





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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





def  __UpperCamelCase   (     a,  a=0.9_9_9,  a="cosine",  )			->Dict:
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(a):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(a):
															return math.exp(t * -1_2.0)

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

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







class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in KarrasDiffusionSchedulers]
					A__								=     2





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.00_085						,	_lowerCAmelCase = 0.012						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = "linspace"						,	_lowerCAmelCase = 0						,	):
										if trained_betas is not None:
															lowerCamelCase__      =							torch.tensor(_lowerCAmelCase						,	dtype=torch.floataa						)
										elif beta_schedule == "linear":
															lowerCamelCase__      =							torch.linspace(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	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						,	_lowerCAmelCase						,	dtype=torch.floataa						) ** 2
															)
										elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowerCamelCase__      =							betas_for_alpha_bar(_lowerCAmelCase						)
										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						)

										#  set all values
										self.set_timesteps(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=None						):
										if schedule_timesteps is None:
															lowerCamelCase__      =							self.timesteps

										lowerCamelCase__      =							(schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter						) == 0:
															lowerCamelCase__      =							1 if len(_lowerCAmelCase						) > 1 else 0
										else:
															lowerCamelCase__      =							timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase						) else timestep
															lowerCamelCase__      =							self._index_counter[timestep_int]

										return indices[pos].item()





					@property
					def        __magic_name__			(			self						):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

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





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							self.index_for_timestep(_lowerCAmelCase						)

										if self.state_in_first_order:
															lowerCamelCase__      =							self.sigmas[step_index]
										else:
															lowerCamelCase__      =							self.sigmas_interpol[step_index - 1]

										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase = None						,	_lowerCAmelCase = None						,	):
										lowerCamelCase__      =							num_inference_steps

										lowerCamelCase__      =							num_train_timesteps or self.config.num_train_timesteps

										# "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						,	num_train_timesteps - 1						,	_lowerCAmelCase						,	dtype=_lowerCAmelCase						)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
															lowerCamelCase__      =							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						,	_lowerCAmelCase						) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase						)
															timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
															lowerCamelCase__      =							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(_lowerCAmelCase						,	0						,	-step_ratio						)).round().copy().astype(_lowerCAmelCase						)
															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__      =							torch.from_numpy(np.log(_lowerCAmelCase						)						).to(_lowerCAmelCase						)

										lowerCamelCase__      =							np.interp(_lowerCAmelCase						,	np.arange(0						,	len(_lowerCAmelCase						)						)						,	_lowerCAmelCase						)
										lowerCamelCase__      =							np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
										lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						)

										# compute up and down sigmas
										lowerCamelCase__      =							sigmas.roll(-1						)
										lowerCamelCase__      =							0.0
										lowerCamelCase__      =							(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
										lowerCamelCase__      =							(sigmas_next**2 - sigmas_up**2) ** 0.5
										lowerCamelCase__      =							0.0

										# compute interpolated sigmas
										lowerCamelCase__      =							sigmas.log().lerp(sigmas_down.log()						,	0.5						).exp()
										lowerCamelCase__      =							0.0

										# set sigmas
										lowerCamelCase__      =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
										lowerCamelCase__      =							torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)
										lowerCamelCase__      =							torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2						), sigmas_up[-1:]]						)
										lowerCamelCase__      =							torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2						), sigmas_down[-1:]]						)

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

										lowerCamelCase__      =							self.sigma_to_t(_lowerCAmelCase						).to(_lowerCAmelCase						,	dtype=timesteps.dtype						)
										lowerCamelCase__      =							torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1						).flatten()

										lowerCamelCase__      =							torch.cat([timesteps[:1], interleaved_timesteps]						)

										lowerCamelCase__      =							None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										lowerCamelCase__      =							defaultdict(_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										# get log sigma
										lowerCamelCase__      =							sigma.log()

