
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

   __lowerCamelCase							=       """0.12"""  # assumed parallelism: 8


if is_torch_available():
   import torch


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Tuple=None    ):
       if rng is None:
              snake_case       :   Optional[Any]							=      random.Random()

       snake_case       :   Union[str, Any]							=      1
       for dim in shape:
              total_dims *= dim

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

       snake_case       :   List[Any]							=      np.array(__lowerCamelCase				,					dtype=jnp.intaa    ).reshape(__lowerCamelCase    )

       return output


def 			UpperCamelCase					(    __lowerCamelCase     : List[str]				,					__lowerCamelCase     : Optional[Any]=None    ):
       snake_case       :   Any							=      ids_tensor(__lowerCamelCase				,					vocab_size=2				,					rng=__lowerCamelCase    )
       # make sure that at least one token is attended to for each batch
       snake_case       :   List[str]							=      1
       return attn_mask






@require_flax
class   UpperCAmelCase		:
   A__					:		Dict						     =     None
   A__					:		Optional[int]						     =     ()



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


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self.model_tester.prepare_config_and_inputs_for_common()

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

          snake_case       :   Tuple							=      jnp.ones_like(snake_case__ )
          snake_case       :   str							=      attention_mask[:max_batch_size, :sequence_length]

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



   @is_pt_flax_cross_test
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Dict:


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case				,					snake_case       :   Tuple							=      self._get_input_ids_and_config()
          snake_case       :   Union[str, Any]							=      False
          snake_case       :   Union[str, Any]							=      max_length
          snake_case       :   List[Any]							=      0

          for model_class in self.all_generative_model_classes:
                 snake_case       :   List[Any]							=      model_class(snake_case__ )

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

                 snake_case       :   str							=      flax_model.generate(snake_case__ ).sequences
                 snake_case       :   str							=      pt_model.generate(torch.tensor(snake_case__    ,						dtype=torch.long ) )

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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


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

          for model_class in self.all_generative_model_classes:
                 snake_case       :   int							=      model_class(snake_case__ )

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

                 snake_case       :   str							=      jit(model.generate )
                 snake_case       :   Optional[int]							=      jit_generate(snake_case__ ).sequences

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



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


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case				,					snake_case       :   List[Any]							=      self._get_input_ids_and_config()
          snake_case       :   Optional[Any]							=      True
          snake_case       :   int							=      max_length

          for model_class in self.all_generative_model_classes:
                 snake_case       :   List[Any]							=      model_class(snake_case__ )

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

                 snake_case       :   Optional[int]							=      jit(model.generate )
                 snake_case       :   int							=      jit_generate(snake_case__ ).sequences

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



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


          '''simple docstring'''


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

          for model_class in self.all_generative_model_classes:
                 snake_case       :   int							=      model_class(snake_case__ )

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

                 snake_case       :   int							=      jit(model.generate )
                 snake_case       :   Dict							=      jit_generate(snake_case__ ).sequences

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



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


          '''simple docstring'''


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

          for model_class in self.all_generative_model_classes:
                 snake_case       :   str							=      model_class(snake_case__ )

                 snake_case       :   Dict							=      model.generate(snake_case__ ).sequences
                 self.assertEqual(generation_outputs.shape[0]    ,						input_ids.shape[0] * config.num_return_sequences )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	str:


          '''simple docstring'''


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

          for model_class in self.all_generative_model_classes:
                 snake_case       :   Optional[int]							=      model_class(snake_case__ )

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

                 snake_case       :   Optional[int]							=      jit(model.generate )
                 snake_case       :   Any							=      jit_generate(snake_case__ ).sequences

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



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


          '''simple docstring'''


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

          for model_class in self.all_generative_model_classes:
                 snake_case       :   Optional[int]							=      model_class(snake_case__ )

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

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

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



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


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case				,					snake_case       :   List[Any]							=      self._get_input_ids_and_config()
          snake_case       :   List[Any]							=      max_length
          snake_case       :   Dict							=      2
          snake_case       :   Any							=      1
          snake_case       :   str							=      8
          snake_case       :   Union[str, Any]							=      9

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

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

                 snake_case       :   Optional[int]							=      jit(model.generate )
                 snake_case       :   Optional[Any]							=      jit_generate(snake_case__ ).sequences

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



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


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case				,					snake_case       :   Tuple							=      self._get_input_ids_and_config()

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

          snake_case       :   Tuple							=      False
          snake_case       :   Tuple							=      max_length

          for model_class in self.all_generative_model_classes:
                 snake_case       :   Optional[int]							=      model_class(snake_case__ )

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

                 snake_case       :   List[str]							=      jit(model.generate )
                 snake_case       :   Dict							=      jit_generate(snake_case__    ,						attention_mask=snake_case__ ).sequences

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


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

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

          snake_case       :   Optional[int]							=      True
          snake_case       :   Any							=      max_length

          for model_class in self.all_generative_model_classes:
                 snake_case       :   str							=      model_class(snake_case__ )

                 snake_case       :   Optional[Any]							=      model.generate(snake_case__    ,						attention_mask=snake_case__ ).sequences
                 self.assertEqual(generation_outputs.shape[-1]    ,						snake_case__ )

                 snake_case       :   Optional[Any]							=      jit(model.generate )
                 snake_case       :   List[str]							=      jit_generate(snake_case__    ,						attention_mask=snake_case__ ).sequences

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


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

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

          snake_case       :   Optional[Any]							=      2
          snake_case       :   Optional[Any]							=      max_length

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

                 snake_case       :   Optional[Any]							=      model.generate(snake_case__    ,						attention_mask=snake_case__ ).sequences
                 self.assertEqual(generation_outputs.shape[-1]    ,						snake_case__ )

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

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






@require_flax
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          snake_case       :   Tuple							=      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
          snake_case       :   List[str]							=      FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )

          snake_case       :   Any							=      "Hello world"
          snake_case       :   str							=      tokenizer(snake_case__    ,						return_tensors="np" ).input_ids

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

          # arbitrary arguments that will not be used anywhere are also not accepted
          with self.assertRaisesRegex(snake_case__    ,						"foo" ):
                 snake_case       :   Optional[Any]							=      {"foo": "bar"}
                 model.generate(snake_case__    ,						**snake_case__ )











def __UpperCamelCase			(       _A  ):
						if not numbers:
												return 0

						if not isinstance(_A ,  (list, tuple)  ) or not all(
						    isinstance(_A ,  _A  ) for number in numbers  ):
												raise ValueError('''numbers must be an iterable of integers'''  )

						lowerCAmelCase_      			=  lowerCAmelCase_      			=  lowerCAmelCase_      			=  numbers[0]

						for i in range(1 ,  len(_A  )  ):
												# update the maximum and minimum subarray products
												lowerCAmelCase_      			=  numbers[i]
												if number < 0:
																		lowerCAmelCase_			,					lowerCAmelCase_      			=  min_till_now, max_till_now
												lowerCAmelCase_      			=  max(_A ,  max_till_now * number  )
												lowerCAmelCase_      			=  min(_A ,  min_till_now * number  )

												# update the maximum product found till now
												lowerCAmelCase_      			=  max(_A ,  _A  )

						return max_prod




