


'''simple docstring'''

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def _SCREAMING_SNAKE_CASE				(A  ,			A=1	)	->	int:






       """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 _SCREAMING_SNAKE_CASE				(A  ,			A=0	)	->	Optional[int]:






       """simple docstring"""







       lowercase__				      =			[]
       for old_item in old_list:
              lowercase__				      =			old_item
              lowercase__				      =			new_item.replace('''block.'''  ,			'''resnets.'''	)
              lowercase__				      =			new_item.replace('''conv_shorcut'''  ,			'''conv1'''	)
              lowercase__				      =			new_item.replace('''in_shortcut'''  ,			'''conv_shortcut'''	)
              lowercase__				      =			new_item.replace('''temb_proj'''  ,			'''time_emb_proj'''	)

              lowercase__				      =			shave_segments(A  ,			n_shave_prefix_segments=A	)

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

       return mapping

def _SCREAMING_SNAKE_CASE				(A  ,			A=0  ,			A=False	)	->	str:






       """simple docstring"""







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

              # In `model.mid`, the layer is called `attn`.
              if not in_mid:
                     lowercase__				      =			new_item.replace('''attn'''  ,			'''attentions'''	)
              lowercase__				      =			new_item.replace('''.k.'''  ,			'''.key.'''	)
              lowercase__				      =			new_item.replace('''.v.'''  ,			'''.value.'''	)
              lowercase__				      =			new_item.replace('''.q.'''  ,			'''.query.'''	)

              lowercase__				      =			new_item.replace('''proj_out'''  ,			'''proj_attn'''	)
              lowercase__				      =			new_item.replace('''norm'''  ,			'''group_norm'''	)

              lowercase__				      =			shave_segments(A  ,			n_shave_prefix_segments=A	)
              mapping.append({'''old''': old_item, '''new''': new_item}	)

       return mapping

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=None  ,			A=None  ,			A=None	)	->	int:






       """simple docstring"""







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

       if attention_paths_to_split is not None:
              if config is None:
                     raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''	)

              for path, path_map in attention_paths_to_split.items():
                     lowercase__				      =			old_checkpoint[path]
                     lowercase__				      =			old_tensor.shape[0] // 3

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

                     lowercase__				      =			old_tensor.shape[0] // config.get('''num_head_channels'''  ,			1	) // 3

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

                     lowercase__				      =			query.reshape(A	).squeeze()
                     lowercase__				      =			key.reshape(A	).squeeze()
                     lowercase__				      =			value.reshape(A	).squeeze()

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

              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

              lowercase__				      =			new_path.replace('''down.'''  ,			'''down_blocks.'''	)
              lowercase__				      =			new_path.replace('''up.'''  ,			'''up_blocks.'''	)

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

              if "attentions" in new_path:
                     lowercase__				      =			old_checkpoint[path['''old''']].squeeze()
              else:
                     lowercase__				      =			old_checkpoint[path['''old''']]

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	List[Any]:






       """simple docstring"""







       lowercase__				      =			{}

       lowercase__				      =			checkpoint['''temb.dense.0.weight''']
       lowercase__				      =			checkpoint['''temb.dense.0.bias''']
       lowercase__				      =			checkpoint['''temb.dense.1.weight''']
       lowercase__				      =			checkpoint['''temb.dense.1.bias''']

       lowercase__				      =			checkpoint['''norm_out.weight''']
       lowercase__				      =			checkpoint['''norm_out.bias''']

       lowercase__				      =			checkpoint['''conv_in.weight''']
       lowercase__				      =			checkpoint['''conv_in.bias''']
       lowercase__				      =			checkpoint['''conv_out.weight''']
       lowercase__				      =			checkpoint['''conv_out.bias''']

       lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''down''' in layer}	)
       lowercase__				      =			{
           layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(A	)
       }

       lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''up''' in layer}	)
       lowercase__				      =			{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(A	)}

       for i in range(A	):
              lowercase__				      =			(i - 1) // (config['''layers_per_block'''] + 1)

              if any('''downsample''' in layer for layer in down_blocks[i]	):
                     lowercase__				      =			checkpoint[
                         f"down.{i}.downsample.op.weight"
                     ]
                     lowercase__				      =			checkpoint[f"down.{i}.downsample.op.bias"]
              #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
              #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

              if any('''block''' in layer for layer in down_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''block''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                         for layer_id in range(A	)
                     }

                     if num_blocks > 0:
                            for j in range(config['''layers_per_block''']	):
                                   lowercase__				      =			renew_resnet_paths(blocks[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A	)

              if any('''attn''' in layer for layer in down_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''attn''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                         for layer_id in range(A	)
                     }

                     if num_attn > 0:
                            for j in range(config['''layers_per_block''']	):
                                   lowercase__				      =			renew_attention_paths(attns[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A  ,			config=A	)

       lowercase__				      =			[key for key in checkpoint if '''mid.block_1''' in key]
       lowercase__				      =			[key for key in checkpoint if '''mid.block_2''' in key]
       lowercase__				      =			[key for key in checkpoint if '''mid.attn_1''' in key]

