"""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


__lowerCAmelCase    :							str													=						logging.get_logger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		def constraint_to_multiple_of(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=0			,  lowerCamelCase__=None     ):
				lowerCAmelCase__        =   round(val / multiple     ) * multiple

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

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

				return x

		lowerCAmelCase__        =   (output_size, output_size) if isinstance(lowerCamelCase__			,  lowerCamelCase__     ) else output_size

		lowerCAmelCase__   ,					lowerCAmelCase__        =   get_image_size(lowerCamelCase__     )
		lowerCAmelCase__   ,					lowerCAmelCase__        =   output_size

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

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

		lowerCAmelCase__        =   constraint_to_multiple_of(scale_height * input_height			,  multiple=lowerCamelCase__     )
		lowerCAmelCase__        =   constraint_to_multiple_of(scale_width * input_width			,  multiple=lowerCamelCase__     )

		return (new_height, new_width)






class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Dict			=    ["pixel_values"]




					def __init__(	self						:     Tuple  ,						snake_case__						:     bool = True  ,						snake_case__						:     Dict[str, int] = None  ,						snake_case__						:     PILImageResampling = PILImageResampling.BILINEAR  ,						snake_case__						:     bool = False  ,						snake_case__						:     int = 1  ,						snake_case__						:     bool = True  ,						snake_case__						:     Union[int, float] = 1 / 255  ,						snake_case__						:     bool = True  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						snake_case__						:     Optional[Union[float, List[float]]] = None  ,						**snake_case__						:     Dict  ,						):
							super().__init__(**snake_case__     )
							lowerCAmelCase__        =   size if size is not None else {"""height""": 384, """width""": 384}
							lowerCAmelCase__        =   get_size_dict(snake_case__     )
							lowerCAmelCase__        =   do_resize
							lowerCAmelCase__        =   size
							lowerCAmelCase__        =   keep_aspect_ratio
							lowerCAmelCase__        =   ensure_multiple_of
							lowerCAmelCase__        =   resample
							lowerCAmelCase__        =   do_rescale
							lowerCAmelCase__        =   rescale_factor
							lowerCAmelCase__        =   do_normalize
							lowerCAmelCase__        =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCAmelCase__        =   image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Dict[str, int]  ,						snake_case__						:     bool = False  ,						snake_case__						:     int = 1  ,						snake_case__						:     PILImageResampling = PILImageResampling.BICUBIC  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     Optional[int]  ,						):
							lowerCAmelCase__        =   get_size_dict(snake_case__     )
							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()}"""     )
							lowerCAmelCase__        =   get_resize_output_image_size(
							    snake_case__  ,						output_size=(size["""height"""], size["""width"""])  ,						keep_aspect_ratio=snake_case__  ,						multiple=snake_case__  ,						)
							return resize(snake_case__  ,						size=snake_case__  ,						resample=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[int, float]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     List[str]  ,						):
							return rescale(snake_case__  ,						scale=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Union[float, List[float]]  ,						snake_case__						:     Optional[Union[str, ChannelDimension]] = None  ,						**snake_case__						:     str  ,						):
							return normalize(snake_case__  ,						mean=snake_case__  ,						std=snake_case__  ,						data_format=snake_case__  ,						**snake_case__     )




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

							lowerCAmelCase__        =   make_list_of_images(snake_case__     )

							if not valid_images(snake_case__     ):
									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.
							lowerCAmelCase__        =   [to_numpy_array(snake_case__     ) for image in images]

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

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

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

							lowerCAmelCase__        =   [to_channel_dimension_format(snake_case__  ,						snake_case__     ) for image in images]

							lowerCAmelCase__        =   {"""pixel_values""": images}
							return BatchFeature(data=snake_case__  ,						tensor_type=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     int  ,						snake_case__						:     List[Tuple] = None     ):
							lowerCAmelCase__        =   outputs.logits

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

									if is_torch_tensor(snake_case__     ):
											lowerCAmelCase__        =   target_sizes.numpy()

									lowerCAmelCase__        =   []

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

							return semantic_segmentation





"""simple docstring"""




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
			from sagemaker import Session, TrainingJobAnalytics
			from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER" ,		"False"			)			) is not True ,		reason="Skipping test because should only be run when releasing minor transformers version" ,		)
@pytest.mark.usefixtures("sm_env"			)
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]			)
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							if self.framework == "pytorch":
									subprocess.run(
									    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()  ,						encoding="""utf-8"""  ,						check=snake_case__  ,						)
							assert hasattr(self  ,						"""env"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Optional[Any]     ):
							# configuration for running training on smdistributed Model Parallel
							lowerCAmelCase__        =   {
							    """enabled""": True,
							    """processes_per_host""": 8,
							}
							lowerCAmelCase__        =   {
							    """enabled""": True,
							    """parameters""": {
							        """microbatches""": 4,
							        """placement_strategy""": """spread""",
							        """pipeline""": """interleaved""",
							        """optimize""": """speed""",
							        """partitions""": 4,
							        """ddp""": True,
							    },
							}

							lowerCAmelCase__        =   {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

							lowerCAmelCase__        =   """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
							# creates estimator
							return HuggingFace(
							    entry_point=self.script  ,						source_dir=self.env.test_path  ,						role=self.env.role  ,						image_uri=self.env.image_uri  ,						base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""  ,						instance_count=snake_case__  ,						instance_type=self.instance_type  ,						debugger_hook_config=snake_case__  ,						hyperparameters={
							        **self.env.hyperparameters,
							        """model_name_or_path""": self.model_name_or_path,
							        """max_steps""": 500,
							    }  ,						metric_definitions=self.env.metric_definitions  ,						distribution=snake_case__  ,						py_version="""py36"""  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str     ):
							TrainingJobAnalytics(snake_case__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )




					@parameterized.expand([(1,)]     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     List[str]     ):
							# create estimator
							lowerCAmelCase__        =   self.create_estimator(snake_case__     )

							# run training
							estimator.fit()

							# result dataframe
							lowerCAmelCase__        =   TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

							# extract kpis
							lowerCAmelCase__        =   list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]     )
							lowerCAmelCase__        =   list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]     )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							lowerCAmelCase__        =   (
							    Session().describe_training_job(estimator.latest_training_job.name     ).get("""TrainingTimeInSeconds"""  ,						999999     )
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy     )
							assert all(t <= self.results["""eval_loss"""] for t in eval_loss     )

							# dump tests result into json file to share in PR
							with open(F"""{estimator.latest_training_job.name}.json"""  ,						"""w"""     ) as outfile:
									json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}  ,						snake_case__     )