										# get distribution
										lowerCamelCase__      =							log_sigma - self.log_sigmas[:, None]

										# get sigmas range
										lowerCamelCase__      =							dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
										lowerCamelCase__      =							low_idx + 1

										lowerCamelCase__      =							self.log_sigmas[low_idx]
										lowerCamelCase__      =							self.log_sigmas[high_idx]

										# interpolate sigmas
										lowerCamelCase__      =							(low - log_sigma) / (low - high)
										lowerCamelCase__      =							w.clamp(0						,	1						)

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





					@property
					def        __magic_name__			(			self						):
										return self.sample is None





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = None						,	_lowerCAmelCase = True						,	):
										lowerCamelCase__      =							self.index_for_timestep(_lowerCAmelCase						)

										# advance index counter by 1
										lowerCamelCase__      =							timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase						) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
															lowerCamelCase__      =							self.sigmas[step_index]
															lowerCamelCase__      =							self.sigmas_interpol[step_index]
															lowerCamelCase__      =							self.sigmas_up[step_index]
															lowerCamelCase__      =							self.sigmas_down[step_index - 1]
										else:
															# 2nd order / KPDM2's method
															lowerCamelCase__      =							self.sigmas[step_index - 1]
															lowerCamelCase__      =							self.sigmas_interpol[step_index - 1]
															lowerCamelCase__      =							self.sigmas_up[step_index - 1]
															lowerCamelCase__      =							self.sigmas_down[step_index - 1]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										lowerCamelCase__      =							0
										lowerCamelCase__      =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										lowerCamelCase__      =							model_output.device
										lowerCamelCase__      =							randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=_lowerCAmelCase						,	generator=_lowerCAmelCase						)

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sigma_hat if self.state_in_first_order else sigma_interpol
															lowerCamelCase__      =							sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
															lowerCamelCase__      =							sigma_hat if self.state_in_first_order else sigma_interpol
															lowerCamelCase__      =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**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`"						)

										if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															lowerCamelCase__      =							(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															lowerCamelCase__      =							sigma_interpol - sigma_hat

															# store for 2nd order step
															lowerCamelCase__      =							sample
															lowerCamelCase__      =							dt
															lowerCamelCase__      =							sample + derivative * dt
										else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															lowerCamelCase__      =							(sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															lowerCamelCase__      =							sigma_down - sigma_hat

															lowerCamelCase__      =							self.sample
															lowerCamelCase__      =							None

															lowerCamelCase__      =							sample + derivative * dt
															lowerCamelCase__      =							prev_sample + noise * sigma_up

										if not return_dict:
															return (prev_sample,)

										return SchedulerOutput(prev_sample=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCamelCase__      =							self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype						)
										if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase						):
															# 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__      =							[self.index_for_timestep(_lowerCAmelCase						,	_lowerCAmelCase						) 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						):
										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 SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=32						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[10, 20, 30, 40]						,	_lowerCAmelCase=[2, 2, 3, 2]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=10						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										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        __magic_name__			(			self						):
										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        __magic_name__			(			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=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFConvNextModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	training=_lowerCAmelCase						)
										# 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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.type_sequence_label_size
										lowerCamelCase__      =							TFConvNextForImageClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						,	training=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)						)






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

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					A__								=     (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModelTester(self						)
										lowerCamelCase__      =							ConfigTester(
										    self						,	config_class=_lowerCAmelCase						,	has_text_modality=_lowerCAmelCase						,	hidden_size=37						,	)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
					def        __magic_name__			(			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        __magic_name__			(			self						):
										super().test_keras_fit()





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





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															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]						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					@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        __magic_name__			(			self						):
										super().test_dataset_conversion()





					def        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

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

															lowerCamelCase__      =							self.model_tester.num_stages
															self.assertEqual(len(_lowerCAmelCase						)						,	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(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

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

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase={}						):
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						).to_tuple()

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

																				recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->List[str]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







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





					"""simple docstring"""


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






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										lowerCamelCase__      =							self.default_image_processor
										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="tf"						)

										# forward pass
										lowerCamelCase__      =							model(**_lowerCAmelCase						)

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

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

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

