


'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A__						:   Dict				=    logging.get_logger(__name__)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         super().__init__()
         __lowerCamelCase  :      str														=						nn.ModuleList(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Union[ControlNetOutput, Tuple]:
         for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	self.nets      )      ):
              __lowerCamelCase							,			__lowerCamelCase  :      str														=						controlnet(
                  SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	)

              # merge samples
              if i == 0:
                   __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						down_samples, mid_sample
              else:
                   __lowerCamelCase  :      str														=						[
                       samples_prev + samples_curr
                       for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   ]
                   mid_block_res_sample += mid_sample

         return down_block_res_samples, mid_block_res_sample







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	List[Any]:
         __lowerCamelCase  :      Any														=						0
         __lowerCamelCase  :      Union[str, Any]														=						save_directory
         for controlnet in self.nets:
              controlnet.save_pretrained(
                  SCREAMING_SNAKE_CASE_     ,	is_main_process=SCREAMING_SNAKE_CASE_     ,	save_function=SCREAMING_SNAKE_CASE_     ,	safe_serialization=SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_     ,	)

              idx += 1
              __lowerCamelCase  :      List[str]														=						model_path_to_save + f'_{idx}'







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      str														=						0
         __lowerCamelCase  :      Any														=						[]

         # load controlnet and append to list until no controlnet directory exists anymore
         # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
         # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
         __lowerCamelCase  :      str														=						pretrained_model_path
         while os.path.isdir(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
              controlnets.append(SCREAMING_SNAKE_CASE_      )

              idx += 1
              __lowerCamelCase  :      Dict														=						pretrained_model_path + f'_{idx}'

         logger.info(f'{len(SCREAMING_SNAKE_CASE_      )} controlnets loaded from {pretrained_model_path}.'      )

         if len(SCREAMING_SNAKE_CASE_      ) == 0:
              raise ValueError(
                  f'No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE_      )}. Expected at least {pretrained_model_path + "_0"}.'      )

         return cls(SCREAMING_SNAKE_CASE_      )













"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor



class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)
