





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 ViltImageProcessor

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=7						,	_lowerCAmelCase=3						,	_lowerCAmelCase=18						,	_lowerCAmelCase=30						,	_lowerCAmelCase=400						,	_lowerCAmelCase=True						,	_lowerCAmelCase=None						,	_lowerCAmelCase=2						,	_lowerCAmelCase=True						,	_lowerCAmelCase=[0.5, 0.5, 0.5]						,	_lowerCAmelCase=[0.5, 0.5, 0.5]						,	):
										lowerCamelCase__      =							size if size is not None else {"shortest_edge": 30}
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							min_resolution
										lowerCamelCase__      =							max_resolution
										lowerCamelCase__      =							do_resize
										lowerCamelCase__      =							size
										lowerCamelCase__      =							size_divisor
										lowerCamelCase__      =							do_normalize
										lowerCamelCase__      =							image_mean
										lowerCamelCase__      =							image_std





					def        __magic_name__			(			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,
										    "size_divisor": self.size_divisor,
										}






					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=False						):
										if not batched:
															lowerCamelCase__      =							self.size["shortest_edge"]
															lowerCamelCase__      =							image_inputs[0]
															if isinstance(_lowerCAmelCase						,	Image.Image						):
																				lowerCamelCase__     ,		lowerCamelCase__      =							image.size
															else:
																				lowerCamelCase__     ,		lowerCamelCase__      =							image.shape[1], image.shape[2]
															lowerCamelCase__      =							size / min(_lowerCAmelCase						,	_lowerCAmelCase						)
															if h < w:
																				lowerCamelCase__     ,		lowerCamelCase__      =							size, scale * w
															else:
																				lowerCamelCase__     ,		lowerCamelCase__      =							scale * h, size

															lowerCamelCase__      =							int((1333 / 800) * size						)
															if max(_lowerCAmelCase						,	_lowerCAmelCase						) > max_size:
																				lowerCamelCase__      =							max_size / max(_lowerCAmelCase						,	_lowerCAmelCase						)
																				lowerCamelCase__      =							newh * scale
																				lowerCamelCase__      =							neww * scale

															lowerCamelCase__     ,		lowerCamelCase__      =							int(newh + 0.5						), int(neww + 0.5						)
															lowerCamelCase__     ,		lowerCamelCase__      =							(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

										else:
															lowerCamelCase__      =							[]
															for image in image_inputs:
																				lowerCamelCase__     ,		lowerCamelCase__      =							self.get_expected_values([image]						)
																				expected_values.append((expected_height, expected_width)						)
															lowerCamelCase__      =							max(_lowerCAmelCase						,	key=lambda _lowerCAmelCase						: item[0]						)[0]
															lowerCamelCase__      =							max(_lowerCAmelCase						,	key=lambda _lowerCAmelCase						: item[1]						)[1]

										return expected_height, expected_width

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     ViltImageProcessor if is_vision_available() else None





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ViltImageProcessingTester(self						)





					@property
					def        __magic_name__			(			self						):
										return self.image_processor_tester.prepare_image_processor_dict()





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"image_mean"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"image_std"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"do_normalize"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"do_resize"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"size"						)						)
										self.assertTrue(hasattr(_lowerCAmelCase						,	"size_divisor"						)						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.image_processing_class.from_dict(self.image_processor_dict						)
										self.assertEqual(image_processor.size						,	{"shortest_edge": 30}						)

										lowerCamelCase__      =							self.image_processing_class.from_dict(self.image_processor_dict						,	size=42						)
										self.assertEqual(image_processor.size						,	{"shortest_edge": 42}						)





					def        __magic_name__			(			self						):
										pass





					def        __magic_name__			(			self						):
										# Initialize image_processing
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										lowerCamelCase__      =							prepare_image_inputs(self.image_processor_tester						,	equal_resolution=_lowerCAmelCase						)
										for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase						,	Image.Image						)

										# Test not batched input
										lowerCamelCase__      =							image_processing(image_inputs[0]						,	return_tensors="pt"						).pixel_values

										lowerCamelCase__     ,		lowerCamelCase__      =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						)
										self.assertEqual(
										    encoded_images.shape						,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,	)

										# Test batched
										lowerCamelCase__      =							image_processing(_lowerCAmelCase						,	return_tensors="pt"						).pixel_values

										lowerCamelCase__     ,		lowerCamelCase__      =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						,	batched=_lowerCAmelCase						)
										self.assertEqual(
										    encoded_images.shape						,	(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,	)





					def        __magic_name__			(			self						):
										# Initialize image_processing
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										lowerCamelCase__      =							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
										lowerCamelCase__      =							image_processing(image_inputs[0]						,	return_tensors="pt"						).pixel_values

										lowerCamelCase__     ,		lowerCamelCase__      =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						)
										self.assertEqual(
										    encoded_images.shape						,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,	)

