'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


a=  logging.get_logger(__name__)

a=  {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''blenderbot-small'''
     SCREAMING_SNAKE_CASE__		   =						['''past_key_values''']
     SCREAMING_SNAKE_CASE__		   =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







     def __init__(		self     ,			_lowerCamelCase=5_0_2_6_5     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=8     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=8     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1     ,			_lowerCamelCase=False     ,			_lowerCamelCase=0     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			_lowerCamelCase=2     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	int		    =  vocab_size
         __UpperCamelCase	:	Dict		    =  max_position_embeddings
         __UpperCamelCase	:	Dict		    =  d_model
         __UpperCamelCase	:	Union[str, Any]		    =  encoder_ffn_dim
         __UpperCamelCase	:	List[str]		    =  encoder_layers
         __UpperCamelCase	:	int		    =  encoder_attention_heads
         __UpperCamelCase	:	Any		    =  decoder_ffn_dim
         __UpperCamelCase	:	List[Any]		    =  decoder_layers
         __UpperCamelCase	:	Tuple		    =  decoder_attention_heads
         __UpperCamelCase	:	List[str]		    =  dropout
         __UpperCamelCase	:	Any		    =  attention_dropout
         __UpperCamelCase	:	Union[str, Any]		    =  activation_dropout
         __UpperCamelCase	:	Optional[Any]		    =  activation_function
         __UpperCamelCase	:	List[str]		    =  init_std
         __UpperCamelCase	:	Tuple		    =  encoder_layerdrop
         __UpperCamelCase	:	Union[str, Any]		    =  decoder_layerdrop
         __UpperCamelCase	:	int		    =  use_cache
         __UpperCamelCase	:	Tuple		    =  encoder_layers
         __UpperCamelCase	:	int		    =  scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			is_encoder_decoder=_lowerCamelCase     ,			decoder_start_token_id=_lowerCamelCase     ,			forced_eos_token_id=_lowerCamelCase     ,			**_lowerCamelCase     ,			)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @property
     def lowerCAmelCase     (		self    ):
         if self.task in ["default", "seq2seq-lm"]:
             __UpperCamelCase	:	Optional[int]		    =  OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]    )

             if self.use_past:
                 __UpperCamelCase	:	Optional[Any]		    =  {0: 'batch'}
                 __UpperCamelCase	:	List[str]		    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                 __UpperCamelCase	:	Optional[int]		    =  {0: 'batch', 1: 'decoder_sequence'}
                 __UpperCamelCase	:	Optional[int]		    =  {0: 'batch', 1: 'decoder_sequence'}

             if self.use_past:
                 self.fill_with_past_key_values_(_lowerCamelCase     ,			direction='inputs'    )
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             __UpperCamelCase	:	List[str]		    =  OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]    )
             if self.use_past:
                 __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.num_layers
                 for i in range(_lowerCamelCase    ):
                     __UpperCamelCase	:	List[Any]		    =  {0: 'batch', 2: 'past_sequence + sequence'}
                     __UpperCamelCase	:	Any		    =  {0: 'batch', 2: 'past_sequence + sequence'}
         else:
             __UpperCamelCase	:	Tuple		    =  OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                     ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                 ]    )

         return common_inputs







     @property
     def lowerCAmelCase     (		self    ):
         if self.task in ["default", "seq2seq-lm"]:
             __UpperCamelCase	:	Union[str, Any]		    =  super().outputs
         else:
             __UpperCamelCase	:	Dict		    =  super(_lowerCamelCase     ,			self    ).outputs
             if self.use_past:
                 __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.num_layers
                 for i in range(_lowerCamelCase    ):
                     __UpperCamelCase	:	Any		    =  {0: 'batch', 2: 'past_sequence + sequence'}
                     __UpperCamelCase	:	Dict		    =  {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			):
         __UpperCamelCase	:	List[str]		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )

         # Generate decoder inputs
         __UpperCamelCase	:	List[str]		    =  seq_length if not self.use_past else 1
         __UpperCamelCase	:	Union[str, Any]		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         __UpperCamelCase	:	List[Any]		    =  dict(**_lowerCamelCase     ,			**_lowerCamelCase    )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'    )
             else:
                 import torch
             __UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  common_inputs['input_ids'].shape
             __UpperCamelCase	:	str		    =  common_inputs['decoder_input_ids'].shape[1]
             __UpperCamelCase    ,					__UpperCamelCase	:	int		    =  self.num_attention_heads
             __UpperCamelCase	:	Optional[int]		    =  (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             __UpperCamelCase	:	str		    =  decoder_seq_length + 3
             __UpperCamelCase	:	Dict		    =  (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             __UpperCamelCase	:	List[Any]		    =  torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(_lowerCamelCase     ,			_lowerCamelCase    )]     ,			dim=1    )

