




'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=1     )		->		List[str]:
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split('.'     )[n_shave_prefix_segments:]     )
			else:
						return ".".join(path.split('.'     )[:n_shave_prefix_segments]     )





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=0     )		->		Tuple:
			A_									= []
			for old_item in old_list:
						A_									= old_item.replace('in_layers.0'       ,'norm1'     )
						A_									= new_item.replace('in_layers.2'       ,'conv1'     )

						A_									= new_item.replace('out_layers.0'       ,'norm2'     )
						A_									= new_item.replace('out_layers.3'       ,'conv2'     )

						A_									= new_item.replace('emb_layers.1'       ,'time_emb_proj'     )
						A_									= new_item.replace('skip_connection'       ,'conv_shortcut'     )

						A_									= shave_segments(SCREAMING_SNAKE_CASE_       ,n_shave_prefix_segments=SCREAMING_SNAKE_CASE_     )

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

			return mapping





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=0     )		->		Optional[int]:
			A_									= []
			for old_item in old_list:
						A_									= old_item

						A_									= new_item.replace('norm.weight'       ,'group_norm.weight'     )
						A_									= new_item.replace('norm.bias'       ,'group_norm.bias'     )

						A_									= new_item.replace('proj_out.weight'       ,'proj_attn.weight'     )
						A_									= new_item.replace('proj_out.bias'       ,'proj_attn.bias'     )

						A_									= shave_segments(SCREAMING_SNAKE_CASE_       ,n_shave_prefix_segments=SCREAMING_SNAKE_CASE_     )

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

			return mapping





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=None       ,SCREAMING_SNAKE_CASE_=None       ,SCREAMING_SNAKE_CASE_=None     )		->		Any:
			assert isinstance(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ), "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():
									A_									= old_checkpoint[path]
									A_									= old_tensor.shape[0] // 3

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

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

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

									A_									= query.reshape(SCREAMING_SNAKE_CASE_     )
									A_									= key.reshape(SCREAMING_SNAKE_CASE_     )
									A_									= value.reshape(SCREAMING_SNAKE_CASE_     )

			for path in paths:
						A_									= 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
						A_									= new_path.replace('middle_block.0'       ,'mid_block.resnets.0'     )
						A_									= new_path.replace('middle_block.1'       ,'mid_block.attentions.0'     )
						A_									= new_path.replace('middle_block.2'       ,'mid_block.resnets.1'     )

						if additional_replacements is not None:
									for replacement in additional_replacements:
												A_									= 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:
									A_									= old_checkpoint[path['old']][:, :, 0]
						else:
									A_									= old_checkpoint[path['old']]





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Tuple:
			A_									= {}

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

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

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

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

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

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

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

						A_									= [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
						A_									= [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:
									A_									= checkpoint[
									    F'input_blocks.{i}.0.op.weight'
									]
									A_									= checkpoint[
									    F'input_blocks.{i}.0.op.bias'
									]
									continue

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

						if len(SCREAMING_SNAKE_CASE_     ):
									A_									= renew_attention_paths(SCREAMING_SNAKE_CASE_     )
									A_									= {
									    'old': F'input_blocks.{i}.1',
									    'new': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
									}
									A_									= {
									    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(
									    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,additional_replacements=[meta_path]       ,attention_paths_to_split=SCREAMING_SNAKE_CASE_       ,config=SCREAMING_SNAKE_CASE_       ,)

			A_									= middle_blocks[0]
			A_									= middle_blocks[1]
			A_									= middle_blocks[2]

			A_									= renew_resnet_paths(SCREAMING_SNAKE_CASE_     )
			assign_to_checkpoint(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,config=SCREAMING_SNAKE_CASE_     )

			A_									= renew_resnet_paths(SCREAMING_SNAKE_CASE_     )
			assign_to_checkpoint(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,config=SCREAMING_SNAKE_CASE_     )

			A_									= renew_attention_paths(SCREAMING_SNAKE_CASE_     )
			A_									= {
			    '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(
			    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,attention_paths_to_split=SCREAMING_SNAKE_CASE_       ,config=SCREAMING_SNAKE_CASE_     )

