import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase      			=     logging.getLogger(__name__)


@dataclass
class _a       :

	'''simple docstring'''



	lowerCamelCase_   :      str       =	field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
	lowerCamelCase_   :      Optional[str]       =	field(
	    default=lowerCAmelCase__   ,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
	lowerCamelCase_   :      Optional[str]       =	field(
	    default=lowerCAmelCase__   ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
	lowerCamelCase_   :      Optional[str]       =	field(
	    default=lowerCAmelCase__   ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,							)


@dataclass
class _a       :

	'''simple docstring'''



	lowerCamelCase_   :      str       =	field(
	    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys() )} )
	lowerCamelCase_   :      str       =	field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
	lowerCamelCase_   :      int       =	field(
	    default=1_2_8   ,							metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }   ,							)
	lowerCamelCase_   :      bool       =	field(
	    default=lowerCAmelCase__   ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )



def    lowerCamelCase_   (  _lowercase    )							->	Dict[str, torch.Tensor]:
		__A							:   Any	  =				default_data_collator(_lowercase    )
		__A							:   Optional[Any]	  =				batch.pop("pairID" ,     _lowercase    )
		return batch



def    lowerCamelCase_   (  )							->	Optional[int]:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		__A							:   Tuple	  =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		__A							:   List[Any]	  =				parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir    )
		    and os.listdir(training_args.output_dir    )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
				    " --overwrite_output_dir to overcome."    )

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,     datefmt="%m/%d/%Y %H:%M:%S" ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,     )
		logger.warning(
		    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,     training_args.local_rank ,     training_args.device ,     training_args.n_gpu ,     bool(training_args.local_rank != -1    ) ,     training_args.fpaa ,     )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank    ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("Training/evaluation parameters %s" ,     _lowercase    )

		# Set seed
		set_seed(training_args.seed    )

		try:
				__A							:   Tuple	  =				hans_tasks_num_labels[data_args.task_name]
		except KeyError:
				raise ValueError("Task not found: %s" % (data_args.task_name)    )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		__A							:   Any	  =				AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,     num_labels=_lowercase ,     finetuning_task=data_args.task_name ,     cache_dir=model_args.cache_dir ,     )
		__A							:   Dict	  =				AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,     cache_dir=model_args.cache_dir ,     )
		__A							:   List[Any]	  =				AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path ,     from_tf=bool(".ckpt" in model_args.model_name_or_path    ) ,     config=_lowercase ,     cache_dir=model_args.cache_dir ,     )

		# Get datasets
		__A							:   Union[str, Any]	  =				(
		    HansDataset(
		        data_dir=data_args.data_dir ,     tokenizer=_lowercase ,     task=data_args.task_name ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     )
		    if training_args.do_train
		    else None
		)
		__A							:   Any	  =				(
		    HansDataset(
		        data_dir=data_args.data_dir ,     tokenizer=_lowercase ,     task=data_args.task_name ,     max_seq_length=data_args.max_seq_length ,     overwrite_cache=data_args.overwrite_cache ,     evaluate=_lowercase ,     )
		    if training_args.do_eval
		    else None
		)

		# Initialize our Trainer
		__A							:   str	  =				Trainer(
		    model=_lowercase ,     args=_lowercase ,     train_dataset=_lowercase ,     eval_dataset=_lowercase ,     data_collator=_lowercase ,     )

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
		if training_args.do_eval:
				logger.info("*** Evaluate ***"    )

				__A							:   Optional[Any]	  =				trainer.predict(_lowercase    )
				__A							:   Any	  =				output.predictions
				__A							:   Tuple	  =				np.argmax(_lowercase ,     axis=1    )

