


'''simple docstring'''
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 						_lowerCamelCase					:




 '''simple docstring'''
 __lowercase				:		Tuple    							=						BlenderbotSmallConfig
 __lowercase				:		List[Any]    							=						{}
 __lowercase				:		Tuple    							=						'gelu'


 def __init__(      self      ,      __lowercase      ,      __lowercase=13      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=99      ,      __lowercase=32      ,      __lowercase=2      ,      __lowercase=4      ,      __lowercase=37      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=20      ,      __lowercase=2      ,      __lowercase=1      ,      __lowercase=0      ,      ):

  """simple docstring"""







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

  __A  :		Optional[Any]       =			hidden_dropout_prob
  __A  :		Optional[int]       =			attention_probs_dropout_prob
  __A  :		List[str]       =			max_position_embeddings
  __A  :		Optional[Any]       =			eos_token_id
  __A  :		str       =			pad_token_id
  __A  :		Any       =			bos_token_id


 def  snake_case__   (      self						):

  """simple docstring"""







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

  __A  :		Tuple       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size						)

  __A  :		Union[str, Any]       =			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  :		List[str]       =			prepare_blenderbot_small_inputs_dict(__A      ,      __A      ,      __A						)
  return config, inputs_dict







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			TFBlenderbotSmallModel(config=__A						).get_decoder()
  __A  :		Any       =			inputs_dict['input_ids']

  __A  :		Optional[int]       =			input_ids[:1, :]
  __A  :		str       =			inputs_dict['attention_mask'][:1, :]
  __A  :		Any       =			inputs_dict['head_mask']
  __A  :		Tuple       =			1

  # first forward pass
  __A  :		Any       =			model(__A      ,      attention_mask=__A      ,      head_mask=__A      ,      use_cache=__A						)

  __A     ,__A  :		List[str]       =			outputs.to_tuple()

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

  # append to next input_ids and
  __A  :		List[str]       =			tf.concat([input_ids, next_tokens]      ,      axis=-1						)
  __A  :		Union[str, Any]       =			tf.concat([attention_mask, next_attn_mask]      ,      axis=-1						)

  __A  :		List[str]       =			model(__A      ,      attention_mask=__A						)[0]
  __A  :		int       =			model(__A      ,      attention_mask=__A      ,      past_key_values=__A						)[0]

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

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

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


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[Any]=None,				UpperCamelCase__       :					Dict=None,				UpperCamelCase__       :					Tuple=None,				UpperCamelCase__       :					str=None,				UpperCamelCase__       :					Any=None,				):






 if attention_mask is None:
  __A  :		Optional[int]       =			tf.cast(tf.math.not_equal(a__,				config.pad_token_id			),				tf.inta			)
 if decoder_attention_mask is None:
  __A  :		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:
  __A  :		Any       =			tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
 if decoder_head_mask is None:
  __A  :		Union[str, Any]       =			tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
 if cross_attn_head_mask is None:
  __A  :		Union[str, Any]       =			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 						_lowerCamelCase					(    __lowercase    ,		__lowercase    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		List[str]    							=						(
     (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
 )
 __lowercase				:		Optional[Any]    							=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
 __lowercase				:		Union[str, Any]    							=						(
     {
         'conversational': TFBlenderbotSmallForConditionalGeneration,
         'feature-extraction': TFBlenderbotSmallModel,
         'summarization': TFBlenderbotSmallForConditionalGeneration,
         'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
         'translation': TFBlenderbotSmallForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 __lowercase				:		List[str]    							=						True
 __lowercase				:		Optional[Any]    							=						False
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			TFBlenderbotSmallModelTester(self						)
  __A  :		Dict       =			ConfigTester(self      ,      config_class=__A						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.config_tester.run_common_tests()







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*__A						)




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




 '''simple docstring'''
 __lowercase				:		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?'
 ]
 __lowercase				:		Any    							=						'facebook/blenderbot_small-90M'


 @cached_property
 def  snake_case__   (      self						):

  """simple docstring"""







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


 @cached_property
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
  return model







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.tokenizer(self.src_text      ,      return_tensors='tf'						)
  __A  :		Optional[Any]       =			self.model.generate(
      model_inputs.input_ids      ,      attention_mask=model_inputs.attention_mask      ,      num_beams=2      ,      use_cache=__A      ,      )
  __A  :		Any       =			self.tokenizer.batch_decode(generated_ids.numpy()      ,      skip_special_tokens=__A						)[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.",
  )










'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = None						):

  """simple docstring"""







  if components is None:
   __A  :		Optional[int]       =			[]
  __A  :		Dict       =			list(__lowercase						)


 def __len__(      self						):

  """simple docstring"""







  return len(self.__components						)


 def __str__(      self						):

  """simple docstring"""







  return "(" + ",".join(map(__lowercase      ,      self.__components						)						) + ")"


