





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
	lowerCamelCase : List[str]						   =       '''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_		(     lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						List[Any]=None       ,       lowerCAmelCase_       :						Optional[int]=None       ,       lowerCAmelCase_       :						Dict=None       ,       lowerCAmelCase_       :						Union[str, Any]=None       ,       lowerCAmelCase_       :						Optional[int]=None       ,       lowerCAmelCase_       :						Dict=None       ,       ):
							if attention_mask is None:
														__lowercase      :							Union[str, Any]				=     np.where(input_ids != config.pad_token_id       ,       1       ,       0      )
							if decoder_attention_mask is None:
														__lowercase      :							str				=     np.where(decoder_input_ids != config.pad_token_id       ,       1       ,       0      )
							if head_mask is None:
														__lowercase      :							int				=     np.ones((config.encoder_layers, config.encoder_attention_heads)      )
							if decoder_head_mask is None:
														__lowercase      :							Optional[int]				=     np.ones((config.decoder_layers, config.decoder_attention_heads)      )
							if cross_attn_head_mask is None:
														__lowercase      :							Any				=     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     :

					'''simple docstring'''

					def __init__(  self				:	Any		,     __a				:	int		,     __a				:	List[Any]=13		,     __a				:	int=7		,     __a				:	Dict=True		,     __a				:	List[str]=False		,     __a				:	Tuple=99		,     __a				:	str=16		,     __a				:	Optional[Any]=2		,     __a				:	int=4		,     __a				:	Dict=4		,     __a				:	Optional[Any]="gelu"		,     __a				:	List[str]=0.1		,     __a				:	Optional[Any]=0.1		,     __a				:	Tuple=32		,     __a				:	List[Any]=2		,     __a				:	Tuple=1		,     __a				:	Optional[int]=0		,     __a				:	Dict=0.02		,     )			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     parent
												__lowercase      :							int				=     batch_size
												__lowercase      :							Dict				=     seq_length
												__lowercase      :							Dict				=     is_training
												__lowercase      :							List[Any]				=     use_labels
												__lowercase      :							str				=     vocab_size
												__lowercase      :							Optional[int]				=     hidden_size
												__lowercase      :							Optional[Any]				=     num_hidden_layers
												__lowercase      :							Tuple				=     num_attention_heads
												__lowercase      :							str				=     intermediate_size
												__lowercase      :							Union[str, Any]				=     hidden_act
												__lowercase      :							Any				=     hidden_dropout_prob
												__lowercase      :							List[Any]				=     attention_probs_dropout_prob
												__lowercase      :							List[Any]				=     max_position_embeddings
												__lowercase      :							Dict				=     eos_token_id
												__lowercase      :							Any				=     pad_token_id
												__lowercase      :							Dict				=     bos_token_id
												__lowercase      :							Optional[Any]				=     initializer_range



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  str:


												"""simple docstring"""

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

												__lowercase      :							int				=     shift_tokens_right(lowerCamelCase_		,     1		,     2							)

												__lowercase      :							List[Any]				=     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_		,     )
												__lowercase      :							Optional[int]				=     prepare_blenderbot_inputs_dict(lowerCamelCase_		,     lowerCamelCase_		,     lowerCamelCase_							)
												return config, inputs_dict



					def       lowerCAmelCase				(  self				:	Any							)			->  List[Any]:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Tuple				=     self.prepare_config_and_inputs()
												return config, inputs_dict



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str		,     __a				:	Optional[Any]		,     __a				:	int							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Dict				=     20
												__lowercase      :							Dict				=     model_class_name(lowerCamelCase_							)

												__lowercase      :							Dict				=     model.encode(inputs_dict["""input_ids"""]							)

												__lowercase			,			__lowercase      :							int				=     (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												__lowercase      :							Union[str, Any]				=     model.init_cache(decoder_input_ids.shape[0]		,     lowerCamelCase_		,     lowerCamelCase_							)
												__lowercase      :							int				=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,     dtype="""i4"""							)

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

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

												__lowercase      :							Optional[int]				=     model.decode(lowerCamelCase_		,     lowerCamelCase_							)

												__lowercase      :							Tuple				=     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				:	List[Any]		,     __a				:	Tuple		,     __a				:	int							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							int				=     20
												__lowercase      :							Any				=     model_class_name(lowerCamelCase_							)

												__lowercase      :							Optional[int]				=     model.encode(inputs_dict["""input_ids"""]							)

												__lowercase			,			__lowercase      :							Union[str, Any]				=     (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

