


"""simple docstring"""


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__lowerCamelCase      :Optional[Any]	    =		'scheduler_config.json'


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													=1
     snake_case__	:					Tuple													=2
     snake_case__	:					Dict													=3
     snake_case__	:					Tuple													=4
     snake_case__	:					Tuple													=5


@dataclass
class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					jnp.ndarray


class 			A__	:


     """simple docstring"""
     snake_case__	:					Tuple													=SCHEDULER_CONFIG_NAME
     snake_case__	:					Union[str, Any]													=['''dtype''']
     snake_case__	:					Union[str, Any]													=[]
     snake_case__	:					Any													=True

     @classmethod
     def 		a__						(	cls:						Union[str, Any]     ,		__a:						Dict[str, Any] = None     ,		__a:						Optional[str] = None     ,		__a:						Dict=False     ,		**__a:						Any     ,		)->    Optional[int]:
           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	cls.load_config(
               pretrained_model_name_or_path=__a     ,		subfolder=__a     ,		return_unused_kwargs=__a     ,		**__a     ,		)
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	cls.from_config(__a     ,		return_unused_kwargs=__a     ,		**__a	)

           if hasattr(__a     ,		"""create_state"""	) and getattr(__a     ,		"""has_state"""     ,		__a	):
                 lowerCamelCase					:					int        =	scheduler.create_state()

           if return_unused_kwargs:
                 return scheduler, state, unused_kwargs

           return scheduler, state

     def 		a__						(	self:						List[Any]     ,		__a:						Union[str, os.PathLike]     ,		__a:						bool = False     ,		**__a:						int	)->    Optional[Any]:
           self.save_config(save_directory=__a     ,		push_to_hub=__a     ,		**__a	)

     @property
     def 		a__						(	self:						str	)->    int:
           return self._get_compatibles()







     @classmethod
     def 		a__						(	cls:						int	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	list(set([cls.__name__] + cls._compatibles	)	)
           lowerCamelCase					:					int        =	importlib.import_module(__name__.split("""."""	)[0]	)
           lowerCamelCase					:					List[str]        =	[
               getattr(__a     ,		__a	) for c in compatible_classes_str if hasattr(__a     ,		__a	)
           ]
           return compatible_classes




def   snake_case		(     UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						Tuple[int]    )   ->      jnp.ndarray:
      assert len(UpperCamelCase__    ) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase__    ) - x.ndim)    )			,				UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str=0.9_9_9			,				UpperCamelCase__       :						Dict=jnp.floataa    )   ->      jnp.ndarray:

      def alpha_bar(UpperCamelCase__       :						List[Any]    ):
            return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

      lowerCamelCase					:					Optional[Any]        =	[]
      for i in range(UpperCamelCase__    ):
            lowerCamelCase					:					Tuple        =	i / num_diffusion_timesteps
            lowerCamelCase					:					str        =	(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(UpperCamelCase__    ) / alpha_bar(UpperCamelCase__    )			,				UpperCamelCase__    )    )
      return jnp.array(UpperCamelCase__			,				dtype=UpperCamelCase__    )


@flax.struct.dataclass
class 			A__	:


     """simple docstring"""
     snake_case__	:					jnp.ndarray
     snake_case__	:					jnp.ndarray
     snake_case__	:					jnp.ndarray

     @classmethod
     def 		a__						(	cls:						Optional[Any]     ,		__a:						List[str]	)->    Optional[Any]:
           lowerCamelCase					:					Optional[int]        =	scheduler.config

