


'''simple docstring'''



import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


_lowercase      :      Optional[int]            =					logging.get_logger(__name__)

_lowercase      :      Union[str, Any]            =					{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class 	UpperCamelCase__(       A__	):
				__magic_name__    :				List[str]  		=	"bart"
				__magic_name__    :				str  		=	["past_key_values"]
				__magic_name__    :				Union[str, Any]  		=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





				def __init__(		self      :			Any				,   lowerCAmelCase      :			Union[str, Any]=50265				,   lowerCAmelCase      :			Tuple=1024				,   lowerCAmelCase      :			Union[str, Any]=12				,   lowerCAmelCase      :			List[Any]=4096				,   lowerCAmelCase      :			Any=16				,   lowerCAmelCase      :			Union[str, Any]=12				,   lowerCAmelCase      :			List[str]=4096				,   lowerCAmelCase      :			Union[str, Any]=16				,   lowerCAmelCase      :			Tuple=0.0				,   lowerCAmelCase      :			List[Any]=0.0				,   lowerCAmelCase      :			int="gelu"				,   lowerCAmelCase      :			Union[str, Any]=1024				,   lowerCAmelCase      :			Optional[Any]=0.1				,   lowerCAmelCase      :			List[str]=0.0				,   lowerCAmelCase      :			Optional[Any]=0.0				,   lowerCAmelCase      :			List[str]=0.02				,   lowerCAmelCase      :			str=0.0				,   lowerCAmelCase      :			Any=False				,   lowerCAmelCase      :			List[str]=True				,   lowerCAmelCase      :			Dict=3				,   lowerCAmelCase      :			Union[str, Any]=1				,   lowerCAmelCase      :			List[str]=0				,   lowerCAmelCase      :			Any=2				,   lowerCAmelCase      :			Any=True				,   lowerCAmelCase      :			List[str]=2				,   lowerCAmelCase      :			Tuple=2				,   **lowerCAmelCase      :			List[str]				,   )->					str:

										"""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					    =     classifier_dropout
										UpperCAmelCase					    =     use_cache
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     scale_embedding  # scale factor will be sqrt(d_model) if True

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

										# ensure backward compatibility for BART CNN models
										if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				,   __snake_case				):
																UpperCAmelCase					    =     self.bos_token_id
																warnings.warn(
																    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
																    '''The config can simply be saved and uploaded again to be fixed.'''				)







class 	UpperCamelCase__(       A__	):





				@property
				def 					a__(		self      :			int				)->					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_(__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(__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 					a__(		self      :			int				)->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase					    =     super().outputs
										else:
																UpperCAmelCase					    =     super(__snake_case				,   self				).outputs
																if self.use_past:
																						UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_layers
																						for i in range(__snake_case				):
																												UpperCAmelCase					    =     {0: '''batch''', 2: '''past_sequence + sequence'''}
																												UpperCAmelCase					    =     {0: '''batch''', 2: '''past_sequence + sequence'''}
										return common_outputs





				def 					a__(		self      :			int				,   lowerCAmelCase      :			PreTrainedTokenizer				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case				,   __snake_case				,   __snake_case				,   __snake_case				,   __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(
										    __snake_case				,   __snake_case				,   __snake_case				,   __snake_case				,   __snake_case				)
										UpperCAmelCase					    =     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										UpperCAmelCase					    =     dict(**__snake_case				,   **__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(__snake_case				,   __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(__snake_case				,   __snake_case				)
																UpperCAmelCase					    =     max(__snake_case				,   __snake_case				) - min_num_layers
																UpperCAmelCase					    =     '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			PreTrainedTokenizer				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case				,   __snake_case				,   __snake_case				,   __snake_case				,   __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(__snake_case				,   __snake_case				,   dtype=__snake_case				)]				,   dim=1				)
																UpperCAmelCase					    =     [
																    (torch.zeros(__snake_case				), torch.zeros(__snake_case				)) for _ in range(__snake_case				)
																]
										return common_inputs





