




import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=[]      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						size[0] - overlap_pixels * 2
    __UpperCamelCase  		=						size[1] - overlap_pixels * 2
    for letter in ["l", "r"]:
        if letter in remove_borders:
            size_x += overlap_pixels
    for letter in ["t", "b"]:
        if letter in remove_borders:
            size_y += overlap_pixels
    __UpperCamelCase  		=						np.ones((size_y, size_x)					,     dtype=np.uinta      ) * 2_55
    __UpperCamelCase  		=						np.pad(lowercase_					,     mode='''linear_ramp'''					,     pad_width=lowercase_					,     end_values=0      )

    if "l" in remove_borders:
        __UpperCamelCase  		=						mask[:, overlap_pixels : mask.shape[1]]
    if "r" in remove_borders:
        __UpperCamelCase  		=						mask[:, 0 : mask.shape[1] - overlap_pixels]
    if "t" in remove_borders:
        __UpperCamelCase  		=						mask[overlap_pixels : mask.shape[0], :]
    if "b" in remove_borders:
        __UpperCamelCase  		=						mask[0 : mask.shape[0] - overlap_pixels, :]
    return mask




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    return max(lowercase_					,     min(lowercase_					,     lowercase_      )      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    return (
        clamp(rect[0]					,     min[0]					,     max[0]      ),
        clamp(rect[1]					,     min[1]					,     max[1]      ),
        clamp(rect[2]					,     min[0]					,     max[0]      ),
        clamp(rect[3]					,     min[1]					,     max[1]      ),
    )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						list(lowercase_      )
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    __UpperCamelCase  		=						clamp_rect(lowercase_					,     [0, 0]					,     [image_size[0], image_size[1]]      )
    return rect




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						Image.new('''RGB'''					,     (tile.size[0] + original_slice, tile.size[1])      )
    result.paste(
        original_image.resize((tile.size[0], tile.size[1])					,     Image.BICUBIC      ).crop(
            (slice_x, 0, slice_x + original_slice, tile.size[1])      )					,     (0, 0)					,     )
    result.paste(lowercase_					,     (original_slice, 0)      )
    return result




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						(original_image_slice * 4, 0, tile.size[0], tile.size[1])
    __UpperCamelCase  		=						tile.crop(lowercase_      )
    return tile




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						n % d
    return n - divisor

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Tuple ,       snake_case					: AutoencoderKL ,       snake_case					: CLIPTextModel ,       snake_case					: CLIPTokenizer ,       snake_case					: UNetaDConditionModel ,       snake_case					: DDPMScheduler ,       snake_case					: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,       snake_case					: int = 350 ,       ):
          super().__init__(
              vae=snake_case ,       text_encoder=snake_case ,       tokenizer=snake_case ,       unet=snake_case ,       low_res_scheduler=snake_case ,       scheduler=snake_case ,       max_noise_level=snake_case ,       )







