

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
   '''simple docstring'''







   lowerCamelCase__           =KandinskyVaaInpaintPipeline
   lowerCamelCase__           =['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   lowerCamelCase__           =[
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   lowerCamelCase__           =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase__           =False
   @property
   def 							__UpperCamelCase (			self							: int	)							->       int:

    """simple docstring"""

    return 32
   @property
   def 							__UpperCamelCase (			self							: int	)							->       Dict:

    """simple docstring"""

    return 32
   @property
   def 							__UpperCamelCase (			self							: int	)							->       Any:

    """simple docstring"""

    return self.time_input_dim
   @property
   def 							__UpperCamelCase (			self							: Any	)							->       Any:

    """simple docstring"""

    return self.time_input_dim * 4
   @property
   def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

    """simple docstring"""

    return 100
   @property
   def 							__UpperCamelCase (			self							: int	)							->       List[Any]:

    """simple docstring"""

    torch.manual_seed(0	)

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {
        "in_channels": 9,
        # Out channels is double in channels because predicts mean and variance
        "out_channels": 8,
        "addition_embed_type": "image",
        "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
        "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
        "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
        "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
        "layers_per_block": 1,
        "encoder_hid_dim": self.text_embedder_hidden_size,
        "encoder_hid_dim_type": "image_proj",
        "cross_attention_dim": self.cross_attention_dim,
        "attention_head_dim": 4,
        "resnet_time_scale_shift": "scale_shift",
        "class_embed_type": None,
    }

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       UNetaDConditionModel(**a	)
    return model
   @property
   def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

    """simple docstring"""

    return {
        "block_out_channels": [32, 64],
        "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
        "in_channels": 3,
        "latent_channels": 4,
        "layers_per_block": 1,
        "norm_num_groups": 8,
        "norm_type": "spatial",
        "num_vq_embeddings": 12,
        "out_channels": 3,
        "up_block_types": [
            "AttnUpDecoderBlock2D",
            "UpDecoderBlock2D",
        ],
        "vq_embed_dim": 4,
    }
   @property
   def 							__UpperCamelCase (			self							: str	)							->       List[Any]:

    """simple docstring"""

    torch.manual_seed(0	)
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       VQModel(**self.dummy_movq_kwargs	)
    return model
   def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      str				     =       self.dummy_unet
    SCREAMING_SNAKE_CASE   :      int				     =       self.dummy_movq

    SCREAMING_SNAKE_CASE   :      List[str]				     =       DDIMScheduler(
        num_train_timesteps=1000      ,      beta_schedule="linear"      ,      beta_start=0.0_0085      ,      beta_end=0.012      ,      clip_sample=a      ,      set_alpha_to_one=a      ,      steps_offset=1      ,      prediction_type="epsilon"      ,      thresholding=a      ,      )

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       {
        "unet": unet,
        "scheduler": scheduler,
        "movq": movq,
    }

    return components
   def 							__UpperCamelCase (			self							: str      ,      a							: List[str]      ,      a							: int=0	)							->       Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(a	)	).to(a	)
    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       floats_tensor((1, self.text_embedder_hidden_size)      ,      rng=random.Random(seed + 1	)	).to(
        a	)
    # create init_image
    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(a	)	).to(a	)
    SCREAMING_SNAKE_CASE   :      Tuple				     =       image.cpu().permute(0      ,      2      ,      3      ,      1	)[0]
    SCREAMING_SNAKE_CASE   :      Dict				     =       Image.fromarray(np.uinta(a	)	).convert("RGB"	).resize((256, 256)	)
    # create mask
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.ones((64, 64)      ,      dtype=np.floataa	)
    SCREAMING_SNAKE_CASE   :      List[Any]				     =       0

    if str(a	).startswith("mps"	):
     SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.manual_seed(a	)
    else:
     SCREAMING_SNAKE_CASE   :      str				     =       torch.Generator(device=a	).manual_seed(a	)
    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {
        "image": init_image,
        "mask_image": mask,
        "image_embeds": image_embeds,
        "negative_image_embeds": negative_image_embeds,
        "generator": generator,
        "height": 64,
        "width": 64,
        "num_inference_steps": 2,
        "guidance_scale": 4.0,
        "output_type": "np",
    }
    return inputs
   def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      Dict				     =       "cpu"

