






"""simple docstring"""







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
			a :				Optional[Any]      					=		[]

			a :				Any      					=		os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
			a :				List[Any]      					=		glob.glob(pattern)
			# TODO: deal with TF/Flax too
			a :				int      					=		[
			    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:
						a :				Dict      					=		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:
												a :				str      					=		tester_class(parent=None)
									except Exception:
												continue
									if hasattr(tester, """get_config"""):
												a :				Any      					=		tester.get_config()
												for k, v in config.to_dict().items():
															if isinstance(v, int):
																		a :				Union[str, Any]      					=		None
																		if k in ["vocab_size"]:
																					a :				Optional[int]      					=		100
																		elif k in ["max_position_embeddings"]:
																					a :				str      					=		128
																		elif k in ["hidden_size", "d_model"]:
																					a :				Union[str, Any]      					=		40
																		elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																					a :				List[Any]      					=		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))














"""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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=7       ,  snake_case__=3       ,  snake_case__=18       ,  snake_case__=30       ,  snake_case__=400       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,  snake_case__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,  snake_case__=True       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       size if size is not None else {"shortest_edge": 20}
												lowercase__				:      int=       crop_size if crop_size is not None else {"height": 18, "width": 18}
												lowercase__				:      Union[str, Any]=       parent
												lowercase__				:      Optional[Any]=       batch_size
												lowercase__				:      List[str]=       num_channels
												lowercase__				:      str=       image_size
												lowercase__				:      Optional[int]=       min_resolution
												lowercase__				:      Union[str, Any]=       max_resolution
												lowercase__				:      List[Any]=       do_resize
												lowercase__				:      Any=       size
												lowercase__				:      Optional[Any]=       do_center_crop
												lowercase__				:      List[str]=       crop_size
												lowercase__				:      List[Any]=       do_normalize
												lowercase__				:      Optional[Any]=       image_mean
												lowercase__				:      Dict=       image_std
												lowercase__				:      Dict=       do_convert_rgb




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_convert_rgb": self.do_convert_rgb,
												}




						def 		UpperCAmelCase_       (   self       ,  snake_case__=False       ,  snake_case__=False       ,  snake_case__=False			):
												'''simple docstring'''
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								image_inputs.append(
																								    np.random.randint(
																								        255       ,  size=(self.num_channels, self.max_resolution, self.max_resolution)       ,  dtype=np.uinta			)			)
												else:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								lowercase__,	lowercase__				:      str=       np.random.choice(np.arange(self.min_resolution       ,  self.max_resolution			)       ,  2			)
																								image_inputs.append(np.random.randint(255       ,  size=(self.num_channels, width, height)       ,  dtype=np.uinta			)			)

												if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		lowercase__				:      Dict=       [Image.fromarray(np.moveaxis(snake_case__       ,  0       ,  -1			)			) for x in image_inputs]

												if torchify:
																		lowercase__				:      Tuple=       [torch.from_numpy(snake_case__			) for x in image_inputs]

												return image_inputs


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       CLIPImageProcessingTester(self			)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Dict=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class.from_dict(self.image_processor_dict			)
												self.assertEqual(image_processor.size       ,  {"shortest_edge": 20}			)
												self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}			)

												lowercase__				:      Dict=       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			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      Optional[Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      List[str]=       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
												lowercase__				:      Dict=       image_processing(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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Any=       self.image_processing_class(**self.image_processor_dict			)
												# create random numpy tensors
												lowercase__				:      Tuple=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  numpify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  np.ndarray			)

												# Test not batched input
												lowercase__				:      Dict=       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
												lowercase__				:      int=       image_processing(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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Optional[Any]=       self.image_processing_class(**self.image_processor_dict			)
												# create random PyTorch tensors
												lowercase__				:      Union[str, Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  torchify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  torch.Tensor			)

												# Test not batched input
												lowercase__				:      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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Union[str, Any]=       image_processing(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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       CLIPImageProcessingTester(self       ,  num_channels=4			)
												lowercase__				:      Optional[Any]=       3




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      int=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      Optional[int]=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Any=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )






