



"""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


snake_case_         = logging.get_logger(__name__)

snake_case_         = {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}







class    A_    (  lowerCAmelCase__       ):



						"""simple docstring"""
						__UpperCamelCase	       =							"""bigbird_pegasus"""
						__UpperCamelCase	       =							["""past_key_values"""]
						__UpperCamelCase	       =							{
						    """num_attention_heads""": """encoder_attention_heads""",
						    """hidden_size""": """d_model""",
						    """attention_probs_dropout_prob""": """attention_dropout""",
						}






						def __init__(      self				:List[Any]     ,						lowercase_				:Union[str, Any]=9_61_03     ,						lowercase_				:Tuple=40_96     ,						lowercase_				:Optional[int]=16     ,						lowercase_				:Any=40_96     ,						lowercase_				:int=16     ,						lowercase_				:Optional[Any]=16     ,						lowercase_				:List[str]=40_96     ,						lowercase_				:List[str]=16     ,						lowercase_				:int=0.0     ,						lowercase_				:str=0.0     ,						lowercase_				:Optional[Any]=True     ,						lowercase_				:Union[str, Any]=True     ,						lowercase_				:Any="gelu_new"     ,						lowercase_				:str=10_24     ,						lowercase_				:Tuple=0.1     ,						lowercase_				:int=0.0     ,						lowercase_				:str=0.0     ,						lowercase_				:Optional[Any]=0.02     ,						lowercase_				:List[str]=2     ,						lowercase_				:Optional[Any]=0.0     ,						lowercase_				:List[Any]=True     ,						lowercase_				:Optional[int]=0     ,						lowercase_				:List[str]=2     ,						lowercase_				:Dict=1     ,						lowercase_				:Optional[Any]="block_sparse"     ,						lowercase_				:int=64     ,						lowercase_				:List[Any]=3     ,						lowercase_				:Optional[Any]=False     ,						**lowercase_				:Dict     ,						)       ->    List[Any]:
												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

												# extra config
												UpperCAmelCase    =				attention_type
												UpperCAmelCase    =				block_size
												UpperCAmelCase    =				num_random_blocks
												UpperCAmelCase    =				use_bias

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







class    A_    (  lowerCAmelCase__       ):



						"""simple docstring"""






						@property
						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    Mapping[str, Mapping[int, str]]:






												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":
																		# 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(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
						def 			UpperCAmelCase__  (      self				:Dict      )       ->    Mapping[str, Mapping[int, str]]:






												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 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:PreTrainedTokenizer     ,						lowercase_				:int = -1     ,						lowercase_				:int = -1     ,						lowercase_				:bool = False     ,						lowercase_				:Optional[TensorType] = None     ,						)       ->    Mapping[str, Any]:
												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 + 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(lowerCAmelCase_     ,						lowerCAmelCase_      )]     ,						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(lowerCAmelCase_     ,						lowerCAmelCase_      )
																		UpperCAmelCase    =				max(lowerCAmelCase_     ,						lowerCAmelCase_      ) - min_num_layers
																		UpperCAmelCase    =				'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    =				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 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:PreTrainedTokenizer     ,						lowercase_				:int = -1     ,						lowercase_				:int = -1     ,						lowercase_				:bool = False     ,						lowercase_				:Optional[TensorType] = None     ,						)       ->    Mapping[str, Any]:
												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
																		# 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(lowerCAmelCase_     ,						lowerCAmelCase_     ,						dtype=lowerCAmelCase_      )]     ,						dim=1      )
																		UpperCAmelCase    =				[
																		    (torch.zeros(lowerCAmelCase_      ), torch.zeros(lowerCAmelCase_      )) for _ in range(lowerCAmelCase_      )
																		]
												return common_inputs






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






												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 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:Dict     ,						lowercase_				:Optional[Any]     ,						lowercase_				:str     ,						lowercase_				:List[Any]      )       ->    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_      )










"""simple docstring"""


from typing import Any

import numpy as np







class    A_    :