           if config.trained_betas is not None:
                 lowerCamelCase					:					List[str]        =	jnp.asarray(config.trained_betas     ,		dtype=scheduler.dtype	)
           elif config.beta_schedule == "linear":
                 lowerCamelCase					:					str        =	jnp.linspace(config.beta_start     ,		config.beta_end     ,		config.num_train_timesteps     ,		dtype=scheduler.dtype	)
           elif config.beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 lowerCamelCase					:					Optional[Any]        =	(
                     jnp.linspace(
                         config.beta_start**0.5     ,		config.beta_end**0.5     ,		config.num_train_timesteps     ,		dtype=scheduler.dtype	)
                     ** 2
                 )
           elif config.beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 lowerCamelCase					:					int        =	betas_for_alpha_bar(config.num_train_timesteps     ,		dtype=scheduler.dtype	)
           else:
                 raise NotImplementedError(
                     f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

           lowerCamelCase					:					Any        =	1.0 - betas

           lowerCamelCase					:					Dict        =	jnp.cumprod(__a     ,		axis=0	)

           return cls(
               alphas=__a     ,		betas=__a     ,		alphas_cumprod=__a     ,		)




def   snake_case		(     UpperCamelCase__       :						CommonSchedulerState			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray    )   ->      List[str]:
      lowerCamelCase					:					str        =	state.alphas_cumprod

      lowerCamelCase					:					Optional[int]        =	alphas_cumprod[timesteps] ** 0.5
      lowerCamelCase					:					str        =	sqrt_alpha_prod.flatten()
      lowerCamelCase					:					Optional[Any]        =	broadcast_to_shape_from_left(UpperCamelCase__			,				original_samples.shape    )

      lowerCamelCase					:					Tuple        =	(1 - alphas_cumprod[timesteps]) ** 0.5
      lowerCamelCase					:					Optional[int]        =	sqrt_one_minus_alpha_prod.flatten()
      lowerCamelCase					:					Any        =	broadcast_to_shape_from_left(UpperCamelCase__			,				original_samples.shape    )

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def   snake_case		(     UpperCamelCase__       :						CommonSchedulerState			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray    )   ->      Tuple:
      lowerCamelCase     ,			lowerCamelCase					:					int        =	get_sqrt_alpha_prod(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      lowerCamelCase					:					Tuple        =	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples






def   snake_case		(     UpperCamelCase__       :						CommonSchedulerState			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray    )   ->      List[Any]:
      lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	get_sqrt_alpha_prod(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      lowerCamelCase					:					Optional[int]        =	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity








"""simple docstring"""


import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
    )
    from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						List[Any]     ,		__a:						List[str]     ,		__a:						Optional[int]=13     ,		__a:						List[str]=32     ,		__a:						int=2     ,		__a:						List[str]=3     ,		__a:						Union[str, Any]=16     ,		__a:						int=[32, 64, 128]     ,		__a:						Optional[Any]=[1, 2, 1]     ,		__a:						Optional[int]=[2, 2, 4]     ,		__a:						Tuple=2     ,		__a:						Dict=2.0     ,		__a:						List[str]=True     ,		__a:						Optional[Any]=0.0     ,		__a:						Any=0.0     ,		__a:						List[Any]=0.1     ,		__a:						List[str]="gelu"     ,		__a:						Tuple=False     ,		__a:						Union[str, Any]=True     ,		__a:						Optional[int]=0.02     ,		__a:						Tuple=1e-5     ,		__a:						int=True     ,		__a:						List[Any]=None     ,		__a:						Optional[int]=True     ,		__a:						Dict=10     ,		__a:						List[str]=8     ,		__a:						Any=["stage1", "stage2"]     ,		__a:						Union[str, Any]=[1, 2]     ,		)->    Dict:
           lowerCamelCase					:					Dict        =	parent
           lowerCamelCase					:					Optional[Any]        =	batch_size
           lowerCamelCase					:					Union[str, Any]        =	image_size
           lowerCamelCase					:					Optional[int]        =	patch_size
           lowerCamelCase					:					Any        =	num_channels
           lowerCamelCase					:					Any        =	embed_dim
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					List[Any]        =	depths
           lowerCamelCase					:					Tuple        =	num_heads
           lowerCamelCase					:					List[Any]        =	window_size
           lowerCamelCase					:					str        =	mlp_ratio
           lowerCamelCase					:					str        =	qkv_bias
           lowerCamelCase					:					str        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Tuple        =	drop_path_rate
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					Tuple        =	use_absolute_embeddings
           lowerCamelCase					:					List[str]        =	patch_norm
           lowerCamelCase					:					List[str]        =	layer_norm_eps
           lowerCamelCase					:					str        =	initializer_range
           lowerCamelCase					:					Tuple        =	is_training
           lowerCamelCase					:					int        =	scope
           lowerCamelCase					:					Union[str, Any]        =	use_labels
           lowerCamelCase					:					List[str]        =	type_sequence_label_size
           lowerCamelCase					:					str        =	encoder_stride
           lowerCamelCase					:					List[str]        =	out_features
           lowerCamelCase					:					Optional[int]        =	out_indices

