
"""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_					:						Optional[Any]				= 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 __lowerCAmelCase				(					_UpperCAmelCase      ):




   """simple docstring"""







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







        """simple docstring"""
        super().__init__(*lowercase__       ,   **lowercase__	)
        A_				 =						eval_examples
        A_				 =						post_process_function
        A_				 =						quant_trainer_args
        A_				 =						128  # default number of calibration samples




   def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Any=None	)      -> Optional[int]:







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

        A_				 =						self._remove_unused_columns(lowercase__       ,   description="Calibration"	)

        return DataLoader(
            lowercase__       ,   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=lowercase__       ,   )




   def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					List[Any]=None	)      -> int:







        """simple docstring"""
        A_				 =						self.train_dataset if calib_dataset is None else calib_dataset
        A_				 =						self.get_calib_dataloader(lowercase__	)

        A_				 =						self.model
        quant_trainer.configure_model(lowercase__       ,   self.quant_trainer_args       ,   calib=lowercase__	)
        model.eval()
        quant_trainer.enable_calibration(lowercase__	)

        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(lowercase__	):
             # Prediction step
             A_				 =						self.prediction_step(lowercase__       ,   lowercase__       ,   prediction_loss_only=lowercase__	)
             if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                  break

        quant_trainer.finish_calibration(lowercase__       ,   self.quant_trainer_args	)
        A_				 =						model




   def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=None       ,   _snake_case						:					int=None       ,   _snake_case						:					Optional[int]=None       ,   _snake_case						:					int = "eval"	)      -> int:







        """simple docstring"""
        A_				 =						self.eval_dataset if eval_dataset is None else eval_dataset
        A_				 =						self.get_eval_dataloader(lowercase__	)
        A_				 =						self.eval_examples if eval_examples is None else eval_examples

        # Temporarily disable metric computation, we will do it in the loop here.
        A_				 =						self.compute_metrics
        A_				 =						None
        A_				 =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
             A_				 =						eval_loop(
                 lowercase__       ,   description="Evaluation"       ,   prediction_loss_only=True if compute_metrics is None else None       ,   ignore_keys=lowercase__       ,   )
        finally:
             A_				 =						compute_metrics

        if self.post_process_function is not None and self.compute_metrics is not None:
             A_				 =						self.post_process_function(lowercase__       ,   lowercase__       ,   output.predictions	)
             A_				 =						self.compute_metrics(lowercase__	)

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

             self.log(lowercase__	)
        else:
             A_				 =						{}

        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()	)

        A_				 =						self.callback_handler.on_evaluate(self.args       ,   self.state       ,   self.control       ,   lowercase__	)
        return metrics




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







        """simple docstring"""
        A_				 =						self.get_test_dataloader(lowercase__	)

        # Temporarily disable metric computation, we will do it in the loop here.
        A_				 =						self.compute_metrics
        A_				 =						None
        A_				 =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        try:
             A_				 =						eval_loop(
                 lowercase__       ,   description="Prediction"       ,   prediction_loss_only=True if compute_metrics is None else None       ,   ignore_keys=lowercase__       ,   )
        finally:
             A_				 =						compute_metrics

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

        A_				 =						self.post_process_function(lowercase__       ,   lowercase__       ,   output.predictions       ,   "predict"	)
        A_				 =						self.compute_metrics(lowercase__	)

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

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






   def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					List[Any]="./"	)      -> Union[str, Any]:







        """simple docstring"""
        A_				 =						self.eval_dataset
        A_				 =						self.get_eval_dataloader(lowercase__	)

        A_				 =						next(iter(lowercase__	)	)

        # saving device - to make it consistent
        A_				 =						torch.device("cuda" if torch.cuda.is_available() else "cpu"	)

        # convert to tuple
        A_				 =						tuple(v.to(lowercase__	) for k, v in batch.items()	)

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

        A_				 =						True

        A_				 =						self.model.to(lowercase__	)

        model.eval()
        model.float()

        A_				 =						model.module if hasattr(lowercase__       ,   "module"	) else model
        quant_trainer.configure_model(lowercase__       ,   self.quant_trainer_args	)

        A_				 =						os.path.join(lowercase__       ,   "model.onnx"	)
        logger.info(F'exporting model to {output_model_file}'	)

        A_				 =						{0: """batch_size""", 1: """seq_len"""}

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






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