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

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

												__lowercase      :							Union[str, Any]				=     model.decode(
												    decoder_input_ids[:, :-1]		,     lowerCamelCase_		,     decoder_attention_mask=lowerCamelCase_		,     past_key_values=lowerCamelCase_		,     decoder_position_ids=lowerCamelCase_		,     )
												__lowercase      :							Tuple				=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,     dtype="""i4"""							)
												__lowercase      :							List[Any]				=     model.decode(
												    decoder_input_ids[:, -1:]		,     lowerCamelCase_		,     past_key_values=outputs_cache.past_key_values		,     decoder_attention_mask=lowerCamelCase_		,     decoder_position_ids=lowerCamelCase_		,     )

												__lowercase      :							int				=     model.decode(lowerCamelCase_		,     lowerCamelCase_		,     decoder_attention_mask=lowerCamelCase_							)

												__lowercase      :							str				=     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			):

					'''simple docstring'''

					_A						:			int									=      99



					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     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		,     )

												__lowercase      :							List[Any]				=     input_ids.shape[0]
												__lowercase      :							Any				=     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]							)			->  List[str]:


												"""simple docstring"""

												__lowercase			,			__lowercase			,			__lowercase      :							int				=     self._get_config_and_data()
												__lowercase      :							Dict				=     FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase_							)
												__lowercase      :							str				=     lm_model(input_ids=lowerCamelCase_							)
												__lowercase      :							Tuple				=     (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape		,     lowerCamelCase_							)



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


												"""simple docstring"""

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



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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,     dtype=np.intaa							)
												__lowercase      :							Optional[int]				=     shift_tokens_right(lowerCamelCase_		,     1		,     2							)
												__lowercase      :							Optional[Any]				=     np.equal(lowerCamelCase_		,     1							).astype(np.floataa							).sum()
												__lowercase      :							Union[str, Any]				=     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 		lowerCAmelCase     ( a__      ,       unittest.TestCase      ,       a__			):

					'''simple docstring'''

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



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


												"""simple docstring"""

												__lowercase      :							Any				=     FlaxBlenderbotSmallModelTester(self							)



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


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Optional[Any]				=     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       lowerCAmelCase				(  self				:	Dict							)			->  Dict:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Optional[Any]				=     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       lowerCAmelCase				(  self				:	Optional[int]							)			->  Tuple:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							List[str]				=     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__							):
																										__lowercase      :							List[str]				=     self._prepare_for_class(lowerCamelCase_		,     lowerCamelCase_							)
																										__lowercase      :							Dict				=     model_class(lowerCamelCase_							)

																										@jax.jit
																										def encode_jitted(__a				:	Tuple		,     __a				:	int=None		,     **__a				:	List[Any]							):
																																	return model.encode(input_ids=lowerCamelCase_		,     attention_mask=lowerCamelCase_							)

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

																										with self.subTest("""JIT Disabled"""							):
																																	with jax.disable_jit():
																																								__lowercase      :							int				=     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       lowerCAmelCase				(  self				:	Tuple							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Tuple				=     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__							):
																										__lowercase      :							List[Any]				=     model_class(lowerCamelCase_							)
																										__lowercase      :							Union[str, Any]				=     model.encode(inputs_dict["""input_ids"""]		,     inputs_dict["""attention_mask"""]							)

																										__lowercase      :							Tuple				=     {
																										    """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				:	Tuple		,     __a				:	int		,     __a				:	Any							):
																																	return model.decode(
																																	    decoder_input_ids=lowerCamelCase_		,     decoder_attention_mask=lowerCamelCase_		,     encoder_outputs=lowerCamelCase_		,     )

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

																										with self.subTest("""JIT Disabled"""							):
																																	with jax.disable_jit():
																																								__lowercase      :							Tuple				=     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       lowerCAmelCase				(  self				:	Dict							)			->  Union[str, Any]:


												"""simple docstring"""

												for model_class_name in self.all_model_classes:
																			__lowercase      :							Dict				=     model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""							)
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			__lowercase      :							Optional[int]				=     np.ones((1, 1)							) * model.config.eos_token_id
																			__lowercase      :							Optional[Any]				=     model(lowerCamelCase_							)
																			self.assertIsNotNone(lowerCamelCase_							)







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(1      ) != 0      )
def       snake_case_		(     ):
							assert or_gate(0       ,       0      ) == 0
							assert or_gate(0       ,       1      ) == 1
							assert or_gate(1       ,       0      ) == 1
							assert or_gate(1       ,       1      ) == 1


if __name__ == "__main__":
	print(or_gate(0, 1))
	print(or_gate(1, 0))
	print(or_gate(0, 0))
	print(or_gate(1, 1))