
'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 						lowercase__	:






   '''simple docstring'''







   _snake_case		   =							BlenderbotConfig
   _snake_case		   =							{}
   _snake_case		   =							"""gelu"""







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=1_3 ,   lowerCamelCase__=7 ,   lowerCamelCase__=True ,   lowerCamelCase__=False ,   lowerCamelCase__=9_9 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=2 ,   lowerCamelCase__=4 ,   lowerCamelCase__=3_7 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=2_0 ,   lowerCamelCase__=2 ,   lowerCamelCase__=1 ,   lowerCamelCase__=0 ,   ):

      '''simple docstring'''







      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  seq_length
      UpperCamelCase       =  is_training
      UpperCamelCase       =  use_labels
      UpperCamelCase       =  vocab_size
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  num_hidden_layers
      UpperCamelCase       =  num_attention_heads
      UpperCamelCase       =  intermediate_size

      UpperCamelCase       =  hidden_dropout_prob
      UpperCamelCase       =  attention_probs_dropout_prob
      UpperCamelCase       =  max_position_embeddings
      UpperCamelCase       =  eos_token_id
      UpperCamelCase       =  pad_token_id
      UpperCamelCase       =  bos_token_id







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length - 1] ,   self.vocab_size   )
      UpperCamelCase       =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   ) ,   1   )
      UpperCamelCase       =  tf.concat([input_ids, eos_tensor] ,   axis=1   )

      UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size   )

      UpperCamelCase       =  self.config_cls(
          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_ids=[2] ,   bos_token_id=self.bos_token_id ,   pad_token_id=self.pad_token_id ,   decoder_start_token_id=self.pad_token_id ,   **self.config_updates ,   )
      UpperCamelCase       =  prepare_blenderbot_inputs_dict(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__   )
      return config, inputs_dict







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  TFBlenderbotModel(config=lowerCAmelCase__   ).get_decoder()
      UpperCamelCase       =  inputs_dict["input_ids"]

      UpperCamelCase       =  input_ids[:1, :]
      UpperCamelCase       =  inputs_dict["attention_mask"][:1, :]
      UpperCamelCase       =  inputs_dict["head_mask"]
      UpperCamelCase       =  1

      # first forward pass
      UpperCamelCase       =  model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ,   head_mask=lowerCAmelCase__ ,   use_cache=lowerCAmelCase__   )

      UpperCamelCase       =  outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      UpperCamelCase       =  ids_tensor((self.batch_size, 3) ,   config.vocab_size   )
      UpperCamelCase       =  tf.cast(ids_tensor((self.batch_size, 3) ,   2   ) ,   tf.inta   )

      # append to next input_ids and
      UpperCamelCase       =  tf.concat([input_ids, next_tokens] ,   axis=-1   )
      UpperCamelCase       =  tf.concat([attention_mask, next_attn_mask] ,   axis=-1   )

      UpperCamelCase       =  model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__   )[0]
      UpperCamelCase       =  model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ,   past_key_values=lowerCAmelCase__   )[0]

      self.parent.assertEqual(next_tokens.shape[1] ,   output_from_past.shape[1]   )

      # select random slice
      UpperCamelCase       =  int(ids_tensor((1,) ,   output_from_past.shape[-1]   )   )
      UpperCamelCase       =  output_from_no_past[:, -3:, random_slice_idx]
      UpperCamelCase       =  output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(lowerCAmelCase__ ,   lowerCAmelCase__ ,   rtol=1e-3   )




def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       Any=None,      _UpperCAmelCase					:       Union[str, Any]=None,      _UpperCAmelCase					:       Union[str, Any]=None,      _UpperCAmelCase					:       Tuple=None,      _UpperCAmelCase					:       Dict=None,      ):






