



'''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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=7    ,a_=3    ,a_=18    ,a_=30    ,a_=400    ,a_=True    ,a_=None    ,a_=True    ,a_=False    ,a_=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,a_=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,a_=True    ,)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					size if size is not None else {"""height""": 20, """width""": 20}
        _UpperCAmelCase    :		Dict     	=					parent
        _UpperCAmelCase    :		Tuple     	=					batch_size
        _UpperCAmelCase    :		Optional[int]     	=					num_channels
        _UpperCAmelCase    :		int     	=					image_size
        _UpperCAmelCase    :		List[Any]     	=					min_resolution
        _UpperCAmelCase    :		str     	=					max_resolution
        _UpperCAmelCase    :		Tuple     	=					do_resize
        _UpperCAmelCase    :		Union[str, Any]     	=					size
        _UpperCAmelCase    :		int     	=					do_normalize
        _UpperCAmelCase    :		List[str]     	=					image_mean
        _UpperCAmelCase    :		Any     	=					image_std
        _UpperCAmelCase    :		Union[str, Any]     	=					do_pad
        _UpperCAmelCase    :		Tuple     	=					do_convert_rgb







 def _snake_case					(				self				)						->  List[str]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }







 def _snake_case					(				self    ,a_=False    ,a_=False    ,a_=False				)						->  Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCAmelCase    :		Union[str, Any]     	=					[]
               for i in range(self.batch_size				):
                      image_inputs.append(
                          np.random.randint(
                              255    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta				)				)
        else:
               _UpperCAmelCase    :		Optional[int]     	=					[]
               for i in range(self.batch_size				):
                      _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					np.random.choice(np.arange(self.min_resolution    ,self.max_resolution				)    ,2				)
                      image_inputs.append(np.random.randint(255    ,size=(self.num_channels, width, height)    ,dtype=np.uinta				)				)

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               _UpperCAmelCase    :		Any     	=					[Image.fromarray(np.moveaxis(a_    ,0    ,-1				)				) for x in image_inputs]

        if torchify:
               _UpperCAmelCase    :		Optional[int]     	=					[torch.from_numpy(a_				) for x in image_inputs]

        return image_inputs







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


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		Dict     	=					BlipImageProcessingTester(self				)







 @property
 def _snake_case					(				self				)						->  str:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Any:
        pass







 def _snake_case					(				self				)						->  Union[str, Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[str]     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		List[str]     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







 def _snake_case					(				self				)						->  Optional[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		str     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_    ,numpify=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,np.ndarray				)

        # Test not batched input
        _UpperCAmelCase    :		Dict     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		int     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







 def _snake_case					(				self				)						->  List[str]:
        # Initialize image_processor
        _UpperCAmelCase    :		Dict     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        _UpperCAmelCase    :		Union[str, Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_    ,torchify=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,torch.Tensor				)

        # Test not batched input
        _UpperCAmelCase    :		str     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Optional[Any]     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







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


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					BlipImageProcessingTester(self    ,num_channels=4				)
        _UpperCAmelCase    :		List[Any]     	=					3







 @property
 def _snake_case					(				self				)						->  int:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Union[str, Any]:
        pass







 def _snake_case					(				self				)						->  List[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		Any     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[Any]     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Dict     	=					image_processor(a_    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)




# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
