






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    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				def __init__(      self    :    str			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    int=1_3			,		UpperCamelCase__    :    Optional[Any]=3			,		UpperCamelCase__    :    List[str]=2_2_4			,		UpperCamelCase__    :    List[Any]=3_0			,		UpperCamelCase__    :    Optional[int]=4_0_0			,		UpperCamelCase__    :    List[Any]=True			,		UpperCamelCase__    :    Dict=None			,		UpperCamelCase__    :    Union[str, Any]=True			,		UpperCamelCase__    :    Dict=[0.5, 0.5, 0.5]			,		UpperCamelCase__    :    int=[0.5, 0.5, 0.5]			,		):






											'''simple docstring'''







											snake_case__	  =       size if size is not None else {"""height""": 1_8, """width""": 1_8}
											snake_case__	  =       parent
											snake_case__	  =       batch_size
											snake_case__	  =       num_channels
											snake_case__	  =       image_size
											snake_case__	  =       min_resolution
											snake_case__	  =       max_resolution
											snake_case__	  =       do_resize
											snake_case__	  =       size
											snake_case__	  =       do_normalize
											snake_case__	  =       image_mean
											snake_case__	  =       image_std



				def 					__magic_name__							(      self    :    int):






											'''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    _lowerCAmelCase			(		lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   List[str]      =  ViTImageProcessor if is_vision_available() else None



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       EfficientFormerImageProcessorTester(self)



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return self.image_proc_tester.prepare_image_processor_dict()



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       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__			,		"""size"""))



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											snake_case__	  =       prepare_image_inputs(self.image_proc_tester			,		equal_resolution=UpperCamelCase__)
											for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__			,		Image.Image)

											# Test not batched input
											snake_case__	  =       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__	  =       image_processor(UpperCamelCase__			,		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 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											snake_case__	  =       prepare_image_inputs(self.image_proc_tester			,		equal_resolution=UpperCamelCase__			,		numpify=UpperCamelCase__)
											for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__			,		np.ndarray)

											# Test not batched input
											snake_case__	  =       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__	  =       image_processor(UpperCamelCase__			,		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 					__magic_name__							(      self    :    str):






											'''simple docstring'''







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

											# Test not batched input
											snake_case__	  =       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__	  =       image_processor(UpperCamelCase__			,		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"""],
											    )			,		)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