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					str        =	None
           if self.use_labels:
                 lowerCamelCase					:					List[str]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)

           lowerCamelCase					:					str        =	self.get_config()

           return config, pixel_values, labels

     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           return FocalNetConfig(
               image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		hidden_sizes=self.hidden_sizes     ,		depths=self.depths     ,		num_heads=self.num_heads     ,		window_size=self.window_size     ,		mlp_ratio=self.mlp_ratio     ,		qkv_bias=self.qkv_bias     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		drop_path_rate=self.drop_path_rate     ,		hidden_act=self.hidden_act     ,		use_absolute_embeddings=self.use_absolute_embeddings     ,		path_norm=self.patch_norm     ,		layer_norm_eps=self.layer_norm_eps     ,		initializer_range=self.initializer_range     ,		encoder_stride=self.encoder_stride     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		)

     def 		a__						(	self:						Tuple     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Tuple        =	model(__a	)

           lowerCamelCase					:					Any        =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
           lowerCamelCase					:					List[Any]        =	int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, expected_seq_len, expected_dim)	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Dict     ,		__a:						Tuple     ,		__a:						List[Any]	)->    int:
           lowerCamelCase					:					List[Any]        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Optional[Any]        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.image_size, 8, 8]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(model.channels     ,		config.hidden_sizes[:-1]	)

           # verify backbone works with out_features=None
           lowerCamelCase					:					Dict        =	None
           lowerCamelCase					:					Dict        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.image_size * 2, 4, 4]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		1	)
           self.parent.assertListEqual(model.channels     ,		[config.hidden_sizes[-1]]	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetForMaskedImageModeling(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a	)
           self.parent.assertEqual(
               result.reconstruction.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

           # test greyscale images
           lowerCamelCase					:					List[str]        =	1
           lowerCamelCase					:					Any        =	FocalNetForMaskedImageModeling(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					str        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Tuple        =	model(__a	)
           self.parent.assertEqual(result.reconstruction.shape     ,		(self.batch_size, 1, self.image_size, self.image_size)	)

     def 		a__						(	self:						str     ,		__a:						Optional[Any]     ,		__a:						Optional[Any]     ,		__a:						Tuple	)->    str:
           lowerCamelCase					:					Optional[Any]        =	self.type_sequence_label_size
           lowerCamelCase					:					Optional[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)

           # test greyscale images
           lowerCamelCase					:					int        =	1
           lowerCamelCase					:					List[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)







     def 		a__						(	self:						int	)->    Optional[int]:
           lowerCamelCase					:					str        =	self.prepare_config_and_inputs()

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	config_and_inputs
           lowerCamelCase					:					List[Any]        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=(
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					Optional[int]													=(
         {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     snake_case__	:					Tuple													=False

     snake_case__	:					Dict													=False
     snake_case__	:					Dict													=False
     snake_case__	:					Tuple													=False
     snake_case__	:					Optional[int]													=False

