
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 ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=7		,	lowerCamelCase=3		,	lowerCamelCase=18		,	lowerCamelCase=30		,	lowerCamelCase=4_00		,	lowerCamelCase=True		,	lowerCamelCase=32		,	lowerCamelCase=True		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				num_channels
          snake_case__						=				image_size
          snake_case__						=				min_resolution
          snake_case__						=				max_resolution
          snake_case__						=				do_resize
          snake_case__						=				size_divisor
          snake_case__						=				do_rescale



      def A_			(			self ):
          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 ):
      _A						:		Optional[Any]    	=     GLPNImageProcessor if is_vision_available() else None
      def A_			(			self ):
          snake_case__						=				GLPNImageProcessingTester(self )
      @property
      def A_			(			self ):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_			(			self ):
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_resize" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"size_divisor" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"resample" ) )
          self.assertTrue(hasattr(lowerCamelCase		,	"do_rescale" ) )
      def A_			(			self ):
          pass
      def A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          snake_case__						=				prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	Image.Image )

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          snake_case__						=				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 A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          snake_case__						=				prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase		,	numpify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	np.ndarray )

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          snake_case__						=				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 A_			(			self ):
          # Initialize image_processing
          snake_case__						=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          snake_case__						=				prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase		,	torchify=lowerCamelCase )
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,	torch.Tensor )

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          snake_case__						=				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 )





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





     def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         if string == "True":
             return True
         elif string == "False":
             return False
         else:
             raise ValueError(F"""could not parse string as bool {string}"""							)

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



