


"""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


a__       :							str        =  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_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       Tuple    ,    *UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       str=None    ,    UpperCAmelCase__    :       Dict=None    ,    UpperCAmelCase__    :       Dict=None    ,    **UpperCAmelCase__    :       Tuple     ) ->			int:
						super().__init__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       eval_examples
						__SCREAMING_SNAKE_CASE		     =       post_process_function
						__SCREAMING_SNAKE_CASE		     =       quant_trainer_args
						__SCREAMING_SNAKE_CASE		     =       1_2_8  # default number of calibration samples




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Tuple=None     ) ->			Optional[int]:
						if calib_dataset is None and self.calib_dataset is None:
											raise ValueError("Trainer: calibration requires an calib_dataset."     )
						__SCREAMING_SNAKE_CASE		     =       calib_dataset if calib_dataset is not None else self.calib_dataset

						__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Tuple=None     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.train_dataset if calib_dataset is None else calib_dataset
						__SCREAMING_SNAKE_CASE		     =       self.get_calib_dataloader(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       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     )
						__SCREAMING_SNAKE_CASE		     =       model




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Tuple=None    ,    UpperCAmelCase__    :       Any=None    ,    UpperCAmelCase__    :       List[Any]=None    ,    UpperCAmelCase__    :       str = "eval"     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.eval_dataset if eval_dataset is None else eval_dataset
						__SCREAMING_SNAKE_CASE		     =       self.get_eval_dataloader(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.eval_examples if eval_examples is None else eval_examples

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

						if self.post_process_function is not None and self.compute_metrics is not None:
											__SCREAMING_SNAKE_CASE		     =       self.post_process_function(UpperCAmelCase__    ,    UpperCAmelCase__    ,    output.predictions     )
											__SCREAMING_SNAKE_CASE		     =       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}_"""     ):
																					__SCREAMING_SNAKE_CASE		     =       metrics.pop(UpperCAmelCase__     )

											self.log(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       {}

						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()     )

						__SCREAMING_SNAKE_CASE		     =       self.callback_handler.on_evaluate(self.args    ,    self.state    ,    self.control    ,    UpperCAmelCase__     )
						return metrics




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Optional[Any]    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       Optional[int]=None    ,    UpperCAmelCase__    :       str = "test"     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.get_test_dataloader(UpperCAmelCase__     )

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

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

						__SCREAMING_SNAKE_CASE		     =       self.post_process_function(UpperCAmelCase__    ,    UpperCAmelCase__    ,    output.predictions    ,    "predict"     )
						__SCREAMING_SNAKE_CASE		     =       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}_"""     ):
																__SCREAMING_SNAKE_CASE		     =       metrics.pop(UpperCAmelCase__     )

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




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int="./"     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.eval_dataset
						__SCREAMING_SNAKE_CASE		     =       self.get_eval_dataloader(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       next(iter(UpperCAmelCase__     )     )

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

						# convert to tuple
						__SCREAMING_SNAKE_CASE		     =       tuple(v.to(UpperCAmelCase__     ) for k, v in batch.items()     )

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

						__SCREAMING_SNAKE_CASE		     =       True

						__SCREAMING_SNAKE_CASE		     =       self.model.to(UpperCAmelCase__     )

						model.eval()
						model.float()

						__SCREAMING_SNAKE_CASE		     =       model.module if hasattr(UpperCAmelCase__    ,    "module"     ) else model
						quant_trainer.configure_model(UpperCAmelCase__    ,    self.quant_trainer_args     )

						__SCREAMING_SNAKE_CASE		     =       os.path.join(UpperCAmelCase__    ,    "model.onnx"     )
						logger.info(F"""exporting model to {output_model_file}"""     )

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

						torch.onnx.export(
						    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    export_params=UpperCAmelCase__    ,    opset_version=1_3    ,    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"     )




"""simple docstring"""







import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


a__       :							Dict        =  logging.get_logger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					def constraint_to_multiple_of(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=0						,		lowerCAmelCase_=None     ):
										__SCREAMING_SNAKE_CASE		     =       round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															__SCREAMING_SNAKE_CASE		     =       math.floor(val / multiple     ) * multiple

										if x < min_val:
															__SCREAMING_SNAKE_CASE		     =       math.ceil(val / multiple     ) * multiple

										return x

					__SCREAMING_SNAKE_CASE		     =       (output_size, output_size) if isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) else output_size

					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_image_size(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       output_size

					# determine new height and width
					__SCREAMING_SNAKE_CASE		     =       output_height / input_height
					__SCREAMING_SNAKE_CASE		     =       output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width     ) < abs(1 - scale_height     ):
															# fit width
															__SCREAMING_SNAKE_CASE		     =       scale_width
										else:
															# fit height
															__SCREAMING_SNAKE_CASE		     =       scale_height

					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_height * input_height						,		multiple=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_width * input_width						,		multiple=lowerCAmelCase_     )

					return (new_height, new_width)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    ["pixel_values"]




	def __init__(				self    :       int    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BILINEAR    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[int, float] = 1 / 2_5_5    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			None:
						super().__init__(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"height": 3_8_4, "width": 3_8_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Dict    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						if "height" not in size or "width" not in size:
											raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
						__SCREAMING_SNAKE_CASE		     =       get_resize_output_image_size(
						    UpperCAmelCase__    ,    output_size=(size["height"], size["width"])    ,    keep_aspect_ratio=UpperCAmelCase__    ,    multiple=UpperCAmelCase__    ,    )
						return resize(UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[int, float]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			Any:
						return rescale(UpperCAmelCase__    ,    scale=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			np.ndarray:
						return normalize(UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       ImageInput    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       PILImageResampling = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[str, TensorType]] = None    ,    UpperCAmelCase__    :       ChannelDimension = ChannelDimension.FIRST    ,    **UpperCAmelCase__    :       int    ,    ) ->			PIL.Image.Image:
						__SCREAMING_SNAKE_CASE		     =       do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		     =       make_list_of_images(UpperCAmelCase__     )

						if not valid_images(UpperCAmelCase__     ):
											raise ValueError(
											    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
											    "torch.Tensor, tf.Tensor or jax.ndarray."     )

						if do_resize and size is None or resample is None:
											raise ValueError("Size and resample must be specified if do_resize is True."     )

						if do_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."     )

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError("Image mean and std must be specified if do_normalize is True."     )

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE		     =       [to_numpy_array(UpperCAmelCase__     ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE		     =       [self.resize(image=UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__     ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE		     =       [self.rescale(image=UpperCAmelCase__    ,    scale=UpperCAmelCase__     ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE		     =       [self.normalize(image=UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       [to_channel_dimension_format(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__    ,    tensor_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       List[Tuple] = None     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
											if len(UpperCAmelCase__     ) != len(UpperCAmelCase__     ):
																raise ValueError(
																    "Make sure that you pass in as many target sizes as the batch dimension of the logits"     )

											if is_torch_tensor(UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       target_sizes.numpy()

											__SCREAMING_SNAKE_CASE		     =       []

											for idx in range(len(UpperCAmelCase__     )     ):
																__SCREAMING_SNAKE_CASE		     =       torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0     )    ,    size=target_sizes[idx]    ,    mode="bilinear"    ,    align_corners=UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       resized_logits[0].argmax(dim=0     )
																semantic_segmentation.append(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       logits.argmax(dim=1     )
											__SCREAMING_SNAKE_CASE		     =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

						return semantic_segmentation
