






'''simple docstring'''



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      SCREAMING_SNAKE_CASE						=	'platform'

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )


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



 """simple docstring"""




 if attention_mask is None:
  lowercase					:   Union[str, Any]				  =np.where(input_ids != config.pad_token_id					,      1					,      0			)
 if decoder_attention_mask is None:
  lowercase					:   Optional[int]				  =np.where(decoder_input_ids != config.pad_token_id					,      1					,      0			)
 if head_mask is None:
  lowercase					:   List[Any]				  =np.ones((config.encoder_layers, config.encoder_attention_heads)			)
 if decoder_head_mask is None:
  lowercase					:   List[Any]				  =np.ones((config.decoder_layers, config.decoder_attention_heads)			)
 if cross_attn_head_mask is None:
  lowercase					:   Optional[Any]				  =np.ones((config.decoder_layers, config.decoder_attention_heads)			)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]=13     ,      UpperCAmelCase			:		Any=7     ,      UpperCAmelCase			:		List[str]=True     ,      UpperCAmelCase			:		Any=False     ,      UpperCAmelCase			:		Optional[int]=99     ,      UpperCAmelCase			:		List[Any]=16     ,      UpperCAmelCase			:		str=2     ,      UpperCAmelCase			:		List[Any]=4     ,      UpperCAmelCase			:		List[Any]=4     ,      UpperCAmelCase			:		Tuple="gelu"     ,      UpperCAmelCase			:		str=0.1     ,      UpperCAmelCase			:		str=0.1     ,      UpperCAmelCase			:		Optional[Any]=32     ,      UpperCAmelCase			:		int=2     ,      UpperCAmelCase			:		Optional[int]=1     ,      UpperCAmelCase			:		str=0     ,      UpperCAmelCase			:		str=0.0_2     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =parent
       lowercase					:   Any				  =batch_size
       lowercase					:   str				  =seq_length
       lowercase					:   Optional[Any]				  =is_training
       lowercase					:   Tuple				  =use_labels
       lowercase					:   List[str]				  =vocab_size
       lowercase					:   Any				  =hidden_size
       lowercase					:   str				  =num_hidden_layers
       lowercase					:   Any				  =num_attention_heads
       lowercase					:   Tuple				  =intermediate_size
       lowercase					:   List[Any]				  =hidden_act
       lowercase					:   Tuple				  =hidden_dropout_prob
       lowercase					:   Dict				  =attention_probs_dropout_prob
       lowercase					:   List[Any]				  =max_position_embeddings
       lowercase					:   List[str]				  =eos_token_id
       lowercase					:   Tuple				  =pad_token_id
       lowercase					:   Tuple				  =bos_token_id
       lowercase					:   int				  =initializer_range






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


       '''simple docstring'''

       lowercase					:   str				  =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,      self.vocab_size		)     ,      3     ,      self.vocab_size		)
       lowercase					:   Union[str, Any]				  =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,      dtype=np.intaa		))     ,      -1		)

       lowercase					:   List[Any]				  =shift_tokens_right(__snake_case     ,      1     ,      2		)

       lowercase					:   Optional[int]				  =BlenderbotSmallConfig(
           vocab_size=self.vocab_size     ,      d_model=self.hidden_size     ,      encoder_layers=self.num_hidden_layers     ,      decoder_layers=self.num_hidden_layers     ,      encoder_attention_heads=self.num_attention_heads     ,      decoder_attention_heads=self.num_attention_heads     ,      encoder_ffn_dim=self.intermediate_size     ,      decoder_ffn_dim=self.intermediate_size     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      eos_token_id=self.eos_token_id     ,      bos_token_id=self.bos_token_id     ,      pad_token_id=self.pad_token_id     ,      initializer_range=self.initializer_range     ,      use_cache=__snake_case     ,      )
       lowercase					:   Dict				  =prepare_blenderbot_inputs_dict(__snake_case     ,      __snake_case     ,      __snake_case		)
       return config, inputs_dict






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


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.prepare_config_and_inputs()
       return config, inputs_dict






