


"""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-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class 				lowerCamelCase							(      _lowerCamelCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   """blenderbot"""
							SCREAMING_SNAKE_CASE              =   ["""past_key_values"""]
							SCREAMING_SNAKE_CASE              =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(    self			:  Any		,			lowerCamelCase_			:  Optional[int]=8008		,			lowerCamelCase_			:  List[Any]=128		,			lowerCamelCase_			:  List[str]=2		,			lowerCamelCase_			:  Dict=10240		,			lowerCamelCase_			:  Union[str, Any]=32		,			lowerCamelCase_			:  str=24		,			lowerCamelCase_			:  Any=10240		,			lowerCamelCase_			:  str=32		,			lowerCamelCase_			:  List[Any]=0.0		,			lowerCamelCase_			:  Union[str, Any]=0.0		,			lowerCamelCase_			:  Union[str, Any]=True		,			lowerCamelCase_			:  List[Any]=True		,			lowerCamelCase_			:  Dict="gelu"		,			lowerCamelCase_			:  Union[str, Any]=2560		,			lowerCamelCase_			:  List[str]=0.1		,			lowerCamelCase_			:  Dict=0.0		,			lowerCamelCase_			:  str=0.0		,			lowerCamelCase_			:  Union[str, Any]=0.02		,			lowerCamelCase_			:  Dict=1		,			lowerCamelCase_			:  str=False		,			lowerCamelCase_			:  List[Any]=0		,			lowerCamelCase_			:  Any=1		,			lowerCamelCase_			:  str=2		,			lowerCamelCase_			:  List[Any]=3		,			lowerCamelCase_			:  int=2		,			**lowerCamelCase_			:  str		,			):
													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=lowerCAmelCase_		,			bos_token_id=lowerCAmelCase_		,			eos_token_id=lowerCAmelCase_		,			is_encoder_decoder=lowerCAmelCase_		,			decoder_start_token_id=lowerCAmelCase_		,			encoder_no_repeat_ngram_size=lowerCAmelCase_		,			forced_eos_token_id=lowerCAmelCase_		,			**lowerCAmelCase_		,			)




class 				lowerCamelCase							(      _lowerCamelCase			):


							"""simple docstring"""






							@property
							def        lowerCAmelCase  (    self			:  int					):
													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_(lowerCAmelCase_		,			direction='''inputs'''					)
													elif self.task == "causal-lm":
																			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(lowerCAmelCase_					):
																															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
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
							def        lowerCAmelCase  (    self			:  int					):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__												=     super().outputs
													else:
																			UpperCAmelCase__												=     super(lowerCAmelCase_		,			self					).outputs
																			if self.use_past:
																									UpperCAmelCase__						,					UpperCAmelCase__												=     self.num_layers
																									for i in range(lowerCAmelCase_					):
																															UpperCAmelCase__												=     {0: '''batch''', 2: '''past_sequence + sequence'''}
																															UpperCAmelCase__												=     {0: '''batch''', 2: '''past_sequence + sequence'''}
													return common_outputs






							def        lowerCAmelCase  (    self			:  List[str]		,			lowerCamelCase_			:  int		,			lowerCamelCase_			:  Dict = -1		,			lowerCamelCase_			:  int = -1		,			lowerCamelCase_			:  Tuple = False		,			lowerCamelCase_			:  Tuple = None		,			):
													UpperCAmelCase__												=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_					)
													# Generate decoder inputs
													UpperCAmelCase__												=     seq_length if not self.use_past else 1
													UpperCAmelCase__												=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_					)
													UpperCAmelCase__												=     {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
													UpperCAmelCase__												=     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__												=     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
																			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(lowerCAmelCase_		,			lowerCAmelCase_					)]		,			dim=1					)
																			UpperCAmelCase__												=     []
																			UpperCAmelCase__						,					UpperCAmelCase__												=     self.num_layers

																			for _ in range(lowerCAmelCase_					):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(lowerCAmelCase_					),
																									        torch.zeros(lowerCAmelCase_					),
																									        torch.zeros(lowerCAmelCase_					),
																									        torch.zeros(lowerCAmelCase_					),
																									    )					)
													return common_inputs






							def        lowerCAmelCase  (    self			:  str		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  List[str] = -1		,			lowerCamelCase_			:  List[Any] = -1		,			lowerCamelCase_			:  List[Any] = False		,			lowerCamelCase_			:  Any = None		,			):
													UpperCAmelCase__												=     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__												=     common_inputs['''input_ids'''].shape
																			UpperCAmelCase__												=     seqlen
																			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(lowerCAmelCase_		,			lowerCAmelCase_		,			dtype=lowerCAmelCase_					)]		,			dim=1					)
																			UpperCAmelCase__												=     [
																			    (torch.zeros(lowerCAmelCase_					), torch.zeros(lowerCAmelCase_					)) for _ in range(lowerCAmelCase_					)
																			]
													return common_inputs






