




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

import numpy as np
import torch
from scipy import integrate

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     List[str]:







				'''simple docstring'''


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(UpperCamelCase__   ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

				elif alpha_transform_type == "exp":

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

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

				__lowerCAmelCase					=  []
				for i in range(UpperCamelCase__   ):
								__lowerCAmelCase					=  i / num_diffusion_timesteps
								__lowerCAmelCase					=  (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(UpperCamelCase__   ) / alpha_bar_fn(UpperCamelCase__   )						,			UpperCamelCase__   )   )
				return torch.tensor(UpperCamelCase__						,			dtype=torch.floataa   )

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Union[str, Any],     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		Optional[bool] = False,     _lowercase:		str = "epsilon",     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


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

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

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

									# setable values
									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  use_karras_sigmas
									self.set_timesteps(_lowercase,     _lowercase)
									__lowerCAmelCase					=  []
									__lowerCAmelCase					=  False




					@property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


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

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




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


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




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		Tuple,     _lowercase:		Optional[int],     _lowercase:		List[str]):


									'''simple docstring'''


									def lms_derivative(_lowercase:		List[str]):
													__lowerCAmelCase					=  1.0
													for k in range(_lowercase):
																	if current_order == k:
																					continue
																	prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
													return prod

									__lowerCAmelCase					=  integrate.quad(_lowercase,     self.sigmas[t],     self.sigmas[t + 1],     epsrel=1e-4)[0]

									return integrated_coeff




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		int,     _lowercase:		Union[str, torch.device] = None):


									'''simple docstring'''


									__lowerCAmelCase					=  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":
													__lowerCAmelCase					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)
									__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)

									if self.use_karras_sigmas:
													__lowerCAmelCase					=  self._convert_to_karras(in_sigmas=_lowercase)
													__lowerCAmelCase					=  np.array([self._sigma_to_t(_lowercase,     _lowercase) for sigma in sigmas])

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)

									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)

									__lowerCAmelCase					=  []




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		List[Any],     _lowercase:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

									# get distribution
									__lowerCAmelCase					=  log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									__lowerCAmelCase					=  np.cumsum((dists >= 0),     axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
									__lowerCAmelCase					=  low_idx + 1

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

									# interpolate sigmas
									__lowerCAmelCase					=  (low - log_sigma) / (low - high)
									__lowerCAmelCase					=  np.clip(_lowercase,     0,     1)

									# transform interpolation to time range
									__lowerCAmelCase					=  (1 - w) * low_idx + w * high_idx
									__lowerCAmelCase					=  t.reshape(sigma.shape)
									return t




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		torch.FloatTensor):


									'''simple docstring'''


									__lowerCAmelCase					=  in_sigmas[-1].item()
									__lowerCAmelCase					=  in_sigmas[0].item()

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     self.num_inference_steps)
									__lowerCAmelCase					=  sigma_min ** (1 / rho)
									__lowerCAmelCase					=  sigma_max ** (1 / rho)
									__lowerCAmelCase					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas




					def     _lowercase						(   self:		List[Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		int = 4,     _lowercase:		bool = True,     ):


									'''simple docstring'''


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

									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

									# 2. Convert to an ODE derivative
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma
									self.derivatives.append(_lowercase)
									if len(self.derivatives) > order:
													self.derivatives.pop(0)

									# 3. Compute linear multistep coefficients
									__lowerCAmelCase					=  min(step_index + 1,     _lowercase)
									__lowerCAmelCase					=  [self.get_lms_coefficient(_lowercase,     _lowercase,     _lowercase) for curr_order in range(_lowercase)]

									# 4. Compute previous sample based on the derivatives path
									__lowerCAmelCase					=  sample + sum(
									    coeff * derivative for coeff, derivative in zip(_lowercase,     reversed(self.derivatives)))

									if not return_dict:
													return (prev_sample,)

									return LMSDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''simple docstring'''


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

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

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

									__lowerCAmelCase					=  original_samples + noise * sigma
									return noisy_samples







					def __len__(   self:		Tuple):


									'''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 		lowercase_       :




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


									'''simple docstring'''


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




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


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

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

									__lowerCAmelCase					=  self.get_config()

									return config, pixel_values, labels




					def     _lowercase						(   self:		int):


									'''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=_lowercase,     initializer_range=self.initializer_range,     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel(config=_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     training=_lowercase)
									# 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     _lowercase						(   self:		Optional[Any],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


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







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.prepare_config_and_inputs()
									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  config_and_inputs
									__lowerCAmelCase					=  {"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_tf
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					__UpperCamelCase     	= (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModelTester(self)
									__lowerCAmelCase					=  ConfigTester(
									    self,     config_class=_lowercase,     has_text_modality=_lowercase,     hidden_size=37,     )




					@unittest.skip(reason="""ConvNext does not use inputs_embeds""")
					def     _lowercase						(   self:		Any):


									'''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     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									super().test_keras_fit()




					@unittest.skip(reason="""ConvNext does not support input and output embeddings""")
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase					=  ["""pixel_values"""]
													self.assertListEqual(arg_names[:1],     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					@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     _lowercase						(   self:		int):


									'''simple docstring'''


									super().test_dataset_conversion()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									def check_hidden_states_output(_lowercase:		Any,     _lowercase:		Dict,     _lowercase:		Any):
													__lowerCAmelCase					=  model_class(_lowercase)

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

													__lowerCAmelCase					=  self.model_tester.num_stages
													self.assertEqual(len(_lowercase),     expected_num_stages + 1)

													# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]),     [self.model_tester.image_size // 4, self.model_tester.image_size // 4],     )

									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)

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

													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(_lowercase:		Tuple,     _lowercase:		Tuple,     _lowercase:		str,     _lowercase:		Dict={}):
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase)
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase).to_tuple()

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

																	recursive_check(_lowercase,     _lowercase)

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)







					@slow
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224""")
									self.assertIsNotNone(_lowercase)





def      UpperCAmelCase  (						)     ->     Any:







				'''simple docstring'''


				__lowerCAmelCase					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				return image

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




					@cached_property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


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







					@slow
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""")

									__lowerCAmelCase					=  self.default_image_processor
									__lowerCAmelCase					=  prepare_img()
									__lowerCAmelCase					=  image_processor(images=_lowercase,     return_tensors="""tf""")

									# forward pass
									__lowerCAmelCase					=  model(**_lowercase)

									# verify the logits
									__lowerCAmelCase					=  tf.TensorShape((1, 1000))
									self.assertEqual(outputs.logits.shape,     _lowercase)

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

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





