



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					:	Dict		,		__lowerCamelCase					:	Optional[int]		,		__lowerCamelCase					:	str=7		,		__lowerCamelCase					:	Optional[Any]=3		,		__lowerCamelCase					:	List[str]=18		,		__lowerCamelCase					:	Optional[int]=30		,		__lowerCamelCase					:	Optional[Any]=4_00		,		__lowerCamelCase					:	List[str]=True		,		__lowerCamelCase					:	Dict=None		,		__lowerCamelCase					:	int=True		,		__lowerCamelCase					:	str=[0.5, 0.5, 0.5]		,		__lowerCamelCase					:	int=[0.5, 0.5, 0.5]		,		)					-> Tuple:
									A							:    str								=  size if size is not None else {"height": 18, "width": 18}
									A							:    Union[str, Any]								=  parent
									A							:    str								=  batch_size
									A							:    str								=  num_channels
									A							:    str								=  image_size
									A							:    Optional[int]								=  min_resolution
									A							:    Optional[int]								=  max_resolution
									A							:    Optional[Any]								=  do_resize
									A							:    List[Any]								=  size
									A							:    Tuple								=  do_normalize
									A							:    Dict								=  image_mean
									A							:    List[str]								=  image_std







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Optional[Any]       )					-> Optional[int]:
									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_			(       SCREAMING_SNAKE_CASE__  ,unittest.TestCase  ):






						'''simple docstring'''
						a__      =     ViTImageProcessor if is_vision_available() else None







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Optional[Any]       )					-> Dict:
									A							:    Optional[Any]								=  ViTImageProcessingTester(self       )







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







						def 	SCREAMING_SNAKE_CASE__						(		self					:	List[str]       )					-> Tuple:
									A							:    Any								=  self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,		"image_mean"       )       )
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,		"image_std"       )       )
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,		"do_normalize"       )       )
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,		"do_resize"       )       )
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,		"size"       )       )







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Dict       )					-> Optional[int]:
									A							:    Dict								=  self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size		,		{"height": 18, "width": 18}       )

									A							:    Dict								=  self.image_processing_class.from_dict(self.image_processor_dict		,		size=42       )
									self.assertEqual(image_processor.size		,		{"height": 42, "width": 42}       )







						def 	SCREAMING_SNAKE_CASE__						(		self					:	int       )					-> Dict:
									pass







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Dict       )					-> Tuple:
									# Initialize image_processing
									A							:    Dict								=  self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									A							:    str								=  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
									A							:    List[Any]								=  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.size["height"],
									        self.image_processor_tester.size["width"],
									    )		,		)

									# Test batched
									A							:    Dict								=  image_processing(_SCREAMING_SNAKE_CASE		,		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 	SCREAMING_SNAKE_CASE__						(		self					:	Optional[Any]       )					-> int:
									# Initialize image_processing
									A							:    Union[str, Any]								=  self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									A							:    str								=  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       )

									# Test not batched input
									A							:    List[Any]								=  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.size["height"],
									        self.image_processor_tester.size["width"],
									    )		,		)

									# Test batched
									A							:    int								=  image_processing(_SCREAMING_SNAKE_CASE		,		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 	SCREAMING_SNAKE_CASE__						(		self					:	Tuple       )					-> Optional[Any]:
									# Initialize image_processing
									A							:    List[Any]								=  self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									A							:    str								=  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
									A							:    Any								=  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.size["height"],
									        self.image_processor_tester.size["width"],
									    )		,		)

									# Test batched
									A							:    Optional[Any]								=  image_processing(_SCREAMING_SNAKE_CASE		,		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"],
									    )		,		)







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
							lowercase     :							List[Any]	     =			[]

							lowercase     :							Optional[int]	     =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
							lowercase     :							Optional[Any]	     =			glob.glob(pattern)
							# TODO: deal with TF/Flax too
							lowercase     :							Tuple	     =			[
							    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
							]

							for test_file in test_files:
														lowercase     :							Union[str, Any]	     =			get_tester_classes(test_file)
														for tester_class in tester_classes:
																					# A few tester classes don't have `parent` parameter in `__init__`.
																					# TODO: deal this better
																					try:
																												lowercase     :							Union[str, Any]	     =			tester_class(parent=None)
																					except Exception:
																												continue
																					if hasattr(tester, '''get_config'''):
																												lowercase     :							Tuple	     =			tester.get_config()
																												for k, v in config.to_dict().items():
																																			if isinstance(v, int):
																																										lowercase     :							Tuple	     =			None
																																										if k in ["vocab_size"]:
																																																	lowercase     :							str	     =			1_00
																																										elif k in ["max_position_embeddings"]:
																																																	lowercase     :							List[str]	     =			1_28
																																										elif k in ["hidden_size", "d_model"]:
																																																	lowercase     :							Union[str, Any]	     =			40
																																										elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																																	lowercase     :							List[str]	     =			5
																																										if target is not None and v > target:
																																																	failures.append(
																																																	    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																																																	    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																																																	    F""" than {target}."""
																																																	)

							if len(failures) > 0:
														raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))
