
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__lowerCamelCase				=			logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   Any       ,		*snake_case__						:   Tuple       ,		snake_case__						:   Optional[Any]=None       ,		snake_case__						:   List[Any]=None       ,		snake_case__						:   List[str]=None       ,		**snake_case__						:   Any					)						->      Dict:


          '''simple docstring'''


          super().__init__(*snake_case__       ,		**snake_case__					)
          snake_case			:  Tuple     							= eval_examples
          snake_case			:  Tuple     							= post_process_function
          snake_case			:  Any     							= quant_trainer_args
          snake_case			:  Optional[int]     							= 1_28  # default number of calibration samples



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   List[Any]=None					)						->      Any:


          '''simple docstring'''


          if calib_dataset is None and self.calib_dataset is None:
                 raise ValueError("Trainer: calibration requires an calib_dataset."					)
          snake_case			:  int     							= calib_dataset if calib_dataset is not None else self.calib_dataset

          snake_case			:  Optional[Any]     							= self._remove_unused_columns(snake_case__       ,		description="Calibration"					)

          return DataLoader(
              snake_case__       ,		batch_size=self.args.eval_batch_size       ,		collate_fn=self.data_collator       ,		drop_last=self.args.dataloader_drop_last       ,		num_workers=self.args.dataloader_num_workers       ,		pin_memory=self.args.dataloader_pin_memory       ,		shuffle=snake_case__       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str=None					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  str     							= self.train_dataset if calib_dataset is None else calib_dataset
          snake_case			:  Optional[int]     							= self.get_calib_dataloader(snake_case__					)

          snake_case			:  List[Any]     							= self.model
          quant_trainer.configure_model(snake_case__       ,		self.quant_trainer_args       ,		calib=snake_case__					)
          model.eval()
          quant_trainer.enable_calibration(snake_case__					)

          logger.info("***** Running calibration *****"					)
          logger.info(f"""  Num examples = {self.calib_num}"""					)
          logger.info(f"""  Batch size = {calib_dataloader.batch_size}"""					)

          for step, inputs in enumerate(snake_case__					):
                 # Prediction step
                 snake_case							,   snake_case							,   snake_case			:  List[str]     							= self.prediction_step(snake_case__       ,		snake_case__       ,		prediction_loss_only=snake_case__					)
                 if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                        break

          quant_trainer.finish_calibration(snake_case__       ,		self.quant_trainer_args					)
          snake_case			:  Any     							= model



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   int=None       ,		snake_case__						:   int=None       ,		snake_case__						:   int=None       ,		snake_case__						:   str = "eval"					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= self.eval_dataset if eval_dataset is None else eval_dataset
          snake_case			:  List[Any]     							= self.get_eval_dataloader(snake_case__					)
          snake_case			:  List[str]     							= self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          snake_case			:  Tuple     							= self.compute_metrics
          snake_case			:  List[Any]     							= None
          snake_case			:  Any     							= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
                 snake_case			:  Optional[Any]     							= eval_loop(
                     snake_case__       ,		description="Evaluation"       ,		prediction_loss_only=True if compute_metrics is None else None       ,		ignore_keys=snake_case__       ,		)
          finally:
                 snake_case			:  Optional[Any]     							= compute_metrics

          if self.post_process_function is not None and self.compute_metrics is not None:
                 snake_case			:  int     							= self.post_process_function(snake_case__       ,		snake_case__       ,		output.predictions					)
                 snake_case			:  Optional[Any]     							= self.compute_metrics(snake_case__					)

                 # Prefix all keys with metric_key_prefix + '_'
                 for key in list(metrics.keys()					):
                        if not key.startswith(f"""{metric_key_prefix}_"""					):
                               snake_case			:  Optional[Any]     							= metrics.pop(snake_case__					)

                 self.log(snake_case__					)
          else:
                 snake_case			:  Optional[Any]     							= {}

          if self.args.tpu_metrics_debug or self.args.debug:
                 # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                 xm.master_print(met.metrics_report()					)

          snake_case			:  str     							= self.callback_handler.on_evaluate(self.args       ,		self.state       ,		self.control       ,		snake_case__					)
          return metrics



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str=None       ,		snake_case__						:   str = "test"					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  str     							= self.get_test_dataloader(snake_case__					)

