



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
						UpperCAmelCase__       =	"platform"

						import jax
						import jax.numpy as jnp

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





def 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Union[str, Any]=None							,       _UpperCAmelCase    :       List[Any]=None							,       _UpperCAmelCase    :       Dict=None							,       _UpperCAmelCase    :       int=None							,       _UpperCAmelCase    :       Optional[Any]=None							,       _UpperCAmelCase    :       Tuple=None							,       )    ->			List[str]:







			'''simple docstring'''







			if attention_mask is None:
						_UpperCAmelCase			=       np.where(input_ids != config.pad_token_id							,       1							,       0   )
			if decoder_attention_mask is None:
						_UpperCAmelCase			=       np.where(decoder_input_ids != config.pad_token_id							,       1							,       0   )
			if head_mask is None:
						_UpperCAmelCase			=       np.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						_UpperCAmelCase			=       np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						_UpperCAmelCase			=       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       __lowerCAmelCase   :



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



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       eos_token_id
						_UpperCAmelCase			=       pad_token_id
						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       initializer_range



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



						"""simple docstring"""






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

						_UpperCAmelCase			=       shift_tokens_right(a_			,		1			,		2)

						_UpperCAmelCase			=       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=a_			,		)
						_UpperCAmelCase			=       prepare_blenderbot_inputs_dict(a_			,		a_			,		a_)
						return config, inputs_dict



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



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.prepare_config_and_inputs()
						return config, inputs_dict



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  int			,		A       :  Any			,		A       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       20
						_UpperCAmelCase			=       model_class_name(a_)

						_UpperCAmelCase			=       model.encode(inputs_dict['input_ids'])

						_UpperCAmelCase	,					_UpperCAmelCase			=       (
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						_UpperCAmelCase			=       model.init_cache(decoder_input_ids.shape[0]			,		a_			,		a_)
						_UpperCAmelCase			=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,		dtype='i4')

						_UpperCAmelCase			=       jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]			,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,		)
						_UpperCAmelCase			=       model.decode(
						    decoder_input_ids[:, :-1]			,		a_			,		decoder_attention_mask=a_			,		past_key_values=a_			,		decoder_position_ids=a_			,		)

						_UpperCAmelCase			=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,		dtype='i4')
						_UpperCAmelCase			=       model.decode(
						    decoder_input_ids[:, -1:]			,		a_			,		decoder_attention_mask=a_			,		past_key_values=outputs_cache.past_key_values			,		decoder_position_ids=a_			,		)

						_UpperCAmelCase			=       model.decode(a_			,		a_)

						_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Optional[int]			,		A       :  Optional[int]			,		A       :  Optional[Any])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       20
						_UpperCAmelCase			=       model_class_name(a_)

						_UpperCAmelCase			=       model.encode(inputs_dict['input_ids'])

						_UpperCAmelCase	,					_UpperCAmelCase			=       (
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

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

						_UpperCAmelCase			=       model.init_cache(decoder_input_ids.shape[0]			,		a_			,		a_)
						_UpperCAmelCase			=       jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]			,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,		)

						_UpperCAmelCase			=       model.decode(
						    decoder_input_ids[:, :-1]			,		a_			,		decoder_attention_mask=a_			,		past_key_values=a_			,		decoder_position_ids=a_			,		)
						_UpperCAmelCase			=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,		dtype='i4')
						_UpperCAmelCase			=       model.decode(
						    decoder_input_ids[:, -1:]			,		a_			,		past_key_values=outputs_cache.past_key_values			,		decoder_attention_mask=a_			,		decoder_position_ids=a_			,		)

						_UpperCAmelCase			=       model.decode(a_			,		a_			,		decoder_attention_mask=a_)

						_UpperCAmelCase			=       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       __lowerCAmelCase   (      unittest.TestCase						):
			UpperCamelCase        =					9_9



			def    _lowerCamelCase	(						self       :  Any)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       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			,		)

