






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 ViTImageProcessor




class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__=13      ,    a__=3      ,    a__=2_24      ,    a__=30      ,    a__=4_00      ,    a__=True      ,    a__=None      ,    a__=True      ,    a__=[0.5, 0.5, 0.5]      ,    a__=[0.5, 0.5, 0.5]      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :Any 							=    size if size is not None else {"""height""": 18, """width""": 18}
            __snake_case      :List[Any] 							=    parent
            __snake_case      :Optional[int] 							=    batch_size
            __snake_case      :Tuple 							=    num_channels
            __snake_case      :int 							=    image_size
            __snake_case      :Optional[Any] 							=    min_resolution
            __snake_case      :Dict 							=    max_resolution
            __snake_case      :Any 							=    do_resize
            __snake_case      :int 							=    size
            __snake_case      :Any 							=    do_normalize
            __snake_case      :Optional[Any] 							=    image_mean
            __snake_case      :List[str] 							=    image_std



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
            }




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

       '''simple docstring'''
       lowerCamelCase				:					int								=			ViTImageProcessor if is_vision_available() else None



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :List[str] 							=    EfficientFormerImageProcessorTester(self     )



       @property
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            return self.image_proc_tester.prepare_image_processor_dict()



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    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"""     )     )



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.image_processing_class(**self.image_processor_dict     )
            # create random PIL images
            __snake_case      :Any 							=    prepare_image_inputs(self.image_proc_tester      ,    equal_resolution=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_      ,    Image.Image     )

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

            # Test batched
            __snake_case      :Dict 							=    image_processor(lowerCamelCase_      ,    return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,    (
                    self.image_proc_tester.batch_size,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size["""height"""],
                    self.image_proc_tester.size["""width"""],
                )      ,    )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.image_processing_class(**self.image_processor_dict     )
            # create random numpy tensors
            __snake_case      :int 							=    prepare_image_inputs(self.image_proc_tester      ,    equal_resolution=lowerCamelCase_      ,    numpify=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_      ,    np.ndarray     )

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

            # Test batched
            __snake_case      :int 							=    image_processor(lowerCamelCase_      ,    return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,    (
                    self.image_proc_tester.batch_size,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size["""height"""],
                    self.image_proc_tester.size["""width"""],
                )      ,    )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.image_processing_class(**self.image_processor_dict     )
            # create random PyTorch tensors
            __snake_case      :Any 							=    prepare_image_inputs(self.image_proc_tester      ,    equal_resolution=lowerCamelCase_      ,    torchify=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_      ,    torch.Tensor     )

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

            # Test batched
            __snake_case      :Optional[Any] 							=    image_processor(lowerCamelCase_      ,    return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,    (
                    self.image_proc_tester.batch_size,
                    self.image_proc_tester.num_channels,
                    self.image_proc_tester.size["""height"""],
                    self.image_proc_tester.size["""width"""],
                )      ,    )






'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d