										# Test batched
										lowerCamelCase__      =							image_processing(_lowerCAmelCase						,	return_tensors="pt"						).pixel_values

										lowerCamelCase__     ,		lowerCamelCase__      =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						,	batched=_lowerCAmelCase						)
										self.assertEqual(
										    encoded_images.shape						,	(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,	)






					def        __magic_name__			(			self						):
										# Initialize image_processing
										lowerCamelCase__      =							self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										lowerCamelCase__      =							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
										lowerCamelCase__      =							image_processing(image_inputs[0]						,	return_tensors="pt"						).pixel_values

										lowerCamelCase__     ,		lowerCamelCase__      =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						)
										self.assertEqual(
										    encoded_images.shape						,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,	)

										# Test batched
										lowerCamelCase__      =							image_processing(_lowerCAmelCase						,	return_tensors="pt"						).pixel_values

										lowerCamelCase__     ,		lowerCamelCase__      =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						,	batched=_lowerCAmelCase						)
										self.assertEqual(
										    encoded_images.shape						,	(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )						,	)








from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=32						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[10, 20, 30, 40]						,	_lowerCAmelCase=[2, 2, 3, 2]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=10						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							num_stages
										lowerCamelCase__      =							hidden_sizes
										lowerCamelCase__      =							depths
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							intermediate_size
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							type_sequence_label_size
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

										lowerCamelCase__      =							None
										if self.use_labels:
															lowerCamelCase__      =							ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)

										lowerCamelCase__      =							self.get_config()

										return config, pixel_values, labels





					def        __magic_name__			(			self						):
										return ConvNextConfig(
										    num_channels=self.num_channels						,	hidden_sizes=self.hidden_sizes						,	depths=self.depths						,	num_stages=self.num_stages						,	hidden_act=self.hidden_act						,	is_decoder=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFConvNextModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	training=_lowerCAmelCase						)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.type_sequence_label_size
										lowerCamelCase__      =							TFConvNextForImageClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						,	training=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__      =							config_and_inputs
										lowerCamelCase__      =							{"pixel_values": pixel_values}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					A__								=     (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModelTester(self						)
										lowerCamelCase__      =							ConfigTester(
										    self						,	config_class=_lowerCAmelCase						,	has_text_modality=_lowerCAmelCase						,	hidden_size=37						,	)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
					def        __magic_name__			(			self						):
										pass





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					@slow
					def        __magic_name__			(			self						):
										super().test_keras_fit()





					@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
					def        __magic_name__			(			self						):
										pass





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															lowerCamelCase__      =							inspect.signature(model.call						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															lowerCamelCase__      =							[*signature.parameters.keys()]

															lowerCamelCase__      =							["pixel_values"]
															self.assertListEqual(arg_names[:1]						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					def        __magic_name__			(			self						):
										super().test_dataset_conversion()





					def        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							model(**self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)						)
															lowerCamelCase__      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowerCamelCase__      =							self.model_tester.num_stages
															self.assertEqual(len(_lowerCAmelCase						)						,	expected_num_stages + 1						)

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)						,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,	)

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							True
															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCamelCase__      =							True

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase={}						):
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						).to_tuple()

															def recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						):
																				if isinstance(_lowerCAmelCase						,	(List, Tuple)						):
																									for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase						,	_lowerCAmelCase						):
																														recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)
																				elif tuple_object is None:
																									return
																				else:
																									self.assertTrue(
																									    all(tf.equal(_lowerCAmelCase						,	_lowerCAmelCase						)						)						,	msg=(
																									        "Tuple and dict output are not equal. Difference:"
																									        F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"
																									    )						,	)

																				recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->List[str]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@cached_property
					def        __magic_name__			(			self						):
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										lowerCamelCase__      =							self.default_image_processor
										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="tf"						)

										# forward pass
										lowerCamelCase__      =							model(**_lowerCAmelCase						)

										# verify the logits
										lowerCamelCase__      =							tf.TensorShape((1, 1000)						)
										self.assertEqual(outputs.logits.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant([-0.0_260, -0.4_739, 0.1_911]						)

										tf.debugging.assert_near(outputs.logits[0, :3]						,	_lowerCAmelCase						,	atol=1E-4						)

