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 : Union[str, Any]      =			BlenderbotSmallConfig
   __UpperCAmelCase : Tuple      =			{}
   __UpperCAmelCase : str      =			'gelu'


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





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Tuple		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TFBlenderbotSmallModel(config=__UpperCAmelCase		).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(__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase       ,  head_mask=__UpperCAmelCase       ,  use_cache=__UpperCAmelCase		)

         UpperCAmelCase__		,      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(__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase		)[0]
         UpperCAmelCase__								=  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
         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(__UpperCAmelCase       ,  __UpperCAmelCase       ,  rtol=1E-3		)




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A=None,  __A=None,  __A=None,  __A=None,  __A=None,  )     ->				Dict:







      '''simple docstring'''


      if attention_mask is None:
            UpperCAmelCase__								=  tf.cast(tf.math.not_equal(__A,  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      A							(				UpperCAmelCase_		,				UpperCAmelCase_		,				unittest.TestCase     ):
   __UpperCAmelCase : List[Any]      =			(
       (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
   )
   __UpperCAmelCase : Union[str, Any]      =			(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
   __UpperCAmelCase : int      =			(
       {
           'conversational': TFBlenderbotSmallForConditionalGeneration,
           'feature-extraction': TFBlenderbotSmallModel,
           'summarization': TFBlenderbotSmallForConditionalGeneration,
           'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
           'translation': TFBlenderbotSmallForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase : Union[str, Any]      =			True
   __UpperCAmelCase : str      =			False
   __UpperCAmelCase : Optional[Any]      =			False


   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TFBlenderbotSmallModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self       ,  config_class=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Tuple		)						->				Tuple:
         """simple docstring"""



         self.config_tester.run_common_tests()





   def 			lowercase_      (self     :						Tuple		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  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     ):
   __UpperCAmelCase : str      =			[
       '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 : Dict      =			'facebook/blenderbot_small-90M'


   @cached_property
   def 			lowercase_      (self     :						Union[str, Any]		)						->				Any:
         """simple docstring"""



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


   @cached_property
   def 			lowercase_      (self     :						int		)						->				str:
         """simple docstring"""



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





   @slow
   def 			lowercase_      (self     :						int		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.tokenizer(self.src_text       ,  return_tensors="tf"		)
         UpperCAmelCase__								=  self.model.generate(
             model_inputs.input_ids       ,  attention_mask=model_inputs.attention_mask       ,  num_beams=2       ,  use_cache=__UpperCAmelCase       ,  )
         UpperCAmelCase__								=  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 __future__ import annotations





class      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[list[int]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Matrices must be formed from a list of zero or more lists containing at "
             "least one and the same number of values, each of which must be of type "
             "int or float."		)
         if len(__UpperCAmelCase		) != 0:
               UpperCAmelCase__								=  len(rows[0]		)
               if cols == 0:
                     raise error
               for row in rows:
                     if len(__UpperCAmelCase		) != cols:
                           raise error
                     for value in row:
                           if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                                 raise error
               UpperCAmelCase__								=  rows
         else:
               UpperCAmelCase__								=  []


   def 			lowercase_      (self     :						Any		)						->				list[list[int]]:
         """simple docstring"""



         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]


   @property
   def 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         return len(self.rows		)


   @property
   def 			lowercase_      (self     :						Union[str, Any]		)						->				int:
         """simple docstring"""



         return len(self.rows[0]		)


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				tuple[int, int]:
         """simple docstring"""



         return (self.num_rows, self.num_columns)


   @property
   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return self.order[0] == self.order[1]


   def 			lowercase_      (self     :						Any		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows		)]
             for row_num in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				int:
         """simple docstring"""



         if not self.is_square:
               return 0
         if self.order == (0, 0):
               return 1
         if self.order == (1, 1):
               return int(self.rows[0][0]		)
         if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])		)
         else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns		)		)


   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return bool(self.determinant()		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns		)
                 if other_column != column
             ]
             for other_row in range(self.num_rows		)
             if other_row != row
         ]
         return Matrix(__UpperCAmelCase		).determinant()


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         if (row + column) % 2 == 0:
               return self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return -1 * self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in range(self.num_columns		)]
                 for row in range(self.num_rows		)
             ]		)


   def 			lowercase_      (self     :						List[str]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns		)
                 ]
                 for row in range(self.minors().num_rows		)
             ]		)


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



         UpperCAmelCase__								=  [
             [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
             for row in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  self.determinant()
         if not determinant:
               raise TypeError("Only matrices with a non-zero determinant have an inverse"		)
         return self.adjugate() * (1 / determinant)


   def __repr__(self     :						Dict		)						->				str:
         """simple docstring"""



         return str(self.rows		)


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



         if self.num_rows == 0:
               return "[]"
         if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]		)		) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     "[" + ". ".join([str(__UpperCAmelCase		) for value in row]		) + ".]"
                     for row in self.rows
                 ]		)
             + "]"
         )


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError("Row must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in row:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_columns:
               raise ValueError(
                   "Row must be equal in length to the other rows in the matrix"		)
         if position is None:
               self.rows.append(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  self.rows[0:position] + [row] + self.rows[position:]


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Column must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in column:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_rows:
               raise ValueError(
                   "Column must be equal in length to the other columns in the matrix"		)
         if position is None:
               UpperCAmelCase__								=  [self.rows[i] + [column[i]] for i in range(self.num_rows		)]
         else:
               UpperCAmelCase__								=  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows		)
               ]


   def __eq__(self     :						Any       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               return NotImplemented
         return self.rows == other.rows


   def __ne__(self     :						int       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         return not self == other


   def __neg__(self     :						Dict		)						->				Matrix:
         """simple docstring"""



         return self * -1


   def __add__(self     :						Dict       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Addition requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __sub__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Subtraction requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __mul__(self     :						Tuple       ,  __UpperCAmelCase     :						Matrix | int | float		)						->				Matrix:
         """simple docstring"""



         if isinstance(__UpperCAmelCase       ,  (int, float)		):
               return Matrix(
                   [[int(element * other		) for element in row] for row in self.rows]		)
         elif isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               if self.num_columns != other.num_rows:
                     raise ValueError(
                         "The number of columns in the first matrix must "
                         "be equal to the number of rows in the second"		)
               return Matrix(
                   [
                       [Matrix.dot_product(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in other.columns()]
                       for row in self.rows
                   ]		)
         else:
               raise TypeError(
                   "A Matrix can only be multiplied by an int, float, or another matrix"		)


   def __pow__(self     :						List[Any]       ,  __UpperCAmelCase     :						int		)						->				Matrix:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise TypeError("A Matrix can only be raised to the power of an int"		)
         if not self.is_square:
               raise ValueError("Only square matrices can be raised to a power"		)
         if other == 0:
               return self.identity()
         if other < 0:
               if self.is_invertable():
                     return self.inverse() ** (-other)
               raise ValueError(
                   "Only invertable matrices can be raised to a negative power"		)
         UpperCAmelCase__								=  self
         for _ in range(other - 1		):
               result *= self
         return result





   @classmethod
   def 			lowercase_      (cls     :						Dict       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						list[int]		)						->				int:
         """simple docstring"""



         return sum(row[i] * column[i] for i in range(len(__UpperCAmelCase		)		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



