



from random import shuffle

import tensorflow as tf
from numpy import array



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Optional[Any] ,				SCREAMING_SNAKE_CASE__				:		Dict )     ->   Tuple:
 _snake_case			:     Dict 		=    int(__lowerCamelCase )
 assert noofclusters < len(__lowerCamelCase )

 # Find out the dimensionality
 _snake_case			:     str 		=    len(vectors[0] )

 # Will help select random centroids from among the available vectors
 _snake_case			:     List[str] 		=    list(range(len(__lowerCamelCase ) ) )
 shuffle(__lowerCamelCase )

 # GRAPH OF COMPUTATION
 # We initialize a new graph and set it as the default during each run
 # of this algorithm. This ensures that as this function is called
 # multiple times, the default graph doesn't keep getting crowded with
 # unused ops and Variables from previous function calls.

 _snake_case			:     str 		=    tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

  _snake_case			:     Optional[Any] 		=    tf.Session()

  ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

  ##First lets ensure we have a Variable vector for each centroid,
  ##initialized to one of the vectors from the available data points
  _snake_case			:     int 		=    [
      tf.Variable(vectors[vector_indices[i]] ) for i in range(__lowerCamelCase )
  ]
  ##These nodes will assign the centroid Variables the appropriate
  ##values
  _snake_case			:     List[Any] 		=    tf.placeholder("""float64""" ,				[dim] )
  _snake_case			:     str 		=    []
  for centroid in centroids:
   cent_assigns.append(tf.assign(__lowerCamelCase ,				__lowerCamelCase ) )

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  _snake_case			:     int 		=    [tf.Variable(0 ) for i in range(len(__lowerCamelCase ) )]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  _snake_case			:     Union[str, Any] 		=    tf.placeholder("""int32""" )
  _snake_case			:     Any 		=    []
  for assignment in assignments:
   cluster_assigns.append(tf.assign(__lowerCamelCase ,				__lowerCamelCase ) )

  ##Now lets construct the node that will compute the mean
  # The placeholder for the input
  _snake_case			:     int 		=    tf.placeholder("""float""" ,				[None, dim] )
  # The Node/op takes the input and computes a mean along the 0th
  # dimension, i.e. the list of input vectors
  _snake_case			:     Optional[int] 		=    tf.reduce_mean(__lowerCamelCase ,				0 )

  ##Node for computing Euclidean distances
  # Placeholders for input
  _snake_case			:     str 		=    tf.placeholder("""float""" ,				[dim] )
  _snake_case			:     Tuple 		=    tf.placeholder("""float""" ,				[dim] )
  _snake_case			:     Dict 		=    tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCamelCase ,				__lowerCamelCase ) ,				2 ) ) )

  ##This node will figure out which cluster to assign a vector to,
  ##based on Euclidean distances of the vector from the centroids.
  # Placeholder for input
  _snake_case			:     Union[str, Any] 		=    tf.placeholder("""float""" ,				[noofclusters] )
  _snake_case			:     str 		=    tf.argmin(__lowerCamelCase ,				0 )

  ##INITIALIZING STATE VARIABLES

  ##This will help initialization of all Variables defined with respect
  ##to the graph. The Variable-initializer should be defined after
  ##all the Variables have been constructed, so that each of them
  ##will be included in the initialization.
  _snake_case			:     str 		=    tf.initialize_all_variables()

  # Initialize all variables
  sess.run(__lowerCamelCase )

  ##CLUSTERING ITERATIONS

  # Now perform the Expectation-Maximization steps of K-Means clustering
  # iterations. To keep things simple, we will only do a set number of
  # iterations, instead of using a Stopping Criterion.
  _snake_case			:     List[str] 		=    100
  for _ in range(__lowerCamelCase ):
   ##EXPECTATION STEP
   ##Based on the centroid locations till last iteration, compute
   ##the _expected_ centroid assignments.
   # Iterate over each vector
   for vector_n in range(len(__lowerCamelCase ) ):
    _snake_case			:     str 		=    vectors[vector_n]
    # Compute Euclidean distance between this vector and each
    # centroid. Remember that this list cannot be named
    #'centroid_distances', since that is the input to the
    # cluster assignment node.
    _snake_case			:     Any 		=    [
        sess.run(__lowerCamelCase ,				feed_dict={va: vect, va: sess.run(__lowerCamelCase )} )
        for centroid in centroids
    ]
    # Now use the cluster assignment node, with the distances
    # as the input
    _snake_case			:     Tuple 		=    sess.run(
        __lowerCamelCase ,				feed_dict={centroid_distances: distances} )
    # Now assign the value to the appropriate state variable
    sess.run(
        cluster_assigns[vector_n] ,				feed_dict={assignment_value: assignment} )

   ##MAXIMIZATION STEP
   # Based on the expected state computed from the Expectation Step,
   # compute the locations of the centroids so as to maximize the
   # overall objective of minimizing within-cluster Sum-of-Squares
   for cluster_n in range(__lowerCamelCase ):
    # Collect all the vectors assigned to this cluster
    _snake_case			:     List[str] 		=    [
        vectors[i]
        for i in range(len(__lowerCamelCase ) )
        if sess.run(assignments[i] ) == cluster_n
    ]
    # Compute new centroid location
    _snake_case			:     Union[str, Any] 		=    sess.run(
        __lowerCamelCase ,				feed_dict={mean_input: array(__lowerCamelCase )} )
    # Assign value to appropriate variable
    sess.run(
        cent_assigns[cluster_n] ,				feed_dict={centroid_value: new_location} )

