
"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase								=						{
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["EfficientFormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EfficientFormerForImageClassification",
							    "EfficientFormerForImageClassificationWithTeacher",
							    "EfficientFormerModel",
							    "EfficientFormerPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEfficientFormerForImageClassification",
							    "TFEfficientFormerForImageClassificationWithTeacher",
							    "TFEfficientFormerModel",
							    "TFEfficientFormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_efficientformer import EfficientFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_efficientformer import (
														    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EfficientFormerForImageClassification,
														    EfficientFormerForImageClassificationWithTeacher,
														    EfficientFormerModel,
														    EfficientFormerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_efficientformer import (
														    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEfficientFormerForImageClassification,
														    TFEfficientFormerForImageClassificationWithTeacher,
														    TFEfficientFormerModel,
														    TFEfficientFormerPreTrainedModel,
														)

else:
							import sys

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

import unittest

import numpy as np
import requests

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

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					SCREAMING_SNAKE_CASE__   :			str												=    False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]=7							,			SCREAMING_SNAKE_CASE__		:							List[str]=3							,			SCREAMING_SNAKE_CASE__		:							Tuple=18							,			SCREAMING_SNAKE_CASE__		:							int=30							,			SCREAMING_SNAKE_CASE__		:							Any=4_00							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			)     ->							Union[str, Any]:
						__lowerCamelCase    					=	size if size is not None else {'''height''': 20, '''width''': 20}
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	min_resolution
						__lowerCamelCase    					=	max_resolution
						__lowerCamelCase    					=	size
						__lowerCamelCase    					=	do_normalize
						__lowerCamelCase    					=	do_convert_rgb
						__lowerCamelCase    					=	[5_12, 10_24, 20_48, 40_96]
						__lowerCamelCase    					=	patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}






		def  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def  __A       (		self		:							int  )     ->							Any:
						__lowerCamelCase    					=	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
						__lowerCamelCase    					=	Image.open(requests.get(SCREAMING_SNAKE_CASE__							,			stream=SCREAMING_SNAKE_CASE__  ).raw  ).convert('''RGB'''  )
						return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Dict        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self  )






		@property
		def  __A       (		self		:							Optional[int]  )     ->							Optional[int]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )






		def  __A       (		self		:							int  )     ->							str:
						__lowerCamelCase    					=	self.image_processor_tester.prepare_dummy_image()

						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						__lowerCamelCase    					=	20_48

						__lowerCamelCase    					=	image_processor(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  )
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,			torch.tensor(0.0606  )							,			atol=1e-3							,			rtol=1e-3  )  )






		def  __A       (		self		:							Union[str, Any]  )     ->							Dict:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							Dict  )     ->							str:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						__lowerCamelCase    					=	True

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
														__lowerCamelCase    					=	image_processor(
														    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches

										__lowerCamelCase    					=	'''Hello'''

										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							List[str]  )     ->							Any:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			numpify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			np.ndarray  )

						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)







		def  __A       (		self		:							Tuple  )     ->							List[str]:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			torchify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			torch.Tensor  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Any        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self							,			num_channels=4  )
						__lowerCamelCase    					=	3






		@property
		def  __A       (		self		:							List[Any]  )     ->							Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )







		def  __A       (		self		:							Optional[int]  )     ->							str:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)