             __UpperCamelCase	:	Union[str, Any]		    =  []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.num_layers
             __UpperCamelCase	:	str		    =  min(_lowerCamelCase     ,			_lowerCamelCase    )
             __UpperCamelCase	:	Dict		    =  max(_lowerCamelCase     ,			_lowerCamelCase    ) - min_num_layers
             __UpperCamelCase	:	Optional[int]		    =  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

             for _ in range(_lowerCamelCase    ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_lowerCamelCase    ),
                         torch.zeros(_lowerCamelCase    ),
                         torch.zeros(_lowerCamelCase    ),
                         torch.zeros(_lowerCamelCase    ),
                     )    )
             # TODO: test this.
             __UpperCamelCase	:	Any		    =  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
             for _ in range(_lowerCamelCase     ,			_lowerCamelCase    ):
                 common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    ))    )
         return common_inputs







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			):
         __UpperCamelCase	:	Dict		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'    )
             else:
                 import torch
             __UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             __UpperCamelCase	:	str		    =  seqlen + 2
             __UpperCamelCase    ,					__UpperCamelCase	:	Dict		    =  self.num_layers
             __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.num_attention_heads
             __UpperCamelCase	:	List[str]		    =  (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             __UpperCamelCase	:	List[Any]		    =  common_inputs['attention_mask'].dtype
             __UpperCamelCase	:	List[Any]		    =  torch.cat(
                 [common_inputs['attention_mask'], torch.ones(_lowerCamelCase     ,			_lowerCamelCase     ,			dtype=_lowerCamelCase    )]     ,			dim=1    )
             __UpperCamelCase	:	Any		    =  [
                 (torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    )) for _ in range(_lowerCamelCase    )
             ]
         return common_inputs







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			):
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __UpperCamelCase	:	Any		    =  compute_effective_axis_dimension(
             _lowerCamelCase     ,			fixed_dimension=OnnxConfig.default_fixed_batch     ,			num_token_to_add=0    )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         __UpperCamelCase	:	Tuple		    =  tokenizer.num_special_tokens_to_add(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  compute_effective_axis_dimension(
             _lowerCamelCase     ,			fixed_dimension=OnnxConfig.default_fixed_sequence     ,			num_token_to_add=_lowerCamelCase    )

         # Generate dummy inputs according to compute batch and sequence
         __UpperCamelCase	:	List[str]		    =  [' '.join([tokenizer.unk_token]    ) * seq_length] * batch_size
         __UpperCamelCase	:	Dict		    =  dict(tokenizer(_lowerCamelCase     ,			return_tensors=_lowerCamelCase    )    )
         return common_inputs







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = -1     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			):
         if self.task in ["default", "seq2seq-lm"]:
             __UpperCamelCase	:	List[str]		    =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			seq_length=_lowerCamelCase     ,			is_pair=_lowerCamelCase     ,			framework=_lowerCamelCase    )

         elif self.task == "causal-lm":
             __UpperCamelCase	:	Tuple		    =  self._generate_dummy_inputs_for_causal_lm(
                 _lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			seq_length=_lowerCamelCase     ,			is_pair=_lowerCamelCase     ,			framework=_lowerCamelCase    )
         else:
             __UpperCamelCase	:	List[str]		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			seq_length=_lowerCamelCase     ,			is_pair=_lowerCamelCase     ,			framework=_lowerCamelCase    )

         return common_inputs







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if self.task in ["default", "seq2seq-lm"]:
             __UpperCamelCase	:	Any		    =  super()._flatten_past_key_values_(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         else:
             __UpperCamelCase	:	Dict		    =  super(_lowerCamelCase     ,			self    )._flatten_past_key_values_(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )




