



'''simple docstring'''



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

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


lowerCAmelCase						:		Dict						      = logging.get_logger(__name__)

lowerCAmelCase						:		Tuple						      = {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  List[Any]   =				"""m2m_100"""
					a   :  Union[str, Any]   =				["""past_key_values"""]
					a   :  Any   =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(     self      ,					UpperCamelCase=12_8112      ,					UpperCamelCase=1024      ,					UpperCamelCase=12      ,					UpperCamelCase=4096      ,					UpperCamelCase=16      ,					UpperCamelCase=12      ,					UpperCamelCase=4096      ,					UpperCamelCase=16      ,					UpperCamelCase=0.05      ,					UpperCamelCase=0.05      ,					UpperCamelCase=True      ,					UpperCamelCase=True      ,					UpperCamelCase="relu"      ,					UpperCamelCase=1024      ,					UpperCamelCase=0.1      ,					UpperCamelCase=0.1      ,					UpperCamelCase=0.0      ,					UpperCamelCase=0.02      ,					UpperCamelCase=2      ,					UpperCamelCase=True      ,					UpperCamelCase=1      ,					UpperCamelCase=0      ,					UpperCamelCase=2      ,					**UpperCamelCase      ,					)  ->      Any:
						__lowerCAmelCase       				=	vocab_size
						__lowerCAmelCase       				=	max_position_embeddings
						__lowerCAmelCase       				=	d_model
						__lowerCAmelCase       				=	encoder_ffn_dim
						__lowerCAmelCase       				=	encoder_layers
						__lowerCAmelCase       				=	encoder_attention_heads
						__lowerCAmelCase       				=	decoder_ffn_dim
						__lowerCAmelCase       				=	decoder_layers
						__lowerCAmelCase       				=	decoder_attention_heads
						__lowerCAmelCase       				=	dropout
						__lowerCAmelCase       				=	attention_dropout
						__lowerCAmelCase       				=	activation_dropout
						__lowerCAmelCase       				=	activation_function
						__lowerCAmelCase       				=	init_std
						__lowerCAmelCase       				=	encoder_layerdrop
						__lowerCAmelCase       				=	decoder_layerdrop
						__lowerCAmelCase       				=	use_cache
						__lowerCAmelCase       				=	encoder_layers
						__lowerCAmelCase       				=	scale_embedding  # scale factor will be sqrt(d_model) if True

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






class        UpperCAmelCase__						(		UpperCamelCase__			):



					@property
					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	OrderedDict(
						    [
						        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
						        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
						    ]  )

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

						if self.use_past:
							self.fill_with_past_key_values_(a_      ,					direction="inputs"  )
						return common_inputs



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase = -1      ,					UpperCamelCase = -1      ,					UpperCamelCase = False      ,					UpperCamelCase = None      ,					)  ->      str:
						# 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
						__lowerCAmelCase       				=	compute_effective_axis_dimension(
						    a_      ,					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
						__lowerCAmelCase       				=	tokenizer.num_special_tokens_to_add(a_  )
						__lowerCAmelCase       				=	compute_effective_axis_dimension(
						    a_      ,					fixed_dimension=OnnxConfig.default_fixed_sequence      ,					num_token_to_add=a_  )

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



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase = -1      ,					UpperCamelCase = -1      ,					UpperCamelCase = False      ,					UpperCamelCase = None      ,					)  ->      Union[str, Any]:
						__lowerCAmelCase       				=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    a_      ,					a_      ,					a_      ,					a_      ,					a_  )

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

						if self.use_past:
							if not is_torch_available():
								raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
							else:
								import torch
							__lowerCAmelCase				,							__lowerCAmelCase       				=	common_inputs["input_ids"].shape
							__lowerCAmelCase       				=	common_inputs["decoder_input_ids"].shape[1]
							__lowerCAmelCase				,							__lowerCAmelCase       				=	self.num_attention_heads
							__lowerCAmelCase       				=	(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							__lowerCAmelCase       				=	decoder_seq_length + 3
							__lowerCAmelCase       				=	(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							__lowerCAmelCase       				=	torch.cat(
							    [common_inputs["decoder_attention_mask"], torch.ones(a_      ,					a_  )]      ,					dim=1  )

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

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



					a   :  Tuple   =				_generate_dummy_inputs_for_default_and_seqaseq_lm




from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											if np.any(tableau[:, -1]	,	where=tableau[:, -1] < 0		):
															raise ValueError("""RHS must be > 0"""		)

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

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

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

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

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

											# Objectives for each stage
											a__					   =		["""max"""]

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

											a__					   =		[""""""]

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

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


							@staticmethod
							def _a   (				*a_		):
											if len(a_		) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

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

											# decision | slack | artificial
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											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 _a   (				self	,	a_		):
											a__					   =		self.objectives[-1]

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

											# RHS
											a__					   =		tableau[s, -1]

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

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

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


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		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
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

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

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

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

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


							def _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																			a__					   =		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		)

															a__   ,      a__					   =		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
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

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

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()

