
'''simple docstring'''


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput

class 		__lowercase			(					nn.Module      ):

   def __init__(self     ,				A     ,				A=1.0 ):
        super().__init__()

        lowerCamelCase_   : Any       =						cut_size
        lowerCamelCase_   : Tuple       =						cut_power






   def     UpperCAmelCase__	(self     ,				A     ,				A ):
        lowerCamelCase_   : Optional[Any]       =						pixel_values.shape[2:4]
        lowerCamelCase_   : List[str]       =						min(A     ,				A )
        lowerCamelCase_   : Optional[int]       =						min(A     ,				A     ,				self.cut_size )
        lowerCamelCase_   : Optional[Any]       =						[]
        for _ in range(A ):
             lowerCamelCase_   : Tuple       =						int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
             lowerCamelCase_   : int       =						torch.randint(0     ,				sideX - size + 1     ,				() )
             lowerCamelCase_   : Union[str, Any]       =						torch.randint(0     ,				sideY - size + 1     ,				() )
             lowerCamelCase_   : Optional[Any]       =						pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
             cutouts.append(F.adaptive_avg_poolad(A     ,				self.cut_size ) )
        return torch.cat(A )




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							List[Any]:
     '''simple docstring'''

     lowerCamelCase_   : List[Any]       =						F.normalize(_lowercase     ,    dim=-1			)
     lowerCamelCase_   : Union[str, Any]       =						F.normalize(_lowercase     ,    dim=-1			)
     return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							Optional[Any]:
     '''simple docstring'''

     for param in model.parameters():
          lowerCamelCase_   : Any       =						value






class 		__lowercase			(					_lowercase      ):

   def __init__(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				):
        super().__init__()
        self.register_modules(
            vae=A     ,				text_encoder=A     ,				clip_model=A     ,				tokenizer=A     ,				unet=A     ,				scheduler=A     ,				feature_extractor=A     ,				)

        lowerCamelCase_   : Union[str, Any]       =						transforms.Normalize(mean=feature_extractor.image_mean     ,				std=feature_extractor.image_std )
        lowerCamelCase_   : Any       =						(
            feature_extractor.size
            if isinstance(feature_extractor.size     ,				A )
            else feature_extractor.size['''shortest_edge''']
        )
        lowerCamelCase_   : Optional[Any]       =						MakeCutouts(self.cut_out_size )

        set_requires_grad(self.text_encoder     ,				A )
        set_requires_grad(self.clip_model     ,				A )

   def     UpperCAmelCase__	(self     ,				A = "auto" ):
        if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             lowerCamelCase_   : Optional[int]       =						self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(A )

   def     UpperCAmelCase__	(self ):
        self.enable_attention_slicing(A )

   def     UpperCAmelCase__	(self ):
        set_requires_grad(self.vae     ,				A )

   def     UpperCAmelCase__	(self ):
        set_requires_grad(self.vae     ,				A )

   def     UpperCAmelCase__	(self ):
        set_requires_grad(self.unet     ,				A )

   def     UpperCAmelCase__	(self ):
        set_requires_grad(self.unet     ,				A )

   @torch.enable_grad()
   def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A=True     ,				):
        lowerCamelCase_   : Dict       =						latents.detach().requires_grad_()

        lowerCamelCase_   : Optional[Any]       =						self.scheduler.scale_model_input(A     ,				A )

