
"""simple docstring"""



import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Tuple     							=					checkpoint

    UpperCAmelCase__	:      Optional[Any]     							=					{}

    UpperCAmelCase__	:      Union[str, Any]     							=					vae_state_dict['encoder.conv_in.weight']
    UpperCAmelCase__	:      List[str]     							=					vae_state_dict['encoder.conv_in.bias']
    UpperCAmelCase__	:      Tuple     							=					vae_state_dict['encoder.conv_out.weight']
    UpperCAmelCase__	:      List[str]     							=					vae_state_dict['encoder.conv_out.bias']
    UpperCAmelCase__	:      Dict     							=					vae_state_dict['encoder.norm_out.weight']
    UpperCAmelCase__	:      str     							=					vae_state_dict['encoder.norm_out.bias']

    UpperCAmelCase__	:      Optional[int]     							=					vae_state_dict['decoder.conv_in.weight']
    UpperCAmelCase__	:      Dict     							=					vae_state_dict['decoder.conv_in.bias']
    UpperCAmelCase__	:      Optional[int]     							=					vae_state_dict['decoder.conv_out.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					vae_state_dict['decoder.conv_out.bias']
    UpperCAmelCase__	:      str     							=					vae_state_dict['decoder.norm_out.weight']
    UpperCAmelCase__	:      Optional[Any]     							=					vae_state_dict['decoder.norm_out.bias']

    UpperCAmelCase__	:      int     							=					vae_state_dict['quant_conv.weight']
    UpperCAmelCase__	:      Optional[Any]     							=					vae_state_dict['quant_conv.bias']
    UpperCAmelCase__	:      int     							=					vae_state_dict['post_quant_conv.weight']
    UpperCAmelCase__	:      Tuple     							=					vae_state_dict['post_quant_conv.bias']

    # Retrieves the keys for the encoder down blocks only
    UpperCAmelCase__	:      List[Any]     							=					len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'encoder.down' in layer} )
    UpperCAmelCase__	:      Optional[int]     							=					{
        layer_id: [key for key in vae_state_dict if F'''down.{layer_id}''' in key] for layer_id in range(_snake_case )
    }

    # Retrieves the keys for the decoder up blocks only
    UpperCAmelCase__	:      List[Any]     							=					len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'decoder.up' in layer} )
    UpperCAmelCase__	:      List[Any]     							=					{
        layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(_snake_case )
    }

    for i in range(_snake_case ):
        UpperCAmelCase__	:      Dict     							=					[key for key in down_blocks[i] if F'''down.{i}''' in key and F'''down.{i}.downsample''' not in key]

        if F'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
            UpperCAmelCase__	:      List[str]     							=					vae_state_dict.pop(
                F'''encoder.down.{i}.downsample.conv.weight''' )
            UpperCAmelCase__	:      Union[str, Any]     							=					vae_state_dict.pop(
                F'''encoder.down.{i}.downsample.conv.bias''' )

        UpperCAmelCase__	:      List[str]     							=					renew_vae_resnet_paths(_snake_case )
        UpperCAmelCase__	:      Tuple     							=					{'old': F'''down.{i}.block''', 'new': F'''down_blocks.{i}.resnets'''}
        assign_to_checkpoint(_snake_case    ,_snake_case    ,_snake_case    ,additional_replacements=[meta_path]    ,config=_snake_case )

    UpperCAmelCase__	:      int     							=					[key for key in vae_state_dict if 'encoder.mid.block' in key]
    UpperCAmelCase__	:      int     							=					2
    for i in range(1    ,num_mid_res_blocks + 1 ):
        UpperCAmelCase__	:      List[str]     							=					[key for key in mid_resnets if F'''encoder.mid.block_{i}''' in key]

        UpperCAmelCase__	:      int     							=					renew_vae_resnet_paths(_snake_case )
        UpperCAmelCase__	:      List[Any]     							=					{'old': F'''mid.block_{i}''', 'new': F'''mid_block.resnets.{i - 1}'''}
        assign_to_checkpoint(_snake_case    ,_snake_case    ,_snake_case    ,additional_replacements=[meta_path]    ,config=_snake_case )

    UpperCAmelCase__	:      Optional[int]     							=					[key for key in vae_state_dict if 'encoder.mid.attn' in key]
    UpperCAmelCase__	:      Optional[Any]     							=					renew_vae_attention_paths(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					{'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
    assign_to_checkpoint(_snake_case    ,_snake_case    ,_snake_case    ,additional_replacements=[meta_path]    ,config=_snake_case )
    conv_attn_to_linear(_snake_case )

    for i in range(_snake_case ):
        UpperCAmelCase__	:      List[Any]     							=					num_up_blocks - 1 - i
        UpperCAmelCase__	:      List[str]     							=					[
            key for key in up_blocks[block_id] if F'''up.{block_id}''' in key and F'''up.{block_id}.upsample''' not in key
        ]

        if F'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
            UpperCAmelCase__	:      List[str]     							=					vae_state_dict[
                F'''decoder.up.{block_id}.upsample.conv.weight'''
            ]
            UpperCAmelCase__	:      int     							=					vae_state_dict[
                F'''decoder.up.{block_id}.upsample.conv.bias'''
            ]