          # Temporarily disable metric computation, we will do it in the loop here.
          snake_case			:  List[Any]     							= self.compute_metrics
          snake_case			:  List[Any]     							= None
          snake_case			:  Optional[Any]     							= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
                 snake_case			:  Union[str, Any]     							= eval_loop(
                     snake_case__       ,		description="Prediction"       ,		prediction_loss_only=True if compute_metrics is None else None       ,		ignore_keys=snake_case__       ,		)
          finally:
                 snake_case			:  Optional[Any]     							= compute_metrics

          if self.post_process_function is None or self.compute_metrics is None:
                 return output

          snake_case			:  Union[str, Any]     							= self.post_process_function(snake_case__       ,		snake_case__       ,		output.predictions       ,		"predict"					)
          snake_case			:  Optional[int]     							= self.compute_metrics(snake_case__					)

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()					):
                 if not key.startswith(f"""{metric_key_prefix}_"""					):
                        snake_case			:  Optional[Any]     							= metrics.pop(snake_case__					)

          return PredictionOutput(predictions=predictions.predictions       ,		label_ids=predictions.label_ids       ,		metrics=snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   List[Any]="./"					)						->      str:


          '''simple docstring'''


          snake_case			:  List[Any]     							= self.eval_dataset
          snake_case			:  Union[str, Any]     							= self.get_eval_dataloader(snake_case__					)

          snake_case			:  int     							= next(iter(snake_case__					)					)

          # saving device - to make it consistent
          snake_case			:  Union[str, Any]     							= torch.device("cuda" if torch.cuda.is_available() else "cpu"					)

          # convert to tuple
          snake_case			:  Dict     							= tuple(v.to(snake_case__					) for k, v in batch.items()					)

          logger.info("Converting model to be onnx compatible"					)
          from pytorch_quantization.nn import TensorQuantizer

          snake_case			:  Union[str, Any]     							= True

          snake_case			:  Optional[Any]     							= self.model.to(snake_case__					)

          model.eval()
          model.float()

          snake_case			:  Tuple     							= model.module if hasattr(snake_case__       ,		"module"					) else model
          quant_trainer.configure_model(snake_case__       ,		self.quant_trainer_args					)

          snake_case			:  Any     							= os.path.join(snake_case__       ,		"model.onnx"					)
          logger.info(f"""exporting model to {output_model_file}"""					)

          snake_case			:  Optional[int]     							= {0: "batch_size", 1: "seq_len"}

          torch.onnx.export(
              snake_case__       ,		snake_case__       ,		snake_case__       ,		export_params=snake_case__       ,		opset_version=13       ,		do_constant_folding=snake_case__       ,		input_names=["input_ids", "attention_mask", "token_type_ids"]       ,		output_names=["output_start_logits", "output_end_logits"]       ,		dynamic_axes={
                  "input_ids": axes,
                  "attention_mask": axes,
                  "token_type_ids": axes,
                  "output_start_logits": axes,
                  "output_end_logits": axes,
              }       ,		verbose=snake_case__       ,		)
          logger.info("onnx export finished"					)







import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCamelCase   (       __lowerCamelCase	:					int = 8       ):
       snake_case			:  int     							= ascii_letters + digits + punctuation
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(__lowerCamelCase       )
       snake_case			:  Any     							= i // 3
       snake_case			:  Optional[int]     							= i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       snake_case			:  Tuple     							= (
           chars_incl
           + random(__lowerCamelCase							,   quotient + remainder       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
       )
       snake_case			:  Optional[Any]     							= list(__lowerCamelCase       )
       shuffle(__lowerCamelCase       )
       return "".join(__lowerCamelCase       )

       # random is a generalised function for letters, characters and numbers


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					List[str]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Any							,   __lowerCamelCase	:					List[Any]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int = 8       ):

       if len(__lowerCamelCase       ) < min_length:
              # Your Password must be at least 8 characters long
              return False

       snake_case			:  Dict     							= any(char in ascii_uppercase for char in password       )
       snake_case			:  Optional[int]     							= any(char in ascii_lowercase for char in password       )
       snake_case			:  str     							= any(char in digits for char in password       )
       snake_case			:  str     							= any(char in punctuation for char in password       )

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters


def      UpperCamelCase   (       ):
       snake_case			:  int     							= int(input("Please indicate the max length of your password: "       ).strip()       )
       snake_case			:  Union[str, Any]     							= input(
           "Please indicate the characters that must be in your password: "       ).strip()
       print("Password generated:"							,   password_generator(__lowerCamelCase       )       )
       print(
           "Alternative Password generated:"							,   alternative_password_generator(__lowerCamelCase							,   __lowerCamelCase       )							,   )
       print("[If you are thinking of using this passsword, You better save it.]"       )


if __name__ == "__main__":
   main()





