





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class      lowerCamelCase:
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'							)

         torch.manual_seed(0							)
         _A     =							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'							)

         torch.manual_seed(0							)
         _A     =							UNetaDConditionModel(
             sample_size=32				,     layers_per_block=1				,     block_out_channels=[32, 64]				,     down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]				,     mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,     up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,     in_channels=3				,     out_channels=6				,     cross_attention_dim=32				,     encoder_hid_dim=32				,     attention_head_dim=8				,     addition_embed_type='text'				,     addition_embed_type_num_heads=2				,     cross_attention_norm='group_norm'				,     resnet_time_scale_shift='scale_shift'				,     act_fn='gelu'				,     )
         unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

         torch.manual_seed(0							)
         _A     =							DDPMScheduler(
             num_train_timesteps=1000				,     beta_schedule='squaredcos_cap_v2'				,     beta_start=0.0001				,     beta_end=0.02				,     thresholding=_a				,     dynamic_thresholding_ratio=0.95				,     sample_max_value=1.0				,     prediction_type='epsilon'				,     variance_type='learned_range'				,     )

         torch.manual_seed(0							)
         _A     =							IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }




     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'							)

         torch.manual_seed(0							)
         _A     =							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'							)

         torch.manual_seed(0							)
         _A     =							UNetaDConditionModel(
             sample_size=32				,     layers_per_block=[1, 2]				,     block_out_channels=[32, 64]				,     down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]				,     mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,     up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,     in_channels=6				,     out_channels=6				,     cross_attention_dim=32				,     encoder_hid_dim=32				,     attention_head_dim=8				,     addition_embed_type='text'				,     addition_embed_type_num_heads=2				,     cross_attention_norm='group_norm'				,     resnet_time_scale_shift='scale_shift'				,     act_fn='gelu'				,     class_embed_type='timestep'				,     mid_block_scale_factor=1.414				,     time_embedding_act_fn='gelu'				,     time_embedding_dim=32				,     )
         unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

         torch.manual_seed(0							)
         _A     =							DDPMScheduler(
             num_train_timesteps=1000				,     beta_schedule='squaredcos_cap_v2'				,     beta_start=0.0001				,     beta_end=0.02				,     thresholding=_a				,     dynamic_thresholding_ratio=0.95				,     sample_max_value=1.0				,     prediction_type='epsilon'				,     variance_type='learned_range'				,     )

         torch.manual_seed(0							)
         _A     =							DDPMScheduler(
             num_train_timesteps=1000				,     beta_schedule='squaredcos_cap_v2'				,     beta_start=0.0001				,     beta_end=0.02				,     )

         torch.manual_seed(0							)
         _A     =							IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }




     def        lowerCAmelCase__		(    self							):
         _A     =							self.get_dummy_components()
         _A     =							self.pipeline_class(**_a							)
         pipe.to(_a							)
         pipe.set_progress_bar_config(disable=_a							)

         _A     =							self.get_dummy_inputs(_a							)

         _A     =							inputs['''prompt''']
         _A     =							inputs['''generator''']
         _A     =							inputs['''num_inference_steps''']
         _A     =							inputs['''output_type''']

         if "image" in inputs:
             _A     =							inputs['''image''']
         else:
             _A     =							None

         if "mask_image" in inputs:
             _A     =							inputs['''mask_image''']
         else:
             _A     =							None

         if "original_image" in inputs:
             _A     =							inputs['''original_image''']
         else:
             _A     =							None

         _A     =							pipe.encode_prompt(_a							)

         # inputs with prompt converted to embeddings
         _A     =							{
             '''prompt_embeds''': prompt_embeds,
             '''negative_prompt_embeds''': negative_prompt_embeds,
             '''generator''': generator,
             '''num_inference_steps''': num_inference_steps,
             '''output_type''': output_type,
         }

         if image is not None:
             _A     =							image

         if mask_image is not None:
             _A     =							mask_image

         if original_image is not None:
             _A     =							original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
             setattr(_a				,     _a				,     _a							)

         _A     =							pipe(**_a							)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_a							)
             _A     =							self.pipeline_class.from_pretrained(_a							)
             pipe_loaded.to(_a							)
             pipe_loaded.set_progress_bar_config(disable=_a							)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

         for optional_component in pipe._optional_components:
             self.assertTrue(
                 getattr(_a				,     _a							) is None				,     F"`{optional_component}` did not stay set to None after loading."				,     )

         _A     =							self.get_dummy_inputs(_a							)

         _A     =							inputs['''generator''']
         _A     =							inputs['''num_inference_steps''']
         _A     =							inputs['''output_type''']

         # inputs with prompt converted to embeddings
         _A     =							{
             '''prompt_embeds''': prompt_embeds,
             '''negative_prompt_embeds''': negative_prompt_embeds,
             '''generator''': generator,
             '''num_inference_steps''': num_inference_steps,
             '''output_type''': output_type,
         }

         if image is not None:
             _A     =							image

         if mask_image is not None:
             _A     =							mask_image

         if original_image is not None:
             _A     =							original_image

         _A     =							pipe_loaded(**_a							)[0]

         _A     =							np.abs(to_np(_a							) - to_np(_a							)							).max()
         self.assertLess(_a				,     1E-4							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.get_dummy_components()
         _A     =							self.pipeline_class(**_a							)
         pipe.to(_a							)
         pipe.set_progress_bar_config(disable=_a							)

         _A     =							self.get_dummy_inputs(_a							)
         _A     =							pipe(**_a							)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_a							)
             _A     =							self.pipeline_class.from_pretrained(_a							)
             pipe_loaded.to(_a							)
             pipe_loaded.set_progress_bar_config(disable=_a							)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

         _A     =							self.get_dummy_inputs(_a							)
         _A     =							pipe_loaded(**_a							)[0]

         _A     =							np.abs(to_np(_a							) - to_np(_a							)							).max()
         self.assertLess(_a				,     1E-4							)








import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						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   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								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)

			a__												=						parser.parse_args()

			a__												=						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)