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	FocalNetModelTester(self	)
           lowerCamelCase					:					Optional[Any]        =	ConfigTester(self     ,		config_class=__a     ,		embed_dim=37     ,		has_text_modality=__a	)

     def 		a__						(	self:						List[str]	)->    List[str]:
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()

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

     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						List[Any]	)->    Dict:
           lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__a	)

     def 		a__						(	self:						List[Any]	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__a	)

     def 		a__						(	self:						List[str]	)->    Dict:
           lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a	)

     @unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
     def 		a__						(	self:						Optional[Any]	)->    str:
           pass

     @unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
     def 		a__						(	self:						Optional[Any]	)->    Dict:
           pass

     def 		a__						(	self:						Optional[Any]	)->    Dict:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					Any        =	model_class(__a	)
                 self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)	)
                 lowerCamelCase					:					Dict        =	model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__a     ,		nn.Linear	)	)

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					int        =	model_class(__a	)
                 lowerCamelCase					:					int        =	inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase					:					Any        =	[*signature.parameters.keys()]

                 lowerCamelCase					:					List[Any]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]     ,		__a:						int     ,		__a:						Tuple     ,		__a:						List[str]	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	model_class(__a	)
           model.to(__a	)
           model.eval()

           with torch.no_grad():
                 lowerCamelCase					:					List[str]        =	model(**self._prepare_for_class(__a     ,		__a	)	)

           lowerCamelCase					:					List[str]        =	outputs.hidden_states

           lowerCamelCase					:					Tuple        =	getattr(
               self.model_tester     ,		"""expected_num_hidden_layers"""     ,		len(self.model_tester.depths	) + 1	)
           self.assertEqual(len(__a	)     ,		__a	)

           # FocalNet has a different seq_length
           lowerCamelCase					:					Tuple        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Dict        =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]	)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

           lowerCamelCase					:					Optional[Any]        =	outputs.reshaped_hidden_states
           self.assertEqual(len(__a	)     ,		__a	)

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	reshaped_hidden_states[0].shape
           lowerCamelCase					:					Tuple        =	(
               reshaped_hidden_states[0].view(__a     ,		__a     ,		height * width	).permute(0     ,		2     ,		1	)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]	)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

     def 		a__						(	self:						Any	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					Union[str, Any]        =	(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					List[str]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					List[Any]        =	True

                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

     def 		a__						(	self:						str	)->    Union[str, Any]:
           lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase					:					List[str]        =	3

           lowerCamelCase					:					Any        =	(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           lowerCamelCase					:					Optional[int]        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Optional[Any]        =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           lowerCamelCase					:					List[str]        =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					str        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					Union[str, Any]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

     @slow
     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					List[str]        =	FocalNetModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)







     def 		a__						(	self:						str	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					int        =	_config_zero_init(__a	)
           for model_class in self.all_model_classes:
                 lowerCamelCase					:					int        =	model_class(config=__a	)
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1e9).round() / 1e9).item()     ,		[0.0, 1.0]     ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized'     ,		)


@require_vision
@require_torch
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					Tuple        =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__a	)
           lowerCamelCase					:					Any        =	self.default_image_processor

           lowerCamelCase					:					Union[str, Any]        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
           lowerCamelCase					:					int        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	model(**__a	)

           # verify the logits
           lowerCamelCase					:					Tuple        =	torch.Size((1, 1_000)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)
           lowerCamelCase					:					List[str]        =	torch.tensor([0.21_66, -0.43_68, 0.21_91]	).to(__a	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		__a     ,		atol=1e-4	)	)
           self.assertTrue(outputs.logits.argmax(dim=-1	).item()     ,		281	)


@require_torch
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=(FocalNetBackbone,) if is_torch_available() else ()
     snake_case__	:					Optional[int]													=FocalNetConfig

     snake_case__	:					str													=False

     def 		a__						(	self:						Union[str, Any]	)->    Tuple:
           lowerCamelCase					:					str        =	FocalNetModelTester(self	)




