


"""simple docstring"""
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


UpperCAmelCase				=						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 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		eval_examples
      _lowerCamelCase	:					Union[str, Any]   =		post_process_function
      _lowerCamelCase	:					List[str]   =		quant_trainer_args
      _lowerCamelCase	:					Optional[int]   =		128  # default number of calibration samples





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("""Trainer: calibration requires an calib_dataset.""")
      _lowerCamelCase	:					Union[str, Any]   =		calib_dataset if calib_dataset is not None else self.calib_dataset

      _lowerCamelCase	:					List[Any]   =		self._remove_unused_columns(SCREAMING_SNAKE_CASE      ,   description="""Calibration""")

      return DataLoader(
          SCREAMING_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=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=None)  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		self.train_dataset if calib_dataset is None else calib_dataset
      _lowerCamelCase	:					Optional[Any]   =		self.get_calib_dataloader(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		self.model
      quant_trainer.configure_model(SCREAMING_SNAKE_CASE      ,   self.quant_trainer_args      ,   calib=SCREAMING_SNAKE_CASE)
      model.eval()
      quant_trainer.enable_calibration(SCREAMING_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(SCREAMING_SNAKE_CASE):
         # Prediction step
         _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.prediction_step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   prediction_loss_only=SCREAMING_SNAKE_CASE)
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
            break

      quant_trainer.finish_calibration(SCREAMING_SNAKE_CASE      ,   self.quant_trainer_args)
      _lowerCamelCase	:					int   =		model





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE = "eval")  ->  Optional[Any]:
      _lowerCamelCase	:					Tuple   =		self.eval_dataset if eval_dataset is None else eval_dataset
      _lowerCamelCase	:					Optional[int]   =		self.get_eval_dataloader(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		self.eval_examples if eval_examples is None else eval_examples

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

      if self.post_process_function is not None and self.compute_metrics is not None:
         _lowerCamelCase	:					Union[str, Any]   =		self.post_process_function(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   output.predictions)
         _lowerCamelCase	:					Union[str, Any]   =		self.compute_metrics(SCREAMING_SNAKE_CASE)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()):
            if not key.startswith(F'{metric_key_prefix}_'):
               _lowerCamelCase	:					Tuple   =		metrics.pop(SCREAMING_SNAKE_CASE)

         self.log(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					str   =		{}

      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())

      _lowerCamelCase	:					Dict   =		self.callback_handler.on_evaluate(self.args      ,   self.state      ,   self.control      ,   SCREAMING_SNAKE_CASE)
      return metrics





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE = "test")  ->  Union[str, Any]:
      _lowerCamelCase	:					Union[str, Any]   =		self.get_test_dataloader(SCREAMING_SNAKE_CASE)

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

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

      _lowerCamelCase	:					List[str]   =		self.post_process_function(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   output.predictions      ,   """predict""")
      _lowerCamelCase	:					Any   =		self.compute_metrics(SCREAMING_SNAKE_CASE)

      # Prefix all keys with metric_key_prefix + '_'
      for key in list(metrics.keys()):
         if not key.startswith(F'{metric_key_prefix}_'):
            _lowerCamelCase	:					int   =		metrics.pop(SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE="./")  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[int]   =		self.eval_dataset
      _lowerCamelCase	:					Any   =		self.get_eval_dataloader(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		next(iter(SCREAMING_SNAKE_CASE))

      # saving device - to make it consistent
      _lowerCamelCase	:					Optional[int]   =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")

      # convert to tuple
      _lowerCamelCase	:					Union[str, Any]   =		tuple(v.to(SCREAMING_SNAKE_CASE) for k, v in batch.items())

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

      _lowerCamelCase	:					Tuple   =		True

      _lowerCamelCase	:					str   =		self.model.to(SCREAMING_SNAKE_CASE)

      model.eval()
      model.float()

