'''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

  UpperCamelCase__     :	List[str]				=			'0.12'  # assumed parallelism: 8


if is_torch_available():
  import torch







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				int  ,			_A					:				Optional[Any]=None    ):
   '''simple docstring'''







   if rng is None:
      UpperCAmelCase__							:				List[Any]   				=  random.Random()

   UpperCAmelCase__							:				Tuple   				=  1
   for dim in shape:
      total_dims *= dim

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

   UpperCAmelCase__							:				str   				=  np.array(_A  ,			dtype=jnp.intaa    ).reshape(_A    )

   return output







def __UpperCamelCase(   _A					:				str  ,			_A					:				Dict=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  ids_tensor(_A  ,			vocab_size=2  ,			rng=_A    )
   # make sure that at least one token is attended to for each batch
   UpperCAmelCase__							:				int   				=  1
   return attn_mask


@require_flax
class 				_lowercase	:
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   None
      UpperCAmelCase_      :      Dict      							=   ()




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self.model_tester.prepare_config_and_inputs_for_common()

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

         UpperCAmelCase__							:				List[Any]   				=  jnp.ones_like(lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         UpperCAmelCase__							:				Tuple   				=  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()`
            UpperCAmelCase__							:				List[Any]   				=  config.eos_token_id
         return config, input_ids, attention_mask, max_length




      @is_pt_flax_cross_test
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				Tuple   				=  False
         UpperCAmelCase__							:				Tuple   				=  max_length
         UpperCAmelCase__							:				List[Any]   				=  0

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				List[Any]   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				Tuple   				=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
            UpperCAmelCase__							:				Union[str, Any]   				=  getattr(lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Dict   				=  pt_model_class(lowerCamelCase_							).eval()
            UpperCAmelCase__							:				Union[str, Any]   				=  load_flax_weights_in_pytorch_model(lowerCamelCase_					,flax_model.params							)

            UpperCAmelCase__							:				str   				=  flax_model.generate(lowerCamelCase_							).sequences
            UpperCAmelCase__							:				str   				=  pt_model.generate(torch.tensor(lowerCamelCase_					,dtype=torch.long							)							)

            if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               UpperCAmelCase__							:				Optional[Any]   				=  flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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




      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				Dict   				=  False
         UpperCAmelCase__							:				str   				=  max_length

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Tuple   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				int   				=  model.generate(lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[-1]					,lowerCamelCase_							)

            UpperCAmelCase__							:				int   				=  jit(model.generate							)
            UpperCAmelCase__							:				List[str]   				=  jit_generate(lowerCamelCase_							).sequences

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				List[str]   				=  True
         UpperCAmelCase__							:				Dict   				=  max_length

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				str   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				Dict   				=  model.generate(lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[-1]					,lowerCamelCase_							)

            UpperCAmelCase__							:				Tuple   				=  jit(model.generate							)
            UpperCAmelCase__							:				List[str]   				=  jit_generate(lowerCamelCase_							).sequences

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




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				Union[str, Any]   				=  False
         UpperCAmelCase__							:				Dict   				=  max_length
         UpperCAmelCase__							:				str   				=  2

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Optional[int]   				=  model_class(lowerCamelCase_							)

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

            UpperCAmelCase__							:				Dict   				=  jit(model.generate							)
            UpperCAmelCase__							:				Optional[int]   				=  jit_generate(lowerCamelCase_							).sequences

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




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				Dict   				=  False
         UpperCAmelCase__							:				List[Any]   				=  max_length
         UpperCAmelCase__							:				List[Any]   				=  2
         UpperCAmelCase__							:				List[str]   				=  2

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Optional[Any]   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				Union[str, Any]   				=  model.generate(lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[0]					,input_ids.shape[0] * config.num_return_sequences							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				Optional[int]   				=  True
         UpperCAmelCase__							:				str   				=  max_length
         UpperCAmelCase__							:				Optional[int]   				=  0.8
         UpperCAmelCase__							:				List[Any]   				=  10
         UpperCAmelCase__							:				int   				=  0.3
         UpperCAmelCase__							:				Union[str, Any]   				=  1
         UpperCAmelCase__							:				int   				=  8
         UpperCAmelCase__							:				Tuple   				=  9

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Optional[Any]   				=  model_class(lowerCamelCase_							)

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

            UpperCAmelCase__							:				Optional[int]   				=  jit(model.generate							)
            UpperCAmelCase__							:				List[Any]   				=  jit_generate(lowerCamelCase_							).sequences

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				str   				=  max_length
         UpperCAmelCase__							:				List[str]   				=  1
         UpperCAmelCase__							:				Optional[int]   				=  8
         UpperCAmelCase__							:				Union[str, Any]   				=  9

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Dict   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				Tuple   				=  model.generate(lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[-1]					,lowerCamelCase_							)

            UpperCAmelCase__							:				Optional[int]   				=  jit(model.generate							)
            UpperCAmelCase__							:				Dict   				=  jit_generate(lowerCamelCase_							).sequences

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self._get_input_ids_and_config()
         UpperCAmelCase__							:				Tuple   				=  max_length
         UpperCAmelCase__							:				Optional[int]   				=  2
         UpperCAmelCase__							:				Union[str, Any]   				=  1
         UpperCAmelCase__							:				Tuple   				=  8
         UpperCAmelCase__							:				Tuple   				=  9

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Optional[int]   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				List[Any]   				=  model.generate(lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[-1]					,lowerCamelCase_							)

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

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




      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self._get_input_ids_and_config()

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

         UpperCAmelCase__							:				List[str]   				=  False
         UpperCAmelCase__							:				str   				=  max_length

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Dict   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				List[str]   				=  model.generate(lowerCamelCase_					,attention_mask=lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[-1]					,lowerCamelCase_							)

            UpperCAmelCase__							:				Tuple   				=  jit(model.generate							)
            UpperCAmelCase__							:				Any   				=  jit_generate(lowerCamelCase_					,attention_mask=lowerCamelCase_							).sequences

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




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self._get_input_ids_and_config()

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

         UpperCAmelCase__							:				int   				=  True
         UpperCAmelCase__							:				str   				=  max_length

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				int   				=  model_class(lowerCamelCase_							)

            UpperCAmelCase__							:				str   				=  model.generate(lowerCamelCase_					,attention_mask=lowerCamelCase_							).sequences
            self.assertEqual(generation_outputs.shape[-1]					,lowerCamelCase_							)

            UpperCAmelCase__							:				List[Any]   				=  jit(model.generate							)
            UpperCAmelCase__							:				List[Any]   				=  jit_generate(lowerCamelCase_					,attention_mask=lowerCamelCase_							).sequences

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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self._get_input_ids_and_config()

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

         UpperCAmelCase__							:				Tuple   				=  2
         UpperCAmelCase__							:				List[Any]   				=  max_length

         for model_class in self.all_generative_model_classes:
            UpperCAmelCase__							:				Optional[Any]   				=  model_class(lowerCamelCase_							)

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

            UpperCAmelCase__							:				int   				=  jit(model.generate							)
            UpperCAmelCase__							:				Any   				=  jit_generate(lowerCamelCase_					,attention_mask=lowerCamelCase_							).sequences

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




@require_flax
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''							)
         UpperCAmelCase__							:				Tuple   				=  FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''							)

         UpperCAmelCase__							:				Dict   				=  """Hello world"""
         UpperCAmelCase__							:				Union[str, Any]   				=  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'''							):
            UpperCAmelCase__							:				Tuple   				=  {"""foo""": """bar"""}
            model.generate(lowerCamelCase_					,**lowerCamelCase_							)





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




