



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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    """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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   List[Any]       =       """m2m_100"""
							UpperCamelCase__					:   Union[str, Any]       =       ["""past_key_values"""]
							UpperCamelCase__					:   Any       =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


							def __init__(				self	,	a_=128_112	,	a_=1_024	,	a_=12	,	a_=4_096	,	a_=16	,	a_=12	,	a_=4_096	,	a_=16	,	a_=0.05	,	a_=0.05	,	a_=True	,	a_=True	,	a_="relu"	,	a_=1_024	,	a_=0.1	,	a_=0.1	,	a_=0.0	,	a_=0.02	,	a_=2	,	a_=True	,	a_=1	,	a_=0	,	a_=2	,	**a_	,	):
											a__					   =		vocab_size
											a__					   =		max_position_embeddings
											a__					   =		d_model
											a__					   =		encoder_ffn_dim
											a__					   =		encoder_layers
											a__					   =		encoder_attention_heads
											a__					   =		decoder_ffn_dim
											a__					   =		decoder_layers
											a__					   =		decoder_attention_heads
											a__					   =		dropout
											a__					   =		attention_dropout
											a__					   =		activation_dropout
											a__					   =		activation_function
											a__					   =		init_std
											a__					   =		encoder_layerdrop
											a__					   =		decoder_layerdrop
											a__					   =		use_cache
											a__					   =		encoder_layers
											a__					   =		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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@property
							def _a   (				self		):
											a__					   =		OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]		)

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

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


							def _a   (				self	,	a_	,	a_ = -1	,	a_ = -1	,	a_ = False	,	a_ = 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
											a__					   =		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
											a__					   =		tokenizer.num_special_tokens_to_add(a_		)
											a__					   =		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
											a__					   =		[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
											a__					   =		dict(tokenizer(a_	,	return_tensors=a_		)		)
											return common_inputs


							def _a   (				self	,	a_	,	a_ = -1	,	a_ = -1	,	a_ = False	,	a_ = None	,	):
											a__					   =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_	,	a_	,	a_	,	a_	,	a_		)

											# Generate decoder inputs
											a__					   =		seq_length if not self.use_past else 1
											a__					   =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_	,	a_	,	a_	,	a_	,	a_		)
											a__					   =		{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
											a__					   =		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
															a__   ,      a__					   =		common_inputs["""input_ids"""].shape
															a__					   =		common_inputs["""decoder_input_ids"""].shape[1]
															a__   ,      a__					   =		self.num_attention_heads
															a__					   =		(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															a__					   =		decoder_seq_length + 3
															a__					   =		(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

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

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







							UpperCamelCase__					:   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()