				__A							:   Optional[int]	  =				[ex.pairID for ex in eval_dataset]
				__A							:   Optional[Any]	  =				os.path.join(training_args.output_dir ,     "hans_predictions.txt"    )
				__A							:   str	  =				eval_dataset.get_labels()
				if trainer.is_world_master():
						with open(_lowercase ,     "w"    ) as writer:
								writer.write("pairID,gold_label\n"    )
								for pid, pred in zip(_lowercase ,     _lowercase    ):
										writer.write("ex" + str(_lowercase    ) + "," + label_list[int(_lowercase    )] + "\n"    )

				trainer._log(output.metrics    )



def    lowerCamelCase_   (  _lowercase    )							->	int:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
					main()


from typing import Any

import numpy as np


class _a       :

	'''simple docstring'''



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

			__A							:   Any	  =				tableau
			__A  ,       __A							:   Dict	  =				tableau.shape

			# Number of decision variables x1, x2, x3...
			__A							:   Any	  =				n_vars

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

			# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
			__A							:   List[Any]	  =				(self.n_art_vars > 0) + 1

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

			# Objectives for each stage
			__A							:   List[Any]	  =				["max"]

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

			__A							:   Tuple	  =				[""]

			# Index of current pivot row and column
			__A							:   Tuple	  =				None
			__A							:   List[str]	  =				None

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

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

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

			# decision | slack | artificial
			__A							:   List[Any]	  =				["x", "s", "a"]
			__A							:   List[str]	  =				[]
			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							,			__UpperCAmelCase   ):
			__A							:   Optional[Any]	  =				self.objectives[-1]

			# Find entries of highest magnitude in objective rows
			__A							:   Optional[int]	  =				(objective == "min") - (objective == "max")
			__A							:   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:
					__A							:   Any	  =				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							:   Optional[int]	  =				slice(self.n_stages							,			self.n_rows   )

			# RHS
			__A							:   Optional[Any]	  =				tableau[s, -1]

			# Elements of pivot column within slice
			__A							:   List[Any]	  =				tableau[s, col_idx]

			# Array filled with nans
			__A							:   Union[str, 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
			__A							:   int	  =				np.divide(__UpperCAmelCase							,			__UpperCAmelCase							,			out=__UpperCAmelCase							,			where=divisor > 0   )

			# Arg of minimum quotient excluding the nan values. n_stages is added
			# to compensate for earlier exclusion of objective columns
			__A							:   Optional[Any]	  =				np.nanargmin(__UpperCAmelCase   ) + self.n_stages
			return row_idx, col_idx

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				tableau[row_idx].copy()

			__A							:   Tuple	  =				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							:   str	  =				piv_row
			return tableau

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			self.objectives.pop()

			if not self.objectives:
					return tableau

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

			# Delete the artificial variable columns
			__A							:   Tuple	  =				np.delete(__UpperCAmelCase							,			__UpperCAmelCase							,			axis=1   )

			# Delete the objective row of the first stage
			__A							:   List[str]	  =				np.delete(__UpperCAmelCase							,			0							,			axis=0   )

			__A							:   Union[str, Any]	  =				1
			self.n_rows -= 1
			__A							:   str	  =				0
			__A							:   str	  =				False
			return tableau

	def 					__UpperCAmelCase(       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							:   Optional[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   )

					__A  ,       __A							:   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
							__A							:   int	  =				self.change_stage(self.tableau   )
					else:
							__A							:   Dict	  =				self.pivot(self.tableau							,			__UpperCAmelCase							,			__UpperCAmelCase   )
			return {}







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				{"P": abs(tableau[0, -1]   )}

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

					# First entry in the nonzero ids
					__A							:   List[Any]	  =				nonzero[0][0]
					__A							:   Tuple	  =				tableau[nonzero_rowidx, i]

					# If there is only one nonzero value in column, which is one
					if n_nonzero == nonzero_val == 1:
							__A							:   Union[str, Any]	  =				tableau[nonzero_rowidx, -1]
							__A							:   Optional[int]	  =				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(__UpperCAmelCase							,			0   )
			return output_dict


if __name__ == "__main__":
					import doctest

					doctest.testmod()