							def        lowerCAmelCase  (    self			:  Tuple		,			lowerCamelCase_			:  Optional[Any]		,			lowerCamelCase_			:  Optional[Any] = -1		,			lowerCamelCase_			:  str = -1		,			lowerCamelCase_			:  Tuple = False		,			lowerCamelCase_			:  Any = 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__												=     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__												=     tokenizer.num_special_tokens_to_add(lowerCAmelCase_					)
													UpperCAmelCase__												=     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__												=     [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
													UpperCAmelCase__												=     dict(tokenizer(lowerCAmelCase_		,			return_tensors=lowerCAmelCase_					)					)
													return common_inputs






							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Optional[Any] = -1		,			lowerCamelCase_			:  str = -1		,			lowerCamelCase_			:  str = False		,			lowerCamelCase_			:  List[str] = None		,			):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__												=     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__												=     self._generate_dummy_inputs_for_causal_lm(
																			    lowerCAmelCase_		,			batch_size=lowerCAmelCase_		,			seq_length=lowerCAmelCase_		,			is_pair=lowerCAmelCase_		,			framework=lowerCAmelCase_					)
													else:
																			UpperCAmelCase__												=     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			:  Dict		,			lowerCamelCase_			:  Union[str, Any]		,			lowerCamelCase_			:  List[Any]		,			lowerCamelCase_			:  Tuple		,			lowerCamelCase_			:  Optional[Any]					):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__												=     super()._flatten_past_key_values_(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_					)
													else:
																			UpperCAmelCase__												=     super(lowerCAmelCase_		,			self					)._flatten_past_key_values_(
																			    lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			lowerCamelCase_			:  Tuple		,			lowerCamelCase_			:  Union[str, Any]					):
													if direction not in ["inputs", "outputs"]:
																			raise ValueError(f"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""					)

													UpperCAmelCase__												=     '''past_key_values''' if direction == '''inputs''' else '''present'''
													UpperCAmelCase__						,					UpperCAmelCase__												=     self.num_layers

													UpperCAmelCase__												=     '''past_encoder_sequence'''
													UpperCAmelCase__												=     '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

													for i in range(lowerCAmelCase_					):
																			UpperCAmelCase__												=     {0: '''batch''', 2: decoder_sequence}
																			UpperCAmelCase__												=     {0: '''batch''', 2: decoder_sequence}
																			UpperCAmelCase__												=     {0: '''batch''', 2: encoder_sequence}
																			UpperCAmelCase__												=     {0: '''batch''', 2: encoder_sequence}






from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(UpperCamelCase__							,     matrix.T    ) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					_snake_case				,  _snake_case						=					np.linalg.eigh(UpperCamelCase__    )

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]
					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[0]
					assert _is_matrix_spd(UpperCamelCase__    )

					# Initialize solution guess, residual, search direction.
					_snake_case						=					np.zeros((np.shape(UpperCamelCase__    )[0], 1)    )
					_snake_case						=					np.copy(UpperCamelCase__    )
					_snake_case						=					np.copy(UpperCamelCase__    )

					# Set initial errors in solution guess and residual.
					_snake_case						=					1e9
					_snake_case						=					1e9
					_snake_case						=					1e9

					# Set iteration counter to threshold number of iterations.
					_snake_case						=					0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

										# The main algorithm.

										# Update search direction magnitude.
										_snake_case						=					np.dot(ra.T							,     UpperCamelCase__    ) / np.dot(pa.T							,     UpperCamelCase__    )
										# Update solution guess.
										_snake_case						=					xa + alpha * pa
										# Calculate new residual.
										_snake_case						=					ra - alpha * w
										# Calculate new Krylov subspace scale.
										_snake_case						=					np.dot(r.T							,     UpperCamelCase__    ) / np.dot(ra.T							,     UpperCamelCase__    )
										# Calculate new A conjuage search direction.
										_snake_case						=					r + beta * pa

										# Calculate errors.
										_snake_case						=					np.linalg.norm(r - ra    )
										_snake_case						=					np.linalg.norm(x - xa    )
										_snake_case						=					np.maximum(UpperCamelCase__							,     UpperCamelCase__    )

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true    ) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true    ) <= 1e-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_conjugate_gradient()



