
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						torch.FloatTensor
							lowerCAmelCase__		:						Optional[torch.FloatTensor]         =							None






def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				int=0.999   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any]="cosine"   ,							)					->						List[str]:
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE	:				Optional[Any]      ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE	:				int      ):
												return math.exp(t * -12.0      )

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

				__lowercase								= []
				for i in range(SCREAMING_SNAKE_CASE      ):
								__lowercase								= i / num_diffusion_timesteps
								__lowercase								= (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE      ) / alpha_bar_fn(SCREAMING_SNAKE_CASE      )   ,							SCREAMING_SNAKE_CASE      )      )
				return torch.tensor(SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa      )
class      A__       (						lowerCAmelCase__   ,						lowerCAmelCase__    ):
							lowerCAmelCase__		:						str         =							[e.name for e in KarrasDiffusionSchedulers]
							lowerCAmelCase__		:						Union[str, Any]         =							1





							@register_to_config
							def __init__(			self  :       str					,			_UpperCAmelCase  :       int = 10_00					,			_UpperCAmelCase  :       float = 0.0_001					,			_UpperCAmelCase  :       float = 0.02					,			_UpperCAmelCase  :       str = "linear"					,			_UpperCAmelCase  :       Optional[Union[np.ndarray, List[float]]] = None					,			_UpperCAmelCase  :       str = "epsilon"					,			_UpperCAmelCase  :       str = "linspace"					,			_UpperCAmelCase  :       int = 0					,			)						->		int:



											"""simple docstring"""


											if trained_betas is not None:
															__lowercase								= torch.tensor(_UpperCAmelCase					,			dtype=torch.floataa    )
											elif beta_schedule == "linear":
															__lowercase								= torch.linspace(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															__lowercase								= (
															    torch.linspace(beta_start**0.5					,			beta_end**0.5					,			_UpperCAmelCase					,			dtype=torch.floataa    ) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															__lowercase								= betas_for_alpha_bar(_UpperCAmelCase    )
											else:
															raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""    )

											__lowercase								= 1.0 - self.betas
											__lowercase								= torch.cumprod(self.alphas					,			dim=0    )

											__lowercase								= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											__lowercase								= np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
											__lowercase								= torch.from_numpy(_UpperCAmelCase    )

											# setable values
											__lowercase								= None
											__lowercase								= np.linspace(0					,			num_train_timesteps - 1					,			_UpperCAmelCase					,			dtype=_UpperCAmelCase    )[::-1].copy()
											__lowercase								= torch.from_numpy(_UpperCAmelCase    )
											__lowercase								= False





							@property
							def       a__			(			self  :       List[str]    )						->		Optional[int]:



											"""simple docstring"""


											if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       Union[float, torch.FloatTensor]    )						->		torch.FloatTensor:



											"""simple docstring"""


											if isinstance(_UpperCAmelCase					,			torch.Tensor    ):
															__lowercase								= timestep.to(self.timesteps.device    )
											__lowercase								= (self.timesteps == timestep).nonzero().item()
											__lowercase								= self.sigmas[step_index]
											__lowercase								= sample / ((sigma**2 + 1) ** 0.5)
											__lowercase								= True
											return sample





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Union[str, torch.device] = None    )						->		Any:



											"""simple docstring"""


											__lowercase								= num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
															__lowercase								= np.linspace(0					,			self.config.num_train_timesteps - 1					,			_UpperCAmelCase					,			dtype=_UpperCAmelCase    )[
															    ::-1
															].copy()
											elif self.config.timestep_spacing == "leading":
															__lowercase								= self.config.num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															__lowercase								= (np.arange(0					,			_UpperCAmelCase    ) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase    )
															timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
															__lowercase								= self.config.num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															__lowercase								= (np.arange(self.config.num_train_timesteps					,			0					,			-step_ratio    )).round().copy().astype(_UpperCAmelCase    )
															timesteps -= 1
											else:
															raise ValueError(
															    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

											__lowercase								= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											__lowercase								= np.interp(_UpperCAmelCase					,			np.arange(0					,			len(_UpperCAmelCase    )    )					,			_UpperCAmelCase    )
											__lowercase								= np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
											__lowercase								= torch.from_numpy(_UpperCAmelCase    ).to(device=_UpperCAmelCase    )
											if str(_UpperCAmelCase    ).startswith('mps'    ):
															# mps does not support float64
															__lowercase								= torch.from_numpy(_UpperCAmelCase    ).to(_UpperCAmelCase					,			dtype=torch.floataa    )
											else:
															__lowercase								= torch.from_numpy(_UpperCAmelCase    ).to(device=_UpperCAmelCase    )





