

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__       :







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =BlenderbotSmallConfig
  UpperCAmelCase__      : str			       ={}
  UpperCAmelCase__      : List[str]			       ="""gelu"""


  def __init__(			self    : Dict				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : str=1_3				,				UpperCAmelCase__    : List[Any]=7				,				UpperCAmelCase__    : int=True				,				UpperCAmelCase__    : int=False				,				UpperCAmelCase__    : Optional[int]=9_9				,				UpperCAmelCase__    : Tuple=3_2				,				UpperCAmelCase__    : Tuple=2				,				UpperCAmelCase__    : Optional[Any]=4				,				UpperCAmelCase__    : List[str]=3_7				,				UpperCAmelCase__    : Any=0.1				,				UpperCAmelCase__    : Any=0.1				,				UpperCAmelCase__    : Union[str, Any]=2_0				,				UpperCAmelCase__    : Dict=2				,				UpperCAmelCase__    : List[str]=1				,				UpperCAmelCase__    : Optional[int]=0				,				)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   parent
        SCREAMING_SNAKE_CASE :      List[str]       =   batch_size
        SCREAMING_SNAKE_CASE :      str       =   seq_length
        SCREAMING_SNAKE_CASE :      Dict       =   is_training
        SCREAMING_SNAKE_CASE :      List[str]       =   use_labels
        SCREAMING_SNAKE_CASE :      int       =   vocab_size
        SCREAMING_SNAKE_CASE :      Any       =   hidden_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   intermediate_size

        SCREAMING_SNAKE_CASE :      Dict       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      int       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Optional[int]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      List[Any]       =   eos_token_id
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   pad_token_id
        SCREAMING_SNAKE_CASE :      str       =   bos_token_id


  def  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size		)
        SCREAMING_SNAKE_CASE :      str       =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)				,				1		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   tf.concat([input_ids, eos_tensor]				,				axis=1		)

        SCREAMING_SNAKE_CASE :      str       =   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size		)

        SCREAMING_SNAKE_CASE :      Tuple       =   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				,				)
        SCREAMING_SNAKE_CASE :      List[Any]       =   prepare_blenderbot_small_inputs_dict(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)
        return config, inputs_dict




  def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Any		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   TFBlenderbotSmallModel(config=UpperCAmelCase__		).get_decoder()
        SCREAMING_SNAKE_CASE :      Any       =   inputs_dict["""input_ids"""]

        SCREAMING_SNAKE_CASE :      int       =   input_ids[:1, :]
        SCREAMING_SNAKE_CASE :      Dict       =   inputs_dict["""attention_mask"""][:1, :]
        SCREAMING_SNAKE_CASE :      Tuple       =   inputs_dict["""head_mask"""]
        SCREAMING_SNAKE_CASE :      Any       =   1

        # first forward pass
        SCREAMING_SNAKE_CASE :      Dict       =   model(UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				head_mask=UpperCAmelCase__				,				use_cache=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Tuple       =   outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE :      str       =   ids_tensor((self.batch_size, 3)				,				config.vocab_size		)
        SCREAMING_SNAKE_CASE :      List[str]       =   tf.cast(ids_tensor((self.batch_size, 3)				,				2		)				,				tf.inta		)

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

        SCREAMING_SNAKE_CASE :      str       =   model(UpperCAmelCase__				,				attention_mask=UpperCAmelCase__		)[0]
        SCREAMING_SNAKE_CASE :      Tuple       =   model(UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				past_key_values=UpperCAmelCase__		)[0]

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

        # select random slice
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(ids_tensor((1,)				,				output_from_past.shape[-1]		)		)
        SCREAMING_SNAKE_CASE :      List[str]       =   output_from_no_past[:, -3:, random_slice_idx]
        SCREAMING_SNAKE_CASE :      List[str]       =   output_from_past[:, :, random_slice_idx]

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






def 					__lowercase      (     _A						,    _A						,    _A						,    _A=None						,    _A=None						,    _A=None						,    _A=None						,    _A=None						,    )     ->						Optional[int]:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   tf.cast(tf.math.not_equal(_A						,    config.pad_token_id			)						,    tf.inta			)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   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:
            SCREAMING_SNAKE_CASE :      Tuple       =   tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE :      Tuple       =   tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE :      int       =   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       ):







  """simple docstring"""




  UpperCAmelCase__      : Dict			       =(
      (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
  )
  UpperCAmelCase__      : Optional[Any]			       =(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
  UpperCAmelCase__      : Dict			       =(
      {
          """conversational""": TFBlenderbotSmallForConditionalGeneration,
          """feature-extraction""": TFBlenderbotSmallModel,
          """summarization""": TFBlenderbotSmallForConditionalGeneration,
          """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
          """translation""": TFBlenderbotSmallForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  UpperCAmelCase__      : Optional[Any]			       =True
  UpperCAmelCase__      : str			       =False
  UpperCAmelCase__      : List[Any]			       =False


  def  _lowercase      (			self    : Optional[Any]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   TFBlenderbotSmallModelTester(self		)
        SCREAMING_SNAKE_CASE :      List[str]       =   ConfigTester(self				,				config_class=UpperCAmelCase__		)


  def  _lowercase      (			self    : str		)					->List[Any]:
        """simple docstring"""







        self.config_tester.run_common_tests()




  def  _lowercase      (			self    : Dict		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__		)







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







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =[
      """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__      : Union[str, Any]			       ="""facebook/blenderbot_small-90M"""


  @cached_property
  def  _lowercase      (			self    : Optional[int]		)					->List[str]:
        """simple docstring"""







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


  @cached_property
  def  _lowercase      (			self    : Optional[int]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
        return model




  @slow
  def  _lowercase      (			self    : str		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   self.tokenizer(self.src_text				,				return_tensors="""tf"""		)
        SCREAMING_SNAKE_CASE :      Dict       =   self.model.generate(
            model_inputs.input_ids				,				attention_mask=model_inputs.attention_mask				,				num_beams=2				,				use_cache=UpperCAmelCase__				,				)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.tokenizer.batch_decode(generated_ids.numpy()				,				skip_special_tokens=UpperCAmelCase__		)[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 math import ceil






def 					__lowercase      (     _A = 1001			)     ->						int:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1

      for i in range(1						,    int(ceil(n / 2.0			)			)			):
            SCREAMING_SNAKE_CASE :      Dict       =   2 * i + 1
            SCREAMING_SNAKE_CASE :      Optional[int]       =   2 * i
            SCREAMING_SNAKE_CASE :      str       =   total + 4 * odd**2 - 6 * even

      return total


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution())
  else:
    try:
      UpperCAmelCase__ :     str				    =    int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number""")




