


"""simple docstring"""


from __future__ import annotations
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self    ,   __a = 0	):
										__lowerCAmelCase				    =			key

							def 			snake_case				(     self    ,   __a    ,   __a	):
										assert isinstance(__a    ,   __a	) and isinstance(__a    ,   __a	)

										__lowerCAmelCase				    =			key or self.__key or 1

										# make sure key is an appropriate size
										key %= 2_55

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

							def 			snake_case				(     self    ,   __a    ,   __a	):
										assert isinstance(__a    ,   __a	) and isinstance(__a    ,   __a	)

										__lowerCAmelCase				    =			key or self.__key or 1

										# make sure key is an appropriate size
										key %= 2_55

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

							def 			snake_case				(     self    ,   __a    ,   __a = 0	):
										assert isinstance(__a    ,   __a	) and isinstance(__a    ,   __a	)

										__lowerCAmelCase				    =			key or self.__key or 1

										# make sure key can be any size
										while key > 2_55:
													key -= 2_55

										# This will be returned
										__lowerCAmelCase				    =			""

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

										return ans

							def 			snake_case				(     self    ,   __a    ,   __a = 0	):
										assert isinstance(__a    ,   __a	) and isinstance(__a    ,   __a	)

										__lowerCAmelCase				    =			key or self.__key or 1

										# make sure key can be any size
										while key > 2_55:
													key -= 2_55

										# This will be returned
										__lowerCAmelCase				    =			""

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

										return ans

							def 			snake_case				(     self    ,   __a    ,   __a = 0	):
										assert isinstance(__a    ,   __a	) and isinstance(__a    ,   __a	)

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

										except OSError:
													return False

										return True



							def 			snake_case				(     self    ,   __a    ,   __a	):
										assert isinstance(__a    ,   __a	) and isinstance(__a    ,   __a	)

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

										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")








"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