        UpperCAmelCase__	:      Optional[Any]     							=					renew_vae_resnet_paths(_snake_case )
        UpperCAmelCase__	:      int     							=					{'old': F'''up.{block_id}.block''', 'new': F'''up_blocks.{i}.resnets'''}
        assign_to_checkpoint(_snake_case    ,_snake_case    ,_snake_case    ,additional_replacements=[meta_path]    ,config=_snake_case )

    UpperCAmelCase__	:      int     							=					[key for key in vae_state_dict if 'decoder.mid.block' in key]
    UpperCAmelCase__	:      Tuple     							=					2
    for i in range(1    ,num_mid_res_blocks + 1 ):
        UpperCAmelCase__	:      int     							=					[key for key in mid_resnets if F'''decoder.mid.block_{i}''' in key]

        UpperCAmelCase__	:      Optional[Any]     							=					renew_vae_resnet_paths(_snake_case )
        UpperCAmelCase__	:      Optional[int]     							=					{'old': F'''mid.block_{i}''', 'new': F'''mid_block.resnets.{i - 1}'''}
        assign_to_checkpoint(_snake_case    ,_snake_case    ,_snake_case    ,additional_replacements=[meta_path]    ,config=_snake_case )

    UpperCAmelCase__	:      Optional[Any]     							=					[key for key in vae_state_dict if 'decoder.mid.attn' in key]
    UpperCAmelCase__	:      Any     							=					renew_vae_attention_paths(_snake_case )
    UpperCAmelCase__	:      Union[str, Any]     							=					{'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
    assign_to_checkpoint(_snake_case    ,_snake_case    ,_snake_case    ,additional_replacements=[meta_path]    ,config=_snake_case )
    conv_attn_to_linear(_snake_case )
    return new_checkpoint
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,):
    # Only support V1
    UpperCAmelCase__	:      Any     							=					requests.get(
        ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml' )
    UpperCAmelCase__	:      Optional[int]     							=					io.BytesIO(r.content )

    UpperCAmelCase__	:      int     							=					OmegaConf.load(_snake_case )
    UpperCAmelCase__	:      List[str]     							=					512
    UpperCAmelCase__	:      Tuple     							=					'cuda' if torch.cuda.is_available() else 'cpu'
    if checkpoint_path.endswith('safetensors' ):
        from safetensors import safe_open

        UpperCAmelCase__	:      Optional[int]     							=					{}
        with safe_open(_snake_case    ,framework='pt'    ,device='cpu' ) as f:
            for key in f.keys():
                UpperCAmelCase__	:      Tuple     							=					f.get_tensor(_snake_case )
    else:
        UpperCAmelCase__	:      Optional[Any]     							=					torch.load(_snake_case    ,map_location=_snake_case )['state_dict']

    # Convert the VAE model.
    UpperCAmelCase__	:      str     							=					create_vae_diffusers_config(_snake_case    ,image_size=_snake_case )
    UpperCAmelCase__	:      List[str]     							=					custom_convert_ldm_vae_checkpoint(_snake_case    ,_snake_case )

    UpperCAmelCase__	:      Optional[Any]     							=					AutoencoderKL(**_snake_case )
    vae.load_state_dict(_snake_case )
    vae.save_pretrained(_snake_case )


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

    parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

    UpperCamelCase__          =	parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)



"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 				a			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[Any]     							=					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.'      )
          if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.'      )

          super().__init__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  'You cannot provide bounding boxes '
                  'if you initialized the image processor with apply_ocr set to True.'      )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

          # first, apply the image processor
          UpperCAmelCase__	:      List[Any]     							=					self.image_processor(images=UpperCamelCase_     ,	return_tensors=UpperCamelCase_      )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      Any     							=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__	:      Union[str, Any]     							=					features['words']

          UpperCAmelCase__	:      List[Any]     							=					self.tokenizer(
              text=text if text is not None else features['words']     ,	text_pair=text_pair if text_pair is not None else None     ,	boxes=boxes if boxes is not None else features['boxes']     ,	word_labels=UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	stride=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_token_type_ids=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	return_overflowing_tokens=UpperCamelCase_     ,	return_special_tokens_mask=UpperCamelCase_     ,	return_offsets_mapping=UpperCamelCase_     ,	return_length=UpperCamelCase_     ,	verbose=UpperCamelCase_     ,	return_tensors=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          # add pixel values
          UpperCAmelCase__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]      )

          if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
              raise ValueError(
                  'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                  F''' {len(UpperCamelCase_      )} and {len(UpperCamelCase_      )}'''      )

          return images_with_overflow



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor_class



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor