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

						for layer in output_block_layers:
									A_		, A_									= layer.split('.'     )[0], shave_segments(SCREAMING_SNAKE_CASE_       ,1     )
									if layer_id in output_block_list:
												output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_     )
									else:
												A_									= [layer_name]

						if len(SCREAMING_SNAKE_CASE_     ) > 1:
									A_									= [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
									A_									= [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

									A_									= renew_resnet_paths(SCREAMING_SNAKE_CASE_     )
									A_									= renew_resnet_paths(SCREAMING_SNAKE_CASE_     )

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

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

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

									if len(SCREAMING_SNAKE_CASE_     ):
												A_									= renew_attention_paths(SCREAMING_SNAKE_CASE_     )
												A_									= {
												    'old': F'output_blocks.{i}.1',
												    'new': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
												}
												A_									= {
												    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(
												    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,additional_replacements=[meta_path]       ,attention_paths_to_split=to_split if any('qkv' in key for key in attentions     ) else None       ,config=SCREAMING_SNAKE_CASE_       ,)
						else:
									A_									= renew_resnet_paths(SCREAMING_SNAKE_CASE_       ,n_shave_prefix_segments=1     )
									for path in resnet_0_paths:
												A_									= '.'.join(['output_blocks', str(SCREAMING_SNAKE_CASE_     ), path['old']]     )
												A_									= '.'.join(['up_blocks', str(SCREAMING_SNAKE_CASE_     ), 'resnets', str(SCREAMING_SNAKE_CASE_     ), path['new']]     )

												A_									= checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						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.")

							_SCREAMING_SNAKE_CASE			=						parser.parse_args()

							_SCREAMING_SNAKE_CASE			=						torch.load(args.checkpoint_path)

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

							_SCREAMING_SNAKE_CASE			=						convert_ldm_checkpoint(checkpoint, config)

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

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

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

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










'''simple docstring'''
from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase				)
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       **_lowercase     :						List[Any])    ->							Union[str, Any]:
								super().__init__(**_lowercase)

								requires_backends(self      ,       'vision')
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)



					def __call__(	self     :						Optional[Any]      ,       _lowercase     :						Union[str, List[str], "Image", List["Image"]]      ,       **_lowercase     :						List[Any])    ->							Any:
								return super().__call__(_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Union[str, Any])    ->							Any:
								A_									= {}
								if "candidate_labels" in kwargs:
											A_									= kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
											A_									= kwargs['hypothesis_template']

								return preprocess_params, {}, {}



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						Tuple      ,       _lowercase     :						Any=None      ,       _lowercase     :						Optional[int]="This is a photo of {}.")    ->							Union[str, Any]:
								A_									= load_image(_lowercase)
								A_									= self.image_processor(images=[image]      ,       return_tensors=self.framework)
								A_									= candidate_labels
								A_									= [hypothesis_template.format(_lowercase) for x in candidate_labels]
								A_									= self.tokenizer(_lowercase      ,       return_tensors=self.framework      ,       padding=_lowercase)
								A_									= [text_inputs]
								return inputs



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Tuple)    ->							Optional[int]:
								A_									= model_inputs.pop('candidate_labels')
								A_									= model_inputs.pop('text_inputs')
								if isinstance(text_inputs[0]      ,       _lowercase):
											A_									= text_inputs[0]
								else:
											# Batching case.
											A_									= text_inputs[0][0]

								A_									= self.model(**_lowercase      ,       **_lowercase)

								A_									= {
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						int)    ->							Optional[int]:
								A_									= model_outputs.pop('candidate_labels')
								A_									= model_outputs['logits'][0]
								if self.framework == "pt":
											A_									= logits.softmax(dim=-1).squeeze(-1)
											A_									= probs.tolist()
											if not isinstance(_lowercase      ,       _lowercase):
														A_									= [scores]
								elif self.framework == "tf":
											A_									= stable_softmax(_lowercase      ,       axis=-1)
											A_									= probs.numpy().tolist()
								else:
											raise ValueError(F'Unsupported framework: {self.framework}')

								A_									= [
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(_lowercase      ,       _lowercase)      ,       key=lambda _lowercase: -x[0])
								]
								return result




