






'''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						=	'0.12'  # assumed parallelism: 8


if is_torch_available():
      import torch


def 							lowercase_				(    __A :       Optional[int]					,      __A :       Optional[int]					,      __A :       Tuple=None			)       ->      List[str]:



 """simple docstring"""




 if rng is None:
  lowercase					:   str				  =random.Random()

 lowercase					:   str				  =1
 for dim in shape:
  total_dims *= dim

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

 lowercase					:   Optional[int]				  =np.array(__A					,      dtype=jnp.intaa			).reshape(__A			)

 return output


def 							lowercase_				(    __A :       Optional[int]					,      __A :       Optional[int]=None			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   Union[str, Any]				  =ids_tensor(__A					,      vocab_size=2					,      rng=__A			)
 # make sure that at least one token is attended to for each batch
 lowercase					:   Optional[int]				  =1
 return attn_mask
@require_flax
class UpperCAmelCase_					:




      """simple docstring"""




      UpperCamelCase_								=			None
      UpperCamelCase_								=			()






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


       '''simple docstring'''

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

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

       lowercase					:   List[str]				  =jnp.ones_like(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =attention_mask[:max_batch_size, :sequence_length]

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






      @is_pt_flax_cross_test
      def 				A__      ( self			:		Optional[int]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   int				  =self._get_input_ids_and_config()
       lowercase					:   Tuple				  =False
       lowercase					:   Tuple				  =max_length
       lowercase					:   Any				  =0

       for model_class in self.all_generative_model_classes:
        lowercase					:   List[Any]				  =model_class(UpperCAmelCase		)

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

        lowercase					:   Any				  =flax_model.generate(UpperCAmelCase		).sequences
        lowercase					:   int				  =pt_model.generate(torch.tensor(UpperCAmelCase     ,      dtype=torch.long		)		)

        if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
         lowercase					:   Optional[int]				  =flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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






      def 				A__      ( self			:		List[Any]		)  ->  int:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Union[str, Any]				  =self._get_input_ids_and_config()
       lowercase					:   Union[str, Any]				  =False
       lowercase					:   Any				  =max_length

       for model_class in self.all_generative_model_classes:
        lowercase					:   Dict				  =model_class(UpperCAmelCase		)

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

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

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






      def 				A__      ( self			:		Any		)  ->  int:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   List[Any]				  =self._get_input_ids_and_config()
       lowercase					:   List[Any]				  =True
       lowercase					:   Tuple				  =max_length

       for model_class in self.all_generative_model_classes:
        lowercase					:   str				  =model_class(UpperCAmelCase		)

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

        lowercase					:   List[Any]				  =jit(model.generate		)
        lowercase					:   Dict				  =jit_generate(UpperCAmelCase		).sequences

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






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


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   List[Any]				  =self._get_input_ids_and_config()
       lowercase					:   Optional[Any]				  =False
       lowercase					:   List[str]				  =max_length
       lowercase					:   List[str]				  =2

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

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

        lowercase					:   Tuple				  =jit(model.generate		)
        lowercase					:   Optional[Any]				  =jit_generate(UpperCAmelCase		).sequences

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






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


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   List[str]				  =self._get_input_ids_and_config()
       lowercase					:   int				  =False
       lowercase					:   List[Any]				  =max_length
       lowercase					:   str				  =2
       lowercase					:   Optional[Any]				  =2

       for model_class in self.all_generative_model_classes:
        lowercase					:   Any				  =model_class(UpperCAmelCase		)

        lowercase					:   Optional[int]				  =model.generate(UpperCAmelCase		).sequences
        self.assertEqual(generation_outputs.shape[0]     ,      input_ids.shape[0] * config.num_return_sequences		)






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


       '''simple docstring'''

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

       for model_class in self.all_generative_model_classes:
        lowercase					:   str				  =model_class(UpperCAmelCase		)

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

        lowercase					:   int				  =jit(model.generate		)
        lowercase					:   List[Any]				  =jit_generate(UpperCAmelCase		).sequences

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






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


       '''simple docstring'''

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

       for model_class in self.all_generative_model_classes:
        lowercase					:   int				  =model_class(UpperCAmelCase		)

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

        lowercase					:   int				  =jit(model.generate		)
        lowercase					:   Tuple				  =jit_generate(UpperCAmelCase		).sequences

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






      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Any				  =self._get_input_ids_and_config()
       lowercase					:   Any				  =max_length
       lowercase					:   int				  =2
       lowercase					:   Any				  =1
       lowercase					:   List[Any]				  =8
       lowercase					:   Dict				  =9

       for model_class in self.all_generative_model_classes:
        lowercase					:   int				  =model_class(UpperCAmelCase		)

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

        lowercase					:   str				  =jit(model.generate		)
        lowercase					:   Dict				  =jit_generate(UpperCAmelCase		).sequences

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






      def 				A__      ( self			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   List[str]				  =self._get_input_ids_and_config()

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

       lowercase					:   List[Any]				  =False
       lowercase					:   Any				  =max_length

       for model_class in self.all_generative_model_classes:
        lowercase					:   Any				  =model_class(UpperCAmelCase		)

        lowercase					:   Tuple				  =model.generate(UpperCAmelCase     ,      attention_mask=UpperCAmelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]     ,      UpperCAmelCase		)

        lowercase					:   Optional[Any]				  =jit(model.generate		)
        lowercase					:   Union[str, Any]				  =jit_generate(UpperCAmelCase     ,      attention_mask=UpperCAmelCase		).sequences

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






      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[int]				  =self._get_input_ids_and_config()

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

       lowercase					:   str				  =True
       lowercase					:   Any				  =max_length

       for model_class in self.all_generative_model_classes:
        lowercase					:   int				  =model_class(UpperCAmelCase		)

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

        lowercase					:   Union[str, Any]				  =jit(model.generate		)
        lowercase					:   Optional[Any]				  =jit_generate(UpperCAmelCase     ,      attention_mask=UpperCAmelCase		).sequences

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






      def 				A__      ( self			:		Union[str, Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Any				  =self._get_input_ids_and_config()

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

       lowercase					:   Union[str, Any]				  =2
       lowercase					:   int				  =max_length

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

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

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

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





@require_flax
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		Tuple		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''		)
       lowercase					:   int				  =FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)

       lowercase					:   Optional[Any]				  ='''Hello world'''
       lowercase					:   Optional[int]				  =tokenizer(UpperCAmelCase     ,      return_tensors='''np'''		).input_ids

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

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(UpperCAmelCase     ,      '''foo'''		):
        lowercase					:   Optional[int]				  ={'''foo''': '''bar'''}
        model.generate(UpperCAmelCase     ,      **UpperCAmelCase		)











'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