        # predict the noise residual
        lowerCamelCase_   : str       =						self.unet(A     ,				A     ,				encoder_hidden_states=A ).sample

        if isinstance(self.scheduler     ,				(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
             lowerCamelCase_   : Union[str, Any]       =						self.scheduler.alphas_cumprod[timestep]
             lowerCamelCase_   : Dict       =						1 - alpha_prod_t
             # compute predicted original sample from predicted noise also called
             # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
             lowerCamelCase_   : Dict       =						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

             lowerCamelCase_   : Any       =						torch.sqrt(A )
             lowerCamelCase_   : Dict       =						pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler     ,				A ):
             lowerCamelCase_   : List[Any]       =						self.scheduler.sigmas[index]
             lowerCamelCase_   : Union[str, Any]       =						latents - sigma * noise_pred
        else:
             raise ValueError(F"""scheduler type {type(self.scheduler )} not supported""" )

        lowerCamelCase_   : str       =						1 / self.vae.config.scaling_factor * sample
        lowerCamelCase_   : int       =						self.vae.decode(A ).sample
        lowerCamelCase_   : Union[str, Any]       =						(image / 2 + 0.5).clamp(0     ,				1 )

        if use_cutouts:
             lowerCamelCase_   : Tuple       =						self.make_cutouts(A     ,				A )
        else:
             lowerCamelCase_   : List[str]       =						transforms.Resize(self.cut_out_size )(A )
        lowerCamelCase_   : Tuple       =						self.normalize(A ).to(latents.dtype )

        lowerCamelCase_   : Dict       =						self.clip_model.get_image_features(A )
        lowerCamelCase_   : Union[str, Any]       =						image_embeddings_clip / image_embeddings_clip.norm(p=2     ,				dim=-1     ,				keepdim=A )

        if use_cutouts:
             lowerCamelCase_   : List[Any]       =						spherical_dist_loss(A     ,				A )
             lowerCamelCase_   : str       =						dists.view([num_cutouts, sample.shape[0], -1] )
             lowerCamelCase_   : Optional[Any]       =						dists.sum(2 ).mean(0 ).sum() * clip_guidance_scale
        else:
             lowerCamelCase_   : str       =						spherical_dist_loss(A     ,				A ).mean() * clip_guidance_scale

        lowerCamelCase_   : Optional[Any]       =						-torch.autograd.grad(A     ,				A )[0]

        if isinstance(self.scheduler     ,				A ):
             lowerCamelCase_   : Optional[int]       =						latents.detach() + grads * (sigma**2)
             lowerCamelCase_   : str       =						noise_pred_original
        else:
             lowerCamelCase_   : List[str]       =						noise_pred_original - torch.sqrt(A ) * grads
        return noise_pred, latents






   @torch.no_grad()
   def __call__(self     ,				A     ,				A = 5_1_2     ,				A = 5_1_2     ,				A = 5_0     ,				A = 7.5     ,				A = 1     ,				A = 0.0     ,				A = 1_0_0     ,				A = None     ,				A = 4     ,				A = True     ,				A = None     ,				A = None     ,				A = "pil"     ,				A = True     ,				):
        if isinstance(A     ,				A ):
             lowerCamelCase_   : List[Any]       =						1
        elif isinstance(A     ,				A ):
             lowerCamelCase_   : int       =						len(A )
        else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(A )}""" )

        if height % 8 != 0 or width % 8 != 0:
             raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )

        # get prompt text embeddings
        lowerCamelCase_   : Any       =						self.tokenizer(
            A     ,				padding='''max_length'''     ,				max_length=self.tokenizer.model_max_length     ,				truncation=A     ,				return_tensors='''pt'''     ,				)
        lowerCamelCase_   : Tuple       =						self.text_encoder(text_input.input_ids.to(self.device ) )[0]
        # duplicate text embeddings for each generation per prompt
        lowerCamelCase_   : List[Any]       =						text_embeddings.repeat_interleave(A     ,				dim=0 )

