




"""simple docstring"""







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 							lowercase    (snake_case__      :       Optional[int]					,				snake_case__      :       Any=1       )   ->     List[Any]:


				'''simple docstring'''






				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("""."""       )[n_shave_prefix_segments:]       )
				else:
								return ".".join(path.split("""."""       )[:n_shave_prefix_segments]       )





def 							lowercase    (snake_case__      :       List[str]					,				snake_case__      :       List[str]=0       )   ->     List[str]:


				'''simple docstring'''






				lowerCAmelCase              = []
				for old_item in old_list:
								lowerCAmelCase              = old_item.replace("""in_layers.0"""					,				"""norm1"""       )
								lowerCAmelCase              = new_item.replace("""in_layers.2"""					,				"""conv1"""       )

								lowerCAmelCase              = new_item.replace("""out_layers.0"""					,				"""norm2"""       )
								lowerCAmelCase              = new_item.replace("""out_layers.3"""					,				"""conv2"""       )

								lowerCAmelCase              = new_item.replace("""emb_layers.1"""					,				"""time_emb_proj"""       )
								lowerCAmelCase              = new_item.replace("""skip_connection"""					,				"""conv_shortcut"""       )

								lowerCAmelCase              = shave_segments(lowerCAmelCase__					,				n_shave_prefix_segments=lowerCAmelCase__       )

								mapping.append({"""old""": old_item, """new""": new_item}       )

				return mapping





def 							lowercase    (snake_case__      :       Union[str, Any]					,				snake_case__      :       Dict=0       )   ->     int:


				'''simple docstring'''






				lowerCAmelCase              = []
				for old_item in old_list:
								lowerCAmelCase              = old_item

								lowerCAmelCase              = new_item.replace("""norm.weight"""					,				"""group_norm.weight"""       )
								lowerCAmelCase              = new_item.replace("""norm.bias"""					,				"""group_norm.bias"""       )

								lowerCAmelCase              = new_item.replace("""proj_out.weight"""					,				"""proj_attn.weight"""       )
								lowerCAmelCase              = new_item.replace("""proj_out.bias"""					,				"""proj_attn.bias"""       )

								lowerCAmelCase              = shave_segments(lowerCAmelCase__					,				n_shave_prefix_segments=lowerCAmelCase__       )

								mapping.append({"""old""": old_item, """new""": new_item}       )

				return mapping





def 							lowercase    (snake_case__      :       Any					,				snake_case__      :       List[Any]					,				snake_case__      :       Union[str, Any]					,				snake_case__      :       Dict=None					,				snake_case__      :       Optional[int]=None					,				snake_case__      :       Any=None       )   ->     Tuple:


				'''simple docstring'''






				assert isinstance(lowerCAmelCase__					,				lowerCAmelCase__       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												lowerCAmelCase              = old_checkpoint[path]
												lowerCAmelCase              = old_tensor.shape[0] // 3

												lowerCAmelCase              = (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

												lowerCAmelCase              = old_tensor.shape[0] // config['num_head_channels'] // 3

												lowerCAmelCase              = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
												lowerCAmelCase              = old_tensor.split(channels // num_heads					,				dim=1       )

												lowerCAmelCase              = query.reshape(lowerCAmelCase__       )
												lowerCAmelCase              = key.reshape(lowerCAmelCase__       )
												lowerCAmelCase              = value.reshape(lowerCAmelCase__       )

				for path in paths:
								lowerCAmelCase              = path['new']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								lowerCAmelCase              = new_path.replace("""middle_block.0"""					,				"""mid_block.resnets.0"""       )
								lowerCAmelCase              = new_path.replace("""middle_block.1"""					,				"""mid_block.attentions.0"""       )
								lowerCAmelCase              = new_path.replace("""middle_block.2"""					,				"""mid_block.resnets.1"""       )

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowerCAmelCase              = new_path.replace(replacement["""old"""]					,				replacement["""new"""]       )

																# proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												lowerCAmelCase              = old_checkpoint[path['old']][:, :, 0]
								else:
												lowerCAmelCase              = old_checkpoint[path['old']]





def 							lowercase    (snake_case__      :       Dict					,				snake_case__      :       Tuple       )   ->     Tuple:


