

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 					_snake_case						:






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=13					,UpperCamelCase=32					,UpperCamelCase=2					,UpperCamelCase=3					,UpperCamelCase=16					,UpperCamelCase=[32, 64, 128]					,UpperCamelCase=[1, 2, 1]					,UpperCamelCase=[2, 2, 4]					,UpperCamelCase=2					,UpperCamelCase=2.0					,UpperCamelCase=True					,UpperCamelCase=0.0					,UpperCamelCase=0.0					,UpperCamelCase=0.1					,UpperCamelCase="gelu"					,UpperCamelCase=False					,UpperCamelCase=True					,UpperCamelCase=0.02					,UpperCamelCase=1E-5					,UpperCamelCase=True					,UpperCamelCase=None					,UpperCamelCase=True					,UpperCamelCase=10					,UpperCamelCase=8					,UpperCamelCase=["stage1", "stage2"]					,UpperCamelCase=[1, 2]					,)						->    Optional[Any]:
					snake_case__      :int         =   parent
					snake_case__      :Tuple         =   batch_size
					snake_case__      :int         =   image_size
					snake_case__      :Any         =   patch_size
					snake_case__      :Any         =   num_channels
					snake_case__      :Union[str, Any]         =   embed_dim
					snake_case__      :Any         =   hidden_sizes
					snake_case__      :Dict         =   depths
					snake_case__      :int         =   num_heads
					snake_case__      :int         =   window_size
					snake_case__      :Optional[int]         =   mlp_ratio
					snake_case__      :List[str]         =   qkv_bias
					snake_case__      :Optional[Any]         =   hidden_dropout_prob
					snake_case__      :Optional[int]         =   attention_probs_dropout_prob
					snake_case__      :Optional[Any]         =   drop_path_rate
					snake_case__      :Optional[int]         =   hidden_act
					snake_case__      :str         =   use_absolute_embeddings
					snake_case__      :Any         =   patch_norm
					snake_case__      :int         =   layer_norm_eps
					snake_case__      :str         =   initializer_range
					snake_case__      :Tuple         =   is_training
					snake_case__      :Any         =   scope
					snake_case__      :Any         =   use_labels
					snake_case__      :List[Any]         =   type_sequence_label_size
					snake_case__      :List[str]         =   encoder_stride
					snake_case__      :str         =   out_features
					snake_case__      :List[str]         =   out_indices






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Optional[int]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

					snake_case__      :Dict         =   None
					if self.use_labels:
									snake_case__      :Optional[int]         =   ids_tensor([self.batch_size]					,self.type_sequence_label_size       )

					snake_case__      :Any         =   self.get_config()

					return config, pixel_values, labels






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[Any]:
					snake_case__      :List[Any]         =   FocalNetModel(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :Any         =   model(UpperCamelCase       )

					snake_case__      :Optional[int]         =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
					snake_case__      :Optional[Any]         =   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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					snake_case__      :List[Any]         =   FocalNetBackbone(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :List[str]         =   model(UpperCamelCase       )

					# 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
					snake_case__      :Optional[int]         =   None
					snake_case__      :Union[str, Any]         =   FocalNetBackbone(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :Tuple         =   model(UpperCamelCase       )

					# 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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :Any         =   FocalNetForMaskedImageModeling(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :str         =   model(UpperCamelCase       )
					self.parent.assertEqual(
					    result.reconstruction.shape					,(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

					# test greyscale images
					snake_case__      :Union[str, Any]         =   1
					snake_case__      :str         =   FocalNetForMaskedImageModeling(UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()

					snake_case__      :str         =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
					snake_case__      :Optional[int]         =   model(UpperCamelCase       )
					self.parent.assertEqual(result.reconstruction.shape					,(self.batch_size, 1, self.image_size, self.image_size)       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Union[str, Any]:
					snake_case__      :List[Any]         =   self.type_sequence_label_size
					snake_case__      :int         =   FocalNetForImageClassification(UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :str         =   model(UpperCamelCase					,labels=UpperCamelCase       )
					self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)       )

					# test greyscale images
					snake_case__      :List[str]         =   1
					snake_case__      :Union[str, Any]         =   FocalNetForImageClassification(UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()

					snake_case__      :str         =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
					snake_case__      :Union[str, Any]         =   model(UpperCamelCase       )
					self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :List[str]         =   self.prepare_config_and_inputs()

					snake_case__      :str         =   config_and_inputs
					snake_case__      :Any         =   {"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class 					_snake_case						(			_A       ,    _A       ,    unittest.TestCase ):
	_A          =   (
	    (
	        FocalNetModel,
	        FocalNetForImageClassification,
	        FocalNetForMaskedImageModeling,
	        FocalNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	_A          =   (
	    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
	    if is_torch_available()
	    else {}
	)
	_A          =   False

