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					:	Optional[int]       =  '''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							:   Any   , lowerCAmelCase							:   str   , lowerCAmelCase							:   int=None   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Tuple=None   , lowerCAmelCase							:   Optional[Any]=None   , lowerCAmelCase							:   Any=None   , lowerCAmelCase							:   Union[str, Any]=None   , ):
		"""simple docstring"""







		if attention_mask is None:
				SCREAMING_SNAKE_CASE_  :		int				      =    np.where(input_ids != config.pad_token_id   , 1   , 0	)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.where(decoder_input_ids != config.pad_token_id   , 1   , 0	)
		if head_mask is None:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    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__						:

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







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    shift_tokens_right(_A,1,2    )

							SCREAMING_SNAKE_CASE_  :		Union[str, 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=_A,)
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    prepare_blenderbot_inputs_dict(_A,_A,_A    )
							return config, inputs_dict

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_config_and_inputs()
							return config, inputs_dict

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Tuple,_A      :       Tuple,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    20
							SCREAMING_SNAKE_CASE_  :		Any				      =    model_class_name(_A    )

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

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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_  :		Dict				      =    model.decode(
							    decoder_input_ids[:, :-1],_A,decoder_attention_mask=_A,past_key_values=_A,decoder_position_ids=_A,)

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

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model.decode(_A,_A    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    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      :       int,_A      :       Tuple,_A      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    20
							SCREAMING_SNAKE_CASE_  :		int				      =    model_class_name(_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model.encode(inputs_dict["input_ids"]    )

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							SCREAMING_SNAKE_CASE_  :		Dict				      =    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_  :		Optional[int]				      =    model.init_cache(decoder_input_ids.shape[0],_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    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_  :		Dict				      =    model.decode(
							    decoder_input_ids[:, :-1],_A,decoder_attention_mask=_A,past_key_values=_A,decoder_position_ids=_A,)
							SCREAMING_SNAKE_CASE_  :		str				      =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"    )
							SCREAMING_SNAKE_CASE_  :		int				      =    model.decode(
							    decoder_input_ids[:, -1:],_A,past_key_values=outputs_cache.past_key_values,decoder_attention_mask=_A,decoder_position_ids=_A,)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model.decode(_A,_A,decoder_attention_mask=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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					):
					A	       =							99

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    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_  :		Any				      =    input_ids.shape[0]
							SCREAMING_SNAKE_CASE_  :		Dict				      =    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      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    self._get_config_and_data()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    FlaxBlenderbotSmallForConditionalGeneration(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    lm_model(input_ids=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape,_A    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],dtype=np.intaa    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    shift_tokens_right(_A,1,2    )
							SCREAMING_SNAKE_CASE_  :		int				      =    np.equal(_A,1    ).astype(np.floataa    ).sum()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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 		a__						(					A__    ,		unittest.TestCase    ,		A__					):
					A	       =							True
					A	       =							(
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					A	       =							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    FlaxBlenderbotSmallModelTester(self    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		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(_A,_A,_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    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_  :		Optional[int]				      =    self._prepare_for_class(_A,_A    )
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_class(_A    )

											@jax.jit
											def encode_jitted(_A      :       Dict,_A      :       Optional[int]=None,**_A      :       int    ):
													return model.encode(input_ids=_A,attention_mask=_A    )

											with self.subTest("JIT Enabled"    ):
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    encode_jitted(**_A    ).to_tuple()

											with self.subTest("JIT Disabled"    ):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE_  :		Dict				      =    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 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    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_  :		str				      =    model_class(_A    )
											SCREAMING_SNAKE_CASE_  :		int				      =    model.encode(inputs_dict["input_ids"],inputs_dict["attention_mask"]    )

											SCREAMING_SNAKE_CASE_  :		Dict				      =    {
											    "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      :       str,_A      :       Any,_A      :       int    ):
													return model.decode(
													    decoder_input_ids=_A,decoder_attention_mask=_A,encoder_outputs=_A,)

											with self.subTest("JIT Enabled"    ):
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    decode_jitted(**_A    ).to_tuple()

											with self.subTest("JIT Disabled"    ):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"    )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									SCREAMING_SNAKE_CASE_  :		str				      =    np.ones((1, 1)    ) * model.config.eos_token_id
									SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A    )
									self.assertIsNotNone(_A    )




'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				return int((input_a, input_a).count(0     ) != 0     )







def 			snake_case_   (  ):







				"""simple docstring"""





				assert nand_gate(0							,      0     ) == 1
				assert nand_gate(0							,      1     ) == 1
				assert nand_gate(1							,      0     ) == 1
				assert nand_gate(1							,      1     ) == 0


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