        if clip_guidance_scale > 0:
             if clip_prompt is not None:
                  lowerCamelCase_   : Optional[Any]       =						self.tokenizer(
                      A     ,				padding='''max_length'''     ,				max_length=self.tokenizer.model_max_length     ,				truncation=A     ,				return_tensors='''pt'''     ,				).input_ids.to(self.device )
             else:
                  lowerCamelCase_   : Dict       =						text_input.input_ids.to(self.device )
             lowerCamelCase_   : Dict       =						self.clip_model.get_text_features(A )
             lowerCamelCase_   : Union[str, Any]       =						text_embeddings_clip / text_embeddings_clip.norm(p=2     ,				dim=-1     ,				keepdim=A )
             # duplicate text embeddings clip for each generation per prompt
             lowerCamelCase_   : int       =						text_embeddings_clip.repeat_interleave(A     ,				dim=0 )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        lowerCamelCase_   : Union[str, Any]       =						guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
             lowerCamelCase_   : Tuple       =						text_input.input_ids.shape[-1]
             lowerCamelCase_   : str       =						self.tokenizer(['''''']     ,				padding='''max_length'''     ,				max_length=A     ,				return_tensors='''pt''' )
             lowerCamelCase_   : Union[str, Any]       =						self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
             # duplicate unconditional embeddings for each generation per prompt
             lowerCamelCase_   : Tuple       =						uncond_embeddings.repeat_interleave(A     ,				dim=0 )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             lowerCamelCase_   : Any       =						torch.cat([uncond_embeddings, text_embeddings] )

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        lowerCamelCase_   : List[Any]       =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        lowerCamelCase_   : Union[str, Any]       =						text_embeddings.dtype
        if latents is None:
             if self.device.type == "mps":
                  # randn does not work reproducibly on mps
                  lowerCamelCase_   : Any       =						torch.randn(A     ,				generator=A     ,				device='''cpu'''     ,				dtype=A ).to(
                      self.device )
             else:
                  lowerCamelCase_   : Any       =						torch.randn(A     ,				generator=A     ,				device=self.device     ,				dtype=A )
        else:
             if latents.shape != latents_shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
             lowerCamelCase_   : Dict       =						latents.to(self.device )

        # set timesteps
        lowerCamelCase_   : Optional[int]       =						'''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
        lowerCamelCase_   : Dict       =						{}
        if accepts_offset:
             lowerCamelCase_   : Tuple       =						1

        self.scheduler.set_timesteps(A     ,				**A )

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        lowerCamelCase_   : List[Any]       =						self.scheduler.timesteps.to(self.device )

        # scale the initial noise by the standard deviation required by the scheduler
        lowerCamelCase_   : str       =						latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        lowerCamelCase_   : str       =						'''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
        lowerCamelCase_   : Dict       =						{}
        if accepts_eta:
             lowerCamelCase_   : Tuple       =						eta

        # check if the scheduler accepts generator
        lowerCamelCase_   : Union[str, Any]       =						'''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
        if accepts_generator:
             lowerCamelCase_   : Optional[int]       =						generator

        for i, t in enumerate(self.progress_bar(A ) ):
             # expand the latents if we are doing classifier free guidance
             lowerCamelCase_   : Union[str, Any]       =						torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
             lowerCamelCase_   : Dict       =						self.scheduler.scale_model_input(A     ,				A )

             # predict the noise residual
             lowerCamelCase_   : Dict       =						self.unet(A     ,				A     ,				encoder_hidden_states=A ).sample

             # perform classifier free guidance
             if do_classifier_free_guidance:
                  lowerCamelCase_   : Union[str, Any]       =						noise_pred.chunk(2 )
                  lowerCamelCase_   : Optional[Any]       =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # perform clip guidance
             if clip_guidance_scale > 0:
                  lowerCamelCase_   : Dict       =						(
                      text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
                  )
                  lowerCamelCase_   : int       =						self.cond_fn(
                      A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				)

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase_   : Optional[Any]       =						self.scheduler.step(A     ,				A     ,				A     ,				**A ).prev_sample