						_UpperCAmelCase			=       input_ids.shape[0]
						_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  Optional[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       self._get_config_and_data()
						_UpperCAmelCase			=       FlaxBlenderbotSmallForConditionalGeneration(a_)
						_UpperCAmelCase			=       lm_model(input_ids=a_)
						_UpperCAmelCase			=       (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs['logits'].shape			,		a_)



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



						"""simple docstring"""






						_UpperCAmelCase			=       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			,		)
						_UpperCAmelCase			=       FlaxBlenderbotSmallForConditionalGeneration(a_)
						_UpperCAmelCase			=       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,		dtype=np.intaa)
						_UpperCAmelCase			=       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,		dtype=np.intaa)
						_UpperCAmelCase			=       lm_model(input_ids=a_			,		decoder_input_ids=a_)
						_UpperCAmelCase			=       (*summary.shape, config.vocab_size)
						self.assertEqual(outputs['logits'].shape			,		a_)



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



						"""simple docstring"""






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







@require_flax
class       __lowerCAmelCase   (      SCREAMING_SNAKE_CASE__    ,						unittest.TestCase    ,						SCREAMING_SNAKE_CASE__						):
			UpperCamelCase        =					True
			UpperCamelCase        =					(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			UpperCamelCase        =					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



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



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxBlenderbotSmallModelTester(self)



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



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(a_			,		a_			,		a_)



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       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(a_			,		a_			,		a_)



			def    _lowerCamelCase	(						self       :  str)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												_UpperCAmelCase			=       self._prepare_for_class(a_			,		a_)
												_UpperCAmelCase			=       model_class(a_)

												@jax.jit
												def encode_jitted(A       :  List[str]			,		A       :  Optional[Any]=None			,		**A       :  Any):
															return model.encode(input_ids=a_			,		attention_mask=a_)

												with self.subTest('JIT Enabled'):
															_UpperCAmelCase			=       encode_jitted(**a_).to_tuple()

												with self.subTest('JIT Disabled'):
															with jax.disable_jit():
																		_UpperCAmelCase			=       encode_jitted(**a_).to_tuple()

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



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



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												_UpperCAmelCase			=       model_class(a_)
												_UpperCAmelCase			=       model.encode(inputs_dict['input_ids']			,		inputs_dict['attention_mask'])

												_UpperCAmelCase			=       {
												    '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(A       :  List[Any]			,		A       :  List[Any]			,		A       :  Any):
															return model.decode(
															    decoder_input_ids=a_			,		decoder_attention_mask=a_			,		encoder_outputs=a_			,		)

												with self.subTest('JIT Enabled'):
															_UpperCAmelCase			=       decode_jitted(**a_).to_tuple()

												with self.subTest('JIT Disabled'):
															with jax.disable_jit():
																		_UpperCAmelCase			=       decode_jitted(**a_).to_tuple()

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



			@slow
			def    _lowerCamelCase	(						self       :  List[str])       ->   Tuple:



						"""simple docstring"""






						for model_class_name in self.all_model_classes:
									_UpperCAmelCase			=       model_class_name.from_pretrained('facebook/blenderbot_small-90M')
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									_UpperCAmelCase			=       np.ones((1, 1)) * model.config.eos_token_id
									_UpperCAmelCase			=       model(a_)
									self.assertIsNotNone(a_)





import string

import numpy





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       int   )    ->			int:







			'''simple docstring'''
			return b if a == 0 else greatest_common_divisor(b % a							,       _UpperCAmelCase   )







class       __lowerCAmelCase   :
			UpperCamelCase        =					string.ascii_uppercase + string.digits
			# This cipher takes alphanumerics into account
			# i.e. a total of 36 characters

			# take x and return x % len(key_string)
			UpperCamelCase        =					numpy.vectorize(lambda A						: x % 3_6						)

			UpperCamelCase        =					numpy.vectorize(A						)



			def __init__(						self       :  Tuple			,		A       :  numpy.ndarray)       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       self.modulus(A)  # mod36 calc's on the encrypt key
						self.check_determinant()  # validate the determinant of the encryption key
						_UpperCAmelCase			=       encrypt_key.shape[0]



			def    _lowerCamelCase	(						self       :  str			,		A       :  str)       ->   int:



						"""simple docstring"""






						return self.key_string.index(A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  int)       ->   str:



						"""simple docstring"""






						return self.key_string[round(A)]



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       round(numpy.linalg.det(self.encrypt_key))

						if det < 0:
									_UpperCAmelCase			=       det % len(self.key_string)

						_UpperCAmelCase			=       len(self.key_string)
						if greatest_common_divisor(A			,		len(self.key_string)) != 1:
									_UpperCAmelCase			=       (
									    F"determinant modular {req_l} of encryption key({det}) "
									    F"is not co prime w.r.t {req_l}.\nTry another key."
									)
									raise ValueError(A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       [char for char in text.upper() if char in self.key_string]

						_UpperCAmelCase			=       chars[-1]
						while len(A) % self.break_key != 0:
									chars.append(A)

						return "".join(A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.process_text(text.upper())
						_UpperCAmelCase			=       ''

						for i in range(0			,		len(A) - self.break_key + 1			,		self.break_key):
									_UpperCAmelCase			=       text[i : i + self.break_key]
									_UpperCAmelCase			=       [self.replace_letters(A) for char in batch]
									_UpperCAmelCase			=       numpy.array([vec]).T
									_UpperCAmelCase			=       self.modulus(self.encrypt_key.dot(A)).T.tolist()[
									    0
									]
									_UpperCAmelCase			=       ''.join(
									    self.replace_digits(A) for num in batch_encrypted)
									encrypted += encrypted_batch

						return encrypted



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   numpy.ndarray:



						"""simple docstring"""






						_UpperCAmelCase			=       round(numpy.linalg.det(self.encrypt_key))

						if det < 0:
									_UpperCAmelCase			=       det % len(self.key_string)
						_UpperCAmelCase			=       None
						for i in range(len(self.key_string)):
									if (det * i) % len(self.key_string) == 1:
												_UpperCAmelCase			=       i
												break

						_UpperCAmelCase			=       (
						    det_inv
						    * numpy.linalg.det(self.encrypt_key)
						    * numpy.linalg.inv(self.encrypt_key)
						)

						return self.to_int(self.modulus(A))



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.make_decrypt_key()
						_UpperCAmelCase			=       self.process_text(text.upper())
						_UpperCAmelCase			=       ''

						for i in range(0			,		len(A) - self.break_key + 1			,		self.break_key):
									_UpperCAmelCase			=       text[i : i + self.break_key]
									_UpperCAmelCase			=       [self.replace_letters(A) for char in batch]
									_UpperCAmelCase			=       numpy.array([vec]).T
									_UpperCAmelCase			=       self.modulus(decrypt_key.dot(A)).T.tolist()[0]
									_UpperCAmelCase			=       ''.join(
									    self.replace_digits(A) for num in batch_decrypted)
									decrypted += decrypted_batch

						return decrypted





def 							A     (					)    ->			None:







			'''simple docstring'''
			_UpperCAmelCase			=       int(input('Enter the order of the encryption key: '   )   )
			_UpperCAmelCase			=       []

			print('Enter each row of the encryption key with space separated integers'   )
			for _ in range(_UpperCAmelCase   ):
						_UpperCAmelCase			=       [int(_UpperCAmelCase   ) for x in input().split()]
						hill_matrix.append(_UpperCAmelCase   )

			_UpperCAmelCase			=       HillCipher(numpy.array(_UpperCAmelCase   )   )

			print('Would you like to encrypt or decrypt some text? (1 or 2)'   )
			_UpperCAmelCase			=       input('\n1. Encrypt\n2. Decrypt\n'   )
			if option == "1":
						_UpperCAmelCase			=       input('What text would you like to encrypt?: '   )
						print('Your encrypted text is:'   )
						print(hc.encrypt(_UpperCAmelCase   )   )
			elif option == "2":
						_UpperCAmelCase			=       input('What text would you like to decrypt?: '   )
						print('Your decrypted text is:'   )
						print(hc.decrypt(_UpperCAmelCase   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()
