

'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							None:
					snake_case							=					{literal: None for literal in literals}

				def __str__(       self )    ->							str:
					return "{" + " , ".join(self.literals ) + "}"

				def __len__(       self )    ->							int:
					return len(self.literals )

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							None:
					for literal in self.literals:
						snake_case							=					literal[:2]
						if symbol in model:
							snake_case							=					model[symbol]
						else:
							continue
						if value is not None:
							# Complement assignment if literal is in complemented form
							if literal.endswith('\'' ):
								snake_case							=					not value
						snake_case							=					value






				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							bool | None:
					for literal in self.literals:
						snake_case							=					literal.rstrip('\'' ) if literal.endswith('\'' ) else literal + '\''
						if symbol in self.literals:
							return True

					self.assign(lowercase_ )
					for value in self.literals.values():
						if value in (True, None):
							return value
					return any(self.literals.values() )







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							None:
					snake_case							=					list(lowercase_ )






				def __str__(       self )    ->							str:
					return "{" + " , ".join(str(lowercase_ ) for clause in self.clauses ) + "}"

def      __magic_name__							(			)      ->		Clause:
	snake_case							=					[]
	snake_case							=					random.randint(1       , 5				)
	snake_case							=					'A'
	snake_case							=					0
	while i < no_of_literals:
		snake_case							=					random.randint(1       , 5				)
		snake_case							=					base_var + str(A				)
		snake_case							=					random.randint(0       , 1				)
		if var_complement == 1:
			var_name += "'"
		if var_name in literals:
			i -= 1
		else:
			literals.append(A				)
		i += 1
	return Clause(A				)

def      __magic_name__							(			)      ->		Formula:
	snake_case							=					set()
	snake_case							=					random.randint(1       , 1_0				)
	while len(A				) < no_of_clauses:
		clauses.add(generate_clause()				)
	return Formula(A				)

def      __magic_name__							(			A				)      ->		tuple[list[Clause], list[str]]:
	snake_case							=					formula.clauses
	snake_case							=					[]
	for clause in formula.clauses:
		for literal in clause.literals:
			snake_case							=					literal[:2]
			if symbol not in symbols_set:
				symbols_set.append(A				)
	return clauses, symbols_set

def      __magic_name__							(			A       , A       , A				)      ->		tuple[list[str], dict[str, bool | None]]:
	snake_case							=					[]
	snake_case							=					{}
	snake_case							=					[]

	for clause in clauses:
		if clause.evaluate(A				):
			continue
		for literal in clause.literals:
			literals.append(A				)

	for s in symbols:
		snake_case							=					s + '\''
		if (s in literals and sym not in literals) or (
		    s not in literals and sym in literals
		):
			pure_symbols.append(A				)
	for p in pure_symbols:
		snake_case							=					None
	for s in pure_symbols:
		snake_case							=					s + '\''
		if s in literals:
			snake_case							=					True
		elif sym in literals:
			snake_case							=					False
	return pure_symbols, assignment

def      __magic_name__							(			A       , A				)      ->		tuple[list[str], dict[str, bool | None]]:
	snake_case							=					[]
	for clause in clauses:
		if len(A				) == 1:
			unit_symbols.append(list(clause.literals.keys()				)[0]				)
		else:
			snake_case			,			snake_case							=					0, 0
			for literal, value in clause.literals.items():
				if value is False:
					f_count += 1
				elif value is None:
					snake_case							=					literal
					n_count += 1
			if f_count == len(A				) - 1 and n_count == 1:
				unit_symbols.append(A				)
	snake_case							=					{}
	for i in unit_symbols:
		snake_case							=					i[:2]
		snake_case							=					len(A				) == 2
	snake_case							=					[i[:2] for i in unit_symbols]

	return unit_symbols, assignment

def      __magic_name__							(			A       , A       , A				)      ->		tuple[bool | None, dict[str, bool | None] | None]:
	snake_case							=					True
	for clause in clauses:
		snake_case							=					clause.evaluate(A				)
		if clause_check is False:
			return False, None
		elif clause_check is None:
			snake_case							=					False
			continue

	if check_clause_all_true:
		return True, model

	try:
		snake_case			,			snake_case							=					find_pure_symbols(A       , A       , A				)
	except RecursionError:
		print('raises a RecursionError and is'				)
		return None, {}
	snake_case							=					None
	if len(A				) > 0:
		snake_case			,			snake_case							=					pure_symbols[0], assignment[pure_symbols[0]]

	if p:
		snake_case							=					model
		snake_case							=					value
		snake_case							=					list(A				)
		if p in tmp_symbols:
			tmp_symbols.remove(A				)
		return dpll_algorithm(A       , A       , A				)

	snake_case			,			snake_case							=					find_unit_clauses(A       , A				)
	snake_case							=					None
	if len(A				) > 0:
		snake_case			,			snake_case							=					unit_symbols[0], assignment[unit_symbols[0]]
	if p:
		snake_case							=					model
		snake_case							=					value
		snake_case							=					list(A				)
		if p in tmp_symbols:
			tmp_symbols.remove(A				)
		return dpll_algorithm(A       , A       , A				)
	snake_case							=					symbols[0]
	snake_case							=					symbols[1:]
	snake_case			,			snake_case							=					model, model
	snake_case			,			snake_case							=					True, False

	return dpll_algorithm(A       , A       , A				) or dpll_algorithm(A       , A       , A				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						lowerCAmelCase_     =      generate_formula()
						print(f"The formula {formula} is", end=" ")

						lowerCAmelCase_ ,						lowerCAmelCase_     =      generate_parameters(formula)
						lowerCAmelCase_ ,						lowerCAmelCase_     =      dpll_algorithm(clauses, symbols, {})

						if solution:
												print(f"satisfiable with the assignment {model}.")
						else:
												print("not satisfiable.")












'''simple docstring'''


import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
    import torch

    from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
    import pyanvml.pyanvml as nvml


A_				     =							logging.get_logger(__name__)


class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      _snake_case           =				42
      _snake_case           =				42
      _snake_case           =				"PyTorch"




      @property
      def 	snake_case  (     self )				->  Dict:
        return torch.__version__




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  float:
        __lowerCAmelCase		:    Any			     =  self._prepare_inference_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_speed(_inference )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  [Memory, Optional[MemorySummary]]:
        __lowerCAmelCase		:    Dict			     =  self._prepare_inference_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_memory(_inference )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  float:
        __lowerCAmelCase		:    Optional[Any]			     =  self._prepare_train_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_speed(_train )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  [Memory, Optional[MemorySummary]]:
        __lowerCAmelCase		:    Optional[int]			     =  self._prepare_train_func(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return self._measure_memory(_train )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Callable[[], None]:
        __lowerCAmelCase		:    str			     =  self.config_dict[model_name]

        if self.args.torchscript:
          __lowerCAmelCase		:    Tuple			     =  True

        __lowerCAmelCase		:    Union[str, Any]			     =  (
            hasattr(SCREAMING_SNAKE_CASE     , 'architectures' )
            and isinstance(config.architectures     , SCREAMING_SNAKE_CASE )
            and len(config.architectures ) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            __lowerCAmelCase		:    Dict			     =  config.architectures[0]
            __lowerCAmelCase		:    List[str]			     =  __import__('transformers'     , fromlist=[model_class] )
            __lowerCAmelCase		:    Tuple			     =  getattr(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    Dict			     =  model_cls(SCREAMING_SNAKE_CASE )
          except ImportError:
            raise ImportError(
                F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.' )
        else:
          __lowerCAmelCase		:    List[Any]			     =  MODEL_MAPPING[config.__class__](SCREAMING_SNAKE_CASE )

        model.eval()
        model.to(self.args.device )

        # encoder-decoder has vocab size saved differently
        __lowerCAmelCase		:    List[str]			     =  config.vocab_size if hasattr(SCREAMING_SNAKE_CASE     , 'vocab_size' ) else config.encoder.vocab_size
        __lowerCAmelCase		:    Optional[Any]			     =  torch.randint(SCREAMING_SNAKE_CASE     , (batch_size, sequence_length)     , dtype=torch.long     , device=self.args.device )

        if self.args.fpaa:
          logger.info('Running training in Mixed Precision...' )
          if not self.args.is_gpu:
            raise ValueError('Mixed precision is possible only for GPU.' )
          # amp seems to have memory leaks so that memory usage
          # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
          model.half()

        if self.args.torchscript:
          with torch.no_grad():
            __lowerCAmelCase		:    List[Any]			     =  torch.jit.trace(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        else:
          __lowerCAmelCase		:    str			     =  model

        def encoder_decoder_forward():
          with torch.no_grad():
            __lowerCAmelCase		:    str			     =  inference_model(SCREAMING_SNAKE_CASE     , decoder_input_ids=SCREAMING_SNAKE_CASE )
          return outputs

        def encoder_forward():
          with torch.no_grad():
            __lowerCAmelCase		:    str			     =  inference_model(SCREAMING_SNAKE_CASE )
          return outputs

        __lowerCAmelCase		:    Optional[Any]			     =  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Callable[[], None]:
        __lowerCAmelCase		:    int			     =  self.config_dict[model_name]

        __lowerCAmelCase		:    Optional[int]			     =  (
            hasattr(SCREAMING_SNAKE_CASE     , 'architectures' )
            and isinstance(config.architectures     , SCREAMING_SNAKE_CASE )
            and len(config.architectures ) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            __lowerCAmelCase		:    List[Any]			     =  config.architectures[0]
            __lowerCAmelCase		:    List[str]			     =  __import__('transformers'     , fromlist=[model_class] )
            __lowerCAmelCase		:    Tuple			     =  getattr(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    Any			     =  model_cls(SCREAMING_SNAKE_CASE )
          except ImportError:
            raise ImportError(
                F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.' )
        else:
          __lowerCAmelCase		:    List[str]			     =  MODEL_WITH_LM_HEAD_MAPPING[config.__class__](SCREAMING_SNAKE_CASE )

        if self.args.torchscript:
          raise NotImplementedError('Training for torchscript is currently not implemented' )
        else:
          __lowerCAmelCase		:    int			     =  model

        model.train()
        model.to(self.args.device )

        # encoder-decoder has vocab size saved differently
        __lowerCAmelCase		:    Tuple			     =  config.vocab_size if hasattr(SCREAMING_SNAKE_CASE     , 'vocab_size' ) else config.encoder.vocab_size
        __lowerCAmelCase		:    Optional[int]			     =  torch.randint(SCREAMING_SNAKE_CASE     , (batch_size, sequence_length)     , dtype=torch.long     , device=self.args.device )

        if self.args.fpaa:
          logger.info('Running training in Mixed Precision...' )
          if not self.args.is_gpu:
            raise ValueError('Mixed precision is possible only for GPU.' )

          # amp seems to have memory leaks so that memory usage
          # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
          model.half()

        def compute_loss_and_backprob_encoder():
          __lowerCAmelCase		:    List[str]			     =  train_model(SCREAMING_SNAKE_CASE     , labels=SCREAMING_SNAKE_CASE )[0]
          loss.backward()
          return loss

        def compute_loss_and_backprob_encoder_decoder():
          __lowerCAmelCase		:    List[Any]			     =  train_model(SCREAMING_SNAKE_CASE     , decoder_input_ids=SCREAMING_SNAKE_CASE     , labels=SCREAMING_SNAKE_CASE )[0]
          loss.backward()
          return loss

        __lowerCAmelCase		:    Dict			     =  (
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  float:
        try:
          if self.args.is_tpu or self.args.torchscript:
            # run additional 10 times to stabilize compilation for tpu and torchscript
            logger.info('Do inference on TPU or torchscript. Running model 5 times to stabilize compilation' )
            timeit.repeat(
                SCREAMING_SNAKE_CASE     , repeat=1     , number=5     , )

          # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
          __lowerCAmelCase		:    Tuple			     =  timeit.repeat(
              SCREAMING_SNAKE_CASE     , repeat=self.args.repeat     , number=10     , )

          if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
            import torch_xla.debug.metrics as met

            self.print_fn(met.metrics_report() )

          return min(SCREAMING_SNAKE_CASE ) / 1_0.0
        except RuntimeError as e:
          self.print_fn(F"""Doesn't fit on GPU. {e}""" )
          return "N/A"







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  [Memory, MemorySummary]:
        try:
          if self.args.trace_memory_line_by_line:
            __lowerCAmelCase		:    Tuple			     =  start_memory_tracing('transformers' )

          if self.args.is_tpu:
            # tpu
            raise NotImplementedError(
                'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'
                ' `--no-memory` or `args.memory=False`' )
          elif self.args.is_gpu:
            if not is_pyanvml_available():
              logger.warning(
                  'py3nvml not installed, we won\'t log GPU memory usage. '
                  'Install py3nvml (pip install py3nvml) to log information about GPU.' )
              __lowerCAmelCase		:    Union[str, Any]			     =  'N/A'
            else:
              logger.info(
                  'Measuring total GPU usage on GPU device. Make sure to not have additional processes running'
                  ' on the same GPU.' )
              # init nvml
              nvml.nvmlInit()
              func()
              __lowerCAmelCase		:    int			     =  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
              __lowerCAmelCase		:    List[str]			     =  nvml.nvmlDeviceGetMemoryInfo(SCREAMING_SNAKE_CASE )
              __lowerCAmelCase		:    Tuple			     =  meminfo.used
              __lowerCAmelCase		:    Dict			     =  Memory(SCREAMING_SNAKE_CASE )
              # shutdown nvml
              nvml.nvmlShutdown()
          else:
            # cpu
            __lowerCAmelCase		:    Tuple			     =  measure_peak_memory_cpu(SCREAMING_SNAKE_CASE )
            __lowerCAmelCase		:    str			     =  Memory(SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) else memory_bytes

          if self.args.trace_memory_line_by_line:
            __lowerCAmelCase		:    int			     =  stop_memory_tracing(SCREAMING_SNAKE_CASE )
          else:
            __lowerCAmelCase		:    Any			     =  None

          return memory, summary
        except RuntimeError as e:
          self.print_fn(F"""Doesn't fit on GPU. {e}""" )
          return "N/A", None