	_A          =   False
	_A          =   False
	_A          =   False
	_A          =   False






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Any         =   FocalNetModelTester(self       )
					snake_case__      :Any         =   ConfigTester(self					,config_class=UpperCamelCase					,embed_dim=37					,has_text_modality=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					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    lowerCAmelCase_							(		self       )						->    str:
					return






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :str         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Optional[Any]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Tuple         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Optional[Any]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*UpperCamelCase       )






	@unittest.skip(reason="FocalNet does not use inputs_embeds"       )
	def    lowerCAmelCase_							(		self       )						->    Dict:
					pass






	@unittest.skip(reason="FocalNet does not use feedforward chunking"       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :int         =   self.model_tester.prepare_config_and_inputs_for_common()

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






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[str]         =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
									snake_case__      :Union[str, Any]         =   model_class(UpperCamelCase       )
									snake_case__      :int         =   inspect.signature(model.forward       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									snake_case__      :Tuple         =   [*signature.parameters.keys()]

									snake_case__      :List[str]         =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Optional[int]:
					snake_case__      :List[str]         =   model_class(UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()

					with torch.no_grad():
									snake_case__      :Any         =   model(**self._prepare_for_class(UpperCamelCase					,UpperCamelCase       )       )

					snake_case__      :Optional[int]         =   outputs.hidden_states

					snake_case__      :Union[str, Any]         =   getattr(
					    self.model_tester					,"expected_num_hidden_layers"					,len(self.model_tester.depths       ) + 1       )
					self.assertEqual(len(UpperCamelCase       )					,UpperCamelCase       )

					# FocalNet has a different seq_length
					snake_case__      :List[str]         =   (
					    config.patch_size
					    if isinstance(config.patch_size					,collections.abc.Iterable       )
					    else (config.patch_size, config.patch_size)
					)

					snake_case__      :Dict         =   (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]					,)

					snake_case__      :List[str]         =   outputs.reshaped_hidden_states
					self.assertEqual(len(UpperCamelCase       )					,UpperCamelCase       )

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






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

					snake_case__      :Optional[Any]         =   (
					    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]:
									snake_case__      :List[Any]         =   True
									self.check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									snake_case__      :Any         =   True

									self.check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :List[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()
					snake_case__      :List[Any]         =   3

					snake_case__      :List[Any]         =   (
					    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)
					)
					snake_case__      :Optional[int]         =   (
					    config.patch_size
					    if isinstance(config.patch_size					,collections.abc.Iterable       )
					    else (config.patch_size, config.patch_size)
					)

					snake_case__      :Tuple         =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					snake_case__      :Optional[int]         =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes[:-1]:
									snake_case__      :Tuple         =   True
									self.check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,(padded_height, padded_width)       )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									snake_case__      :int         =   True
									self.check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,(padded_height, padded_width)       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    str:
					for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__      :Tuple         =   FocalNetModel.from_pretrained(UpperCamelCase       )
									self.assertIsNotNone(UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

					snake_case__      :int         =   _config_zero_init(UpperCamelCase       )
					for model_class in self.all_model_classes:
									snake_case__      :Dict         =   model_class(config=UpperCamelCase       )
									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 					_snake_case						(			unittest.TestCase ):






	@cached_property
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					# TODO update organization
					return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"       ) if is_vision_available() else None






	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :str         =   FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"       ).to(UpperCamelCase       )
					snake_case__      :Optional[int]         =   self.default_image_processor

					snake_case__      :Tuple         =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
					snake_case__      :int         =   image_processor(images=UpperCamelCase					,return_tensors="pt"       ).to(UpperCamelCase       )

					# forward pass
					with torch.no_grad():
									snake_case__      :Union[str, Any]         =   model(**UpperCamelCase       )

					# verify the logits
					snake_case__      :Tuple         =   torch.Size((1, 1_000)       )
					self.assertEqual(outputs.logits.shape					,UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   torch.tensor([0.2166, -0.4368, 0.2191]       ).to(UpperCamelCase       )
					self.assertTrue(torch.allclose(outputs.logits[0, :3]					,UpperCamelCase					,atol=1E-4       )       )
					self.assertTrue(outputs.logits.argmax(dim=-1       ).item()					,281       )




@require_torch
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   (FocalNetBackbone,) if is_torch_available() else ()
	_A          =   FocalNetConfig

	_A          =   False






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Any         =   FocalNetModelTester(self       )







from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .multicontrolnet import MultiControlNetModel
	from .pipeline_controlnet import StableDiffusionControlNetPipeline
	from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
	from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
	from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline