


import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    FocalNetBackbone,
						    FocalNetForImageClassification,
						    FocalNetForMaskedImageModeling,
						    FocalNetModel,
						)
						from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=32      ,						snake_case=2      ,						snake_case=3      ,						snake_case=16      ,						snake_case=[32, 64, 128]      ,						snake_case=[1, 2, 1]      ,						snake_case=[2, 2, 4]      ,						snake_case=2      ,						snake_case=2.0      ,						snake_case=True      ,						snake_case=0.0      ,						snake_case=0.0      ,						snake_case=0.1      ,						snake_case="gelu"      ,						snake_case=False      ,						snake_case=True      ,						snake_case=0.02      ,						snake_case=1E-5      ,						snake_case=True      ,						snake_case=None      ,						snake_case=True      ,						snake_case=10      ,						snake_case=8      ,						snake_case=["stage1", "stage2"]      ,						snake_case=[1, 2]      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       patch_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       embed_dim
						UpperCamelCase__					=       hidden_sizes
						UpperCamelCase__					=       depths
						UpperCamelCase__					=       num_heads
						UpperCamelCase__					=       window_size
						UpperCamelCase__					=       mlp_ratio
						UpperCamelCase__					=       qkv_bias
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       drop_path_rate
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       use_absolute_embeddings
						UpperCamelCase__					=       patch_norm
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       scope
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       type_sequence_label_size
						UpperCamelCase__					=       encoder_stride
						UpperCamelCase__					=       out_features
						UpperCamelCase__					=       out_indices




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return FocalNetConfig(
						    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						embed_dim=self.embed_dim      ,						hidden_sizes=self.hidden_sizes      ,						depths=self.depths      ,						num_heads=self.num_heads      ,						window_size=self.window_size      ,						mlp_ratio=self.mlp_ratio      ,						qkv_bias=self.qkv_bias      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						drop_path_rate=self.drop_path_rate      ,						hidden_act=self.hidden_act      ,						use_absolute_embeddings=self.use_absolute_embeddings      ,						path_norm=self.patch_norm      ,						layer_norm_eps=self.layer_norm_eps      ,						initializer_range=self.initializer_range      ,						encoder_stride=self.encoder_stride      ,						out_features=self.out_features      ,						out_indices=self.out_indices      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       FocalNetModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )

						UpperCamelCase__					=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
						UpperCamelCase__					=       int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, expected_seq_len, expected_dim)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       FocalNetBackbone(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps     )      ,						len(config.out_features     )     )
						self.parent.assertListEqual(list(result.feature_maps[0].shape     )      ,						[self.batch_size, self.image_size, 8, 8]     )

						# verify channels
						self.parent.assertEqual(len(model.channels     )      ,						len(config.out_features     )     )
						self.parent.assertListEqual(model.channels      ,						config.hidden_sizes[:-1]     )

						# verify backbone works with out_features=None
						UpperCamelCase__					=       None
						UpperCamelCase__					=       FocalNetBackbone(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps     )      ,						1     )
						self.parent.assertListEqual(list(result.feature_maps[0].shape     )      ,						[self.batch_size, self.image_size * 2, 4, 4]     )

						# verify channels
						self.parent.assertEqual(len(model.channels     )      ,						1     )
						self.parent.assertListEqual(model.channels      ,						[config.hidden_sizes[-1]]     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       FocalNetForMaskedImageModeling(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(
						    result.reconstruction.shape      ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

						# test greyscale images
						UpperCamelCase__					=       1
						UpperCamelCase__					=       FocalNetForMaskedImageModeling(snake_case     )
						model.to(snake_case     )
						model.eval()

						UpperCamelCase__					=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.reconstruction.shape      ,						(self.batch_size, 1, self.image_size, self.image_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.type_sequence_label_size
						UpperCamelCase__					=       FocalNetForImageClassification(snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)     )

						# test greyscale images
						UpperCamelCase__					=       1
						UpperCamelCase__					=       FocalNetForImageClassification(snake_case     )
						model.to(snake_case     )
						model.eval()

						UpperCamelCase__					=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()