'''simple docstring'''
import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  Vector([1, 2, 3]    )
         self.assertEqual(x.component(0    )     ,			1    )
         self.assertEqual(x.component(2    )     ,			3    )
         __UpperCamelCase	:	Dict		    =  Vector()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  Vector([0, 0, 0, 0, 0, 1]    )
         self.assertEqual(str(_lowerCamelCase    )     ,			'(0,0,0,0,0,1)'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  Vector([1, 2, 3, 4]    )
         self.assertEqual(len(_lowerCamelCase    )     ,			4    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  Vector([1, 2]    )
         __UpperCamelCase	:	Optional[int]		    =  Vector([1, 2, 3, 4, 5]    )
         __UpperCamelCase	:	Optional[Any]		    =  Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]    )
         __UpperCamelCase	:	List[str]		    =  Vector([1, -1, 1, -1, 2, -3, 4, -5]    )
         self.assertAlmostEqual(x.euclidean_length()     ,			2.2_3_6     ,			3    )
         self.assertAlmostEqual(y.euclidean_length()     ,			7.4_1_6     ,			3    )
         self.assertEqual(z.euclidean_length()     ,			0    )
         self.assertAlmostEqual(w.euclidean_length()     ,			7.6_1_6     ,			3    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  Vector([1, 2, 3]    )
         __UpperCamelCase	:	List[str]		    =  Vector([1, 1, 1]    )
         self.assertEqual((x + y).component(0    )     ,			2    )
         self.assertEqual((x + y).component(1    )     ,			3    )
         self.assertEqual((x + y).component(2    )     ,			4    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  Vector([1, 2, 3]    )
         __UpperCamelCase	:	Dict		    =  Vector([1, 1, 1]    )
         self.assertEqual((x - y).component(0    )     ,			0    )
         self.assertEqual((x - y).component(1    )     ,			1    )
         self.assertEqual((x - y).component(2    )     ,			2    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  Vector([1, 2, 3]    )
         __UpperCamelCase	:	int		    =  Vector([2, -1, 4]    )  # for test of dot product
         __UpperCamelCase	:	int		    =  Vector([1, -2, -1]    )
         self.assertEqual(str(x * 3.0    )     ,			'(3.0,6.0,9.0)'    )
         self.assertEqual((a * b)     ,			0    )







     def lowerCAmelCase     (		self    ):
         self.assertEqual(str(zero_vector(1_0    )    ).count('0'    )     ,			1_0    )







     def lowerCAmelCase     (		self    ):
         self.assertEqual(str(unit_basis_vector(3     ,			1    )    )     ,			'(0,1,0)'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  Vector([1, 2, 3]    )
         __UpperCamelCase	:	Union[str, Any]		    =  Vector([1, 0, 1]    )
         self.assertEqual(str(axpy(2     ,			_lowerCamelCase     ,			_lowerCamelCase    )    )     ,			'(3,4,7)'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  Vector([1, 0, 0, 0, 0, 0]    )
         __UpperCamelCase	:	Optional[Any]		    =  x.copy()
         self.assertEqual(str(_lowerCamelCase    )     ,			str(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  Vector([1, 0, 0]    )
         x.change_component(0     ,			0    )
         x.change_component(1     ,			1    )
         self.assertEqual(str(_lowerCamelCase    )     ,			'(0,1,0)'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n'     ,			str(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         __UpperCamelCase	:	List[str]		    =  [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
         for x in range(a.height()    ):
             for y in range(a.width()    ):
                 self.assertEqual(minors[x][y]     ,			a.minor(_lowerCamelCase     ,			_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         __UpperCamelCase	:	List[str]		    =  [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
         for x in range(a.height()    ):
             for y in range(a.width()    ):
                 self.assertEqual(cofactors[x][y]     ,			a.cofactor(_lowerCamelCase     ,			_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         self.assertEqual(-5     ,			a.determinant()    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     ,			3     ,			3    )
         __UpperCamelCase	:	Union[str, Any]		    =  Vector([1, 2, 3]    )
         self.assertEqual('(14,32,50)'     ,			str(a * x    )    )
         self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n'     ,			str(a * 2    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         a.change_component(0     ,			2     ,			5    )
         self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n'     ,			str(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         self.assertEqual(7     ,			a.component(2     ,			1    )     ,			0.0_1    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         __UpperCamelCase	:	Optional[Any]		    =  Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]     ,			3     ,			3    )
         self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n'     ,			str(a + b    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,			3     ,			3    )
         __UpperCamelCase	:	Optional[int]		    =  Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]     ,			3     ,			3    )
         self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'     ,			str(a - b    )    )







     def lowerCAmelCase     (		self    ):
         self.assertEqual(
             '|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'     ,			str(square_zero_matrix(5    )    )     ,			)


if __name__ == "__main__":
  unittest.main()



