

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=True					,						_A=None					,						**_A							):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							config_class
												__lowerCAmelCase					=							has_text_modality
												__lowerCAmelCase					=							kwargs
												__lowerCAmelCase					=							common_properties




							def        A__  ( self							):
												__lowerCAmelCase					=							self.config_class(**self.inputs_dict							)
												__lowerCAmelCase					=							(
												    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
												    if self.common_properties is None
												    else self.common_properties
												)

												# Add common fields for text models
												if self.has_text_modality:
																	common_properties.extend(['vocab_size']							)

												# Test that config has the common properties as getters
												for prop in common_properties:
																	self.parent.assertTrue(hasattr(_A					,						_A							)					,						msg=F"""`{prop}` does not exist"""							)

												# Test that config has the common properties as setter
												for idx, name in enumerate(_A							):
																	try:
																						setattr(_A					,						_A					,						_A							)
																						self.parent.assertEqual(
																						    getattr(_A					,						_A							)					,						_A					,						msg=F"""`{name} value {idx} expected, but was {getattr(_A					,						_A							)}"""							)
																	except NotImplementedError:
																						# Some models might not be able to implement setters for common_properties
																						# In that case, a NotImplementedError is raised
																						pass

        # Test if config class can be called with Config(prop_name=..)
												for idx, name in enumerate(_A							):
																	try:
																						__lowerCAmelCase					=							self.config_class(**{name: idx}							)
																						self.parent.assertEqual(
																						    getattr(_A					,						_A							)					,						_A					,						msg=F"""`{name} value {idx} expected, but was {getattr(_A					,						_A							)}"""							)
																	except NotImplementedError:
																						# Some models might not be able to implement setters for common_properties
																						# In that case, a NotImplementedError is raised
																						pass




							def        A__  ( self							):
												__lowerCAmelCase					=							self.config_class(**self.inputs_dict							)
												__lowerCAmelCase					=							json.loads(config.to_json_string()							)
												for key, value in self.inputs_dict.items():
																	self.parent.assertEqual(obj[key]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.config_class(**self.inputs_dict							)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	__lowerCAmelCase					=							os.path.join(_A					,						'config.json'							)
																	config_first.to_json_file(_A							)
																	__lowerCAmelCase					=							self.config_class.from_json_file(_A							)

												self.parent.assertEqual(config_second.to_dict()					,						config_first.to_dict()							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.config_class(**self.inputs_dict							)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	config_first.save_pretrained(_A							)
																	__lowerCAmelCase					=							self.config_class.from_pretrained(_A							)

												self.parent.assertEqual(config_second.to_dict()					,						config_first.to_dict()							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.config_class(**self.inputs_dict							)

												__lowerCAmelCase					=							'test'
												with tempfile.TemporaryDirectory() as tmpdirname:
																	__lowerCAmelCase					=							os.path.join(_A					,						_A							)
																	config_first.save_pretrained(_A							)
																	__lowerCAmelCase					=							self.config_class.from_pretrained(_A					,						subfolder=_A							)

												self.parent.assertEqual(config_second.to_dict()					,						config_first.to_dict()							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.config_class(**self.inputs_dict					,						num_labels=5							)
												self.parent.assertEqual(len(config.idalabel							)					,						5							)
												self.parent.assertEqual(len(config.labelaid							)					,						5							)

												__lowerCAmelCase					=							3
												self.parent.assertEqual(len(config.idalabel							)					,						3							)
												self.parent.assertEqual(len(config.labelaid							)					,						3							)




							def        A__  ( self							):
												if self.config_class.is_composition:
																	return
												__lowerCAmelCase					=							self.config_class()
												self.parent.assertIsNotNone(_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							copy.deepcopy(_A							)
												__lowerCAmelCase					=							self.config_class(**_A							)
												__lowerCAmelCase					=							[]
												for key, value in config_common_kwargs.items():
																	if key == "torch_dtype":
																						if not is_torch_available():
																											continue
																						else:
																											import torch

																											if config.torch_dtype != torch.floataa:
																																wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)							)
																	elif getattr(_A					,						_A							) != value:
																						wrong_values.append((key, getattr(_A					,						_A							), value)							)

												if len(_A							) > 0:
																	__lowerCAmelCase					=							'\n'.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]							)
																	raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""							)




							def        A__  ( self							):
												self.create_and_test_config_common_properties()
												self.create_and_test_config_to_json_string()
												self.create_and_test_config_to_json_file()
												self.create_and_test_config_from_and_save_pretrained()
												self.create_and_test_config_from_and_save_pretrained_subfolder()
												self.create_and_test_config_with_num_labels()
												self.check_config_can_be_init_without_params()
												self.check_config_arguments_init()









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     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3					,						_A=2_2_4					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( self							):
												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     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =ViTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							EfficientFormerImageProcessorTester(self							)




							@property
							def        A__  ( self							):
												return self.image_proc_tester.prepare_image_processor_dict()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												self.assertTrue(hasattr(_A					,						'image_mean'							)							)
												self.assertTrue(hasattr(_A					,						'image_std'							)							)
												self.assertTrue(hasattr(_A					,						'do_normalize'							)							)
												self.assertTrue(hasattr(_A					,						'do_resize'							)							)
												self.assertTrue(hasattr(_A					,						'size'							)							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						Image.Image							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						numpify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						np.ndarray							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						torchify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						torch.Tensor							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)






