




from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class        __snake_case  :
				lowerCAmelCase__				=     BlenderbotConfig
				lowerCAmelCase__				=     {}
				lowerCAmelCase__				=     '''gelu'''



				def __init__(      self      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Any=13		,	_UpperCAmelCase      :		List[str]=7		,	_UpperCAmelCase      :		Any=True		,	_UpperCAmelCase      :		Optional[Any]=False		,	_UpperCAmelCase      :		Tuple=99		,	_UpperCAmelCase      :		Optional[int]=32		,	_UpperCAmelCase      :		str=2		,	_UpperCAmelCase      :		Dict=4		,	_UpperCAmelCase      :		Dict=37		,	_UpperCAmelCase      :		Tuple=0.1		,	_UpperCAmelCase      :		Optional[int]=0.1		,	_UpperCAmelCase      :		Optional[Any]=20		,	_UpperCAmelCase      :		Optional[int]=2		,	_UpperCAmelCase      :		Union[str, Any]=1		,	_UpperCAmelCase      :		int=0		,	)  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      parent
								_lowerCAmelCase    :    Any										=      batch_size
								_lowerCAmelCase    :    Union[str, Any]										=      seq_length
								_lowerCAmelCase    :    List[str]										=      is_training
								_lowerCAmelCase    :    Dict										=      use_labels
								_lowerCAmelCase    :    str										=      vocab_size
								_lowerCAmelCase    :    int										=      hidden_size
								_lowerCAmelCase    :    str										=      num_hidden_layers
								_lowerCAmelCase    :    Tuple										=      num_attention_heads
								_lowerCAmelCase    :    Tuple										=      intermediate_size

								_lowerCAmelCase    :    List[str]										=      hidden_dropout_prob
								_lowerCAmelCase    :    int										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    Dict										=      max_position_embeddings
								_lowerCAmelCase    :    List[str]										=      eos_token_id
								_lowerCAmelCase    :    int										=      pad_token_id
								_lowerCAmelCase    :    List[Any]										=      bos_token_id



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size      )
								_lowerCAmelCase    :    List[Any]										=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )		,	1      )
								_lowerCAmelCase    :    Dict										=      tf.concat([input_ids, eos_tensor]		,	axis=1      )

								_lowerCAmelCase    :    int										=      ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size      )

								_lowerCAmelCase    :    Tuple										=      self.config_cls(
								    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_ids=[2]		,	bos_token_id=self.bos_token_id		,	pad_token_id=self.pad_token_id		,	decoder_start_token_id=self.pad_token_id		,	**self.config_updates		,	)
								_lowerCAmelCase    :    Tuple										=      prepare_blenderbot_inputs_dict(A_		,	A_		,	A_      )
								return config, inputs_dict



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      TFBlenderbotModel(config=A_      ).get_decoder()
								_lowerCAmelCase    :    Tuple										=      inputs_dict["""input_ids"""]

								_lowerCAmelCase    :    Tuple										=      input_ids[:1, :]
								_lowerCAmelCase    :    List[str]										=      inputs_dict["""attention_mask"""][:1, :]
								_lowerCAmelCase    :    int										=      inputs_dict["""head_mask"""]
								_lowerCAmelCase    :    List[str]										=      1

								# first forward pass
								_lowerCAmelCase    :    Tuple										=      model(A_		,	attention_mask=A_		,	head_mask=A_		,	use_cache=A_      )

								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_lowerCAmelCase    :    int										=      ids_tensor((self.batch_size, 3)		,	config.vocab_size      )
								_lowerCAmelCase    :    int										=      tf.cast(ids_tensor((self.batch_size, 3)		,	2      )		,	tf.inta      )

								# append to next input_ids and
								_lowerCAmelCase    :    List[Any]										=      tf.concat([input_ids, next_tokens]		,	axis=-1      )
								_lowerCAmelCase    :    Dict										=      tf.concat([attention_mask, next_attn_mask]		,	axis=-1      )

								_lowerCAmelCase    :    List[str]										=      model(A_		,	attention_mask=A_      )[0]
								_lowerCAmelCase    :    Optional[Any]										=      model(A_		,	attention_mask=A_		,	past_key_values=A_      )[0]

								self.parent.assertEqual(next_tokens.shape[1]		,	output_from_past.shape[1]      )

								# select random slice
								_lowerCAmelCase    :    Optional[int]										=      int(ids_tensor((1,)		,	output_from_past.shape[-1]      )      )
								_lowerCAmelCase    :    Dict										=      output_from_no_past[:, -3:, random_slice_idx]
								_lowerCAmelCase    :    str										=      output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(A_		,	A_		,	rtol=1E-3      )






def 							_UpperCAmelCase  (UpperCamelCase_ : Dict      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : List[Any]=None      ,    UpperCamelCase_ : Optional[int]=None      ,    UpperCamelCase_ : List[Any]=None      ,    UpperCamelCase_ : str=None      ,    UpperCamelCase_ : Optional[Any]=None      ,    ):
				'''simple docstring'''
				if attention_mask is None:
								_lowerCAmelCase    :    List[str]										=      tf.cast(tf.math.not_equal(UpperCamelCase_      ,    config.pad_token_id       )      ,    tf.inta       )
				if decoder_attention_mask is None:
								_lowerCAmelCase    :    Dict										=      tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape      ,    dtype=tf.inta       ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,    config.pad_token_id       )      ,    tf.inta       ),
								    ]      ,    axis=-1      ,    )
				if head_mask is None:
								_lowerCAmelCase    :    List[str]										=      tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								_lowerCAmelCase    :    Tuple										=      tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								_lowerCAmelCase    :    Tuple										=      tf.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": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}
@require_tf
class        __snake_case  (_UpperCAmelCase						,	_UpperCAmelCase						,	unittest.TestCase		):
				lowerCAmelCase__				=     (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				lowerCAmelCase__				=     (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				lowerCAmelCase__				=     (
				    {
				        '''conversational''': TFBlenderbotForConditionalGeneration,
				        '''feature-extraction''': TFBlenderbotModel,
				        '''summarization''': TFBlenderbotForConditionalGeneration,
				        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
				        '''translation''': TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				lowerCAmelCase__				=     True
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False



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





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      TFBlenderbotModelTester(self      )
								_lowerCAmelCase    :    Tuple										=      ConfigTester(self		,	config_class=A_      )



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





								'''simple docstring'''




								self.config_tester.run_common_tests()



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





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*A_      )






@require_tokenizers
@require_tf
class        __snake_case  (unittest.TestCase		):
				lowerCAmelCase__				=     ['''My friends are cool but they eat too many carbs.''']
				lowerCAmelCase__				=     '''facebook/blenderbot-400M-distill'''



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





								'''simple docstring'''




								return BlenderbotTokenizer.from_pretrained(self.model_name      )



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
								return model



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





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.tokenizer(self.src_text		,	return_tensors="""tf"""      )
								_lowerCAmelCase    :    List[Any]										=      self.model.generate(
								    model_inputs.input_ids		,	)
								_lowerCAmelCase    :    Dict										=      self.tokenizer.batch_decode(generated_ids.numpy()		,	skip_special_tokens=A_      )[0]
								assert (
								    generated_words
								    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
								)







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")