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


       '''simple docstring'''

       lowercase					:   Tuple				  =20
       lowercase					:   Union[str, Any]				  =model_class_name(__snake_case		)

       lowercase					:   Optional[Any]				  =model.encode(inputs_dict['''input_ids''']		)

       lowercase					:   Dict				  =(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       lowercase					:   Optional[Any]				  =model.init_cache(decoder_input_ids.shape[0]     ,      __snake_case     ,      __snake_case		)
       lowercase					:   Union[str, Any]				  =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,      dtype='''i4'''		)

       lowercase					:   int				  =jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,      )
       lowercase					:   Optional[Any]				  =model.decode(
           decoder_input_ids[:, :-1]     ,      __snake_case     ,      decoder_attention_mask=__snake_case     ,      past_key_values=__snake_case     ,      decoder_position_ids=__snake_case     ,      )

       lowercase					:   Optional[int]				  =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,      dtype='''i4'''		)
       lowercase					:   str				  =model.decode(
           decoder_input_ids[:, -1:]     ,      __snake_case     ,      decoder_attention_mask=__snake_case     ,      past_key_values=outputs_cache.past_key_values     ,      decoder_position_ids=__snake_case     ,      )

       lowercase					:   Optional[int]				  =model.decode(__snake_case     ,      __snake_case		)

       lowercase					:   Tuple				  =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
       self.parent.assertTrue(diff < 1e-3     ,      msg=f'Max diff is {diff}'		)






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Tuple		)  ->  int:


       '''simple docstring'''

       lowercase					:   List[str]				  =20
       lowercase					:   Union[str, Any]				  =model_class_name(__snake_case		)

       lowercase					:   Any				  =model.encode(inputs_dict['''input_ids''']		)

       lowercase					:   Optional[int]				  =(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       lowercase					:   str				  =jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
           ]     ,      axis=-1     ,      )

       lowercase					:   Optional[int]				  =model.init_cache(decoder_input_ids.shape[0]     ,      __snake_case     ,      __snake_case		)
       lowercase					:   Any				  =jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,      )

       lowercase					:   int				  =model.decode(
           decoder_input_ids[:, :-1]     ,      __snake_case     ,      decoder_attention_mask=__snake_case     ,      past_key_values=__snake_case     ,      decoder_position_ids=__snake_case     ,      )
       lowercase					:   Union[str, Any]				  =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,      dtype='''i4'''		)
       lowercase					:   int				  =model.decode(
           decoder_input_ids[:, -1:]     ,      __snake_case     ,      past_key_values=outputs_cache.past_key_values     ,      decoder_attention_mask=__snake_case     ,      decoder_position_ids=__snake_case     ,      )

       lowercase					:   str				  =model.decode(__snake_case     ,      __snake_case     ,      decoder_attention_mask=__snake_case		)

       lowercase					:   Any				  =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
       self.parent.assertTrue(diff < 1e-3     ,      msg=f'Max diff is {diff}'		)





@require_flax
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			99






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


       '''simple docstring'''

       lowercase					:   Dict				  =np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]     ,      dtype=np.intaa     ,      )

       lowercase					:   Union[str, Any]				  =input_ids.shape[0]
       lowercase					:   Any				  =BlenderbotSmallConfig(
           vocab_size=self.vocab_size     ,      d_model=24     ,      encoder_layers=2     ,      decoder_layers=2     ,      encoder_attention_heads=2     ,      decoder_attention_heads=2     ,      encoder_ffn_dim=32     ,      decoder_ffn_dim=32     ,      max_position_embeddings=48     ,      eos_token_id=2     ,      pad_token_id=1     ,      bos_token_id=0     ,      )
       return config, input_ids, batch_size






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


       '''simple docstring'''

       lowercase					:   List[Any]				  =self._get_config_and_data()
       lowercase					:   str				  =FlaxBlenderbotSmallForConditionalGeneration(__snake_case		)
       lowercase					:   Any				  =lm_model(input_ids=__snake_case		)
       lowercase					:   Union[str, Any]				  =(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape     ,      __snake_case		)