      def       snake_case	(     self					: int ,       snake_case					: Any ,       snake_case					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: Tuple ,       snake_case					: Any ,       snake_case					: Union[str, Any] ,       snake_case					: List[str] ,       **snake_case					: Dict				):
          torch.manual_seed(0				)
          __UpperCamelCase  		=						(
              min(image.size[0] - (tile_size + original_image_slice) ,       x * tile_size				),
              min(image.size[1] - (tile_size + original_image_slice) ,       y * tile_size				),
              min(image.size[0] ,       (x + 1) * tile_size				),
              min(image.size[1] ,       (y + 1) * tile_size				),
          )
          __UpperCamelCase  		=						add_overlap_rect(snake_case ,       snake_case ,       image.size				)
          __UpperCamelCase  		=						image.crop(snake_case				)
          __UpperCamelCase  		=						((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
          __UpperCamelCase  		=						translated_slice_x - (original_image_slice / 2)
          __UpperCamelCase  		=						max(0 ,       snake_case				)
          __UpperCamelCase  		=						squeeze_tile(snake_case ,       snake_case ,       snake_case ,       snake_case				)
          __UpperCamelCase  		=						to_input.size
          __UpperCamelCase  		=						to_input.resize((tile_size, tile_size) ,       Image.BICUBIC				)
          __UpperCamelCase  		=						super(snake_case ,       self				).__call__(image=snake_case ,       **snake_case				).images[0]
          __UpperCamelCase  		=						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,       Image.BICUBIC				)
          __UpperCamelCase  		=						unsqueeze_tile(snake_case ,       snake_case				)
          __UpperCamelCase  		=						upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,       Image.BICUBIC				)
          __UpperCamelCase  		=						[]
          if x == 0:
              remove_borders.append('''l'''				)
          elif crop_rect[2] == image.size[0]:
              remove_borders.append('''r'''				)
          if y == 0:
              remove_borders.append('''t'''				)
          elif crop_rect[3] == image.size[1]:
              remove_borders.append('''b'''				)
          __UpperCamelCase  		=						Image.fromarray(
              make_transparency_mask(
                  (upscaled_tile.size[0], upscaled_tile.size[1]) ,       tile_border * 4 ,       remove_borders=snake_case				) ,       mode='''L''' ,       )
          final_image.paste(
              snake_case ,       (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) ,       snake_case				)







      @torch.no_grad()
      def __call__(     self					: int ,       snake_case					: Union[str, List[str]] ,       snake_case					: Union[PIL.Image.Image, List[PIL.Image.Image]] ,       snake_case					: int = 75 ,       snake_case					: float = 9.0 ,       snake_case					: int = 50 ,       snake_case					: Optional[Union[str, List[str]]] = None ,       snake_case					: Optional[int] = 1 ,       snake_case					: float = 0.0 ,       snake_case					: Optional[torch.Generator] = None ,       snake_case					: Optional[torch.FloatTensor] = None ,       snake_case					: Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,       snake_case					: int = 1 ,       snake_case					: int = 128 ,       snake_case					: int = 32 ,       snake_case					: int = 32 ,       ):
          __UpperCamelCase  		=						Image.new('''RGB''' ,       (image.size[0] * 4, image.size[1] * 4)				)
          __UpperCamelCase  		=						math.ceil(image.size[0] / tile_size				)
          __UpperCamelCase  		=						math.ceil(image.size[1] / tile_size				)
          __UpperCamelCase  		=						tcx * tcy
          __UpperCamelCase  		=						0
          for y in range(snake_case				):
              for x in range(snake_case				):
                  self._process_tile(
                      snake_case ,       snake_case ,       snake_case ,       snake_case ,       snake_case ,       snake_case ,       snake_case ,       prompt=snake_case ,       num_inference_steps=snake_case ,       guidance_scale=snake_case ,       noise_level=snake_case ,       negative_prompt=snake_case ,       num_images_per_prompt=snake_case ,       eta=snake_case ,       generator=snake_case ,       latents=snake_case ,       )
                  current_count += 1
                  if callback is not None:
                      callback({'''progress''': current_count / total_tile_count, '''image''': final_image}				)
          return final_image







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

    """simple docstring"""


    __UpperCamelCase  		=						'''stabilityai/stable-diffusion-x4-upscaler'''
    __UpperCamelCase  		=						StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase_					,     revision='''fp16'''					,     torch_dtype=torch.floataa      )
    __UpperCamelCase  		=						pipe.to('''cuda'''      )
    __UpperCamelCase  		=						Image.open('''../../docs/source/imgs/diffusers_library.jpg'''      )

    def callback(lowercase_      ):
        print(F"progress: {obj['progress']:.4f}"      )
        obj["image"].save('''diffusers_library_progress.jpg'''      )

    __UpperCamelCase  		=						pipe(image=lowercase_					,     prompt='''Black font, white background, vector'''					,     noise_level=40					,     callback=lowercase_      )
    final_image.save('''diffusers_library.jpg'''      )


if __name__ == "__main__":
 main()












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  _lowerCamelCase						:

      """simple docstring"""

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







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          __UpperCamelCase  		=						self.get_config()

          return config, pixel_values, labels







      def       snake_case	(     self					: List[str]				):
          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       snake_case	(     self					: int ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						FocalNetModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          __UpperCamelCase  		=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
          __UpperCamelCase  		=						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       snake_case	(     self					: Optional[Any] ,       snake_case					: Any ,       snake_case					: Dict ,       snake_case					: int				):
          __UpperCamelCase  		=						FocalNetBackbone(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          # 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
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						FocalNetBackbone(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          # 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       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: Any				):
          __UpperCamelCase  		=						FocalNetForMaskedImageModeling(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(
              result.reconstruction.shape ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						FocalNetForMaskedImageModeling(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.reconstruction.shape ,       (self.batch_size, 1, self.image_size, self.image_size)				)







      def       snake_case	(     self					: str ,       snake_case					: Tuple ,       snake_case					: List[str] ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						self.type_sequence_label_size
          __UpperCamelCase  		=						FocalNetForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						FocalNetForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()

          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: Optional[Any]    =					(
          {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: List[str]    =					False

      lowerCAmelCase__			: Any    =					False
      lowerCAmelCase__			: Tuple    =					False
      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: Optional[Any]    =					False







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						FocalNetModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       embed_dim=37 ,       has_text_modality=snake_case				)







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







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case				)







      @unittest.skip(reason='''FocalNet does not use inputs_embeds'''				)
      def       snake_case	(     self					: Optional[Any]				):
          pass







      @unittest.skip(reason='''FocalNet does not use feedforward chunking'''				)
      def       snake_case	(     self					: int				):
          pass







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

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







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase  		=						['''pixel_values''']
              self.assertListEqual(arg_names[:1] ,       snake_case				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						model_class(snake_case				)
          model.to(snake_case				)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase  		=						model(**self._prepare_for_class(snake_case ,       snake_case				)				)

          __UpperCamelCase  		=						outputs.hidden_states

          __UpperCamelCase  		=						getattr(
              self.model_tester ,       '''expected_num_hidden_layers''' ,       len(self.model_tester.depths				) + 1				)
          self.assertEqual(len(snake_case				) ,       snake_case				)

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

          __UpperCamelCase  		=						(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] ,       )

          __UpperCamelCase  		=						outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case				) ,       snake_case				)

          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						reshaped_hidden_states[0].shape
          __UpperCamelCase  		=						(
              reshaped_hidden_states[0].view(snake_case ,       snake_case ,       height * width				).permute(0 ,       2 ,       1				)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]				) ,       [num_patches, self.model_tester.embed_dim] ,       )







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase  		=						(
              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]:
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       snake_case				)

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

              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase  		=						3

          __UpperCamelCase  		=						(
              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)
          )
          __UpperCamelCase  		=						(
              config.patch_size
              if isinstance(config.patch_size ,       collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          __UpperCamelCase  		=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          __UpperCamelCase  		=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       (padded_height, padded_width)				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       (padded_height, padded_width)				)







      @slow
      def       snake_case	(     self					: Union[str, Any]				):
          for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						FocalNetModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase  		=						_config_zero_init(snake_case				)
          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						model_class(config=snake_case				)
              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  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: str				):
          # TODO update organization
          return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''				).to(snake_case				)
          __UpperCamelCase  		=						self.default_image_processor

          __UpperCamelCase  		=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape ,       snake_case				)
          __UpperCamelCase  		=						torch.tensor([0.21_66, -0.43_68, 0.21_91]				).to(snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3] ,       snake_case ,       atol=1E-4				)				)
          self.assertTrue(outputs.logits.argmax(dim=-1				).item() ,       281				)







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					(FocalNetBackbone,) if is_torch_available() else ()
      lowerCAmelCase__			: Dict    =					FocalNetConfig

      lowerCAmelCase__			: List[str]    =					False







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						FocalNetModelTester(self				)