				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			PreTrainedTokenizer				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     compute_effective_axis_dimension(
										    __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(__snake_case				)
										UpperCAmelCase					    =     compute_effective_axis_dimension(
										    __snake_case				,   fixed_dimension=OnnxConfig.default_fixed_sequence				,   num_token_to_add=__snake_case				)

										# Generate dummy inputs according to compute batch and sequence
										UpperCAmelCase					    =     [''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
										UpperCAmelCase					    =     dict(tokenizer(__snake_case				,   return_tensors=__snake_case				)				)
										return common_inputs





				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			PreTrainedTokenizer				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




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

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

										return common_inputs





				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[int]				)->					str:

										"""simple docstring"""




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









'''simple docstring'''



from __future__ import annotations

from typing import Any






class 	UpperCamelCase__:




				def __init__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			float = 0				)->					None:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     row, column
										UpperCAmelCase					    =     [[default_value for c in range(lowerCAmelCase				)] for r in range(lowerCAmelCase				)]




				def __str__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

										# Make string identifier
										UpperCAmelCase					    =     0
										for row_vector in self.array:
																for obj in row_vector:
																						UpperCAmelCase					    =     max(lowerCAmelCase				,   len(str(lowerCAmelCase				)				)				)
										UpperCAmelCase					    =     F"""%{max_element_length}s"""

										# Make string and return
										def single_line(lowerCAmelCase      :			list[float]				) -> str:
																nonlocal string_format_identifier
																UpperCAmelCase					    =     '''['''
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector				)
																line += "]"
																return line

										s += "\n".join(single_line(lowerCAmelCase				) for row_vector in self.array				)
										return s




				def __repr__(		self      :			Tuple				)->					str:

										"""simple docstring"""




										return str(self				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			tuple[int, int]				)->					bool:

										"""simple docstring"""




										if not (isinstance(lowerCAmelCase				,   (list, tuple)				) and len(lowerCAmelCase				) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True




				def __getitem__(		self      :			int				,   lowerCAmelCase      :			tuple[int, int]				)->					Any:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										return self.array[loc[0]][loc[1]]




				def __setitem__(		self      :			List[str]				,   lowerCAmelCase      :			tuple[int, int]				,   lowerCAmelCase      :			float				)->					None:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										UpperCAmelCase					    =     value




				def __add__(		self      :			int				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == another.row and self.column == another.column

										# Add
										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c] + another[r, c]
										return result




				def __neg__(		self      :			Dict				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     -self[r, c]
										return result




				def __sub__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										return self + (-another)




				def __mul__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			int | float | Matrix				)->					Matrix:

										"""simple docstring"""




										if isinstance(lowerCAmelCase				,   (int, float)				):  # Scalar multiplication
																UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
																for r in range(self.row				):
																						for c in range(self.column				):
																												UpperCAmelCase					    =     self[r, c] * another
																return result
										elif isinstance(lowerCAmelCase				,   lowerCAmelCase				):  # Matrix multiplication
																assert self.column == another.row
																UpperCAmelCase					    =     Matrix(self.row				,   another.column				)
																for r in range(self.row				):
																						for c in range(another.column				):
																												for i in range(self.column				):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																UpperCAmelCase					    =     F"""Unsupported type given for another ({type(lowerCAmelCase				)})"""
																raise TypeError(lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.column				,   self.row				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c]
										return result





				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Matrix				,   lowerCAmelCase      :			Matrix				)->					Any:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				) and isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										UpperCAmelCase					    =     v.transpose()
										UpperCAmelCase					    =     (v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							UpperCAmelCase					    =     Matrix(3				,       3				,       0					)
							for i in range(3					):
													UpperCAmelCase					    =     1
							print(f"""a^(-1) is {ainv}"""					)
							# u, v
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     1, 2, -3
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     4, -2, 5
							print(f"""u is {u}"""					)
							print(f"""v is {v}"""					)
							print(f"""uv^T is {u * v.transpose()}"""					)
							# Sherman Morrison
							print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(A				,       A					)}"""					)


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							import doctest

							doctest.testmod()




	testa()