				'''simple docstring'''






				lowerCAmelCase              = {}

				lowerCAmelCase              = checkpoint['time_embed.0.weight']
				lowerCAmelCase              = checkpoint['time_embed.0.bias']
				lowerCAmelCase              = checkpoint['time_embed.2.weight']
				lowerCAmelCase              = checkpoint['time_embed.2.bias']

				lowerCAmelCase              = checkpoint['input_blocks.0.0.weight']
				lowerCAmelCase              = checkpoint['input_blocks.0.0.bias']

				lowerCAmelCase              = checkpoint['out.0.weight']
				lowerCAmelCase              = checkpoint['out.0.bias']
				lowerCAmelCase              = checkpoint['out.2.weight']
				lowerCAmelCase              = checkpoint['out.2.bias']

				# Retrieves the keys for the input blocks only
				lowerCAmelCase              = len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """input_blocks""" in layer}       )
				lowerCAmelCase              = {
				    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(lowerCAmelCase__       )
				}

				# Retrieves the keys for the middle blocks only
				lowerCAmelCase              = len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """middle_block""" in layer}       )
				lowerCAmelCase              = {
				    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
				    for layer_id in range(lowerCAmelCase__       )
				}

				# Retrieves the keys for the output blocks only
				lowerCAmelCase              = len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """output_blocks""" in layer}       )
				lowerCAmelCase              = {
				    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(lowerCAmelCase__       )
				}

				for i in range(1					,				lowerCAmelCase__       ):
								lowerCAmelCase              = (i - 1) // (config['num_res_blocks'] + 1)
								lowerCAmelCase              = (i - 1) % (config['num_res_blocks'] + 1)

								lowerCAmelCase              = [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
								lowerCAmelCase              = [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

								if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
												lowerCAmelCase              = checkpoint[
												    f'''input_blocks.{i}.0.op.weight'''
												]
												lowerCAmelCase              = checkpoint[
												    f'''input_blocks.{i}.0.op.bias'''
												]
												continue

								lowerCAmelCase              = renew_resnet_paths(lowerCAmelCase__       )
								lowerCAmelCase              = {'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
								lowerCAmelCase              = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
								assign_to_checkpoint(
								    lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				additional_replacements=[meta_path, resnet_op]					,				config=lowerCAmelCase__       )

								if len(lowerCAmelCase__       ):
												lowerCAmelCase              = renew_attention_paths(lowerCAmelCase__       )
												lowerCAmelCase              = {
												    'old': f'''input_blocks.{i}.1''',
												    'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												lowerCAmelCase              = {
												    f'''input_blocks.{i}.1.qkv.bias''': {
												        'key': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        'query': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        'value': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    f'''input_blocks.{i}.1.qkv.weight''': {
												        'key': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        'query': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        'value': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				additional_replacements=[meta_path]					,				attention_paths_to_split=lowerCAmelCase__					,				config=lowerCAmelCase__					,				)

				lowerCAmelCase              = middle_blocks[0]
				lowerCAmelCase              = middle_blocks[1]
				lowerCAmelCase              = middle_blocks[2]

				lowerCAmelCase              = renew_resnet_paths(lowerCAmelCase__       )
				assign_to_checkpoint(lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				config=lowerCAmelCase__       )

				lowerCAmelCase              = renew_resnet_paths(lowerCAmelCase__       )
				assign_to_checkpoint(lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				config=lowerCAmelCase__       )

				lowerCAmelCase              = renew_attention_paths(lowerCAmelCase__       )
				lowerCAmelCase              = {
				    'middle_block.1.qkv.bias': {
				        'key': 'mid_block.attentions.0.key.bias',
				        'query': 'mid_block.attentions.0.query.bias',
				        'value': 'mid_block.attentions.0.value.bias',
				    },
				    'middle_block.1.qkv.weight': {
				        'key': 'mid_block.attentions.0.key.weight',
				        'query': 'mid_block.attentions.0.query.weight',
				        'value': 'mid_block.attentions.0.value.weight',
				    },
				}
				assign_to_checkpoint(
				    lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				attention_paths_to_split=lowerCAmelCase__					,				config=lowerCAmelCase__       )

