


'''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

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


if is_torch_available():
       import torch





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Optional[Any]=None				):
   if rng is None:
      lowerCAmelCase     =			random.Random()

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

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

   lowerCAmelCase     =			np.array(lowerCamelCase	, dtype=jnp.intaa				).reshape(lowerCamelCase				)

   return output





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Tuple=None				):
   lowerCAmelCase     =			ids_tensor(lowerCamelCase	, vocab_size=2	, rng=lowerCamelCase				)
   # make sure that at least one token is attended to for each batch
   lowerCAmelCase     =			1
   return attn_mask

@require_flax
class 		UpperCAmelCase_      :
     lowerCamelCase				:	Dict		=     None
     lowerCamelCase				:	Dict		=     ()





     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase     =			jnp.ones_like(UpperCAmelCase__    )
        lowerCAmelCase     =			attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        lowerCAmelCase     =			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()`
           lowerCAmelCase     =			config.eos_token_id
        return config, input_ids, attention_mask, max_length





     @is_pt_flax_cross_test
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			0

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			flax_model.generate(UpperCAmelCase__    ).sequences
           lowerCAmelCase     =			pt_model.generate(torch.tensor(UpperCAmelCase__    ,      dtype=torch.long    )    )

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

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





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			True
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			2

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			2
        lowerCAmelCase     =			2

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			True
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			0.8
        lowerCAmelCase     =			1_0
        lowerCAmelCase     =			0.3
        lowerCAmelCase     =			1
        lowerCAmelCase     =			8
        lowerCAmelCase     =			9

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			1
        lowerCAmelCase     =			8
        lowerCAmelCase     =			9

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  int:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			2
        lowerCAmelCase     =			1
        lowerCAmelCase     =			8
        lowerCAmelCase     =			9

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()

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

        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      int    )				->  int:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()

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

        lowerCAmelCase     =			True
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences

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





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()

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

        lowerCAmelCase     =			2
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

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

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences

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




@require_flax
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        lowerCAmelCase     =			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'    )
        lowerCAmelCase     =			FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'    )

        lowerCAmelCase     =			'Hello world'
        lowerCAmelCase     =			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'    ):
           lowerCAmelCase     =			{'foo': 'bar'}
           model.generate(UpperCAmelCase__    ,      **UpperCAmelCase__    )






'''simple docstring'''

from __future__ import annotations

from random import random



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase = None )  ->  str:
        lowercase__				:    Any  =					value
        lowercase__				:    Union[str, Any]  =					random()
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None

       def __repr__(						self )  ->  str:
        from pprint import pformat

        if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
        else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}	,   indent=1 )


       def __str__(						self )  ->  str:
        lowercase__				:    Any  =					str(self.value ) + ''' '''
        lowercase__				:    str  =					str(self.left or '''''' )
        lowercase__				:    List[str]  =					str(self.right or '''''' )
        return value + left + right


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if root is None:  # None tree is split into 2 Nones
  return None, None
 elif root.value is None:
  return None, None
 else:
  if value < root.value:
   lowercase__ ,							lowercase__				:    int  =					split(root.left		,     UpperCAmelCase     )
   return left, root
  else:
   lowercase__ ,							lowercase__				:    Optional[Any]  =					split(root.right		,     UpperCAmelCase     )
   return root, right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if (not left) or (not right):  # If one node is None, return the other
  return left or right
 elif left.prior < right.prior:
  lowercase__				:    Any  =					merge(left.right		,     UpperCAmelCase     )
  return left
 else:
  lowercase__				:    Dict  =					merge(UpperCAmelCase		,     right.left     )
  return right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					Node(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    Tuple  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(merge(UpperCAmelCase		,     UpperCAmelCase     )		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Dict  =					split(UpperCAmelCase		,     value - 1     )
 lowercase__ ,							lowercase__				:    int  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if not root:  # None
  return
 else:
  inorder(root.left     )
  print(root.value		,     end=''','''     )
  inorder(root.right     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 for arg in args.split():
  if arg[0] == "+":
   lowercase__				:    Tuple  =					insert(UpperCAmelCase		,     int(arg[1:]     )     )

  elif arg[0] == "-":
   lowercase__				:    Union[str, Any]  =					erase(UpperCAmelCase		,     int(arg[1:]     )     )

  else:
   print('''Unknown command'''     )

 return root
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					None
 print(
     '''enter numbers to create a tree, + value to add value into treap, '''
     '''- value to erase all nodes with value. \'q\' to quit. '''     )

 lowercase__				:    Dict  =					input()
 while args != "q":
  lowercase__				:    Any  =					interact_treap(UpperCAmelCase		,     UpperCAmelCase     )
  print(UpperCAmelCase     )
  lowercase__				:    List[Any]  =					input()

 print('''good by!'''     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

