

'''simple docstring'''

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			0  # The first color of the flag.
SCREAMING_SNAKE_CASE							:				List[str]     	=			1  # The second color of the flag.
SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			2  # The third color of the flag.
SCREAMING_SNAKE_CASE							:				Tuple     	=			(red, white, blue)




def 							_UpperCamelCase				(   lowerCAmelCase__:					list   )      -> list:
							if not sequence:
														return []
							if len(lowerCAmelCase__   ) == 1:
														return list(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				0
							SCREAMING_SNAKE_CASE_				=				len(lowerCAmelCase__   ) - 1
							SCREAMING_SNAKE_CASE_				=				0
							while mid <= high:
														if sequence[mid] == colors[0]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[mid], sequence[low]
																					low += 1
																					mid += 1
														elif sequence[mid] == colors[1]:
																					mid += 1
														elif sequence[mid] == colors[2]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[high], sequence[mid]
																					high -= 1
														else:
																					SCREAMING_SNAKE_CASE_				=				F"""The elements inside the sequence must contains only {colors} values"""
																					raise ValueError(lowerCAmelCase__   )
							return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE							:				str     	=			input("Enter numbers separated by commas:\n").strip()
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(item.strip()) for item in user_input.split(",")]
							print(f"{dutch_national_flag_sort(unsorted)}")








'''simple docstring'''

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					int		,lowerCAmelCase__:					Tuple=None   )      -> Union[str, Any]:
							if rng is None:
														SCREAMING_SNAKE_CASE_				=				random.Random()

							SCREAMING_SNAKE_CASE_				=				1
							for dim in shape:
														total_dims *= dim

							SCREAMING_SNAKE_CASE_				=				[]
							for _ in range(lowerCAmelCase__   ):
														values.append(rng.randint(0		,vocab_size - 1   )   )

							SCREAMING_SNAKE_CASE_				=				np.array(lowerCAmelCase__		,dtype=jnp.intaa   ).reshape(lowerCAmelCase__   )

							return output




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					str=None   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				ids_tensor(lowerCAmelCase__		,vocab_size=2		,rng=lowerCAmelCase__   )
							# make sure that at least one token is attended to for each batch
							SCREAMING_SNAKE_CASE_				=				1
							return attn_mask






@require_flax
class    snake_case     :






				"""simple docstring"""
				_a    	=     None
				_a    	=     ()



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				inputs['input_ids'].shape[-1] // 2
											SCREAMING_SNAKE_CASE_				=				inputs['input_ids'][:max_batch_size, :sequence_length]

											SCREAMING_SNAKE_CASE_				=				jnp.ones_like(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											SCREAMING_SNAKE_CASE_				=				input_ids.shape[-1] + 5
											if config.eos_token_id is not None and config.pad_token_id is None:
																		# hack to allow generate for models such as GPT2 as is done in `generate()`
																		SCREAMING_SNAKE_CASE_				=				config.eos_token_id
											return config, input_ids, attention_mask, max_length



				@is_pt_flax_cross_test
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				pt_model_class(_lowercase       ).eval()
																		SCREAMING_SNAKE_CASE_				=				load_flax_weights_in_pytorch_model(_lowercase,     flax_model.params       )

																		SCREAMING_SNAKE_CASE_				=				flax_model.generate(_lowercase       ).sequences
																		SCREAMING_SNAKE_CASE_				=				pt_model.generate(torch.tensor(_lowercase,     dtype=torch.long       )       )

																		if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																									SCREAMING_SNAKE_CASE_				=				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																		self.assertListEqual(pt_generation_outputs.numpy().tolist(),     flax_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				2

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[0],     input_ids.shape[0] * config.num_return_sequences       )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				0.8
											SCREAMING_SNAKE_CASE_				=				10
											SCREAMING_SNAKE_CASE_				=				0.3
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()
											SCREAMING_SNAKE_CASE_				=				max_length
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				8
											SCREAMING_SNAKE_CASE_				=				9

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )





				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self._get_input_ids_and_config()

											# pad attention mask on the left
											SCREAMING_SNAKE_CASE_				=				attention_mask.at[(0, 0)].set(0       )

											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				max_length

											for model_class in self.all_generative_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model.generate(_lowercase,     attention_mask=_lowercase       ).sequences
																		self.assertEqual(generation_outputs.shape[-1],     _lowercase       )

																		SCREAMING_SNAKE_CASE_				=				jit(model.generate       )
																		SCREAMING_SNAKE_CASE_				=				jit_generate(_lowercase,     attention_mask=_lowercase       ).sequences

																		self.assertListEqual(generation_outputs.tolist(),     jit_generation_outputs.tolist()       )






@require_flax
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'       )
											SCREAMING_SNAKE_CASE_				=				FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'       )

											SCREAMING_SNAKE_CASE_				=				'Hello world'
											SCREAMING_SNAKE_CASE_				=				tokenizer(_lowercase,     return_tensors='np'       ).input_ids

											# typos are quickly detected (the correct argument is `do_sample`)
											with self.assertRaisesRegex(_lowercase,     'do_samples'       ):
																		model.generate(_lowercase,     do_samples=_lowercase       )

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(_lowercase,     'foo'       ):
																		SCREAMING_SNAKE_CASE_				=				{'foo': 'bar'}
																		model.generate(_lowercase,     **_lowercase       )





