





"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       float       ,			_lowercase		:       float   )					->float:






	'''simple docstring'''






	return price * (1 + tax_rate)


if __name__ == "__main__":
		print(F'''{price_plus_tax(100, 0.25) = }''')
		print(F'''{price_plus_tax(125.50, 0.05) = }''')









"""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

		a		:					Optional[Any]	      =     '''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       Any       ,			_lowercase		:       Union[str, Any]=None   )					->Union[str, Any]:






	'''simple docstring'''






	if rng is None:
		a :						Dict      =							random.Random()

	a :						Any      =							1
	for dim in shape:
		total_dims *= dim

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

	a :						Dict      =							np.array(_lowercase       ,			dtype=jnp.intaa   ).reshape(_lowercase   )

	return output






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       int=None   )					->Optional[Any]:






	'''simple docstring'''






	a :						str      =							ids_tensor(_lowercase       ,			vocab_size=2       ,			rng=_lowercase   )
	# make sure that at least one token is attended to for each batch
	a :						Union[str, Any]      =							1
	return attn_mask


@require_flax
class 			__UpperCamelCase							:
							lowerCamelCase  :   Dict    =None
							lowerCamelCase  :   List[Any]    =()






							def        __a  (  self )      ->					Union[str, Any]:
								a,			a :						List[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								a :						Any      =							2
								a :						List[Any]      =							inputs["input_ids"].shape[-1] // 2
								a :						Union[str, Any]      =							inputs["input_ids"][:max_batch_size, :sequence_length]

								a :						Optional[Any]      =							jnp.ones_like(lowerCAmelCase__ )
								a :						Any      =							attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								a :						int      =							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()`
									a :						List[str]      =							config.eos_token_id
								return config, input_ids, attention_mask, max_length






							@is_pt_flax_cross_test
							def        __a  (  self )      ->					List[Any]:
								a,			a,			a,			a :						int      =							self._get_input_ids_and_config()
								a :						Dict      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							0

								for model_class in self.all_generative_model_classes:
									a :						List[str]      =							model_class(lowerCAmelCase__ )

									a :						int      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
									a :						List[Any]      =							getattr(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Any      =							pt_model_class(lowerCAmelCase__ ).eval()
									a :						int      =							load_flax_weights_in_pytorch_model(lowerCAmelCase__ ,   flax_model.params )

									a :						Optional[Any]      =							flax_model.generate(lowerCAmelCase__ ).sequences
									a :						Tuple      =							pt_model.generate(torch.tensor(lowerCAmelCase__ ,   dtype=torch.long ) )

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
										a :						List[str]      =							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						List[str]      =							False
								a :						Tuple      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

									a :						Union[str, Any]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Dict      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Any:
								a,			a,			a,			a :						List[Any]      =							self._get_input_ids_and_config()
								a :						Dict      =							True
								a :						Any      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						int      =							model_class(lowerCAmelCase__ )

									a :						Optional[int]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						List[Any]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Dict:
								a,			a,			a,			a :						str      =							self._get_input_ids_and_config()
								a :						str      =							False
								a :						Union[str, Any]      =							max_length
								a :						Optional[int]      =							2

								for model_class in self.all_generative_model_classes:
									a :						List[Any]      =							model_class(lowerCAmelCase__ )

									a :						Optional[Any]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Union[str, Any]      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						Optional[Any]      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							2
								a :						Any      =							2

								for model_class in self.all_generative_model_classes:
									a :						Any      =							model_class(lowerCAmelCase__ )

									a :						List[str]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[0] ,   input_ids.shape[0] * config.num_return_sequences )






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()
								a :						Optional[int]      =							True
								a :						Dict      =							max_length
								a :						Dict      =							0.8
								a :						Any      =							10
								a :						Union[str, Any]      =							0.3
								a :						List[Any]      =							1
								a :						Optional[int]      =							8
								a :						List[Any]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Tuple      =							model_class(lowerCAmelCase__ )

									a :						Any      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Tuple      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()
								a :						Dict      =							max_length
								a :						List[str]      =							1
								a :						List[str]      =							8
								a :						List[str]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

									a :						str      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[str]      =							jit(model.generate )
									a :						Union[str, Any]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					int:
								a,			a,			a,			a :						Tuple      =							self._get_input_ids_and_config()
								a :						List[Any]      =							max_length
								a :						Dict      =							2
								a :						List[Any]      =							1
								a :						List[str]      =							8
								a :						Any      =							9

								for model_class in self.all_generative_model_classes:
									a :						Optional[int]      =							model_class(lowerCAmelCase__ )

									a :						List[str]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						int      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()

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

								a :						Any      =							False
								a :						Union[str, Any]      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Union[str, Any]      =							model_class(lowerCAmelCase__ )

									a :						Union[str, Any]      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					List[str]:
								a,			a,			a,			a :						Union[str, Any]      =							self._get_input_ids_and_config()

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

								a :						Dict      =							True
								a :						List[Any]      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						List[str]      =							model_class(lowerCAmelCase__ )

									a :						int      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Any      =							jit(model.generate )
									a :						List[str]      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					str:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()

								# pad attention mask on the left
								a :						Union[str, Any]      =							attention_mask.at[(0, 0)].set(0 )

								a :						int      =							2
								a :						Any      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Optional[Any]      =							model_class(lowerCAmelCase__ )

									a :						List[Any]      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

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


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					List[Any]:
								a :						Any      =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
								a :						Tuple      =							FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )

								a :						int      =							"Hello world"
								a :						int      =							tokenizer(lowerCAmelCase__ ,   return_tensors="np" ).input_ids

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

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