				for i in range(lowerCAmelCase__       ):
								lowerCAmelCase              = i // (config['num_res_blocks'] + 1)
								lowerCAmelCase              = i % (config['num_res_blocks'] + 1)
								lowerCAmelCase              = [shave_segments(lowerCAmelCase__					,				2       ) for name in output_blocks[i]]
								lowerCAmelCase              = {}

								for layer in output_block_layers:
												lowerCAmelCase              = layer.split("""."""       )[0], shave_segments(lowerCAmelCase__					,				1       )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCAmelCase__       )
												else:
																lowerCAmelCase              = [layer_name]

								if len(lowerCAmelCase__       ) > 1:
												lowerCAmelCase              = [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
												lowerCAmelCase              = [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

												lowerCAmelCase              = renew_resnet_paths(lowerCAmelCase__       )
												lowerCAmelCase              = renew_resnet_paths(lowerCAmelCase__       )

												lowerCAmelCase              = {'old': f'''output_blocks.{i}.0''', 'new': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				additional_replacements=[meta_path]					,				config=lowerCAmelCase__       )

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																lowerCAmelCase              = list(output_block_list.values()       ).index(["""conv.weight""", """conv.bias"""]       )
																lowerCAmelCase              = checkpoint[
																    f'''output_blocks.{i}.{index}.conv.weight'''
																]
																lowerCAmelCase              = checkpoint[
																    f'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(lowerCAmelCase__       ) == 2:
																				lowerCAmelCase              = []

												if len(lowerCAmelCase__       ):
																lowerCAmelCase              = renew_attention_paths(lowerCAmelCase__       )
																lowerCAmelCase              = {
																    'old': f'''output_blocks.{i}.1''',
																    'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																lowerCAmelCase              = {
																    f'''output_blocks.{i}.1.qkv.bias''': {
																        'key': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																        'query': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																        'value': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																    },
																    f'''output_blocks.{i}.1.qkv.weight''': {
																        'key': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																        'query': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																        'value': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																    },
																}
																assign_to_checkpoint(
																    lowerCAmelCase__					,				lowerCAmelCase__					,				lowerCAmelCase__					,				additional_replacements=[meta_path]					,				attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions       ) else None					,				config=lowerCAmelCase__					,				)
								else:
												lowerCAmelCase              = renew_resnet_paths(lowerCAmelCase__					,				n_shave_prefix_segments=1       )
												for path in resnet_0_paths:
																lowerCAmelCase              = '.'.join(["""output_blocks""", str(lowerCAmelCase__       ), path["""old"""]]       )
																lowerCAmelCase              = '.'.join(["""up_blocks""", str(lowerCAmelCase__       ), """resnets""", str(lowerCAmelCase__       ), path["""new"""]]       )

																lowerCAmelCase              = checkpoint[old_path]

				return new_checkpoint


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(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help='The config json file corresponding to the architecture.',
		)

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		a            =							parser.parse_args()

		a            =							torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				a            =							json.loads(f.read())

		a            =							convert_ldm_checkpoint(checkpoint, config)

		if "ldm" in config:
				del config["ldm"]

		a            =							UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				a            =							DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
				a            =							VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

				a            =							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)










"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (
          """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
          """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
          """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
      )
      _SCREAMING_SNAKE_CASE  				=     """CIDAS/clipseg-rd64-refined"""
      _SCREAMING_SNAKE_CASE  				=     """image_segmenter"""
      _SCREAMING_SNAKE_CASE  				=     CLIPSegForImageSegmentation

      _SCREAMING_SNAKE_CASE  				=     ["""image""", """text"""]
      _SCREAMING_SNAKE_CASE  				=     ["""image"""]



      def __init__(      self				:      Dict    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        requires_backends(self    ,			['vision']       )
        super().__init__(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      "Image"    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        return self.pre_processor(text=[label]    ,			images=[image]    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        with torch.no_grad():
          lowerCAmelCase_   :				List[str]								= self.model(**SCREAMING_SNAKE_CASE_       ).logits
        return logits





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= outputs.cpu().detach().numpy()
        lowerCAmelCase_   :				Optional[Any]								= 0
        lowerCAmelCase_   :				Optional[Any]								= 1
        return Image.fromarray((array * 2_5_5).astype(np.uinta       )       )





