


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 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      Any       ,		*UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      int=None       ,		**UpperCamelCase_:      Union[str, Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									super().__init__(*UpperCamelCase_       ,		**UpperCamelCase_				)
									lowercase__				= eval_examples
									lowercase__				= post_process_function
									lowercase__				= quant_trainer_args
									lowercase__				= 128  # default number of calibration samples

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      int=None				)     ->  Optional[Any]:





									"""simple docstring"""







									if calib_dataset is None and self.calib_dataset is None:
											raise ValueError('''Trainer: calibration requires an calib_dataset.'''				)
									lowercase__				= calib_dataset if calib_dataset is not None else self.calib_dataset

									lowercase__				= self._remove_unused_columns(UpperCamelCase_       ,		description='''Calibration'''				)

									return DataLoader(
									    UpperCamelCase_       ,		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=UpperCamelCase_       ,		)

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      List[Any]=None				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.train_dataset if calib_dataset is None else calib_dataset
									lowercase__				= self.get_calib_dataloader(UpperCamelCase_				)

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

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

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

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      str=None       ,		UpperCamelCase_:      str = "eval"				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.eval_dataset if eval_dataset is None else eval_dataset
									lowercase__				= self.get_eval_dataloader(UpperCamelCase_				)
									lowercase__				= self.eval_examples if eval_examples is None else eval_examples

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

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

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

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

									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()				)

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

							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      Optional[int]       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      str = "test"				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= self.get_test_dataloader(UpperCamelCase_				)

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

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

									lowercase__				= self.post_process_function(UpperCamelCase_       ,		UpperCamelCase_       ,		output.predictions       ,		'''predict'''				)
									lowercase__				= self.compute_metrics(UpperCamelCase_				)

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

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


							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      Union[str, Any]="./"				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.eval_dataset
									lowercase__				= self.get_eval_dataloader(UpperCamelCase_				)

									lowercase__				= next(iter(UpperCamelCase_				)				)

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

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

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

									lowercase__				= True

									lowercase__				= self.model.to(UpperCamelCase_				)

									model.eval()
									model.float()

									lowercase__				= model.module if hasattr(UpperCamelCase_       ,		'''module'''				) else model
									quant_trainer.configure_model(UpperCamelCase_       ,		self.quant_trainer_args				)

									lowercase__				= os.path.join(UpperCamelCase_       ,		'''model.onnx'''				)
									logger.info(f'exporting model to {output_model_file}'				)

									lowercase__				= {0: '''batch_size''', 1: '''seq_len'''}

									torch.onnx.export(
									    UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		export_params=UpperCamelCase_       ,		opset_version=13       ,		do_constant_folding=UpperCamelCase_       ,		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=UpperCamelCase_       ,		)
									logger.info('''onnx export finished'''				)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:       str					 =	{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       Optional[Any]					 =	[
							    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PegasusXForConditionalGeneration""",
							    """PegasusXModel""",
							    """PegasusXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus_x import (
														    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusXForConditionalGeneration,
														    PegasusXModel,
														    PegasusXPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       str					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