       # Mid new 2
       lowercase__				      =			renew_resnet_paths(A	)
       assign_to_checkpoint(
           A  ,			A  ,			A  ,			additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]  ,			)

       lowercase__				      =			renew_resnet_paths(A	)
       assign_to_checkpoint(
           A  ,			A  ,			A  ,			additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]  ,			)

       lowercase__				      =			renew_attention_paths(A  ,			in_mid=A	)
       assign_to_checkpoint(
           A  ,			A  ,			A  ,			additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]  ,			)

       for i in range(A	):
              lowercase__				      =			num_up_blocks - 1 - i

              if any('''upsample''' in layer for layer in up_blocks[i]	):
                     lowercase__				      =			checkpoint[
                         f"up.{i}.upsample.conv.weight"
                     ]
                     lowercase__				      =			checkpoint[f"up.{i}.upsample.conv.bias"]

              if any('''block''' in layer for layer in up_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''block''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(A	)
                     }

                     if num_blocks > 0:
                            for j in range(config['''layers_per_block'''] + 1	):
                                   lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
                                   lowercase__				      =			renew_resnet_paths(blocks[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A  ,			additional_replacements=[replace_indices]	)

              if any('''attn''' in layer for layer in up_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''attn''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(A	)
                     }

                     if num_attn > 0:
                            for j in range(config['''layers_per_block'''] + 1	):
                                   lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
                                   lowercase__				      =			renew_attention_paths(attns[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A  ,			additional_replacements=[replace_indices]	)

       lowercase__				      =			{k.replace('''mid_new_2'''  ,			'''mid_block'''	): v for k, v in new_checkpoint.items()}
       return new_checkpoint

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	List[str]:






       """simple docstring"""







       lowercase__				      =			{}

       lowercase__				      =			checkpoint['''encoder.norm_out.weight''']
       lowercase__				      =			checkpoint['''encoder.norm_out.bias''']

       lowercase__				      =			checkpoint['''encoder.conv_in.weight''']
       lowercase__				      =			checkpoint['''encoder.conv_in.bias''']
       lowercase__				      =			checkpoint['''encoder.conv_out.weight''']
       lowercase__				      =			checkpoint['''encoder.conv_out.bias''']

       lowercase__				      =			checkpoint['''decoder.norm_out.weight''']
       lowercase__				      =			checkpoint['''decoder.norm_out.bias''']

       lowercase__				      =			checkpoint['''decoder.conv_in.weight''']
       lowercase__				      =			checkpoint['''decoder.conv_in.bias''']
       lowercase__				      =			checkpoint['''decoder.conv_out.weight''']
       lowercase__				      =			checkpoint['''decoder.conv_out.bias''']

       lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''down''' in layer}	)
       lowercase__				      =			{
           layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(A	)
       }

       lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''up''' in layer}	)
       lowercase__				      =			{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(A	)}

       for i in range(A	):
              lowercase__				      =			(i - 1) // (config['''layers_per_block'''] + 1)

              if any('''downsample''' in layer for layer in down_blocks[i]	):
                     lowercase__				      =			checkpoint[
                         f"encoder.down.{i}.downsample.conv.weight"
                     ]
                     lowercase__				      =			checkpoint[
                         f"encoder.down.{i}.downsample.conv.bias"
                     ]

              if any('''block''' in layer for layer in down_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''block''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                         for layer_id in range(A	)
                     }

                     if num_blocks > 0:
                            for j in range(config['''layers_per_block''']	):
                                   lowercase__				      =			renew_resnet_paths(blocks[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A	)

              if any('''attn''' in layer for layer in down_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''attn''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                         for layer_id in range(A	)
                     }

                     if num_attn > 0:
                            for j in range(config['''layers_per_block''']	):
                                   lowercase__				      =			renew_attention_paths(attns[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A  ,			config=A	)

       lowercase__				      =			[key for key in checkpoint if '''mid.block_1''' in key]
       lowercase__				      =			[key for key in checkpoint if '''mid.block_2''' in key]
       lowercase__				      =			[key for key in checkpoint if '''mid.attn_1''' in key]

       # Mid new 2
       lowercase__				      =			renew_resnet_paths(A	)
       assign_to_checkpoint(
           A  ,			A  ,			A  ,			additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]  ,			)

       lowercase__				      =			renew_resnet_paths(A	)
       assign_to_checkpoint(
           A  ,			A  ,			A  ,			additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]  ,			)

       lowercase__				      =			renew_attention_paths(A  ,			in_mid=A	)
       assign_to_checkpoint(
           A  ,			A  ,			A  ,			additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]  ,			)

       for i in range(A	):
              lowercase__				      =			num_up_blocks - 1 - i

              if any('''upsample''' in layer for layer in up_blocks[i]	):
                     lowercase__				      =			checkpoint[
                         f"decoder.up.{i}.upsample.conv.weight"
                     ]
                     lowercase__				      =			checkpoint[
                         f"decoder.up.{i}.upsample.conv.bias"
                     ]

              if any('''block''' in layer for layer in up_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''block''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(A	)
                     }

                     if num_blocks > 0:
                            for j in range(config['''layers_per_block'''] + 1	):
                                   lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
                                   lowercase__				      =			renew_resnet_paths(blocks[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A  ,			additional_replacements=[replace_indices]	)

              if any('''attn''' in layer for layer in up_blocks[i]	):
                     lowercase__				      =			len(
                         {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''attn''' in layer}	)
                     lowercase__				      =			{
                         layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(A	)
                     }

                     if num_attn > 0:
                            for j in range(config['''layers_per_block'''] + 1	):
                                   lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
                                   lowercase__				      =			renew_attention_paths(attns[j]	)
                                   assign_to_checkpoint(A  ,			A  ,			A  ,			additional_replacements=[replace_indices]	)

       lowercase__				      =			{k.replace('''mid_new_2'''  ,			'''mid_block'''	): v for k, v in new_checkpoint.items()}
       lowercase__				      =			checkpoint['''quant_conv.weight''']
       lowercase__				      =			checkpoint['''quant_conv.bias''']
       if "quantize.embedding.weight" in checkpoint:
              lowercase__				      =			checkpoint['''quantize.embedding.weight''']
       lowercase__				      =			checkpoint['''post_quant_conv.weight''']
       lowercase__				      =			checkpoint['''post_quant_conv.bias''']

       return new_checkpoint


if __name__ == "__main__":
 lowerCamelCase							:       List[str]  						=     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.')

 lowerCamelCase							:       Tuple  						=     parser.parse_args()
 lowerCamelCase							:       List[Any]  						=     torch.load(args.checkpoint_path)

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

 # unet case
 lowerCamelCase							:       str  						=     {key.split('.')[0] for key in checkpoint.keys()}
 if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  lowerCamelCase							:       str  						=     convert_vq_autoenc_checkpoint(checkpoint, config)
 else:
  lowerCamelCase							:       Any  						=     convert_ddpm_checkpoint(checkpoint, config)

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

 if config["_class_name"] == "VQModel":
  lowerCamelCase							:       int  						=     VQModel(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 elif config["_class_name"] == "AutoencoderKL":
  lowerCamelCase							:       Optional[Any]  						=     AutoencoderKL(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 else:
  lowerCamelCase							:       Tuple  						=     UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  lowerCamelCase							:       Optional[int]  						=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

  lowerCamelCase							:       Optional[int]  						=     DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCamelCase							:       Any  						=     'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class   __lowerCAmelCase						(lowercase_					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							"""google/flan-t5-base"""
      lowerCAmelCase__ :					List[Any]   =							(
          """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
          """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
      )
      lowerCAmelCase__ :					Any   =							"""text_qa"""
      lowerCAmelCase__ :					Union[str, Any]   =							AutoTokenizer
      lowerCAmelCase__ :					Any   =							AutoModelForSeqaSeqLM

      lowerCAmelCase__ :					Any   =							["""text""", """text"""]
      lowerCAmelCase__ :					Optional[int]   =							["""text"""]



      def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   str			):

             '''simple docstring'''







             lowercase__				      =			QA_PROMPT.format(text=UpperCamelCase       ,						question=UpperCamelCase			)
             return self.pre_processor(UpperCamelCase       ,						return_tensors='''pt'''			)



      def 				UpperCamelCase__		(self   :   Dict       ,						UpperCamelCase   :   List[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.model.generate(**UpperCamelCase			)

             lowercase__      ,lowercase__				      =			inputs['''input_ids'''].shape
             lowercase__				      =			output_ids.shape[0]

             return output_ids.reshape(UpperCamelCase       ,						out_b // in_b       ,						*output_ids.shape[1:]			)[0][0]






      def 				UpperCamelCase__		(self   :   Tuple       ,						UpperCamelCase   :   Optional[Any]			):

             '''simple docstring'''







             return self.pre_processor.decode(UpperCamelCase       ,						skip_special_tokens=UpperCamelCase       ,						clean_up_tokenization_spaces=UpperCamelCase			)




