'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor






class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=7			,       __lowerCAmelCase=3			,       __lowerCAmelCase=18			,       __lowerCAmelCase=30			,       __lowerCAmelCase=400			,       __lowerCAmelCase=True			,       __lowerCAmelCase=None			,       __lowerCAmelCase=True			,       __lowerCAmelCase=None			,       __lowerCAmelCase=True			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    size if size is not None else {"""shortest_edge""": 20}
					lowerCAmelCase     =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
					lowerCAmelCase     =    parent
					lowerCAmelCase     =    batch_size
					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    min_resolution
					lowerCAmelCase     =    max_resolution
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    do_center_crop
					lowerCAmelCase     =    crop_size
					lowerCAmelCase     =    do_flip_channel_order







			def 				a_			(  self):




					"""simple docstring"""


					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_flip_channel_order": self.do_flip_channel_order,
					}







@require_torch
@require_vision
class 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			MobileViTImageProcessor if is_vision_available() else None



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    MobileViTImageProcessingTester(self)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(__lowerCAmelCase			,       """do_resize"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """size"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """do_center_crop"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """center_crop"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """do_flip_channel_order"""))



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size			,       {"""shortest_edge""": 20})
					self.assertEqual(image_processor.crop_size			,       {"""height""": 18, """width""": 18})

					lowerCAmelCase     =    self.image_processing_class.from_dict(self.image_processor_dict			,       size=42			,       crop_size=84)
					self.assertEqual(image_processor.size			,       {"""shortest_edge""": 42})
					self.assertEqual(image_processor.crop_size			,       {"""height""": 84, """width""": 84})



			def 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					lowerCAmelCase     =    prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase			,       Image.Image)

					# Test not batched input
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )

					# Test batched
					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase     =    prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowerCAmelCase			,       numpify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase			,       np.ndarray)

					# Test not batched input
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )

					# Test batched
					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase     =    prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowerCAmelCase			,       torchify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase			,       torch.Tensor)

					# Test not batched input
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )

					# Test batched
					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )





'''simple docstring'''




import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       *__lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					super().__init__(*__lowerCAmelCase			,       **__lowerCAmelCase)
					lowerCAmelCase     =    eval_examples
					lowerCAmelCase     =    post_process_function



			def 				a_			(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "eval"):




					"""simple docstring"""


					lowerCAmelCase     =    self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCAmelCase     =    self.get_eval_dataloader(__lowerCAmelCase)
					lowerCAmelCase     =    self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Evaluation"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    self.args.eval_batch_size * self.args.world_size
					if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
							start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
					output.metrics.update(
					    speed_metrics(
					        __lowerCAmelCase			,       __lowerCAmelCase			,       num_samples=output.num_samples			,       num_steps=math.ceil(output.num_samples / total_batch_size)			,       ))
					if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
							# Only the main node write the results by default
							lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions)
							lowerCAmelCase     =    self.compute_metrics(__lowerCAmelCase)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()):
									if not key.startswith(f"{metric_key_prefix}_"):
											lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
							metrics.update(output.metrics)
					else:
							lowerCAmelCase     =    output.metrics

					if self.args.should_log:
							# Only the main node log the results by default
							self.log(__lowerCAmelCase)

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







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "test"):




					"""simple docstring"""


					lowerCAmelCase     =    self.get_test_dataloader(__lowerCAmelCase)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Prediction"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    self.args.eval_batch_size * self.args.world_size
					if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
							start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
					output.metrics.update(
					    speed_metrics(
					        __lowerCAmelCase			,       __lowerCAmelCase			,       num_samples=output.num_samples			,       num_steps=math.ceil(output.num_samples / total_batch_size)			,       ))

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

					lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions			,       """predict""")
					lowerCAmelCase     =    self.compute_metrics(__lowerCAmelCase)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()):
							if not key.startswith(f"{metric_key_prefix}_"):
									lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
					metrics.update(output.metrics)
					return PredictionOutput(predictions=predictions.predictions			,       label_ids=predictions.label_ids			,       metrics=__lowerCAmelCase)




