


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 DeiTImageProcessor
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				def __init__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[Any]=7      ,      lowerCAmelCase__      :Optional[Any]=3      ,      lowerCAmelCase__      :Optional[Any]=18      ,      lowerCAmelCase__      :Tuple=30      ,      lowerCAmelCase__      :str=400      ,      lowerCAmelCase__      :Dict=True      ,      lowerCAmelCase__      :Optional[Any]=None      ,      lowerCAmelCase__      :Dict=True      ,      lowerCAmelCase__      :Optional[int]=None      ,      lowerCAmelCase__      :int=True      ,      lowerCAmelCase__      :Tuple=[0.5, 0.5, 0.5]      ,      lowerCAmelCase__      :Tuple=[0.5, 0.5, 0.5]      ,      )		->      str:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		size if size is not None else {'''height''': 20, '''width''': 20}
										__SCREAMING_SNAKE_CASE						:					Any		      =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		parent
										__SCREAMING_SNAKE_CASE						:					Dict		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		num_channels
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		image_size
										__SCREAMING_SNAKE_CASE						:					Dict		      =		min_resolution
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		max_resolution
										__SCREAMING_SNAKE_CASE						:					str		      =		do_resize
										__SCREAMING_SNAKE_CASE						:					str		      =		size
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		do_center_crop
										__SCREAMING_SNAKE_CASE						:					int		      =		crop_size
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		do_normalize
										__SCREAMING_SNAKE_CASE						:					str		      =		image_mean
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		image_std



				def 		__magic_name__(	self      :Union[str, Any]     )		->      List[str]:
										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,
										}




@require_torch
@require_vision
class 	_lowercase				(      A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		DeiTImageProcessor if is_vision_available() else None
				SCREAMING_SNAKE_CASE__    :      Dict      =		True


				def 		__magic_name__(	self      :Union[str, Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		DeiTImageProcessingTester(self     )


				@property
				def 		__magic_name__(	self      :Dict     )		->      List[str]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(	self      :Optional[Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_resize'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''size'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_center_crop'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''center_crop'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''do_normalize'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''image_mean'''     )     )
										self.assertTrue(hasattr(lowerCAmelCase__      ,      '''image_std'''     )     )


				def 		__magic_name__(	self      :Tuple     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size      ,      {'''height''': 20, '''width''': 20}     )
										self.assertEqual(image_processor.crop_size      ,      {'''height''': 18, '''width''': 18}     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84     )
										self.assertEqual(image_processor.size      ,      {'''height''': 42, '''width''': 42}     )
										self.assertEqual(image_processor.crop_size      ,      {'''height''': 84, '''width''': 84}     )


				def 		__magic_name__(	self      :Optional[Any]     )		->      List[str]:
										pass


				def 		__magic_name__(	self      :List[str]     )		->      int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase__     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,      Image.Image     )

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:					Union[str, 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
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		image_processing(lowerCAmelCase__      ,      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 		__magic_name__(	self      :List[Any]     )		->      List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase__      ,      numpify=lowerCAmelCase__     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,      np.ndarray     )

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:					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
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		image_processing(lowerCAmelCase__      ,      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 		__magic_name__(	self      :Dict     )		->      str:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:					str		      =		prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase__      ,      torchify=lowerCAmelCase__     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__      ,      torch.Tensor     )

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:					Optional[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
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		image_processing(lowerCAmelCase__      ,      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'''],
										    )      ,      )




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