        # scale and decode the image latents with vae
        lowerCamelCase_   : Union[str, Any]       =						1 / self.vae.config.scaling_factor * latents
        lowerCamelCase_   : Optional[Any]       =						self.vae.decode(A ).sample

        lowerCamelCase_   : Optional[Any]       =						(image / 2 + 0.5).clamp(0     ,				1 )
        lowerCamelCase_   : List[Any]       =						image.cpu().permute(0     ,				2     ,				3     ,				1 ).numpy()

        if output_type == "pil":
             lowerCamelCase_   : Union[str, Any]       =						self.numpy_to_pil(A )

        if not return_dict:
             return (image, None)

        return StableDiffusionPipelineOutput(images=A     ,				nsfw_content_detected=A )




'''simple docstring'''


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor

class 		__lowercase			:

			def __init__(self     ,				A     ,				A=1_3     ,				A=3_2     ,				A=3     ,				A=4     ,				A=[1_0, 2_0, 3_0, 4_0]     ,				A=[2, 2, 3, 2]     ,				A=True     ,				A=True     ,				A=3_7     ,				A="gelu"     ,				A=1_0     ,				A=0.02     ,				A=3     ,				A=None     ,				):
								lowerCamelCase_   : List[str]       =						parent
								lowerCamelCase_   : List[Any]       =						batch_size
								lowerCamelCase_   : List[str]       =						image_size
								lowerCamelCase_   : List[str]       =						num_channels
								lowerCamelCase_   : Union[str, Any]       =						num_stages
								lowerCamelCase_   : Optional[Any]       =						hidden_sizes
								lowerCamelCase_   : Optional[int]       =						depths
								lowerCamelCase_   : Optional[Any]       =						is_training
								lowerCamelCase_   : Dict       =						use_labels
								lowerCamelCase_   : Optional[int]       =						intermediate_size
								lowerCamelCase_   : Union[str, Any]       =						hidden_act
								lowerCamelCase_   : Tuple       =						type_sequence_label_size
								lowerCamelCase_   : Optional[Any]       =						initializer_range
								lowerCamelCase_   : str       =						scope

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								lowerCamelCase_   : str       =						None
								if self.use_labels:
													lowerCamelCase_   : Optional[Any]       =						ids_tensor([self.batch_size]     ,				self.type_sequence_label_size )

								lowerCamelCase_   : List[Any]       =						self.get_config()

								return config, pixel_values, labels

			def     UpperCAmelCase__	(self ):
								return ConvNextConfig(
								    num_channels=self.num_channels     ,				hidden_sizes=self.hidden_sizes     ,				depths=self.depths     ,				num_stages=self.num_stages     ,				hidden_act=self.hidden_act     ,				is_decoder=A     ,				initializer_range=self.initializer_range     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : List[str]       =						TFConvNextModel(config=A )
								lowerCamelCase_   : Tuple       =						model(A     ,				training=A )
								# expected last hidden states: B, C, H // 32, W // 32
								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     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : Union[str, Any]       =						self.type_sequence_label_size
								lowerCamelCase_   : Dict       =						TFConvNextForImageClassification(A )
								lowerCamelCase_   : Optional[Any]       =						model(A     ,				labels=A     ,				training=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size) )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.prepare_config_and_inputs()
								lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : List[str]       =						config_and_inputs
								lowerCamelCase_   : Tuple       =						{'''pixel_values''': pixel_values}
								return config, inputs_dict

@require_tf
class 		__lowercase			(					_lowercase ,       _lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						Tuple		 =     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
			lowerCamelCase     :						List[str]		 =     (
			    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase     :						List[Any]		 =     False
			lowerCamelCase     :						List[str]		 =     False
			lowerCamelCase     :						Optional[int]		 =     False
			lowerCamelCase     :						List[Any]		 =     False
			lowerCamelCase     :						List[Any]		 =     False

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextModelTester(self )
								lowerCamelCase_   : int       =						ConfigTester(
								    self     ,				config_class=A     ,				has_text_modality=A     ,				hidden_size=3_7     ,				)

			@unittest.skip(reason='''ConvNext does not use inputs_embeds''' )
			def     UpperCAmelCase__	(self ):
								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.'''     ,				)
			@slow
			def     UpperCAmelCase__	(self ):
								super().test_keras_fit()