    # Return centroids and assignments
  _snake_case			:     Optional[int] 		=    sess.run(__lowerCamelCase )
  _snake_case			:     Any 		=    sess.run(__lowerCamelCase )
  return centroids, assignments








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__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     int		):
  __UpperCAmelCase							:	List[Any]           =  checkpoint

  __UpperCAmelCase							:	Tuple           =  {}

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""encoder.conv_in.weight"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""encoder.conv_in.bias"""]
  __UpperCAmelCase							:	Dict           =  vae_state_dict["""encoder.conv_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""encoder.conv_out.bias"""]
  __UpperCAmelCase							:	List[Any]           =  vae_state_dict["""encoder.norm_out.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""encoder.norm_out.bias"""]

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""decoder.conv_in.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""decoder.conv_in.bias"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.weight"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.bias"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""decoder.norm_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""decoder.norm_out.bias"""]

  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""quant_conv.weight"""]
  __UpperCAmelCase							:	int           =  vae_state_dict["""quant_conv.bias"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""post_quant_conv.weight"""]
  __UpperCAmelCase							:	Any           =  vae_state_dict["""post_quant_conv.bias"""]

  # Retrieves the keys for the encoder down blocks only
  __UpperCAmelCase							:	int           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """encoder.down""" in layer}		)
  __UpperCAmelCase							:	Optional[Any]           =  {
      layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase		)
  }

  # Retrieves the keys for the decoder up blocks only
  __UpperCAmelCase							:	Dict           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """decoder.up""" in layer}		)
  __UpperCAmelCase							:	Optional[int]           =  {
      layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase		)
  }

  for i in range(__lowerCamelCase		):
    __UpperCAmelCase							:	List[Any]           =  [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							:	Optional[Any]           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""		)
      __UpperCAmelCase							:	int           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""		)

    __UpperCAmelCase							:	Optional[int]           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	Tuple           =  [key for key in vae_state_dict if """encoder.mid.block""" in key]
  __UpperCAmelCase							:	Optional[Any]           =  2
  for i in range(1  ,		num_mid_res_blocks + 1		):
    __UpperCAmelCase							:	Dict           =  [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

    __UpperCAmelCase							:	Tuple           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Tuple           =  {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	List[Any]           =  [key for key in vae_state_dict if """encoder.mid.attn""" in key]
  __UpperCAmelCase							:	str           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)
  conv_attn_to_linear(__lowerCamelCase		)

  for i in range(__lowerCamelCase		):
    __UpperCAmelCase							:	Optional[Any]           =  num_up_blocks - 1 - i
    __UpperCAmelCase							:	Union[str, Any]           =  [
        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							:	int           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      __UpperCAmelCase							:	Dict           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    __UpperCAmelCase							:	Dict           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	Tuple           =  [key for key in vae_state_dict if """decoder.mid.block""" in key]
  __UpperCAmelCase							:	Union[str, Any]           =  2
  for i in range(1  ,		num_mid_res_blocks + 1		):
    __UpperCAmelCase							:	Dict           =  [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

    __UpperCAmelCase							:	List[Any]           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	int           =  {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
    assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)

  __UpperCAmelCase							:	Dict           =  [key for key in vae_state_dict if """decoder.mid.attn""" in key]
  __UpperCAmelCase							:	List[Any]           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		additional_replacements=[meta_path]  ,		config=__lowerCamelCase		)
  conv_attn_to_linear(__lowerCamelCase		)
  return new_checkpoint






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     str  ,		):
  # Only support V1
  __UpperCAmelCase							:	Optional[int]           =  requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""		)
  __UpperCAmelCase							:	Optional[int]           =  io.BytesIO(r.content		)

  __UpperCAmelCase							:	Dict           =  OmegaConf.load(__lowerCamelCase		)
  __UpperCAmelCase							:	str           =  512
  __UpperCAmelCase							:	Any           =  """cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""		):
    from safetensors import safe_open

    __UpperCAmelCase							:	List[Any]           =  {}
    with safe_open(__lowerCamelCase  ,		framework="""pt"""  ,		device="""cpu"""		) as f:
      for key in f.keys():
        __UpperCAmelCase							:	str           =  f.get_tensor(__lowerCamelCase		)
  else:
    __UpperCAmelCase							:	Optional[int]           =  torch.load(__lowerCamelCase  ,		map_location=__lowerCamelCase		)["""state_dict"""]

  # Convert the VAE model.
  __UpperCAmelCase							:	Optional[int]           =  create_vae_diffusers_config(__lowerCamelCase  ,		image_size=__lowerCamelCase		)
  __UpperCAmelCase							:	Union[str, Any]           =  custom_convert_ldm_vae_checkpoint(__lowerCamelCase  ,		__lowerCamelCase		)

  __UpperCAmelCase							:	Optional[int]           =  AutoencoderKL(**__lowerCamelCase		)
  vae.load_state_dict(__lowerCamelCase		)
  vae.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
       a		:       List[Any]			     = 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.")

       a		:       Optional[int]			     = parser.parse_args()

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


