

import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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 transformers import UperNetForSemanticSegmentation
     from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class  A						:







    """simple docstring"""





    def __init__(      self :       str,lowercase_ :       str,lowercase_ :       Tuple=1_3,lowercase_ :       List[Any]=3_2,lowercase_ :       Union[str, Any]=3,lowercase_ :       Dict=4,lowercase_ :       Dict=[1_0, 2_0, 3_0, 4_0],lowercase_ :       Any=[2, 2, 3, 2],lowercase_ :       str=True,lowercase_ :       Optional[Any]=True,lowercase_ :       Optional[Any]=3_7,lowercase_ :       Optional[int]="gelu",lowercase_ :       Optional[Any]=1_0,lowercase_ :       Dict=0.02,lowercase_ :       Dict=["stage2", "stage3", "stage4"],lowercase_ :       Union[str, Any]=3,lowercase_ :       Optional[Any]=None,)->    Optional[int]:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   num_channels
        A__          =   num_stages
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   is_training
        A__          =   use_labels
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   out_features
        A__          =   num_labels
        A__          =   scope
        A__          =   num_stages


    def 						snake_case__     (      self :       Tuple		)->    Optional[Any]:





        '''simple docstring'''







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

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

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       Dict		)->    Union[str, Any]:





        '''simple docstring'''







        return ConvNextConfig(
            num_channels=self.num_channels,num_stages=self.num_stages,hidden_sizes=self.hidden_sizes,depths=self.depths,is_training=self.is_training,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,out_features=self.out_features,)


    def 						snake_case__     (      self :       Tuple		)->    Union[str, Any]:





        '''simple docstring'''







        return UperNetConfig(
            backbone_config=self.get_backbone_config(),hidden_size=5_1_2,pool_scales=[1, 2, 3, 6],use_auxiliary_head=lowercase_,auxiliary_loss_weight=0.4,auxiliary_in_channels=4_0,auxiliary_channels=2_5_6,auxiliary_num_convs=1,auxiliary_concat_input=lowercase_,loss_ignore_index=2_5_5,num_labels=self.num_labels,)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Dict,lowercase_ :       int,lowercase_ :       Optional[int]		)->    Dict:





        '''simple docstring'''







        A__          =   UperNetForSemanticSegmentation(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)
        self.parent.assertEqual(
            result.logits.shape,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)


    def 						snake_case__     (      self :       int		)->    Dict:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        (
            (
            A__
        )      ,						(
            A__
        )      ,						(
            A__
        )      ,						
        )          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(UperNetForSemanticSegmentation,) if is_torch_available() else ()
    lowerCamelCase			=							{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Optional[Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   UperNetModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Dict:





        '''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 :       int		)->    Tuple:





        '''simple docstring'''







        return


    def 						snake_case__     (      self :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_		)


    @unittest.skip(reason='UperNet does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Any		)->    List[Any]:





        '''simple docstring'''







        pass


    @unittest.skip(reason='UperNet does not support input and output embeddings'		)
    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        pass


    @unittest.skip(reason='UperNet does not have a base model'		)
    def 						snake_case__     (      self :       int		)->    Any:





        '''simple docstring'''







        pass


    @unittest.skip(reason='UperNet does not have a base model'		)
    def 						snake_case__     (      self :       Union[str, Any]		)->    Dict:





        '''simple docstring'''







        pass


    @require_torch_multi_gpu
    @unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'		)
    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        pass


    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
    def 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       int		)->    Any:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_ :       str,lowercase_ :       Union[str, Any],lowercase_ :       Any		):
            A__          =   model_class(lowercase_		)
            model.to(lowercase_		)
            model.eval()

            with torch.no_grad():
                A__          =   model(**self._prepare_for_class(lowercase_,lowercase_		)		)

            A__          =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A__          =   self.model_tester.num_stages
            self.assertEqual(len(lowercase_		),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],)

        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   True
            check_hidden_states_output(lowercase_,lowercase_,lowercase_		)

