


'''simple docstring'''






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       _snake_case     (  A				,					A				,					A=None				,					A=None				,					A=None				,					A=None				,					A=None				,					A=None				,					)		->					Tuple:
							if attention_mask is None:
														lowerCAmelCase__      =			np.where(input_ids != config.pad_token_id				,					1				,					0 )
							if decoder_attention_mask is None:
														lowerCAmelCase__      =			np.where(decoder_input_ids != config.pad_token_id				,					1				,					0 )
							if head_mask is None:
														lowerCAmelCase__      =			np.ones((config.encoder_layers, config.encoder_attention_heads) )
							if decoder_head_mask is None:
														lowerCAmelCase__      =			np.ones((config.decoder_layers, config.decoder_attention_heads) )
							if cross_attn_head_mask is None:
														lowerCAmelCase__      =			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 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=13      ,					lowerCamelCase_=7      ,					lowerCamelCase_=True      ,					lowerCamelCase_=False      ,					lowerCamelCase_=99      ,					lowerCamelCase_=16      ,					lowerCamelCase_=2      ,					lowerCamelCase_=4      ,					lowerCamelCase_=4      ,					lowerCamelCase_="gelu"      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=32      ,					lowerCamelCase_=2      ,					lowerCamelCase_=1      ,					lowerCamelCase_=0      ,					lowerCamelCase_=0.02      ,					) ->							Optional[Any]:
														lowerCAmelCase__      =			parent
														lowerCAmelCase__      =			batch_size
														lowerCAmelCase__      =			seq_length
														lowerCAmelCase__      =			is_training
														lowerCAmelCase__      =			use_labels
														lowerCAmelCase__      =			vocab_size
														lowerCAmelCase__      =			hidden_size
														lowerCAmelCase__      =			num_hidden_layers
														lowerCAmelCase__      =			num_attention_heads
														lowerCAmelCase__      =			intermediate_size
														lowerCAmelCase__      =			hidden_act
														lowerCAmelCase__      =			hidden_dropout_prob
														lowerCAmelCase__      =			attention_probs_dropout_prob
														lowerCAmelCase__      =			max_position_embeddings
														lowerCAmelCase__      =			eos_token_id
														lowerCAmelCase__      =			pad_token_id
														lowerCAmelCase__      =			bos_token_id
														lowerCAmelCase__      =			initializer_range







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,					self.vocab_size     )      ,					3      ,					self.vocab_size     )
														lowerCAmelCase__      =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,					dtype=np.intaa     ))      ,					-1     )

														lowerCAmelCase__      =			shift_tokens_right(lowerCamelCase_      ,					1      ,					2     )

														lowerCAmelCase__      =			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=lowerCamelCase_      ,					)
														lowerCAmelCase__      =			prepare_blenderbot_inputs_dict(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )
														return config, inputs_dict







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.prepare_config_and_inputs()
														return config, inputs_dict







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			20
														lowerCAmelCase__      =			model_class_name(lowerCamelCase_     )

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

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

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

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

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

														lowerCAmelCase__      =			model.decode(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			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      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			20
														lowerCAmelCase__      =			model_class_name(lowerCamelCase_     )

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

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

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

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

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

														lowerCAmelCase__      =			model.decode(lowerCamelCase_      ,					lowerCamelCase_      ,					decoder_attention_mask=lowerCamelCase_     )

														lowerCAmelCase__      =			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 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     int					=   9_9







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			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__      =			input_ids.shape[0]
														lowerCAmelCase__      =			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     ) ->							List[str]:
														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			self._get_config_and_data()
														lowerCAmelCase__      =			FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_     )
														lowerCAmelCase__      =			lm_model(input_ids=lowerCamelCase_     )
														lowerCAmelCase__      =			(batch_size, input_ids.shape[1], config.vocab_size)
														self.assertEqual(outputs['''logits'''].shape      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			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__      =			FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_     )
														lowerCAmelCase__      =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,					dtype=np.intaa     )
														lowerCAmelCase__      =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,					dtype=np.intaa     )
														lowerCAmelCase__      =			lm_model(input_ids=lowerCamelCase_      ,					decoder_input_ids=lowerCamelCase_     )
														lowerCAmelCase__      =			(*summary.shape, config.vocab_size)
														self.assertEqual(outputs['''logits'''].shape      ,					lowerCamelCase_     )







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




@require_flax
class 	a__       (							a__       ,       unittest.TestCase       ,       a__						):





							'''simple docstring'''






							lowercase__    :     Optional[int]					=   True
							lowercase__    :     Tuple					=   (
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							lowercase__    :     List[Any]					=   (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			FlaxBlenderbotSmallModelTester(self     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			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(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__     ):
																												lowerCAmelCase__      =			self._prepare_for_class(lowerCamelCase_      ,					lowerCamelCase_     )
																												lowerCAmelCase__      =			model_class(lowerCamelCase_     )

																												@jax.jit
																												def encode_jitted(lowerCamelCase_      ,					lowerCamelCase_=None      ,					**lowerCamelCase_     ):
																																			return model.encode(input_ids=lowerCamelCase_      ,					attention_mask=lowerCamelCase_     )

																												with self.subTest('''JIT Enabled'''     ):
																																			lowerCAmelCase__      =			encode_jitted(**lowerCamelCase_     ).to_tuple()

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

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

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

																												lowerCAmelCase__      =			{
																												    '''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(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ):
																																			return model.decode(
																																			    decoder_input_ids=lowerCamelCase_      ,					decoder_attention_mask=lowerCamelCase_      ,					encoder_outputs=lowerCamelCase_      ,					)

																												with self.subTest('''JIT Enabled'''     ):
																																			lowerCAmelCase__      =			decode_jitted(**lowerCamelCase_     ).to_tuple()

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

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







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														for model_class_name in self.all_model_classes:
																					lowerCAmelCase__      =			model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''     )
																					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																					lowerCAmelCase__      =			np.ones((1, 1)     ) * model.config.eos_token_id
																					lowerCAmelCase__      =			model(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )



'''simple docstring'''






from __future__ import annotations




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_ = 0     ) ->							List[Any]:
														lowerCAmelCase__      =			key







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							list[str]:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			key or self.__key or 1

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

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							list[str]:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			key or self.__key or 1

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

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 0     ) ->							str:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														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(lowerCamelCase_     ) ^ key     )

														return ans







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 0     ) ->							str:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

														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(lowerCamelCase_     ) ^ key     )

														return ans







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 0     ) ->							bool:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

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

														except OSError:
																					return False

														return True







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							bool:
														assert isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) and isinstance(lowerCamelCase_      ,					lowerCamelCase_     )

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

														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")