



'''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 LevitImageProcessor






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase=7			,				UpperCamelCase=3			,				UpperCamelCase=18			,				UpperCamelCase=30			,				UpperCamelCase=400			,				UpperCamelCase=True			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase=[0.5, 0.5, 0.5]			,				UpperCamelCase=[0.5, 0.5, 0.5]			,				)     ->			Optional[int]:
						__lowerCAmelCase												=  size if size is not None else {"shortest_edge": 18}
						__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_normalize
						__lowerCAmelCase												=  image_mean
						__lowerCAmelCase												=  image_std



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}






@require_torch
@require_vision
class 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     Optional[int]        =							LevitImageProcessor if is_vision_available() else None



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  LevitImageProcessingTester(self     )



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



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict     )
						self.assertTrue(hasattr(UpperCamelCase			,				"image_mean"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"image_std"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"do_normalize"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"do_resize"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"do_center_crop"     )     )
						self.assertTrue(hasattr(UpperCamelCase			,				"size"     )     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  self.image_processing_class.from_dict(self.image_processor_dict     )
						self.assertEqual(image_processor.size			,				{"shortest_edge": 18}     )
						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        UpperCAmelCase_ (    self     )     ->			Tuple:
						pass



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						# Initialize image_processing
						__lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict     )
						# create random PIL images
						__lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester			,				equal_resolution=UpperCamelCase     )
						for image in image_inputs:
							self.assertIsInstance(UpperCamelCase			,				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(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        UpperCAmelCase_ (    self     )     ->			Tuple:
						# Initialize image_processing
						__lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict     )
						# create random numpy tensors
						__lowerCAmelCase												=  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
						__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(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        UpperCAmelCase_ (    self     )     ->			Tuple:
						# Initialize image_processing
						__lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict     )
						# create random PyTorch tensors
						__lowerCAmelCase												=  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
						__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(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"],
						    )			,				)




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase	:		Optional[Any]				=   {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrOCRForCausalLM''',
	    '''TrOCRPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

	lowerCAmelCase	:		Optional[Any]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)