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       Union[float, torch.FloatTensor]					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       Optional[torch.Generator] = None					,			_UpperCAmelCase  :       bool = True					,			)						->		Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:



											"""simple docstring"""


											if (
											    isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )
											    or isinstance(_UpperCAmelCase					,			torch.IntTensor    )
											    or isinstance(_UpperCAmelCase					,			torch.LongTensor    )
											):
															raise ValueError(
															    (
															        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
															        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
															        ' one of the `scheduler.timesteps` as a timestep.'
															    )					,			)

											if not self.is_scale_input_called:
															logger.warning(
															    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
															    'See `StableDiffusionPipeline` for a usage example.'    )

											if isinstance(_UpperCAmelCase					,			torch.Tensor    ):
															__lowercase								= timestep.to(self.timesteps.device    )

											__lowercase								= (self.timesteps == timestep).nonzero().item()
											__lowercase								= self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
															__lowercase								= sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															__lowercase								= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
															raise NotImplementedError('prediction_type not implemented yet: sample'    )
											else:
															raise ValueError(
															    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

											__lowercase								= self.sigmas[step_index]
											__lowercase								= self.sigmas[step_index + 1]
											__lowercase								= (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
											__lowercase								= (sigma_to**2 - sigma_up**2) ** 0.5

											# 2. Convert to an ODE derivative
											__lowercase								= (sample - pred_original_sample) / sigma

											__lowercase								= sigma_down - sigma

											__lowercase								= sample + derivative * dt

											__lowercase								= model_output.device
											__lowercase								= randn_tensor(model_output.shape					,			dtype=model_output.dtype					,			device=_UpperCAmelCase					,			generator=_UpperCAmelCase    )

											__lowercase								= prev_sample + noise * sigma_up

											if not return_dict:
															return (prev_sample,)

											return EulerAncestralDiscreteSchedulerOutput(
											    prev_sample=_UpperCAmelCase					,			pred_original_sample=_UpperCAmelCase    )





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       torch.FloatTensor					,			)						->		torch.FloatTensor:



											"""simple docstring"""


											__lowercase								= self.sigmas.to(device=original_samples.device					,			dtype=original_samples.dtype    )
											if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase    ):
															# mps does not support float64
															__lowercase								= self.timesteps.to(original_samples.device					,			dtype=torch.floataa    )
															__lowercase								= timesteps.to(original_samples.device					,			dtype=torch.floataa    )
											else:
															__lowercase								= self.timesteps.to(original_samples.device    )
															__lowercase								= timesteps.to(original_samples.device    )

											__lowercase								= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

											__lowercase								= sigmas[step_indices].flatten()
											while len(sigma.shape    ) < len(original_samples.shape    ):
															__lowercase								= sigma.unsqueeze(-1    )

											__lowercase								= original_samples + noise * sigma
											return noisy_samples





							def __len__(			self  :       str    )						->		str:



											"""simple docstring"""


											return self.config.num_train_timesteps







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      A__       :





							def __init__(			self  :       Any					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]=13					,			_UpperCAmelCase  :       Union[str, Any]=32					,			_UpperCAmelCase  :       Optional[Any]=3					,			_UpperCAmelCase  :       Optional[Any]=4					,			_UpperCAmelCase  :       Optional[Any]=[10, 20, 30, 40]					,			_UpperCAmelCase  :       Dict=[2, 2, 3, 2]					,			_UpperCAmelCase  :       Union[str, Any]=True					,			_UpperCAmelCase  :       str=True					,			_UpperCAmelCase  :       str=37					,			_UpperCAmelCase  :       List[str]="gelu"					,			_UpperCAmelCase  :       List[str]=10					,			_UpperCAmelCase  :       Optional[int]=0.02					,			_UpperCAmelCase  :       List[Any]=3					,			_UpperCAmelCase  :       List[Any]=None					,			)						->		str:



											"""simple docstring"""


											__lowercase								= parent
											__lowercase								= batch_size
											__lowercase								= image_size
											__lowercase								= num_channels
											__lowercase								= num_stages
											__lowercase								= hidden_sizes
											__lowercase								= depths
											__lowercase								= is_training
											__lowercase								= use_labels
											__lowercase								= intermediate_size
											__lowercase								= hidden_act
											__lowercase								= type_sequence_label_size
											__lowercase								= initializer_range
											__lowercase								= scope





