




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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      :  List[str]      						=						"platform"

	import jax
	import jax.numpy as jnp

	from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
	    FlaxBlenderbotSmallForConditionalGeneration,
	    FlaxBlenderbotSmallModel,
	    shift_tokens_right,
	)




def 							_UpperCAmelCase  (UpperCamelCase_ : Dict      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : Optional[int]=None      ,    UpperCamelCase_ : List[str]=None      ,    UpperCamelCase_ : List[str]=None      ,    UpperCamelCase_ : Tuple=None      ,    UpperCamelCase_ : Tuple=None      ,    UpperCamelCase_ : int=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    :    Any										=      np.where(decoder_input_ids != config.pad_token_id      ,    1      ,    0       )
				if head_mask is None:
								_lowerCAmelCase    :    List[str]										=      np.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								_lowerCAmelCase    :    Dict										=      np.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								_lowerCAmelCase    :    Tuple										=      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  :



				def __init__(      self      :		Any		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		List[str]=13		,	_UpperCAmelCase      :		Optional[Any]=7		,	_UpperCAmelCase      :		Union[str, Any]=True		,	_UpperCAmelCase      :		List[str]=False		,	_UpperCAmelCase      :		Dict=99		,	_UpperCAmelCase      :		Tuple=16		,	_UpperCAmelCase      :		Any=2		,	_UpperCAmelCase      :		Dict=4		,	_UpperCAmelCase      :		List[str]=4		,	_UpperCAmelCase      :		Dict="gelu"		,	_UpperCAmelCase      :		Any=0.1		,	_UpperCAmelCase      :		str=0.1		,	_UpperCAmelCase      :		List[Any]=32		,	_UpperCAmelCase      :		Tuple=2		,	_UpperCAmelCase      :		int=1		,	_UpperCAmelCase      :		Optional[int]=0		,	_UpperCAmelCase      :		List[Any]=0.02		,	)  ->    Dict:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    int										=      shift_tokens_right(_UpperCAmelCase		,	1		,	2      )

								_lowerCAmelCase    :    int										=      BlenderbotSmallConfig(
								    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    :    List[Any]										=      prepare_blenderbot_inputs_dict(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )
								return config, inputs_dict



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





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.prepare_config_and_inputs()
								return config, inputs_dict



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		List[Any]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      20
								_lowerCAmelCase    :    Optional[int]										=      model_class_name(_UpperCAmelCase      )

								_lowerCAmelCase    :    Union[str, Any]										=      model.encode(inputs_dict["""input_ids"""]      )

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

								_lowerCAmelCase    :    str										=      model.init_cache(decoder_input_ids.shape[0]		,	_UpperCAmelCase		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,	dtype="""i4"""      )

								_lowerCAmelCase    :    Union[str, 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    :    List[str]										=      model.decode(
								    decoder_input_ids[:, :-1]		,	_UpperCAmelCase		,	decoder_attention_mask=_UpperCAmelCase		,	past_key_values=_UpperCAmelCase		,	decoder_position_ids=_UpperCAmelCase		,	)

								_lowerCAmelCase    :    Union[str, Any]										=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="""i4"""      )
								_lowerCAmelCase    :    Union[str, Any]										=      model.decode(
								    decoder_input_ids[:, -1:]		,	_UpperCAmelCase		,	decoder_attention_mask=_UpperCAmelCase		,	past_key_values=outputs_cache.past_key_values		,	decoder_position_ids=_UpperCAmelCase		,	)

								_lowerCAmelCase    :    str										=      model.decode(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[int]										=      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      :		Optional[Any]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      20
								_lowerCAmelCase    :    str										=      model_class_name(_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      model.encode(inputs_dict["""input_ids"""]      )

								_lowerCAmelCase    :    List[str]										=      (
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								_lowerCAmelCase    :    Optional[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    :    Tuple										=      jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]		,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,	)

								_lowerCAmelCase    :    str										=      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    :    Optional[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    :    str										=      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		):
				lowerCAmelCase__				=     9_9



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      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    :    Union[str, Any]										=      input_ids.shape[0]
								_lowerCAmelCase    :    str										=      BlenderbotSmallConfig(
								    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      :		Dict      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      self._get_config_and_data()
								_lowerCAmelCase    :    Dict										=      FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      lm_model(input_ids=_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs["""logits"""].shape		,	_UpperCAmelCase      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      BlenderbotSmallConfig(
								    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    :    Any										=      FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,	dtype=np.intaa      )
								_lowerCAmelCase    :    List[Any]										=      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,	dtype=np.intaa      )
								_lowerCAmelCase    :    Optional[Any]										=      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[str]										=      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,	dtype=np.intaa      )
								_lowerCAmelCase    :    List[Any]										=      shift_tokens_right(_UpperCAmelCase		,	1		,	2      )
								_lowerCAmelCase    :    int										=      np.equal(_UpperCAmelCase		,	1      ).astype(np.floataa      ).sum()
								_lowerCAmelCase    :    List[str]										=      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  (_a						,	unittest.TestCase						,	_a		):
				lowerCAmelCase__				=     True
				lowerCAmelCase__				=     (
				    (
				        FlaxBlenderbotSmallModel,
				        FlaxBlenderbotSmallForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				lowerCAmelCase__				=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



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





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      FlaxBlenderbotSmallModelTester(self      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      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      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      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      :		Union[str, Any]      )  ->    List[str]:





								'''simple docstring'''




								_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__      ):
																_lowerCAmelCase    :    Tuple										=      self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )
																_lowerCAmelCase    :    Optional[Any]										=      model_class(_UpperCAmelCase      )

																@jax.jit
																def encode_jitted(_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Dict=None		,	**_UpperCAmelCase      :		int      ):
																				return model.encode(input_ids=_UpperCAmelCase		,	attention_mask=_UpperCAmelCase      )

																with self.subTest("""JIT Enabled"""      ):
																				_lowerCAmelCase    :    Optional[int]										=      encode_jitted(**_UpperCAmelCase      ).to_tuple()

																with self.subTest("""JIT Disabled"""      ):
																				with jax.disable_jit():
																								_lowerCAmelCase    :    Optional[int]										=      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      :		List[str]      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[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    :    Union[str, Any]										=      model_class(_UpperCAmelCase      )
																_lowerCAmelCase    :    int										=      model.encode(inputs_dict["""input_ids"""]		,	inputs_dict["""attention_mask"""]      )

																_lowerCAmelCase    :    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      :		Dict		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Union[str, 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    :    List[str]										=      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      :		Dict      )  ->    str:





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase    :    str										=      model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""      )
												# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
												_lowerCAmelCase    :    Union[str, Any]										=      np.ones((1, 1)      ) * model.config.eos_token_id
												_lowerCAmelCase    :    Any										=      model(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )







from __future__ import annotations
class        __snake_case  :



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int = 0      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      key



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    list[str]:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[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      :		List[str]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int      )  ->    list[str]:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    List[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      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    str:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

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

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

								# This will be returned
								_lowerCAmelCase    :    List[str]										=      """"""

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

								return ans



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		int = 0      )  ->    str:





								'''simple docstring'''




								assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      key or self.__key or 1

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

								# This will be returned
								_lowerCAmelCase    :    List[Any]										=      """"""

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

								return ans



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_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      :		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")