						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		(
			    (
			        FocalNetModel,
			        FocalNetForImageClassification,
			        FocalNetForMaskedImageModeling,
			        FocalNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase		:					List[str]    =		(
			    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_UpperCamelCase		:					Union[str, Any]    =		False

			_UpperCamelCase		:					Any    =		False
			_UpperCamelCase		:					Optional[Any]    =		False
			_UpperCamelCase		:					Optional[int]    =		False
			_UpperCamelCase		:					Union[str, Any]    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       FocalNetModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						embed_dim=37      ,						has_text_modality=snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case     )




			@unittest.skip(reason="FocalNet does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="FocalNet does not use feedforward chunking"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes[:-1]:
									UpperCamelCase__					=       model_class(snake_case     )
									self.assertIsInstance(model.get_input_embeddings()      ,						(nn.Module)     )
									UpperCamelCase__					=       model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case      ,						nn.Linear     )     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes[:-1]:
									UpperCamelCase__					=       model_class(snake_case     )
									UpperCamelCase__					=       inspect.signature(model.forward     )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCamelCase__					=       [*signature.parameters.keys()]

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       model_class(snake_case     )
						model.to(snake_case     )
						model.eval()

						with torch.no_grad():
									UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

						UpperCamelCase__					=       outputs.hidden_states

						UpperCamelCase__					=       getattr(
						    self.model_tester      ,						"expected_num_hidden_layers"      ,						len(self.model_tester.depths     ) + 1     )
						self.assertEqual(len(snake_case     )      ,						snake_case     )

						# FocalNet has a different seq_length
						UpperCamelCase__					=       (
						    config.patch_size
						    if isinstance(config.patch_size      ,						collections.abc.Iterable     )
						    else (config.patch_size, config.patch_size)
						)

						UpperCamelCase__					=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]     )      ,						[num_patches, self.model_tester.embed_dim]      ,						)

						UpperCamelCase__					=       outputs.reshaped_hidden_states
						self.assertEqual(len(snake_case     )      ,						snake_case     )

						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       reshaped_hidden_states[0].shape
						UpperCamelCase__					=       (
						    reshaped_hidden_states[0].view(snake_case      ,						snake_case      ,						height * width     ).permute(0      ,						2      ,						1     )
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]     )      ,						[num_patches, self.model_tester.embed_dim]      ,						)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						UpperCamelCase__					=       (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size      ,						collections.abc.Iterable     )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes[:-1]:
									UpperCamelCase__					=       True
									self.check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case      ,						snake_case     )

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

									self.check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase__					=       3

						UpperCamelCase__					=       (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size      ,						collections.abc.Iterable     )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						UpperCamelCase__					=       (
						    config.patch_size
						    if isinstance(config.patch_size      ,						collections.abc.Iterable     )
						    else (config.patch_size, config.patch_size)
						)

						UpperCamelCase__					=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						UpperCamelCase__					=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes[:-1]:
									UpperCamelCase__					=       True
									self.check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case      ,						(padded_height, padded_width)     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__					=       True
									self.check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case      ,						(padded_height, padded_width)     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       FocalNetModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						UpperCamelCase__					=       _config_zero_init(snake_case     )
						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(config=snake_case     )
									for name, param in model.named_parameters():
												if "embeddings" not in name and param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item()      ,						[0.0, 1.0]      ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,						)







@require_vision
@require_torch
class        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"     ) if is_vision_available() else None







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"     ).to(snake_case     )
						UpperCamelCase__					=       self.default_image_processor

						UpperCamelCase__					=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
						UpperCamelCase__					=       image_processor(images=snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 1000)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )
						UpperCamelCase__					=       torch.tensor([0.2166, -0.4368, 0.2191]     ).to(snake_case     )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,						snake_case      ,						atol=1E-4     )     )
						self.assertTrue(outputs.logits.argmax(dim=-1     ).item()      ,						281     )







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		(FocalNetBackbone,) if is_torch_available() else ()
			_UpperCamelCase		:					Union[str, Any]    =		FocalNetConfig

			_UpperCamelCase		:					Union[str, Any]    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       FocalNetModelTester(self     )






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


