





'''simple docstring'''




from typing import Any

import numpy as np




class       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	Dict     ,		lowerCamelCase_							:	np.ndarray     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							if np.any(tableau[:, -1]     ,		where=tableau[:, -1] < 0		):
									raise ValueError("""RHS must be > 0"""		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	tableau
							SCREAMING_SNAKE_CASE				:							List[Any]						=	tableau.shape

							# Number of decision variables x1, x2, x3...
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	n_vars

							# Number of artificial variables to be minimised
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	len(np.where(tableau[self.n_vars : -1] == -1		)[0]		)

							# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
							SCREAMING_SNAKE_CASE				:							Tuple						=	(self.n_art_vars > 0) + 1

							# Number of slack variables added to make inequalities into equalities
							SCREAMING_SNAKE_CASE				:							int						=	self.n_rows - self.n_stages

							# Objectives for each stage
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	["""max"""]

							# In two stage simplex, first minimise then maximise
							if self.n_art_vars:
									self.objectives.append("""min"""		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[""""""]

							# Index of current pivot row and column
							SCREAMING_SNAKE_CASE				:							Dict						=	None
							SCREAMING_SNAKE_CASE				:							List[Any]						=	None

							# Does objective row only contain (non)-negative values?
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	False


					@staticmethod
					def lowerCamelCase_	(		*lowerCamelCase_							:	int		):

							'''simple docstring'''



							if len(lowerCamelCase_		) != 3:
									raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

							if not all(x >= 0 and isinstance(lowerCamelCase_     ,		lowerCamelCase_		) for x in args		):
									raise ValueError("""All arguments must be non-negative integers"""		)

							# decision | slack | artificial
							SCREAMING_SNAKE_CASE				:							List[Any]						=	["""x""", """s""", """a"""]
							SCREAMING_SNAKE_CASE				:							Dict						=	[]
							for i in range(3		):
									for j in range(args[i]		):
											titles.append(string_starts[i] + str(j + 1		)		)
							titles.append("""RHS"""		)
							return titles


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	np.ndarray		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.objectives[-1]

							# Find entries of highest magnitude in objective rows
							SCREAMING_SNAKE_CASE				:							List[Any]						=	(objective == """min""") - (objective == """max""")
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	np.argmax(sign * tableau[0, : self.n_vars]		)

							# Choice is only valid if below 0 for maximise, and above for minimise
							if sign * self.tableau[0, col_idx] <= 0:
									SCREAMING_SNAKE_CASE				:							str						=	True
									return 0, 0

							# Pivot row is chosen as having the lowest quotient when elements of
							# the pivot column divide the right-hand side

							# Slice excluding the objective rows
							SCREAMING_SNAKE_CASE				:							List[Any]						=	slice(self.n_stages     ,		self.n_rows		)

							# RHS
							SCREAMING_SNAKE_CASE				:							str						=	tableau[s, -1]

							# Elements of pivot column within slice
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	tableau[s, col_idx]

							# Array filled with nans
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	np.full(self.n_rows - self.n_stages     ,		np.nan		)

							# If element in pivot column is greater than zeron_stages, return
							# quotient or nan otherwise
							SCREAMING_SNAKE_CASE				:							int						=	np.divide(lowerCamelCase_     ,		lowerCamelCase_     ,		out=lowerCamelCase_     ,		where=divisor > 0		)

							# Arg of minimum quotient excluding the nan values. n_stages is added
							# to compensate for earlier exclusion of objective columns
							SCREAMING_SNAKE_CASE				:							int						=	np.nanargmin(lowerCamelCase_		) + self.n_stages
							return row_idx, col_idx


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	np.ndarray     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	tableau[row_idx].copy()

							SCREAMING_SNAKE_CASE				:							str						=	piv_row[col_idx]

							# Entry becomes 1
							piv_row *= 1 / piv_val

							# Variable in pivot column becomes basic, ie the only non-zero entry
							for idx, coeff in enumerate(tableau[:, col_idx]		):
									tableau[idx] += -coeff * piv_row
							SCREAMING_SNAKE_CASE				:							List[str]						=	piv_row
							return tableau


					def lowerCamelCase_	(		self							:	Optional[Any]     ,		lowerCamelCase_							:	np.ndarray		):

							'''simple docstring'''



							self.objectives.pop()

							if not self.objectives:
									return tableau

							# Slice containing ids for artificial columns
							SCREAMING_SNAKE_CASE				:							List[str]						=	slice(-self.n_art_vars - 1     ,		-1		)

							# Delete the artificial variable columns
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	np.delete(lowerCamelCase_     ,		lowerCamelCase_     ,		axis=1		)

							# Delete the objective row of the first stage
							SCREAMING_SNAKE_CASE				:							Tuple						=	np.delete(lowerCamelCase_     ,		0     ,		axis=0		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	1
							self.n_rows -= 1
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	0
							SCREAMING_SNAKE_CASE				:							Any						=	False
							return tableau


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							for _ in range(1_00		):
									# Completion of each stage removes an objective. If both stages
									# are complete, then no objectives are left
									if not self.objectives:
											SCREAMING_SNAKE_CASE				:							List[Any]						=	self.generate_col_titles(
											    self.n_vars     ,		self.n_slack     ,		self.n_art_vars		)

											# Find the values of each variable at optimal solution
											return self.interpret_tableau(self.tableau     ,		self.col_titles		)

									SCREAMING_SNAKE_CASE				:							Dict						=	self.find_pivot(self.tableau		)

									# If there are no more negative values in objective row
									if self.stop_iter:
											# Delete artificial variable columns and rows. Update attributes
											SCREAMING_SNAKE_CASE				:							int						=	self.change_stage(self.tableau		)
									else:
											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.pivot(self.tableau     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							return {}



					def lowerCamelCase_	(		self							:	Tuple     ,		lowerCamelCase_							:	np.ndarray     ,		lowerCamelCase_							:	list[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	{"""P""": abs(tableau[0, -1]		)}

							for i in range(self.n_vars		):
									# Gives ids of nonzero entries in the ith column
									SCREAMING_SNAKE_CASE				:							List[str]						=	np.nonzero(tableau[:, i]		)
									SCREAMING_SNAKE_CASE				:							Tuple						=	len(nonzero[0]		)

									# First entry in the nonzero ids
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	nonzero[0][0]
									SCREAMING_SNAKE_CASE				:							List[str]						=	tableau[nonzero_rowidx, i]

									# If there is only one nonzero value in column, which is one
									if n_nonzero == nonzero_val == 1:
											SCREAMING_SNAKE_CASE				:							str						=	tableau[nonzero_rowidx, -1]
											SCREAMING_SNAKE_CASE				:							str						=	rhs_val

        # Check for basic variables
							for title in col_titles:
									# Don't add RHS or slack variables to output dict
									if title[0] not in "R-s-a":
											output_dict.setdefault(lowerCamelCase_     ,		0		)
							return output_dict


if __name__ == "__main__":
							import doctest

							doctest.testmod()










'''simple docstring'''




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


__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					'''mbart'''
					SCREAMING_SNAKE_CASE__						=					['''past_key_values''']
					SCREAMING_SNAKE_CASE__						=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(		self							:	Union[str, Any]     ,		lowerCamelCase_							:	List[str]=5_02_65     ,		lowerCamelCase_							:	Tuple=10_24     ,		lowerCamelCase_							:	Union[str, Any]=12     ,		lowerCamelCase_							:	int=40_96     ,		lowerCamelCase_							:	Dict=16     ,		lowerCamelCase_							:	Any=12     ,		lowerCamelCase_							:	Any=40_96     ,		lowerCamelCase_							:	Tuple=16     ,		lowerCamelCase_							:	List[Any]=0.0     ,		lowerCamelCase_							:	Any=0.0     ,		lowerCamelCase_							:	Any=True     ,		lowerCamelCase_							:	Dict=True     ,		lowerCamelCase_							:	List[str]="gelu"     ,		lowerCamelCase_							:	List[str]=10_24     ,		lowerCamelCase_							:	int=0.1     ,		lowerCamelCase_							:	List[Any]=0.0     ,		lowerCamelCase_							:	Tuple=0.0     ,		lowerCamelCase_							:	Optional[int]=0.02     ,		lowerCamelCase_							:	Any=0.0     ,		lowerCamelCase_							:	Tuple=False     ,		lowerCamelCase_							:	Tuple=1     ,		lowerCamelCase_							:	Tuple=0     ,		lowerCamelCase_							:	List[str]=2     ,		lowerCamelCase_							:	Optional[int]=2     ,		**lowerCamelCase_							:	int     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	vocab_size
							SCREAMING_SNAKE_CASE				:							Any						=	max_position_embeddings
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	d_model
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	encoder_ffn_dim
							SCREAMING_SNAKE_CASE				:							Tuple						=	encoder_layers
							SCREAMING_SNAKE_CASE				:							Dict						=	encoder_attention_heads
							SCREAMING_SNAKE_CASE				:							str						=	decoder_ffn_dim
							SCREAMING_SNAKE_CASE				:							List[Any]						=	decoder_layers
							SCREAMING_SNAKE_CASE				:							List[str]						=	decoder_attention_heads
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	dropout
							SCREAMING_SNAKE_CASE				:							Dict						=	attention_dropout
							SCREAMING_SNAKE_CASE				:							Any						=	activation_dropout
							SCREAMING_SNAKE_CASE				:							Dict						=	activation_function
							SCREAMING_SNAKE_CASE				:							str						=	init_std
							SCREAMING_SNAKE_CASE				:							Tuple						=	encoder_layerdrop
							SCREAMING_SNAKE_CASE				:							Tuple						=	decoder_layerdrop
							SCREAMING_SNAKE_CASE				:							List[str]						=	classifier_dropout
							SCREAMING_SNAKE_CASE				:							int						=	use_cache
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	encoder_layers
							SCREAMING_SNAKE_CASE				:							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_     ,		forced_eos_token_id=lowerCamelCase_     ,		**lowerCamelCase_     ,		)







