




import numpy

# List of input, output pairs
lowerCamelCase__            = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCamelCase__            = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase__            = [2, 4, 1, 5]
lowerCamelCase__            = len(train_data)
lowerCamelCase__            = 0.009







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Any						,						SCREAMING_SNAKE_CASE						:		str="train" ):






	"""simple docstring"""
	return calculate_hypothesis_value(a__						,						a__ ) - output(
	    a__						,						a__ )







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optional[Any] ):






	"""simple docstring"""
	snake_case__						:     Any		=0
	for i in range(len(a__ ) - 1 ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[Any]						,						SCREAMING_SNAKE_CASE						:		List[str] ):






	"""simple docstring"""





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Tuple						,						SCREAMING_SNAKE_CASE						:		int ):






	"""simple docstring"""





	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0] )
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0] )
	return None







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optional[int]						,						SCREAMING_SNAKE_CASE						:		Tuple=m ):






	"""simple docstring"""
	snake_case__						:     int		=0
	for i in range(a__ ):
		if index == -1:
			summation_value += _error(a__ )
		else:
			summation_value += _error(a__ ) * train_data[i][0][index]
	return summation_value







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optional[int] ):






	"""simple docstring"""
	snake_case__						:     Dict		=summation_of_cost_derivative(a__						,						a__ ) / m
	return cost_derivative_value







def 	lowercase_		(     ):






	"""simple docstring"""
	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	snake_case__						:     Optional[Any]		=0.00_0002
	snake_case__						:     Dict		=0
	snake_case__						:     Optional[Any]		=0
	while True:
		j += 1
		snake_case__						:     int		=[0, 0, 0, 0]
		for i in range(0						,						len(a__ ) ):
			snake_case__						:     Dict		=get_cost_derivative(i - 1 )
			snake_case__						:     Union[str, Any]		=(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    a__						,						a__						,						atol=a__						,						rtol=a__						,						):
			break
		snake_case__						:     Tuple		=temp_parameter_vector
	print(('''Number of iterations:''', j) )







def 	lowercase_		(     ):






	"""simple docstring"""





	for i in range(len(a__ ) ):
		print(('''Actual output value:''', output(a__						,						'''test''' )) )
		print(('''Hypothesis output:''', calculate_hypothesis_value(a__						,						'''test''' )) )


if __name__ == "__main__":
		run_gradient_descent()
		print('''\nTesting gradient descent for a linear hypothesis function.\n''')
		test_gradient_descent()








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


lowerCAmelCase__    :List[Any]       =				logging.get_logger(__name__)

lowerCAmelCase__    :int       =				{
    '''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 						__a (							UpperCAmelCase     ):
		_a      :    Dict           =      'blenderbot-small'
		_a      :    Tuple           =      ['past_key_values']
		_a      :    List[str]           =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50265     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=2     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     encoder_ffn_dim
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     encoder_attention_heads
									_UpperCAmelCase											=     decoder_ffn_dim
									_UpperCAmelCase											=     decoder_layers
									_UpperCAmelCase											=     decoder_attention_heads
									_UpperCAmelCase											=     dropout
									_UpperCAmelCase											=     attention_dropout
									_UpperCAmelCase											=     activation_dropout
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     init_std
									_UpperCAmelCase											=     encoder_layerdrop
									_UpperCAmelCase											=     decoder_layerdrop
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)

																if self.use_past:
																							_UpperCAmelCase											=     {0: 'batch'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																else:
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}

																if self.use_past:
																							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,       direction='inputs'		)
									elif self.task == "causal-lm":
																# TODO: figure this case out.
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super().outputs
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		).outputs
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									# Generate decoder inputs
									_UpperCAmelCase											=     seq_length if not self.use_past else 1
									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									_UpperCAmelCase											=     dict(**_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									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											=     common_inputs['input_ids'].shape
																_UpperCAmelCase											=     common_inputs['decoder_input_ids'].shape[1]
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																_UpperCAmelCase											=     decoder_seq_length + 3
																_UpperCAmelCase											=     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)]     ,       dim=1		)

																_UpperCAmelCase											=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase											=     min(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     max(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) - min_num_layers
																_UpperCAmelCase											=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																for _ in range(_SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							        torch.zeros(_SCREAMING_SNAKE_CASE		),
																							    )		)
																# TODO: test this.
																_UpperCAmelCase											=     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																for _ in range(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																							common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		))		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									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											=     common_inputs['input_ids'].shape
																# Not using the same length for past_key_values
																_UpperCAmelCase											=     seqlen + 2
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																_UpperCAmelCase											=     common_inputs['attention_mask'].dtype
																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE		)]     ,       dim=1		)
																_UpperCAmelCase											=     [
																    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(_SCREAMING_SNAKE_CASE		)
																]
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       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											=     tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     compute_effective_axis_dimension(
									    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)

									# Generate dummy inputs according to compute batch and sequence
									_UpperCAmelCase											=     [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
									_UpperCAmelCase											=     dict(tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

									elif self.task == "causal-lm":
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_causal_lm(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    _SCREAMING_SNAKE_CASE     ,       batch_size=_SCREAMING_SNAKE_CASE     ,       seq_length=_SCREAMING_SNAKE_CASE     ,       is_pair=_SCREAMING_SNAKE_CASE     ,       framework=_SCREAMING_SNAKE_CASE		)

									return common_inputs






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		)._flatten_past_key_values_(
																    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
