowerCamelCase     :		int 						=						model.decode(
              decoder_input_ids[:, -1:]    ,  _UpperCamelCase    ,  past_key_values=outputs_cache.past_key_values    ,  decoder_attention_mask=_UpperCamelCase    ,  decoder_position_ids=_UpperCamelCase    ,  )

          _lowerCamelCase     :		Tuple 						=						model.decode(_UpperCamelCase    ,  _UpperCamelCase    ,  decoder_attention_mask=_UpperCamelCase)

          _lowerCamelCase     :		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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       99



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ]    ,  dtype=np.intaa    ,  )

          _lowerCamelCase     :		List[Any] 						=						input_ids.shape[0]
          _lowerCamelCase     :		Tuple 						=						BlenderbotConfig(
              vocab_size=self.vocab_size    ,  d_model=24    ,  encoder_layers=2    ,  decoder_layers=2    ,  encoder_attention_heads=2    ,  decoder_attention_heads=2    ,  encoder_ffn_dim=32    ,  decoder_ffn_dim=32    ,  max_position_embeddings=48    ,  eos_token_id=2    ,  pad_token_id=1    ,  bos_token_id=0    ,  )
          return config, input_ids, batch_size



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self._get_config_and_data()
          _lowerCamelCase     :		Any 						=						FlaxBlenderbotForConditionalGeneration(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						lm_model(input_ids=_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["""logits"""].shape    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						BlenderbotConfig(
              vocab_size=self.vocab_size    ,  d_model=14    ,  encoder_layers=2    ,  decoder_layers=2    ,  encoder_attention_heads=2    ,  decoder_attention_heads=2    ,  encoder_ffn_dim=8    ,  decoder_ffn_dim=8    ,  max_position_embeddings=48    ,  )
          _lowerCamelCase     :		Optional[int] 						=						FlaxBlenderbotForConditionalGeneration(_UpperCamelCase)
          _lowerCamelCase     :		int 						=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,  dtype=np.intaa)
          _lowerCamelCase     :		Tuple 						=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,  dtype=np.intaa)
          _lowerCamelCase     :		Dict 						=						lm_model(input_ids=_UpperCamelCase    ,  decoder_input_ids=_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						(*summary.shape, config.vocab_size)
          self.assertEqual(outputs["""logits"""].shape    ,  _UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,  dtype=np.intaa)
          _lowerCamelCase     :		int 						=						shift_tokens_right(_UpperCamelCase    ,  1    ,  2)
          _lowerCamelCase     :		Tuple 						=						np.equal(_UpperCamelCase    ,  1).astype(np.floataa).sum()
          _lowerCamelCase     :		Dict 						=						np.equal(_UpperCamelCase    ,  1).astype(np.floataa).sum()
          self.assertEqual(shifted.shape    ,  input_ids.shape)
          self.assertEqual(_UpperCamelCase    ,  n_pad_before - 1)
          self.assertTrue(np.equal(shifted[:, 0]    ,  2).all())





@require_flax
class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       True
      _snake_case    		=       (
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      _snake_case    		=       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						FlaxBlenderbotModelTester(self)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  _lowerCamelCase     :		Tuple 						=						self._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase)
                  _lowerCamelCase     :		List[str] 						=						model_class(_UpperCamelCase)

                  @jax.jit
                  def encode_jitted(_UpperCamelCase     :				str    ,  _UpperCamelCase     :				Any=None    ,  **_UpperCamelCase     :				Dict):
                      return model.encode(input_ids=_UpperCamelCase    ,  attention_mask=_UpperCamelCase)

                  with self.subTest("""JIT Enabled"""):
                      _lowerCamelCase     :		Dict 						=						encode_jitted(**_UpperCamelCase).to_tuple()

                  with self.subTest("""JIT Disabled"""):
                      with jax.disable_jit():
                          _lowerCamelCase     :		str 						=						encode_jitted(**_UpperCamelCase).to_tuple()

                  self.assertEqual(len(_UpperCamelCase)    ,  len(_UpperCamelCase))
                  for jitted_output, output in zip(_UpperCamelCase    ,  _UpperCamelCase):
                      self.assertEqual(jitted_output.shape    ,  output.shape)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  _lowerCamelCase     :		Any 						=						model_class(_UpperCamelCase)
                  _lowerCamelCase     :		int 						=						model.encode(inputs_dict["""input_ids"""]    ,  inputs_dict["""attention_mask"""])

                  _lowerCamelCase     :		Union[str, Any] 						=						{
                      """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                      """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                      """encoder_outputs""": encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(_UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any]):
                      return model.decode(
                          decoder_input_ids=_UpperCamelCase    ,  decoder_attention_mask=_UpperCamelCase    ,  encoder_outputs=_UpperCamelCase    ,  )

                  with self.subTest("""JIT Enabled"""):
                      _lowerCamelCase     :		Dict 						=						decode_jitted(**_UpperCamelCase).to_tuple()

                  with self.subTest("""JIT Disabled"""):
                      with jax.disable_jit():
                          _lowerCamelCase     :		Optional[int] 						=						decode_jitted(**_UpperCamelCase).to_tuple()

                  self.assertEqual(len(_UpperCamelCase)    ,  len(_UpperCamelCase))
                  for jitted_output, output in zip(_UpperCamelCase    ,  _UpperCamelCase):
                      self.assertEqual(jitted_output.shape    ,  output.shape)



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          for model_class_name in self.all_model_classes:
              _lowerCamelCase     :		Any 						=						model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""")
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              _lowerCamelCase     :		Dict 						=						np.ones((1, 1)) * model.config.eos_token_id
              _lowerCamelCase     :		int 						=						model(_UpperCamelCase)
              self.assertIsNotNone(_UpperCamelCase)




      @unittest.skipUnless(jax_device != """cpu"""    ,  """3B test too slow on CPU.""")
      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
          _lowerCamelCase     :		Dict 						=						{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

          _lowerCamelCase     :		Dict 						=						FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""    ,  from_pt=_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""")

          _lowerCamelCase     :		List[Any] 						=						["""Sam"""]
          _lowerCamelCase     :		Dict 						=						tokenizer(_UpperCamelCase    ,  return_tensors="""jax""")

          _lowerCamelCase     :		Optional[int] 						=						model.generate(**_UpperCamelCase    ,  **_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						"""Sam is a great name. It means \"sun\" in Gaelic."""

          _lowerCamelCase     :		Union[str, Any] 						=						tokenizer.batch_decode(_UpperCamelCase    ,  **_UpperCamelCase)
          assert generated_txt[0].strip() == tgt_text





from __future__ import annotations




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						key



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Any 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Optional[Any] 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""encrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""decrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