   if attention_mask is None:
      UpperCamelCase       =  tf.cast(tf.math.not_equal(_lowercase,      config.pad_token_id),      tf.inta)
   if decoder_attention_mask is None:
      UpperCamelCase       =  tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape,      dtype=tf.inta),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],      config.pad_token_id),      tf.inta),
          ],      axis=-1,      )
   if head_mask is None:
      UpperCamelCase       =  tf.ones((config.encoder_layers, config.encoder_attention_heads))
   if decoder_head_mask is None:
      UpperCamelCase       =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
   if cross_attn_head_mask is None:
      UpperCamelCase       =  tf.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": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class 						lowercase__	(  a__,					a__,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
   _snake_case		   =							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
   _snake_case		   =							(
       {
           """conversational""": TFBlenderbotForConditionalGeneration,
           """feature-extraction""": TFBlenderbotModel,
           """summarization""": TFBlenderbotForConditionalGeneration,
           """text2text-generation""": TFBlenderbotForConditionalGeneration,
           """translation""": TFBlenderbotForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _snake_case		   =							True
   _snake_case		   =							False
   _snake_case		   =							False







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  TFBlenderbotModelTester(self   )
      UpperCamelCase       =  ConfigTester(self ,   config_class=lowerCAmelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.config_tester.run_common_tests()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__   )




@require_tokenizers
@require_tf
class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							["""My friends are cool but they eat too many carbs."""]
   _snake_case		   =							"""facebook/blenderbot-400M-distill"""







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return BlenderbotTokenizer.from_pretrained(self.model_name   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
      return model







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer(self.src_text ,   return_tensors='''tf'''   )
      UpperCamelCase       =  self.model.generate(
          model_inputs.input_ids ,   )
      UpperCamelCase       =  self.tokenizer.batch_decode(generated_ids.numpy() ,   skip_special_tokens=lowerCAmelCase__   )[0]
      assert (
          generated_words
          == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
      )





'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  []
   UpperCamelCase       =  []
   UpperCamelCase       =  {
       '''^''': 3,
       '''*''': 2,
       '''/''': 2,
       '''%''': 2,
       '''+''': 1,
       '''-''': 1,
   }  # Priority of each operator
   UpperCamelCase       =  len(_UpperCAmelCase) if (len(_UpperCAmelCase) > 7) else 7

   # Print table header for output
   print(
       '''Symbol'''.center(8),      '''Stack'''.center(_UpperCAmelCase),      '''Postfix'''.center(_UpperCAmelCase),      sep=''' | ''',      )
   print('''-''' * (print_width * 3 + 7))

   for x in infix:
      if x.isalpha() or x.isdigit():
         post_fix.append(_UpperCAmelCase)  # if x is Alphabet / Digit, add it to Postfix
      elif x == "(":
         stack.append(_UpperCAmelCase)  # if x is "(" push to Stack
      elif x == ")":  # if x is ")" pop stack until "(" is encountered
         while stack[-1] != "(":
            post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
         stack.pop()
      else:
         if len(_UpperCAmelCase) == 0:
            stack.append(_UpperCAmelCase)  # If stack is empty, push x to stack
         else:  # while priority of x is not > priority of element in the stack
            while len(_UpperCAmelCase) > 0 and priority[x] <= priority[stack[-1]]:
               post_fix.append(stack.pop())  # pop stack & add to Postfix
            stack.append(_UpperCAmelCase)  # push x to stack

      print(
          x.center(8),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      sep=''' | ''',      )  # Output in tabular format

   while len(_UpperCAmelCase) > 0:  # while stack is not empty
      post_fix.append(stack.pop())  # pop stack & add to Postfix
      print(
          ''' '''.center(8),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      sep=''' | ''',      )  # Output in tabular format

   return "".join(_UpperCAmelCase)  # return Postfix as str



def  __snake_case     (    _UpperCAmelCase					:       str):
   UpperCamelCase       =  list(infix[::-1])  # reverse the infix equation

   for i in range(len(_UpperCAmelCase)):
      if infix[i] == "(":
         UpperCamelCase       =  ''')'''  # change "(" to ")"
      elif infix[i] == ")":
         UpperCamelCase       =  '''('''  # change ")" to "("

   return (infix_2_postfix(''''''.join(_UpperCAmelCase)))[
       ::-1
   ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
   snake_case_ :  str     =					input('\nEnter an Infix Equation = ')  # Input an Infix equation
   snake_case_ :  Union[str, Any]     =					''.join(Infix.split())  # Remove spaces from the input
   print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')