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			len(self						)
  if size == len(__lowercase						):
   __A  :		Dict       =			[self.__components[i] + other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:
   raise Exception('must have the same size'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			len(self						)
  if size == len(__lowercase						):
   __A  :		str       =			[self.__components[i] - other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:  # error case
   raise Exception('must have the same size'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      (float, int)						):
   __A  :		Dict       =			[c * other for c in self.__components]
   return Vector(__lowercase						)
  elif isinstance(__lowercase      ,      __lowercase						) and len(self						) == len(__lowercase						):
   __A  :		Optional[Any]       =			len(self						)
   __A  :		Optional[int]       =			[self.__components[i] * other.component(__lowercase						) for i in range(__lowercase						)]
   return sum(__lowercase						)
  else:  # error case
   raise Exception('invalid operand!'						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return Vector(self.__components						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						) and -len(self.__components						) <= i < len(self.__components						):
   return self.__components[i]
  else:
   raise Exception('index out of range'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  assert -len(self.__components						) <= pos < len(self.__components						)
  __A  :		str       =			value


 def  snake_case__   (      self						):

  """simple docstring"""







  if len(self.__components						) == 0:
   raise Exception('Vector is empty'						)
  __A  :		List[Any]       =			[c**2 for c in self.__components]
  return math.sqrt(sum(__lowercase						)						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		List[Any]       =			self * other
  __A  :		Any       =			self.euclidean_length() * other.euclidean_length()
  if deg:
   return math.degrees(math.acos(num / den						)						)
  else:
   return math.acos(num / den						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			)
 return Vector([0] * dimension			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			) and (isinstance(UpperCamelCase__,				UpperCamelCase__			))
 __A  :		List[str]       =			[0] * dimension
 __A  :		Tuple       =			1
 return Vector(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					Vector,				UpperCamelCase__       :					Vector			):
 assert (
     isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and (isinstance(UpperCamelCase__,				(int, float)			))
 )
 return x * scalar + y


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		Any       =			[random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)]
 return Vector(UpperCamelCase__			)

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			matrix
  __A  :		str       =			w
  __A  :		Dict       =			h


 def __str__(      self						):

  """simple docstring"""







  __A  :		Tuple       =			''
  for i in range(self.__height						):
   ans += "|"
   for j in range(self.__width						):
    if j < self.__width - 1:
     ans += str(self.__matrix[i][j]						) + ","
    else:
     ans += str(self.__matrix[i][j]						) + "|\n"
  return ans


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Union[str, Any]       =			[]
   for i in range(self.__height						):
    __A  :		List[Any]       =			[
        self.__matrix[i][j] + other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrix must have the same dimension!'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Dict       =			[]
   for i in range(self.__height						):
    __A  :		str       =			[
        self.__matrix[i][j] - other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrices must have the same dimension!'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						):  # matrix-vector
   if len(__lowercase						) == self.__width:
    __A  :		Tuple       =			zero_vector(self.__height						)
    for i in range(self.__height						):
     __A  :		Any       =			[
         self.__matrix[i][j] * other.component(__lowercase						)
         for j in range(self.__width						)
     ]
     ans.change_component(__lowercase      ,      sum(__lowercase						)						)
    return ans
   else:
    raise Exception(
        'vector must have the same size as the '
        'number of columns of the matrix!'						)
  elif isinstance(__lowercase      ,      (int, float)						):  # matrix-scalar
   __A  :		Optional[Any]       =			[
       [self.__matrix[i][j] * other for j in range(self.__width						)]
       for i in range(self.__height						)
   ]
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  return None


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__height


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__width


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   return self.__matrix[x][y]
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   __A  :		Dict       =			value
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  __A  :		Union[str, Any]       =			self.__matrix[:x] + self.__matrix[x + 1 :]
  for i in range(len(__lowercase						)						):
   __A  :		Dict       =			minor[i][:y] + minor[i][y + 1 :]
  return Matrix(__lowercase      ,      self.__width - 1      ,      self.__height - 1						).determinant()


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if 0 <= x < self.__height and 0 <= y < self.__width:
   return (-1) ** (x + y) * self.minor(__lowercase      ,      __lowercase						)
  else:
   raise Exception('Indices out of bounds'						)







 def  snake_case__   (      self						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if self.__height < 1:
   raise Exception('Matrix has no element'						)
  elif self.__height == 1:
   return self.__matrix[0][0]
  elif self.__height == 2:
   return (
       self.__matrix[0][0] * self.__matrix[1][1]
       - self.__matrix[0][1] * self.__matrix[1][0]
   )
  else:
   __A  :		List[str]       =			[
       self.__matrix[0][y] * self.cofactor(0      ,      __lowercase						) for y in range(self.__width						)
   ]
   return sum(__lowercase						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		list[list[float]]       =			[[0] * n for _ in range(UpperCamelCase__			)]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		list[list[float]]       =			[
     [random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)] for _ in range(UpperCamelCase__			)
 ]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)