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

            check_hidden_states_output(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        A__          =   _config_zero_init(lowercase_		)
        A__          =   _config_zero_init(configs_no_init.backbone_config		)
        for model_class in self.all_model_classes:
            A__          =   model_class(config=lowercase_		)
            for name, param in model.named_parameters():
                if 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',)


    @unittest.skip(reason='UperNet does not have tied weights'		)
    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        pass


    @slow
    def 						snake_case__     (      self :       Dict		)->    Dict:





        '''simple docstring'''







        for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__          =   UperNetForSemanticSegmentation.from_pretrained(lowercase_		)
            self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      int:





    '''simple docstring'''






    A__          =   hf_hub_download(
        repo_id='hf-internal-testing/fixtures_ade20k'    ,			repo_type='dataset'    ,			filename='ADE_val_00000001.jpg'      )
    A__          =   Image.open(SCREAMING_SNAKE_CASE__      ).convert('RGB'      )
    return image


@require_torch
@require_vision
@slow
class  A						(							unittest.TestCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       Optional[int]		)->    List[Any]:





        '''simple docstring'''







        A__          =   AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'		)
        A__          =   UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'		).to(lowercase_		)

        A__          =   prepare_img()
        A__          =   processor(images=lowercase_,return_tensors='pt'		).to(lowercase_		)

        with torch.no_grad():
            A__          =   model(**lowercase_		)

        A__          =   torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]		).to(lowercase_		)
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],lowercase_,atol=1E-4		)		)


    def 						snake_case__     (      self :       Any		)->    Any:





        '''simple docstring'''







        A__          =   AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'		)
        A__          =   UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'		).to(lowercase_		)

        A__          =   prepare_img()
        A__          =   processor(images=lowercase_,return_tensors='pt'		).to(lowercase_		)

        with torch.no_grad():
            A__          =   model(**lowercase_		)

        A__          =   torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   torch.tensor(
            [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]		).to(lowercase_		)
        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],lowercase_,atol=1E-4		)		)




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowercase_       		=  False


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionAttendAndExcitePipeline
    lowerCamelCase			=							False
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[Any]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),cross_attention_dim=3_2,attention_head_dim=(2, 4),use_linear_projection=lowercase_,)
        A__          =   DDIMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',clip_sample=lowercase_,set_alpha_to_one=lowercase_,)
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=1_2_8,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,hidden_act='gelu',projection_dim=5_1_2,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

        A__          =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components


    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       List[Any]=0		)->    int:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__          =   A__          =   {
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs


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





        '''simple docstring'''







        A__          =   'cpu'

        A__          =   self.get_dummy_components()
        A__          =   self.pipeline_class(**lowercase_		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   pipe(**lowercase_		).images
        A__          =   image[0, -3:, -3:, -1]

        self.assertEqual(image.shape,(1, 6_4, 6_4, 3)		)
        A__          =   np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
        A__          =   np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowercase_,1E-3		)


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        self._test_inference_batch_single_identical(batch_size=2,expected_max_diff=7E-4		)


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





        '''simple docstring'''







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().test_save_load_local(expected_max_difference=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    List[str]:





        '''simple docstring'''







        super().test_save_load_optional_components(expected_max_difference=4E-4		)




@require_torch_gpu
@slow
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[int]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       int		)->    List[Any]:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


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





        '''simple docstring'''







        A__          =   torch.manual_seed(5_1		)

        A__          =   StableDiffusionAttendAndExcitePipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4',safety_checker=lowercase_,torch_dtype=torch.floataa		)
        pipe.to('cuda'		)

        A__          =   'a painting of an elephant with glasses'
        A__          =   [5, 7]

        A__          =   pipe(
            prompt=lowercase_,token_indices=lowercase_,guidance_scale=7.5,generator=lowercase_,num_inference_steps=5,max_iter_to_alter=5,output_type='numpy',).images[0]

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'		)
        assert np.abs((expected_image - image).max()		) < 5E-1