class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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

									if self.use_past:
											SCREAMING_SNAKE_CASE				:							str						=	{0: """batch"""}
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
											SCREAMING_SNAKE_CASE				:							Dict						=	{0: """batch""", 1: """decoder_sequence"""}
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{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.
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]		)
									if self.use_past:
											SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.num_layers
											for i in range(lowerCamelCase_		):
													SCREAMING_SNAKE_CASE				:							Any						=	{0: """batch""", 2: """past_sequence + sequence"""}
													SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{0: """batch""", 2: """past_sequence + sequence"""}
							else:
									SCREAMING_SNAKE_CASE				:							List[str]						=	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							:	List[str]		):

							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	super().outputs
							else:
									SCREAMING_SNAKE_CASE				:							int						=	super(lowerCamelCase_     ,		self		).outputs
									if self.use_past:
											SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[str]						=	self.num_layers
											for i in range(lowerCamelCase_		):
													SCREAMING_SNAKE_CASE				:							str						=	{0: """batch""", 2: """past_sequence + sequence"""}
													SCREAMING_SNAKE_CASE				:							Tuple						=	{0: """batch""", 2: """past_sequence + sequence"""}
							return common_outputs


					def lowerCamelCase_	(		self							:	List[Any]     ,		lowerCamelCase_							:	PreTrainedTokenizer     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	bool = False     ,		lowerCamelCase_							:	Optional[TensorType] = None     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)

							# Generate decoder inputs
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	seq_length if not self.use_past else 1
							SCREAMING_SNAKE_CASE				:							List[str]						=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Any						=	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							SCREAMING_SNAKE_CASE				:							Union[str, 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
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	common_inputs["""input_ids"""].shape
									SCREAMING_SNAKE_CASE				:							Dict						=	common_inputs["""decoder_input_ids"""].shape[1]
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	self.num_attention_heads
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	(
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									SCREAMING_SNAKE_CASE				:							Any						=	decoder_seq_length + 3
									SCREAMING_SNAKE_CASE				:							Dict						=	(
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.cat(
									    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase_     ,		lowerCamelCase_		)]     ,		dim=1		)

									SCREAMING_SNAKE_CASE				:							List[str]						=	[]
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	self.num_layers
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	min(lowerCamelCase_     ,		lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							str						=	max(lowerCamelCase_     ,		lowerCamelCase_		) - min_num_layers
									SCREAMING_SNAKE_CASE				:							List[str]						=	"""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.
									SCREAMING_SNAKE_CASE				:							List[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							:	Optional[int]     ,		lowerCamelCase_							:	PreTrainedTokenizer     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	bool = False     ,		lowerCamelCase_							:	Optional[TensorType] = None     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	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
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[Any]						=	common_inputs["""input_ids"""].shape
									# Not using the same length for past_key_values
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	seqlen + 2
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.num_layers
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	self.num_attention_heads
									SCREAMING_SNAKE_CASE				:							int						=	(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE				:							Dict						=	common_inputs["""attention_mask"""].dtype
									SCREAMING_SNAKE_CASE				:							Dict						=	torch.cat(
									    [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase_     ,		lowerCamelCase_     ,		dtype=lowerCamelCase_		)]     ,		dim=1		)
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[
									    (torch.zeros(lowerCamelCase_		), torch.zeros(lowerCamelCase_		)) for _ in range(lowerCamelCase_		)
									]
							return common_inputs


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	PreTrainedTokenizer     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	bool = False     ,		lowerCamelCase_							:	Optional[TensorType] = None     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[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
							SCREAMING_SNAKE_CASE				:							int						=	tokenizer.num_special_tokens_to_add(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							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
							SCREAMING_SNAKE_CASE				:							Any						=	[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	dict(tokenizer(lowerCamelCase_     ,		return_tensors=lowerCamelCase_		)		)
							return common_inputs


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	PreTrainedTokenizer     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	bool = False     ,		lowerCamelCase_							:	Optional[TensorType] = None     ,		):

							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE				:							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":
									SCREAMING_SNAKE_CASE				:							str						=	self._generate_dummy_inputs_for_causal_lm(
									    lowerCamelCase_     ,		batch_size=lowerCamelCase_     ,		seq_length=lowerCamelCase_     ,		is_pair=lowerCamelCase_     ,		framework=lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	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							:	Union[str, Any]     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	List[Any]		):

							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE				:							Tuple						=	super()._flatten_past_key_values_(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	super(lowerCamelCase_     ,		self		)._flatten_past_key_values_(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)