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:np.ndarray     ,						lowercase_				:int      )       ->    None:
												# Check if RHS is negative
												if np.any(tableau[:, -1]     ,						where=tableau[:, -1] < 0      ):
																		raise ValueError('RHS must be > 0'      )

												UpperCAmelCase    =				tableau
												UpperCAmelCase   ,					UpperCAmelCase    =				tableau.shape

												# Number of decision variables x1, x2, x3...
												UpperCAmelCase    =				n_vars

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

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

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

												# Objectives for each stage
												UpperCAmelCase    =				['max']

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

												UpperCAmelCase    =				['']

												# Index of current pivot row and column
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None

												# Does objective row only contain (non)-negative values?
												UpperCAmelCase    =				False






						@staticmethod
						def 			UpperCAmelCase__  (      *lowercase_				:int      )       ->    list[str]:
												if len(lowercase_      ) != 3:
																		raise ValueError('Must provide n_vars, n_slack, and n_art_vars'      )

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

												# decision | slack | artificial
												UpperCAmelCase    =				['x', 's', 'a']
												UpperCAmelCase    =				[]
												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 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:np.ndarray      )       ->    tuple[Any, Any]:
												UpperCAmelCase    =				self.objectives[-1]

												# Find entries of highest magnitude in objective rows
												UpperCAmelCase    =				(objective == 'min') - (objective == 'max')
												UpperCAmelCase    =				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:
																		UpperCAmelCase    =				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
												UpperCAmelCase    =				slice(self.n_stages     ,						self.n_rows      )

												# RHS
												UpperCAmelCase    =				tableau[s, -1]

												# Elements of pivot column within slice
												UpperCAmelCase    =				tableau[s, col_idx]

												# Array filled with nans
												UpperCAmelCase    =				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
												UpperCAmelCase    =				np.divide(lowercase_     ,						lowercase_     ,						out=lowercase_     ,						where=divisor > 0      )

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






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:np.ndarray     ,						lowercase_				:int     ,						lowercase_				:int      )       ->    np.ndarray:
												UpperCAmelCase    =				tableau[row_idx].copy()

												UpperCAmelCase    =				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
												UpperCAmelCase    =				piv_row
												return tableau






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:np.ndarray      )       ->    np.ndarray:
												self.objectives.pop()

												if not self.objectives:
																		return tableau

												# Slice containing ids for artificial columns
												UpperCAmelCase    =				slice(-self.n_art_vars - 1     ,						-1      )

												# Delete the artificial variable columns
												UpperCAmelCase    =				np.delete(lowercase_     ,						lowercase_     ,						axis=1      )

												# Delete the objective row of the first stage
												UpperCAmelCase    =				np.delete(lowercase_     ,						0     ,						axis=0      )

												UpperCAmelCase    =				1
												self.n_rows -= 1
												UpperCAmelCase    =				0
												UpperCAmelCase    =				False
												return tableau






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    dict[Any, Any]:
												# Stop simplex algorithm from cycling.
												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:
																								UpperCAmelCase    =				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      )

																		UpperCAmelCase   ,					UpperCAmelCase    =				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
																								UpperCAmelCase    =				self.change_stage(self.tableau      )
																		else:
																								UpperCAmelCase    =				self.pivot(self.tableau     ,						lowercase_     ,						lowercase_      )
												return {}






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:np.ndarray     ,						lowercase_				:list[str]      )       ->    dict[str, float]:
												UpperCAmelCase    =				{'P': abs(tableau[0, -1]      )}

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

																		# First entry in the nonzero ids
																		UpperCAmelCase    =				nonzero[0][0]
																		UpperCAmelCase    =				tableau[nonzero_rowidx, i]

																		# If there is only one nonzero value in column, which is one
																		if n_nonzero == nonzero_val == 1:
																								UpperCAmelCase    =				tableau[nonzero_rowidx, -1]
																								UpperCAmelCase    =				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(lowercase_     ,						0      )
												return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()





