



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class     _A       :
       UpperCamelCase__  :    Dict											=						BlenderbotSmallConfig
       UpperCamelCase__  :    Tuple											=						{}
       UpperCamelCase__  :    str											=						'''gelu'''



       def __init__(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=13     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=7     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Dict=99     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=32     ,		__SCREAMING_SNAKE_CASE							:     Tuple=2     ,		__SCREAMING_SNAKE_CASE							:     str=4     ,		__SCREAMING_SNAKE_CASE							:     int=37     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=20     ,		__SCREAMING_SNAKE_CASE							:     List[str]=2     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0     ,		):


           '''simple docstring'''

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

           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				eos_token_id
           __a						       =				pad_token_id
           __a						       =				bos_token_id



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

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

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

           __a						       =				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     ,		)
           __a						       =				prepare_blenderbot_small_inputs_dict(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return config, inputs_dict





       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				TFBlenderbotSmallModel(config=__SCREAMING_SNAKE_CASE).get_decoder()
           __a						       =				inputs_dict['''input_ids''']

           __a						       =				input_ids[:1, :]
           __a						       =				inputs_dict['''attention_mask'''][:1, :]
           __a						       =				inputs_dict['''head_mask''']
           __a						       =				1

           # first forward pass
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		head_mask=__SCREAMING_SNAKE_CASE     ,		use_cache=__SCREAMING_SNAKE_CASE)

           __a       ,   __a						       =				outputs.to_tuple()

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

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

           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)[0]
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		past_key_values=__SCREAMING_SNAKE_CASE)[0]

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

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

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=None   , _UpperCAmelCase=None   , _UpperCAmelCase=None   , _UpperCAmelCase=None   , _UpperCAmelCase=None   , ):
    if attention_mask is None:
        __a						       =				tf.cast(tf.math.not_equal(_UpperCAmelCase   , config.pad_token_id				)   , tf.inta				)
    if decoder_attention_mask is None:
        __a						       =				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:
        __a						       =				tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
    if decoder_head_mask is None:
        __a						       =				tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
    if cross_attn_head_mask is None:
        __a						       =				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     _A       (	__UpperCAmelCase      ,__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						(
           (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
       )
       UpperCamelCase__  :    Union[str, Any]											=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
       UpperCamelCase__  :    str											=						(
           {
               '''conversational''': TFBlenderbotSmallForConditionalGeneration,
               '''feature-extraction''': TFBlenderbotSmallModel,
               '''summarization''': TFBlenderbotSmallForConditionalGeneration,
               '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
               '''translation''': TFBlenderbotSmallForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       UpperCamelCase__  :    Optional[int]											=						True
       UpperCamelCase__  :    int											=						False
       UpperCamelCase__  :    str											=						False



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				TFBlenderbotSmallModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           self.config_tester.run_common_tests()





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE)


@require_tokenizers
@require_tf
class     _A       (	unittest.TestCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						[
           '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
           '''  i\'m going to throw up.\nand why is that?'''
       ]
       UpperCamelCase__  :    str											=						'''facebook/blenderbot_small-90M'''



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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





       @slow
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				self.tokenizer(self.src_text     ,		return_tensors='''tf''')
           __a						       =				self.model.generate(
               model_inputs.input_ids     ,		attention_mask=model_inputs.attention_mask     ,		num_beams=2     ,		use_cache=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				self.tokenizer.batch_decode(generated_ids.numpy()     ,		skip_special_tokens=__SCREAMING_SNAKE_CASE)[0]
           assert generated_words in (
               "i don't know. i just feel like i'm going to throw up. it's not fun.",
               "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
               "i'm not sure. i just feel like i've been in a bad situation.",
           )










from __future__ import annotations

from typing import Any





def      __snake_case	(			_UpperCAmelCase				):
    if not postfix_notation:
        return 0

    __a						       =				{'''+''', '''-''', '''*''', '''/'''}
    __a						       =				[]

    for token in postfix_notation:
        if token in operations:
            __a       ,   __a						       =				stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(_UpperCAmelCase				)				)

    return stack.pop()


if __name__ == "__main__":
 import doctest

 doctest.testmod()