			@unittest.skip(reason='''ConvNext does not support input and output embeddings''' )
			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Union[str, Any]       =						model_class(A )
													lowerCamelCase_   : str       =						inspect.signature(model.call )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase_   : Optional[int]       =						[*signature.parameters.keys()]

													lowerCamelCase_   : str       =						['''pixel_values''']
													self.assertListEqual(arg_names[:1]     ,				A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A )

			@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 ):
								super().test_dataset_conversion()

			def     UpperCAmelCase__	(self ):
								def check_hidden_states_output(A     ,				A     ,				A ):
													lowerCamelCase_   : str       =						model_class(A )

													lowerCamelCase_   : Dict       =						model(**self._prepare_for_class(A     ,				A ) )
													lowerCamelCase_   : List[str]       =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowerCamelCase_   : Tuple       =						self.model_tester.num_stages
													self.assertEqual(len(A )     ,				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]     ,				)

								lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_   : 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"]
													lowerCamelCase_   : Any       =						True

													check_hidden_states_output(A     ,				A     ,				A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : Dict       =						self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(A     ,				A     ,				A     ,				A={} ):
													lowerCamelCase_   : Tuple       =						model(A     ,				return_dict=A     ,				**A )
													lowerCamelCase_   : List[str]       =						model(A     ,				return_dict=A     ,				**A ).to_tuple()

													def recursive_check(A     ,				A ):
																		if isinstance(A     ,				(List, Tuple) ):
																							for tuple_iterable_value, dict_iterable_value in zip(A     ,				A ):
																												recursive_check(A     ,				A )
																		elif tuple_object is None:
																							return
																		else:
																							self.assertTrue(
																							    all(tf.equal(A     ,				A ) )     ,				msg=(
																							        '''Tuple and dict output are not equal. Difference:'''
																							        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
																							    )     ,				)

																		recursive_check(A     ,				A )

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Optional[Any]       =						model_class(A )

													lowerCamelCase_   : Any       =						self._prepare_for_class(A     ,				A )
													lowerCamelCase_   : Any       =						self._prepare_for_class(A     ,				A )
													check_equivalence(A     ,				A     ,				A )

													lowerCamelCase_   : Optional[Any]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													lowerCamelCase_   : int       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													check_equivalence(A     ,				A     ,				A )

													lowerCamelCase_   : Union[str, Any]       =						self._prepare_for_class(A     ,				A )
													lowerCamelCase_   : List[str]       =						self._prepare_for_class(A     ,				A )
													check_equivalence(A     ,				A     ,				A     ,				{'''output_hidden_states''': True} )

													lowerCamelCase_   : Optional[int]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													lowerCamelCase_   : List[str]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													check_equivalence(A     ,				A     ,				A     ,				{'''output_hidden_states''': True} )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A )






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224''' )
								self.assertIsNotNone(A )




def lowercase_		(				)       ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					return image






@require_tf
@require_vision
class 		__lowercase			(					unittest.TestCase      ):

			@cached_property
			def     UpperCAmelCase__	(self ):
								return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224''' ) if is_vision_available() else None






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''' )

								lowerCamelCase_   : Tuple       =						self.default_image_processor
								lowerCamelCase_   : List[str]       =						prepare_img()
								lowerCamelCase_   : Optional[Any]       =						image_processor(images=A     ,				return_tensors='''tf''' )

								# forward pass
								lowerCamelCase_   : Optional[int]       =						model(**A )

								# verify the logits
								lowerCamelCase_   : List[str]       =						tf.TensorShape((1, 1_0_0_0) )
								self.assertEqual(outputs.logits.shape     ,				A )

								lowerCamelCase_   : Optional[int]       =						tf.constant([-0.02_60, -0.47_39, 0.19_11] )

								tf.debugging.assert_near(outputs.logits[0, :3]     ,				A     ,				atol=1E-4 )


