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
		__A						:							Tuple							=  """platform"""

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	):
			'''simple docstring'''
			if attention_mask is None:
						SCREAMING_SNAKE_CASE           =					np.where(input_ids != config.pad_token_id					,	1					,	0   )
			if decoder_attention_mask is None:
						SCREAMING_SNAKE_CASE           =					np.where(decoder_input_ids != config.pad_token_id					,	1					,	0   )
			if head_mask is None:
						SCREAMING_SNAKE_CASE           =					np.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						SCREAMING_SNAKE_CASE           =					np.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      :




							'''simple docstring'''



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





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

										SCREAMING_SNAKE_CASE           =					shift_tokens_right(lowerCamelCase_       ,     1       ,     2			)

										SCREAMING_SNAKE_CASE           =					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_       ,     )
										SCREAMING_SNAKE_CASE           =					prepare_blenderbot_inputs_dict(lowerCamelCase_       ,     lowerCamelCase_       ,     lowerCamelCase_			)
										return config, inputs_dict





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.prepare_config_and_inputs()
										return config, inputs_dict





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	int       ,     a       :	Dict       ,     a       :	int			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					20
										SCREAMING_SNAKE_CASE           =					model_class_name(lowerCamelCase_			)

										SCREAMING_SNAKE_CASE           =					model.encode(inputs_dict["""input_ids"""]			)

										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

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

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

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

										SCREAMING_SNAKE_CASE           =					model.decode(lowerCamelCase_       ,     lowerCamelCase_			)

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Dict       ,     a       :	str       ,     a       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					20
										SCREAMING_SNAKE_CASE           =					model_class_name(lowerCamelCase_			)

										SCREAMING_SNAKE_CASE           =					model.encode(inputs_dict["""input_ids"""]			)

										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

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

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

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

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

										SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     9_9





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					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       ,     )

										SCREAMING_SNAKE_CASE           =					input_ids.shape[0]
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Tuple			)			->					List[str]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self._get_config_and_data()
										SCREAMING_SNAKE_CASE           =					FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_			)
										SCREAMING_SNAKE_CASE           =					lm_model(input_ids=lowerCamelCase_			)
										SCREAMING_SNAKE_CASE           =					(batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["""logits"""].shape       ,     lowerCamelCase_			)





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







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										SCREAMING_SNAKE_CASE           =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,     dtype=np.intaa			)
										SCREAMING_SNAKE_CASE           =					shift_tokens_right(lowerCamelCase_       ,     1       ,     2			)
										SCREAMING_SNAKE_CASE           =					np.equal(lowerCamelCase_       ,     1			).astype(np.floataa			).sum()
										SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   a__		,      unittest.TestCase		,      a__       ):




							'''simple docstring'''



							a__						  =     True
							a__						  =     (
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							a__						  =     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





							def    _UpperCAmelCase     (					self       :	int			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					FlaxBlenderbotSmallModelTester(self			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	str			)			->					Dict:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Tuple:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs_for_common()

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

																@jax.jit
																def encode_jitted(a       :	Dict       ,     a       :	int=None       ,     **a       :	List[str]			):
																			return model.encode(input_ids=lowerCamelCase_       ,     attention_mask=lowerCamelCase_			)

																with self.subTest("""JIT Enabled"""			):
																			SCREAMING_SNAKE_CASE           =					encode_jitted(**lowerCamelCase_			).to_tuple()

																with self.subTest("""JIT Disabled"""			):
																			with jax.disable_jit():
																						SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs_for_common()

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

																SCREAMING_SNAKE_CASE           =					{
																    """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       :	Union[str, Any]       ,     a       :	List[Any]       ,     a       :	Optional[int]			):
																			return model.decode(
																			    decoder_input_ids=lowerCamelCase_       ,     decoder_attention_mask=lowerCamelCase_       ,     encoder_outputs=lowerCamelCase_       ,     )

																with self.subTest("""JIT Enabled"""			):
																			SCREAMING_SNAKE_CASE           =					decode_jitted(**lowerCamelCase_			).to_tuple()

																with self.subTest("""JIT Disabled"""			):
																			with jax.disable_jit():
																						SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE           =					model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""			)
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													SCREAMING_SNAKE_CASE           =					np.ones((1, 1)			) * model.config.eos_token_id
													SCREAMING_SNAKE_CASE           =					model(lowerCamelCase_			)
													self.assertIsNotNone(lowerCamelCase_			)












from __future__ import annotations




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	int = 0			)			->					str:
										SCREAMING_SNAKE_CASE           =					key





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

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





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

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





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

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

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

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

										return ans





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

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

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

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

										return ans





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	str       ,     a       :	int = 0			)			->					bool:
										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    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int			)			->					bool:
										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")






