






'''simple docstring'''







import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor





class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def __init__(			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      int = 32	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Union[int, float] = 1 / 255	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=7	,    __SCREAMING_SNAKE_CASE   :      List[str]=30	,    __SCREAMING_SNAKE_CASE   :      str=400	,    __SCREAMING_SNAKE_CASE   :      Any=3	,    )      ->      Any:
							a_		:	int									=   parent
							a_		:	Any									=   do_resize
							a_		:	Optional[int]									=   size if size is not None else {'''shortest_edge''': 288}
							a_		:	List[str]									=   size_divisor
							a_		:	Optional[int]									=   do_rescale
							a_		:	int									=   rescale_factor
							a_		:	Any									=   do_normalize
							a_		:	List[str]									=   do_center_crop
							a_		:	Union[str, Any]									=   image_mean
							a_		:	str									=   image_std
							a_		:	Any									=   do_pad
							a_		:	List[str]									=   batch_size
							a_		:	int									=   num_channels
							a_		:	int									=   min_resolution
							a_		:	str									=   max_resolution






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=False       )      ->      int:







							if not batched:
											a_		:	Dict									=   self.size['''shortest_edge''']
											a_		:	Tuple									=   image_inputs[0]
											if isinstance(__lowerCamelCase	,    Image.Image       ):
															a_					,      a_		:	Union[str, Any]									=   image.size
											else:
															a_					,      a_		:	Optional[int]									=   image.shape[1], image.shape[2]
											a_		:	int									=   size / min(__lowerCamelCase	,    __lowerCamelCase       )
											if h < w:
															a_					,      a_		:	Optional[int]									=   size, scale * w
											else:
															a_					,      a_		:	int									=   scale * h, size

											a_		:	Union[str, Any]									=   int((1333 / 800) * size       )
											if max(__lowerCamelCase	,    __lowerCamelCase       ) > max_size:
															a_		:	List[str]									=   max_size / max(__lowerCamelCase	,    __lowerCamelCase       )
															a_		:	Dict									=   newh * scale
															a_		:	Optional[Any]									=   neww * scale

											a_					,      a_		:	Optional[int]									=   int(newh + 0.5       ), int(neww + 0.5       )
											a_					,      a_		:	List[Any]									=   (
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

							else:
											a_		:	Union[str, Any]									=   []
											for image in image_inputs:
															a_					,      a_		:	List[str]									=   self.get_expected_values([image]       )
															expected_values.append((expected_height, expected_width)       )
											a_		:	int									=   max(__lowerCamelCase	,    key=lambda __SCREAMING_SNAKE_CASE       : item[0]       )[0]
											a_		:	Tuple									=   max(__lowerCamelCase	,    key=lambda __SCREAMING_SNAKE_CASE       : item[1]       )[1]

							return expected_height, expected_width





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       __snake_case						, unittest.TestCase							):
			snake_case__	=					BridgeTowerImageProcessor if is_vision_available() else None






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Optional[Any]:
							a_		:	Union[str, Any]									=   BridgeTowerImageProcessingTester(self       )






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      Dict:
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__lowerCamelCase	,    '''image_mean'''       )       )
							self.assertTrue(hasattr(__lowerCamelCase	,    '''image_std'''       )       )
							self.assertTrue(hasattr(__lowerCamelCase	,    '''do_normalize'''       )       )
							self.assertTrue(hasattr(__lowerCamelCase	,    '''do_resize'''       )       )
							self.assertTrue(hasattr(__lowerCamelCase	,    '''size'''       )       )
							self.assertTrue(hasattr(__lowerCamelCase	,    '''size_divisor'''       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Union[str, Any]:
							a_		:	Optional[int]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							a_		:	Dict									=   prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase       )
							for image in image_inputs:
											self.assertIsInstance(__lowerCamelCase	,    Image.Image       )

							# Test not batched input
							a_		:	Optional[int]									=   image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	Optional[Any]									=   self.image_processor_tester.get_expected_values(__lowerCamelCase       )
							self.assertEqual(
							    encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

							# Test batched
							a_		:	Tuple									=   image_processing(__lowerCamelCase	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	List[str]									=   self.image_processor_tester.get_expected_values(__lowerCamelCase	,    batched=__lowerCamelCase       )
							self.assertEqual(
							    encoded_images.shape	,    (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,    )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							a_		:	str									=   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
							a_		:	Union[str, Any]									=   image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	int									=   self.image_processor_tester.get_expected_values(__lowerCamelCase       )
							self.assertEqual(
							    encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

							# Test batched
							a_		:	Optional[int]									=   image_processing(__lowerCamelCase	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	int									=   self.image_processor_tester.get_expected_values(__lowerCamelCase	,    batched=__lowerCamelCase       )
							self.assertEqual(
							    encoded_images.shape	,    (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,    )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[Any]:
							a_		:	str									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							a_		:	Dict									=   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
							a_		:	Dict									=   image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	int									=   self.image_processor_tester.get_expected_values(__lowerCamelCase       )
							self.assertEqual(
							    encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

							# Test batched
							a_		:	int									=   image_processing(__lowerCamelCase	,    return_tensors='''pt'''       ).pixel_values

							a_					,      a_		:	Any									=   self.image_processor_tester.get_expected_values(__lowerCamelCase	,    batched=__lowerCamelCase       )
							self.assertEqual(
							    encoded_images.shape	,    (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,    )








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 	__lowercase					(	__snake_case	):





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






         """simple docstring"""







         super().__init__(*__lowerCamelCase       ,							**__lowerCamelCase							)
         UpperCAmelCase      					=  eval_examples
         UpperCAmelCase      					=  post_process_function





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       int=None       ,							__lowerCamelCase    :       int=None       ,							__lowerCamelCase    :       Tuple=None       ,							__lowerCamelCase    :       str = "eval"							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.eval_dataset if eval_dataset is None else eval_dataset
         UpperCAmelCase      					=  self.get_eval_dataloader(__lowerCamelCase							)
         UpperCAmelCase      					=  self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         UpperCAmelCase      					=  self.compute_metrics
         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         UpperCAmelCase      					=  time.time()
         try:
            UpperCAmelCase      					=  eval_loop(
                __lowerCamelCase       ,							description="""Evaluation"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=__lowerCamelCase       ,							metric_key_prefix=__lowerCamelCase       ,							)
         finally:
            UpperCAmelCase      					=  compute_metrics
         UpperCAmelCase      					=  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
            UpperCAmelCase      					=  self.post_process_function(__lowerCamelCase       ,							__lowerCamelCase       ,							output.predictions							)
            UpperCAmelCase      					=  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}_"""							):
                  UpperCAmelCase      					=  metrics.pop(__lowerCamelCase							)
            metrics.update(output.metrics							)
         else:
            UpperCAmelCase      					=  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()							)

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





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Dict=None       ,							__lowerCamelCase    :       str = "test"							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_test_dataloader(__lowerCamelCase							)

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

         UpperCAmelCase      					=  self.post_process_function(__lowerCamelCase       ,							__lowerCamelCase       ,							output.predictions       ,							"""predict"""							)
         UpperCAmelCase      					=  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}_"""							):
               UpperCAmelCase      					=  metrics.pop(__lowerCamelCase							)
         metrics.update(output.metrics							)
         return PredictionOutput(predictions=predictions.predictions       ,							label_ids=predictions.label_ids       ,							metrics=__lowerCamelCase							)