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



											"""simple docstring"""


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

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

											__lowercase								= self.get_config()

											return config, pixel_values, labels





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



											"""simple docstring"""


											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=_UpperCAmelCase					,			initializer_range=self.initializer_range					,			)





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Any    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= TFConvNextModel(config=_UpperCAmelCase    )
											__lowercase								= model(_UpperCAmelCase					,			training=_UpperCAmelCase    )
											# 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 // 32, self.image_size // 32)					,			)





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.type_sequence_label_size
											__lowercase								= TFConvNextForImageClassification(_UpperCAmelCase    )
											__lowercase								= model(_UpperCAmelCase					,			labels=_UpperCAmelCase					,			training=_UpperCAmelCase    )
											self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.type_sequence_label_size)    )





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



											"""simple docstring"""


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






@require_tf
class      A__       (						lowerCAmelCase__   ,						lowerCAmelCase__   ,						unittest.TestCase    ):
							lowerCAmelCase__		:						List[Any]         =							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
							lowerCAmelCase__		:						List[Any]         =							(
							    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
							    if is_tf_available()
							    else {}
							)

							lowerCAmelCase__		:						Union[str, Any]         =							False
							lowerCAmelCase__		:						Union[str, Any]         =							False
							lowerCAmelCase__		:						Any         =							False
							lowerCAmelCase__		:						Optional[Any]         =							False
							lowerCAmelCase__		:						Optional[int]         =							False





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



											"""simple docstring"""


											__lowercase								= TFConvNextModelTester(self    )
											__lowercase								= ConfigTester(
											    self					,			config_class=_UpperCAmelCase					,			has_text_modality=_UpperCAmelCase					,			hidden_size=37					,			)





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



											"""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.'					,			)
							@slow
							def       a__			(			self  :       Union[str, Any]    )						->		str:



											"""simple docstring"""


											super().test_keras_fit()





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



											"""simple docstring"""


											pass





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



											"""simple docstring"""


											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

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

															__lowercase								= ['pixel_values']
															self.assertListEqual(arg_names[:1]					,			_UpperCAmelCase    )





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



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase    )





							@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       a__			(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											super().test_dataset_conversion()





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



											"""simple docstring"""


											def check_hidden_states_output(_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       str    ):
															__lowercase								= model_class(_UpperCAmelCase    )

															__lowercase								= model(**self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )    )
															__lowercase								= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__lowercase								= self.model_tester.num_stages
															self.assertEqual(len(_UpperCAmelCase    )					,			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]					,			)

											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__lowercase								= True
															check_hidden_states_output(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

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

															check_hidden_states_output(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )





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



											"""simple docstring"""


											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]={}    ):
															__lowercase								= model(_UpperCAmelCase					,			return_dict=_UpperCAmelCase					,			**_UpperCAmelCase    )
															__lowercase								= model(_UpperCAmelCase					,			return_dict=_UpperCAmelCase					,			**_UpperCAmelCase    ).to_tuple()

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

																			recursive_check(_UpperCAmelCase					,			_UpperCAmelCase    )

											for model_class in self.all_model_classes:
															__lowercase								= model_class(_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			{'output_hidden_states': True}    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			{'output_hidden_states': True}    )





							def       a__			(			self  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase    )





							@slow
							def       a__			(			self  :       Tuple    )						->		Any:



											"""simple docstring"""


											__lowercase								= TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'    )
											self.assertIsNotNone(_UpperCAmelCase    )






def 							__SCREAMING_SNAKE_CASE       (		)					->						List[str]:
				__lowercase								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
				return image
@require_tf
@require_vision
class      A__       (						unittest.TestCase    ):





							@cached_property
							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'    ) if is_vision_available() else None





							@slow
							def       a__			(			self  :       List[Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'    )

											__lowercase								= self.default_image_processor
											__lowercase								= prepare_img()
											__lowercase								= image_processor(images=_UpperCAmelCase					,			return_tensors='tf'    )

											# forward pass
											__lowercase								= model(**_UpperCAmelCase    )

											# verify the logits
											__lowercase								= tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape					,			_UpperCAmelCase    )

											__lowercase								= tf.constant([-0.0_260, -0.4_739, 0.1_911]    )

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





