



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 MobileNetVaImageProcessor






class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def __init__(     self     :						List[str]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						int=7						,  __UpperCAmelCase     :						List[Any]=3						,  __UpperCAmelCase     :						List[Any]=18						,  __UpperCAmelCase     :						List[Any]=30						,  __UpperCAmelCase     :						List[Any]=400						,  __UpperCAmelCase     :						str=True						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						Optional[Any]=True						,  __UpperCAmelCase     :						Optional[int]=None						,  )      ->			Any:
							UpperCAmelCase_=       size if size is not None else {"""shortest_edge""": 20}
							UpperCAmelCase_=       crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       num_channels
							UpperCAmelCase_=       image_size
							UpperCAmelCase_=       min_resolution
							UpperCAmelCase_=       max_resolution
							UpperCAmelCase_=       do_resize
							UpperCAmelCase_=       size
							UpperCAmelCase_=       do_center_crop
							UpperCAmelCase_=       crop_size




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}






@require_torch
@require_vision
class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: int		       =							MobileNetVaImageProcessor if is_vision_available() else None




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[int]:
							UpperCAmelCase_=       MobileNetVaImageProcessingTester(self					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_resize"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """size"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_center_crop"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """crop_size"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							UpperCAmelCase_=       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}					)

							UpperCAmelCase_=       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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Tuple:
							pass




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			str:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  Image.Image					)

							# Test not batched input
							UpperCAmelCase_=       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
							UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase						,  numpify=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  np.ndarray					)

							# Test not batched input
							UpperCAmelCase_=       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
							UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase						,  torchify=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  torch.Tensor					)

							# Test not batched input
							UpperCAmelCase_=       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
							UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  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"""],
							    )						,  )









import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, 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__):






	"""simple docstring"""


	def __init__(     self     :						Any						,  *__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[int]=None						,  **__UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							super().__init__(*__UpperCAmelCase						,  **__UpperCAmelCase					)
							UpperCAmelCase_=       eval_examples
							UpperCAmelCase_=       post_process_function




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Dataset] = None						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "eval"						,  **__UpperCAmelCase     :						Any						,  )      ->			Dict[str, float]:
							UpperCAmelCase_=       gen_kwargs.copy()
							UpperCAmelCase_=       (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
							)
							UpperCAmelCase_=       (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=       gen_kwargs

							UpperCAmelCase_=       self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_=       self.get_eval_dataloader(__UpperCAmelCase					)
							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_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Evaluation"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							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(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  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(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       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}_"""					):
																									UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)

													metrics.update(output.metrics					)
							else:
													UpperCAmelCase_=       output.metrics

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

							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						,  __UpperCAmelCase					)
							return metrics




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str = "test"						,  **__UpperCAmelCase     :						List[str]					)      ->			Tuple:
							UpperCAmelCase_=       gen_kwargs.copy()

							UpperCAmelCase_=       self.get_test_dataloader(__UpperCAmelCase					)

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Prediction"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							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(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  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(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  """predict"""					)
							UpperCAmelCase_=       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}_"""					):
																			UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)
							metrics.update(output.metrics					)
							return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=__UpperCAmelCase					)




