
import copy
import re





class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					"hp"
	snake_case_    =					{}
	snake_case_    =					None






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			Dict							,A :			Any			):
					__A             =			prefix
					__A             =			defaults
					cls.build_naming_info()






	@staticmethod
	def     UpperCamelCase_  (	A :			Dict							,A :			int			):
					if len(A			) == 0:
									return ""
					__A             =			None
					if any(char.isdigit() for char in word			):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''			)
					if word in info["short_word"]:
									return info["short_word"][word]
					for prefix_len in range(1							,len(A			) + 1			):
									__A             =			word[:prefix_len]
									if prefix in info["reverse_short_word"]:
													continue
									else:
													__A             =			prefix
													break

					if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(A :			str			):
													__A             =			""
													while integer != 0:
																	__A             =			chr(ord("A"			) + integer % 10			) + s
																	integer //= 10
													return s

									__A             =			0
									while True:
													__A             =			word + "#" + int_to_alphabetic(A			)
													if sword in info["reverse_short_word"]:
																	continue
													else:
																	__A             =			sword
																	break

					__A             =			short_word
					__A             =			word
					return short_word






	@staticmethod
	def     UpperCamelCase_  (	A :			int							,A :			Tuple			):
					__A             =			param_name.split("_"			)

					__A             =			[TrialShortNamer.shortname_for_word(A							,A			) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					__A             =			["", "_"]

					for separator in separators:
									__A             =			separator.join(A			)
									if shortname not in info["reverse_short_param"]:
													__A             =			shortname
													__A             =			param_name
													return shortname

					return param_name






	@staticmethod
	def     UpperCamelCase_  (	A :			Optional[Any]							,A :			Tuple			):
					__A             =			TrialShortNamer.shortname_for_key(A							,A			)
					__A             =			short_name
					__A             =			param_name






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict			):
					if cls.NAMING_INFO is not None:
									return

					__A             =			{
					    "short_word": {},
					    "reverse_short_word": {},
					    "short_param": {},
					    "reverse_short_param": {},
					}

					__A             =			list(cls.DEFAULTS.keys()			)

					for k in field_keys:
									cls.add_new_param_name(A							,A			)

					__A             =			info






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			List[str]			):
					cls.build_naming_info()
					assert cls.PREFIX is not None
					__A             =			[copy.copy(cls.PREFIX			)]

					for k, v in params.items():
									if k not in cls.DEFAULTS:
													raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''			)
									if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

									__A             =			cls.NAMING_INFO["short_param"][k]

									if isinstance(A							,A			):
													__A             =			1 if v else 0

									__A             =			"" if isinstance(A							,(int, float)			) else "-"
									__A             =			f'''{key}{sep}{v}'''
									name.append(A			)

					return "_".join(A			)







	@classmethod
	def     UpperCamelCase_  (	cls :			Tuple							,A :			Tuple			):
					__A             =			repr[len(cls.PREFIX			) + 1 :]
					if repr == "":
									__A             =			[]
					else:
									__A             =			repr.split("_"			)

					__A             =			{}

					for value in values:
									if "-" in value:
													__A      ,							__A             =			value.split("-"			)
									else:
													__A             =			re.sub("[0-9.]"							,""							,A			)
													__A             =			float(re.sub("[^0-9.]"							,""							,A			)			)

									__A             =			cls.NAMING_INFO["reverse_short_param"][p_k]

									__A             =			p_v

					for k in cls.DEFAULTS:
									if k not in parameters:
													__A             =			cls.DEFAULTS[k]

					return parameters


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 PoolFormerImageProcessor





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Optional[int]							,A :			List[str]							,A :			List[Any]=7							,A :			Any=3							,A :			int=30							,A :			List[Any]=4_00							,A :			str=True							,A :			int=None							,A :			List[str]=0.9							,A :			Dict=None							,A :			int=True							,A :			Any=[0.5, 0.5, 0.5]							,A :			Optional[int]=[0.5, 0.5, 0.5]							,):
					__A             =			size if size is not None else {"shortest_edge": 30}
					__A             =			crop_size if crop_size is not None else {"height": 30, "width": 30}
					__A             =			parent
					__A             =			batch_size
					__A             =			num_channels
					__A             =			min_resolution
					__A             =			max_resolution
					__A             =			do_resize_and_center_crop
					__A             =			size
					__A             =			crop_pct
					__A             =			crop_size
					__A             =			do_normalize
					__A             =			image_mean
					__A             =			image_std







	def     UpperCamelCase_  (	self :			int			):
					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}





@require_torch
@require_vision
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					PoolFormerImageProcessor if is_vision_available() else None






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			PoolFormerImageProcessingTester(self			)






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self.image_processor_tester.prepare_image_processor_dict()






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(A							,"do_resize_and_center_crop"			)			)
					self.assertTrue(hasattr(A							,"size"			)			)
					self.assertTrue(hasattr(A							,"crop_pct"			)			)
					self.assertTrue(hasattr(A							,"do_normalize"			)			)
					self.assertTrue(hasattr(A							,"image_mean"			)			)
					self.assertTrue(hasattr(A							,"image_std"			)			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size							,{"shortest_edge": 30}			)
					self.assertEqual(image_processor.crop_size							,{"height": 30, "width": 30}			)

					__A             =			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 :			List[str]			):
					pass






	def     UpperCamelCase_  (	self :			Optional[int]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__A             =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image			)

					# Test not batched input
					__A             =			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
					__A             =			image_processing(A							,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 :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__A             =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=A							,numpify=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,np.ndarray			)

					# Test not batched input
					__A             =			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
					__A             =			image_processing(A							,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 :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__A             =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=A							,torchify=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,torch.Tensor			)

					# Test not batched input
					__A             =			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
					__A             =			image_processing(A							,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"],
					    )							,)