    SCREAMING_SNAKE_CASE   :      int				     =       self.get_dummy_components()

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.pipeline_class(**a	)
    SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipe.to(a	)

    pipe.set_progress_bar_config(disable=a	)

    SCREAMING_SNAKE_CASE   :      Tuple				     =       pipe(**self.get_dummy_inputs(a	)	)
    SCREAMING_SNAKE_CASE   :      Any				     =       output.images

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipe(
        **self.get_dummy_inputs(a	)      ,      return_dict=a      ,      )[0]

    SCREAMING_SNAKE_CASE   :      Any				     =       image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_from_tuple[0, -3:, -3:, -1]

    print(F"image.shape {image.shape}"	)

    assert image.shape == (1, 64, 64, 3)

    SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.array(
        [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]	)

    assert (
        np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
    ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
    assert (
        np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
    ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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

    """simple docstring"""

    super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






@slow
@require_torch_gpu
class _UpperCamelCase							(							unittest.TestCase   ):
   '''simple docstring'''







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

    """simple docstring"""

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






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

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      str				     =       load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"	)

    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
    SCREAMING_SNAKE_CASE   :      List[str]				     =       np.ones((768, 768)      ,      dtype=np.floataa	)
    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0

    SCREAMING_SNAKE_CASE   :      Any				     =       "a hat"

    SCREAMING_SNAKE_CASE   :      List[Any]				     =       KandinskyVaaPriorPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-prior"      ,      torch_dtype=torch.floataa	)
    pipe_prior.to(a	)

    SCREAMING_SNAKE_CASE   :      str				     =       KandinskyVaaInpaintPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-decoder-inpaint"      ,      torch_dtype=torch.floataa	)
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pipeline.to(a	)
    pipeline.set_progress_bar_config(disable=a	)

    SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.Generator(device="cpu"	).manual_seed(0	)
    SCREAMING_SNAKE_CASE   :      Dict				     =       pipe_prior(
        a      ,      generator=a      ,      num_inference_steps=5      ,      negative_prompt=""      ,      ).to_tuple()

    SCREAMING_SNAKE_CASE   :      Tuple				     =       pipeline(
        image=a      ,      mask_image=a      ,      image_embeds=a      ,      negative_image_embeds=a      ,      generator=a      ,      num_inference_steps=100      ,      height=768      ,      width=768      ,      output_type="np"      ,      )

    SCREAMING_SNAKE_CASE   :      str				     =       output.images[0]

    assert image.shape == (768, 768, 3)

    assert_mean_pixel_difference(a      ,      a	)


from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
			from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(a      ,      "embed_dim"	)	)
				self.parent.assertTrue(hasattr(a      ,      "num_heads"	)	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: int      ,      a							: List[str]=13      ,      a							: Optional[int]=64      ,      a							: List[str]=3      ,      a							: Dict=[16, 48, 96]      ,      a							: List[Any]=[1, 3, 6]      ,      a							: int=[1, 2, 10]      ,      a							: List[str]=[7, 3, 3]      ,      a							: List[Any]=[4, 2, 2]      ,      a							: Tuple=[2, 1, 1]      ,      a							: Union[str, Any]=[2, 2, 2]      ,      a							: List[str]=[False, False, True]      ,      a							: int=[0.0, 0.0, 0.0]      ,      a							: Dict=0.02      ,      a							: List[Any]=1e-12      ,      a							: Tuple=True      ,      a							: Optional[int]=True      ,      a							: Union[str, Any]=2      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       patch_sizes
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       patch_stride
				SCREAMING_SNAKE_CASE   :      str				     =       patch_padding
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       is_training
				SCREAMING_SNAKE_CASE   :      Dict				     =       use_labels
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_labels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Any				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      str				     =       num_heads
				SCREAMING_SNAKE_CASE   :      Any				     =       stride_kv
				SCREAMING_SNAKE_CASE   :      Tuple				     =       depth
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       cls_token
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       attention_drop_rate
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       layer_norm_eps
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				SCREAMING_SNAKE_CASE   :      Any				     =       None
				if self.use_labels:
					# create a random int32 tensor of given shape
					SCREAMING_SNAKE_CASE   :      str				     =       ids_tensor([self.batch_size]      ,      self.num_labels	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_config()
				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[int]:

				"""simple docstring"""

				return CvtConfig(
				    image_size=self.image_size      ,      num_labels=self.num_labels      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      num_heads=self.num_heads      ,      patch_sizes=self.patch_sizes      ,      patch_padding=self.patch_padding      ,      patch_stride=self.patch_stride      ,      stride_kv=self.stride_kv      ,      depth=self.depth      ,      cls_token=self.cls_token      ,      attention_drop_rate=self.attention_drop_rate      ,      initializer_range=self.initializer_range      ,      )
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: str      ,      a							: List[str]      ,      a							: str	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       TFCvtModel(config=a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       model(a      ,      training=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (self.image_size, self.image_size)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       image_size[0], image_size[1]
				for i in range(len(self.depth	)	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.embed_dim[-1], height, width)	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: int      ,      a							: List[Any]      ,      a							: List[str]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.num_labels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       TFCvtForImageClassification(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(a      ,      labels=a      ,      training=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)	)






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      str				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
			lowerCamelCase__           =(
			    {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFCvtModelTester(self	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFCvtConfigTester(self      ,      config_class=a      ,      has_text_modality=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				self.config_tester.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()
			@unittest.skip(reason="Cvt does not output attentions"	)
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Cvt does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Cvt does not support input and output embeddings"	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

				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."      ,      )
			def 							__UpperCamelCase (			self							: int	)							->       Tuple:

				"""simple docstring"""

				super().test_dataset_conversion()
			@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 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

				super().test_keras_fit()
			@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.keras.mixed_precision.Policy("mixed_float16"	)
				tf.keras.mixed_precision.set_global_policy(a	)
				super().test_keras_fit()
				tf.keras.mixed_precision.set_global_policy("float32"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       inspect.signature(model.call	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE   :      Tuple				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				def check_hidden_states_output(a							: List[str]      ,      a							: Union[str, Any]      ,      a							: List[str]	):
					SCREAMING_SNAKE_CASE   :      Tuple				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(**self._prepare_for_class(a      ,      a	)	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       outputs.hidden_states

					SCREAMING_SNAKE_CASE   :      int				     =       len(self.model_tester.depth	)
					self.assertEqual(len(a	)      ,      a	)

					# verify the first hidden states (first block)
					self.assertListEqual(
					    list(hidden_states[0].shape[-3:]	)      ,      [
					        self.model_tester.embed_dim[0],
					        self.model_tester.image_size // 4,
					        self.model_tester.image_size // 4,
					    ]      ,      )

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True
					check_hidden_states_output(a      ,      a      ,      a	)

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

					check_hidden_states_output(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)






			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       TFCvtModel.from_pretrained(a	)
					self.assertIsNotNone(a	)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Tuple	)							->       Tuple:

				"""simple docstring"""

				return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)






			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processor(images=a      ,      return_tensors="tf"	)

				# forward pass
				SCREAMING_SNAKE_CASE   :      List[str]				     =       model(**a	)

				# verify the logits
				SCREAMING_SNAKE_CASE   :      Dict				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.constant([0.9285, 0.9015, -0.3150]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()      ,      a      ,      atol=1e-4	)	)