'''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
			lowerCAmelCase_	:		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 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=None     ,UpperCAmelCase=None     ,UpperCAmelCase=None     ,UpperCAmelCase=None     ,UpperCAmelCase=None     ,UpperCAmelCase=None     ,)     ->			Tuple:




						'''simple docstring'''
						if attention_mask is None:
												_UpperCamelCase			:			Dict   			=						np.where(input_ids != config.pad_token_id     ,1     ,0					)
						if decoder_attention_mask is None:
												_UpperCamelCase			:			str   			=						np.where(decoder_input_ids != config.pad_token_id     ,1     ,0					)
						if head_mask is None:
												_UpperCamelCase			:			Optional[Any]   			=						np.ones((config.encoder_layers, config.encoder_attention_heads)					)
						if decoder_head_mask is None:
												_UpperCamelCase			:			Optional[Any]   			=						np.ones((config.decoder_layers, config.decoder_attention_heads)					)
						if cross_attn_head_mask is None:
												_UpperCamelCase			:			Optional[int]   			=						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 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





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


										'''simple docstring'''




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





				def  snake_case__				(						self					:						str					)							->Any:


										'''simple docstring'''




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

										_UpperCamelCase			:			Optional[Any]   			=						shift_tokens_right(lowercase__       ,		1       ,		2					)

										_UpperCamelCase			:			Optional[int]   			=						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=lowercase__       ,		)
										_UpperCamelCase			:			Dict   			=						prepare_blenderbot_inputs_dict(lowercase__       ,		lowercase__       ,		lowercase__					)
										return config, inputs_dict





				def  snake_case__				(						self					:						str					)							->Any:


										'''simple docstring'''




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





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


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						20
										_UpperCamelCase			:			int   			=						model_class_name(lowercase__					)

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

										_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										_UpperCamelCase			:			Tuple   			=						model.init_cache(decoder_input_ids.shape[0]       ,		lowercase__       ,		lowercase__					)
										_UpperCamelCase			:			Union[str, Any]   			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,		dtype="i4"					)

										_UpperCamelCase			:			Optional[int]   			=						jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,		)
										_UpperCamelCase			:			Dict   			=						model.decode(
										    decoder_input_ids[:, :-1]       ,		lowercase__       ,		decoder_attention_mask=lowercase__       ,		past_key_values=lowercase__       ,		decoder_position_ids=lowercase__       ,		)

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

										_UpperCamelCase			:			List[str]   			=						model.decode(lowercase__       ,		lowercase__					)

										_UpperCamelCase			:			Union[str, 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  snake_case__				(						self					:						Dict       ,		lowercase__					:						str       ,		lowercase__					:						Tuple       ,		lowercase__					:						Optional[int]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						20
										_UpperCamelCase			:			Tuple   			=						model_class_name(lowercase__					)

										_UpperCamelCase			:			Optional[Any]   			=						model.encode(inputs_dict["input_ids"]					)

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

										_UpperCamelCase			:			Optional[int]   			=						jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
										    ]       ,		axis=-1       ,		)

										_UpperCamelCase			:			List[str]   			=						model.init_cache(decoder_input_ids.shape[0]       ,		lowercase__       ,		lowercase__					)
										_UpperCamelCase			:			Any   			=						jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]       ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,		)

										_UpperCamelCase			:			List[str]   			=						model.decode(
										    decoder_input_ids[:, :-1]       ,		lowercase__       ,		decoder_attention_mask=lowercase__       ,		past_key_values=lowercase__       ,		decoder_position_ids=lowercase__       ,		)
										_UpperCamelCase			:			List[Any]   			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,		dtype="i4"					)
										_UpperCamelCase			:			Optional[int]   			=						model.decode(
										    decoder_input_ids[:, -1:]       ,		lowercase__       ,		past_key_values=outputs_cache.past_key_values       ,		decoder_attention_mask=lowercase__       ,		decoder_position_ids=lowercase__       ,		)

										_UpperCamelCase			:			Dict   			=						model.decode(lowercase__       ,		lowercase__       ,		decoder_attention_mask=lowercase__					)

										_UpperCamelCase			:			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}'''					)





@require_flax
class 		SCREAMING_SNAKE_CASE     (					unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							99





				def  snake_case__				(						self					:						Tuple					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			List[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       ,		)

										_UpperCamelCase			:			List[str]   			=						input_ids.shape[0]
										_UpperCamelCase			:			Tuple   			=						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  snake_case__				(						self					:						Any					)							->int:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						self._get_config_and_data()
										_UpperCamelCase			:			int   			=						FlaxBlenderbotSmallForConditionalGeneration(lowercase__					)
										_UpperCamelCase			:			Optional[int]   			=						lm_model(input_ids=lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						(batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape       ,		lowercase__					)





				def  snake_case__				(						self					:						Any					)							->Tuple:


										'''simple docstring'''




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







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


										'''simple docstring'''




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





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







				'''simple docstring'''
				UpperCAmelCase__							=							True
				UpperCAmelCase__							=							(
				    (
				        FlaxBlenderbotSmallModel,
				        FlaxBlenderbotSmallForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__							=							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





				def  snake_case__				(						self					:						Tuple					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						FlaxBlenderbotSmallModelTester(self					)





				def  snake_case__				(						self					:						Tuple					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowercase__       ,		lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						Any					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			str   			=						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(lowercase__       ,		lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						List[Any]					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																						_UpperCamelCase			:			List[Any]   			=						self._prepare_for_class(lowercase__       ,		lowercase__					)
																						_UpperCamelCase			:			Any   			=						model_class(lowercase__					)

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

																						with self.subTest("JIT Enabled"					):
																												_UpperCamelCase			:			List[Any]   			=						encode_jitted(**lowercase__					).to_tuple()

																						with self.subTest("JIT Disabled"					):
																												with jax.disable_jit():
																																		_UpperCamelCase			:			Union[str, Any]   			=						encode_jitted(**lowercase__					).to_tuple()

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





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


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			str   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																						_UpperCamelCase			:			List[Any]   			=						model_class(lowercase__					)
																						_UpperCamelCase			:			Dict   			=						model.encode(inputs_dict["input_ids"]       ,		inputs_dict["attention_mask"]					)

																						_UpperCamelCase			:			Optional[int]   			=						{
																						    "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(lowercase__					:						str       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Dict					):
																												return model.decode(
																												    decoder_input_ids=lowercase__       ,		decoder_attention_mask=lowercase__       ,		encoder_outputs=lowercase__       ,		)

																						with self.subTest("JIT Enabled"					):
																												_UpperCamelCase			:			Any   			=						decode_jitted(**lowercase__					).to_tuple()

																						with self.subTest("JIT Disabled"					):
																												with jax.disable_jit():
																																		_UpperCamelCase			:			Union[str, Any]   			=						decode_jitted(**lowercase__					).to_tuple()

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







				@slow
				def  snake_case__				(						self					:						Optional[int]					)							->Dict:


										'''simple docstring'''




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



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