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


       '''simple docstring'''

       lowercase					:   int				  =BlenderbotSmallConfig(
           vocab_size=self.vocab_size     ,      d_model=14     ,      encoder_layers=2     ,      decoder_layers=2     ,      encoder_attention_heads=2     ,      decoder_attention_heads=2     ,      encoder_ffn_dim=8     ,      decoder_ffn_dim=8     ,      max_position_embeddings=48     ,      )
       lowercase					:   Optional[int]				  =FlaxBlenderbotSmallForConditionalGeneration(__snake_case		)
       lowercase					:   List[Any]				  =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,      dtype=np.intaa		)
       lowercase					:   List[Any]				  =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,      dtype=np.intaa		)
       lowercase					:   List[str]				  =lm_model(input_ids=__snake_case     ,      decoder_input_ids=__snake_case		)
       lowercase					:   Tuple				  =(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape     ,      __snake_case		)






      def 				A__      ( self			:		Dict		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,      dtype=np.intaa		)
       lowercase					:   Union[str, Any]				  =shift_tokens_right(__snake_case     ,      1     ,      2		)
       lowercase					:   Dict				  =np.equal(__snake_case     ,      1		).astype(np.floataa		).sum()
       lowercase					:   str				  =np.equal(__snake_case     ,      1		).astype(np.floataa		).sum()
       self.assertEqual(shifted.shape     ,      input_ids.shape		)
       self.assertEqual(__snake_case     ,      n_pad_before - 1		)
       self.assertTrue(np.equal(shifted[:, 0]     ,      2		).all()		)





@require_flax
class UpperCAmelCase_					(    __SCREAMING_SNAKE_CASE				,       unittest.TestCase				,       __SCREAMING_SNAKE_CASE				):




      """simple docstring"""




      UpperCamelCase_								=			True
      UpperCamelCase_								=			(
          (
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      UpperCamelCase_								=			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =FlaxBlenderbotSmallModelTester(self		)






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


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(__snake_case     ,      __snake_case     ,      __snake_case		)






      def 				A__      ( self			:		str		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(__snake_case     ,      __snake_case     ,      __snake_case		)






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


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__		):
         lowercase					:   Tuple				  =self._prepare_for_class(__snake_case     ,      __snake_case		)
         lowercase					:   Optional[int]				  =model_class(__snake_case		)

         @jax.jit
         def encode_jitted(UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[int]=None     ,      **UpperCAmelCase			:		Optional[Any]		):
          return model.encode(input_ids=__snake_case     ,      attention_mask=__snake_case		)

         with self.subTest('''JIT Enabled'''		):
          lowercase					:   Optional[int]				  =encode_jitted(**__snake_case		).to_tuple()

         with self.subTest('''JIT Disabled'''		):
          with jax.disable_jit():
           lowercase					:   Any				  =encode_jitted(**__snake_case		).to_tuple()

         self.assertEqual(len(__snake_case		)     ,      len(__snake_case		)		)
         for jitted_output, output in zip(__snake_case     ,      __snake_case		):
          self.assertEqual(jitted_output.shape     ,      output.shape		)






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


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__		):
         lowercase					:   Tuple				  =model_class(__snake_case		)
         lowercase					:   Dict				  =model.encode(inputs_dict['''input_ids''']     ,      inputs_dict['''attention_mask''']		)

         lowercase					:   Tuple				  ={
             '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
             '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
             '''encoder_outputs''': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Union[str, Any]		):
          return model.decode(
              decoder_input_ids=__snake_case     ,      decoder_attention_mask=__snake_case     ,      encoder_outputs=__snake_case     ,      )

         with self.subTest('''JIT Enabled'''		):
          lowercase					:   Tuple				  =decode_jitted(**__snake_case		).to_tuple()

         with self.subTest('''JIT Disabled'''		):
          with jax.disable_jit():
           lowercase					:   Optional[Any]				  =decode_jitted(**__snake_case		).to_tuple()

         self.assertEqual(len(__snake_case		)     ,      len(__snake_case		)		)
         for jitted_output, output in zip(__snake_case     ,      __snake_case		):
          self.assertEqual(jitted_output.shape     ,      output.shape		)






      @slow
      def 				A__      ( self			:		List[str]		)  ->  Optional[Any]:


       '''simple docstring'''

       for model_class_name in self.all_model_classes:
        lowercase					:   Optional[Any]				  =model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''		)
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        lowercase					:   Optional[int]				  =np.ones((1, 1)		) * model.config.eos_token_id
        lowercase					:   Union[str, Any]				  =model(__snake_case		)
        self.assertIsNotNone(__snake_case		)











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




 lowercase					:   int				  ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

 lowercase					:   Stack[int]				  =Stack()
 lowercase					:   Stack[str]				  =Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	'(5 + ((4 * 2) * (2 + 3)))'
      # answer = 45
      print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