      _lowerCamelCase	:					List[Any]   =		model.module if hasattr(SCREAMING_SNAKE_CASE      ,   """module""") else model
      quant_trainer.configure_model(SCREAMING_SNAKE_CASE      ,   self.quant_trainer_args)

      _lowerCamelCase	:					Optional[Any]   =		os.path.join(SCREAMING_SNAKE_CASE      ,   """model.onnx""")
      logger.info(F'exporting model to {output_model_file}')

      _lowerCamelCase	:					Union[str, Any]   =		{0: """batch_size""", 1: """seq_len"""}

      torch.onnx.export(
          SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   export_params=SCREAMING_SNAKE_CASE      ,   opset_version=13      ,   do_constant_folding=SCREAMING_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=SCREAMING_SNAKE_CASE      ,   )
      logger.info("""onnx export finished""")





"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      # Input as list
      _lowerCamelCase	:					Any   =		list(poly_a or [0])[:]
      _lowerCamelCase	:					Optional[Any]   =		list(poly_b or [0])[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
         self.polyA.pop()
      _lowerCamelCase	:					int   =		len(self.polyA)

      while self.polyB[-1] == 0:
         self.polyB.pop()
      _lowerCamelCase	:					Union[str, Any]   =		len(self.polyB)

      # Add 0 to make lengths equal a power of 2
      _lowerCamelCase	:					List[Any]   =		int(
          2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

      while len(self.polyA) < self.c_max_length:
         self.polyA.append(0)
      while len(self.polyB) < self.c_max_length:
         self.polyB.append(0)
      # A complex root used for the fourier transform
      _lowerCamelCase	:					Optional[Any]   =		complex(mpmath.root(x=1      ,   n=self.c_max_length      ,   k=1))

      # The product
      _lowerCamelCase	:					int   =		self.__multiply()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
      # Corner case
      if len(SCREAMING_SNAKE_CASE) <= 1:
         return dft[0]
      #
      _lowerCamelCase	:					str   =		self.c_max_length // 2
      while next_ncol > 0:
         _lowerCamelCase	:					Dict   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					Tuple   =		self.root**next_ncol

         # First half of next step
         _lowerCamelCase	:					int   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
            current_root *= root
         # Second half of next step
         _lowerCamelCase	:					Optional[int]   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
            current_root *= root
         # Update
         _lowerCamelCase	:					Union[str, Any]   =		new_dft
         _lowerCamelCase	:					List[str]   =		next_ncol // 2
      return dft[0]





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		self.__dft("""A""")
      _lowerCamelCase	:					List[str]   =		self.__dft("""B""")
      _lowerCamelCase	:					List[Any]   =		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
      del dft_a
      del dft_b

      # Corner Case
      if len(inverce_c[0]) <= 1:
         return inverce_c[0]
      # Inverse DFT
      _lowerCamelCase	:					List[str]   =		2
      while next_ncol <= self.c_max_length:
         _lowerCamelCase	:					Any   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					List[Any]   =		self.root ** (next_ncol // 2)
         _lowerCamelCase	:					str   =		1
         # First half of next step
         for j in range(self.c_max_length // next_ncol):
            for i in range(next_ncol // 2):
               # Even positions
               new_inverse_c[i].append(
                   (
                       inverce_c[i][j]
                       + inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / 2)
               # Odd positions
               new_inverse_c[i + next_ncol // 2].append(
                   (
                       inverce_c[i][j]
                       - inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / (2 * current_root))
            current_root *= root
         # Update
         _lowerCamelCase	:					Any   =		new_inverse_c
         next_ncol *= 2
      # Unpack
      _lowerCamelCase	:					Optional[Any]   =		[round(x[0].real      ,   8) + round(x[0].imag      ,   8) * 1j for x in inverce_c]

      # Remove leading 0's
      while inverce_c[-1] == 0:
         inverce_c.pop()
      return inverce_c





   def __str__(							self)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""A = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
      _lowerCamelCase	:					List[Any]   =		"""B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
      _lowerCamelCase	:					int   =		"""A*B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.product))

      return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()

