

import inspect
import unittest

from transformers import BitConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       int,lowercase_ :       List[Any]=3,lowercase_ :       Dict=3_2,lowercase_ :       List[str]=3,lowercase_ :       List[str]=1_0,lowercase_ :       List[Any]=[8, 1_6, 3_2, 6_4],lowercase_ :       Any=[1, 1, 2, 1],lowercase_ :       Any=True,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]="relu",lowercase_ :       int=3,lowercase_ :       Tuple=None,lowercase_ :       Union[str, Any]=["stage2", "stage3", "stage4"],lowercase_ :       Tuple=[2, 3, 4],lowercase_ :       List[Any]=1,)->    int:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   num_channels
        A__          =   embeddings_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_act
        A__          =   num_labels
        A__          =   scope
        A__          =   len(lowercase_		)
        A__          =   out_features
        A__          =   out_indices
        A__          =   num_groups


    def 						snake_case__     (      self :       Any		)->    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.num_labels		)

        A__          =   self.get_config()

        return config, pixel_values, labels


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





        '''simple docstring'''







        return BitConfig(
            num_channels=self.num_channels,embeddings_size=self.embeddings_size,hidden_sizes=self.hidden_sizes,depths=self.depths,hidden_act=self.hidden_act,num_labels=self.num_labels,out_features=self.out_features,out_indices=self.out_indices,num_groups=self.num_groups,)


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





        '''simple docstring'''







        A__          =   BitModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),)


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





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   BitForImageClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


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





        '''simple docstring'''







        A__          =   BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)

        # 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.hidden_sizes[1], 4, 4]		)

        # 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
        A__          =   None
        A__          =   BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		),1		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		),[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

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


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





        '''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			=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


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





        '''simple docstring'''







        A__          =   BitModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''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 :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        return


    @unittest.skip(reason='Bit does not output attentions'		)
    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        pass


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





        '''simple docstring'''







        pass


    @unittest.skip(reason='Bit does not support input and output embeddings'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        pass


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





        '''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[Any]		)->    int:





        '''simple docstring'''







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


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





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Any		)->    Any:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(config=lowercase_		)
            for name, module in model.named_modules():
                if isinstance(lowercase_,(nn.BatchNormad, nn.GroupNorm)		):
                    self.assertTrue(
                        torch.all(module.weight == 1		),msg=F'Parameter {name} of model {model_class} seems not properly initialized',)
                    self.assertTrue(
                        torch.all(module.bias == 0		),msg=F'Parameter {name} of model {model_class} seems not properly initialized',)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_ :       Any,lowercase_ :       Union[str, Any],lowercase_ :       Dict		):
            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		)

            # Bit'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()
        A__          =   ['preactivation', 'bottleneck']
        for model_class in self.all_model_classes:
            for layer_type in layers_type:
                A__          =   layer_type
                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_		)


    @unittest.skip(reason='Bit does not use feedforward chunking'		)
    def 						snake_case__     (      self :       int		)->    str:





        '''simple docstring'''







        pass


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





        '''simple docstring'''







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


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





        '''simple docstring'''







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




def 						_snake_case(    )							->      str:





    '''simple docstring'''






    A__          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image


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







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
        )


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    Dict:





        '''simple docstring'''







        A__          =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowercase_		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='pt'		).to(lowercase_		)

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

        # verify the logits
        A__          =   torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]		).to(lowercase_		)

        self.assertTrue(torch.allclose(outputs.logits[0, :3],lowercase_,atol=1E-4		)		)




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







    """simple docstring"""





    lowerCamelCase			=							(BitBackbone,) if is_torch_available() else ()
    lowerCamelCase			=							BitConfig

    lowerCamelCase			=							False


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





        '''simple docstring'''







        A__          =   BitModelTester(self		)




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

