'''simple docstring'''




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowercase        =    {
    """sample_size""": 3_2,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [3_2, 6_4],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowercase        =    {
    """sample_size""": 6_4,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    """attention_head_dim""": 6_4,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowercase        =    {
    """sample_size""": 2_5_6,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    """attention_head_dim""": 6_4,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowercase        =    {
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.002,
    """sigma_max""": 8_0.0,
}

__lowercase        =    {
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.002,
    """sigma_max""": 8_0.0,
}

__lowercase        =    {
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.002,
    """sigma_max""": 8_0.0,
}






def 							snake_case__				(     _A:   Dict					)       ->      Any:

		'''simple docstring'''


		if isinstance(_lowerCAmelCase ,  _lowerCAmelCase					):
				return v
		if v.lower() in ("yes", "true", "t", "y", "1"):
				return True
		elif v.lower() in ("no", "false", "f", "n", "0"):
				return False
		else:
				raise argparse.ArgumentTypeError("""boolean value expected"""					)






def 							snake_case__				(     _A:   str ,  _A:   int ,  _A:   Tuple ,  _A:   Optional[Any] ,  _A:   str=False					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    checkpoint[f"{old_prefix}.in_layers.0.weight"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.in_layers.0.bias"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.in_layers.2.weight"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.in_layers.2.bias"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.emb_layers.1.weight"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.emb_layers.1.bias"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.out_layers.0.weight"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.out_layers.0.bias"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.out_layers.3.weight"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.out_layers.3.bias"]

		if has_skip:
				lowerCAmelCase     =    checkpoint[f"{old_prefix}.skip_connection.weight"]
				lowerCAmelCase     =    checkpoint[f"{old_prefix}.skip_connection.bias"]

		return new_checkpoint






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

		'''simple docstring'''


		lowerCAmelCase     =    checkpoint[f"{old_prefix}.qkv.weight"].chunk(3 ,  dim=0					)
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.qkv.bias"].chunk(3 ,  dim=0					)

		lowerCAmelCase     =    checkpoint[f"{old_prefix}.norm.weight"]
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.norm.bias"]

		lowerCAmelCase     =    weight_q.squeeze(-1					).squeeze(-1					)
		lowerCAmelCase     =    bias_q.squeeze(-1					).squeeze(-1					)
		lowerCAmelCase     =    weight_k.squeeze(-1					).squeeze(-1					)
		lowerCAmelCase     =    bias_k.squeeze(-1					).squeeze(-1					)
		lowerCAmelCase     =    weight_v.squeeze(-1					).squeeze(-1					)
		lowerCAmelCase     =    bias_v.squeeze(-1					).squeeze(-1					)

		lowerCAmelCase     =    (
		    checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1					).squeeze(-1					)
		)
		lowerCAmelCase     =    checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1					).squeeze(-1					)

		return new_checkpoint






def 							snake_case__				(     _A:   Optional[Any] ,  _A:   str					)       ->      Tuple:

		'''simple docstring'''


		lowerCAmelCase     =    torch.load(_lowerCAmelCase ,  map_location="""cpu"""					)
		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"]

		if unet_config["num_class_embeds"] is not None:
				lowerCAmelCase     =    checkpoint["label_emb.weight"]

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

		lowerCAmelCase     =    unet_config["down_block_types"]
		lowerCAmelCase     =    unet_config["layers_per_block"]
		lowerCAmelCase     =    unet_config["attention_head_dim"]
		lowerCAmelCase     =    unet_config["block_out_channels"]
		lowerCAmelCase     =    1
		lowerCAmelCase     =    channels_list[0]

		for i, layer_type in enumerate(_lowerCAmelCase					):
				lowerCAmelCase     =    channels_list[i]
				lowerCAmelCase     =    current_channels != prev_channels
				if layer_type == "ResnetDownsampleBlock2D":
						for j in range(_lowerCAmelCase					):
								lowerCAmelCase     =    f"down_blocks.{i}.resnets.{j}"
								lowerCAmelCase     =    f"input_blocks.{current_layer}.0"
								lowerCAmelCase     =    True if j == 0 and downsample_block_has_skip else False
								lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  has_skip=_lowerCAmelCase					)
								current_layer += 1

				elif layer_type == "AttnDownBlock2D":
						for j in range(_lowerCAmelCase					):
								lowerCAmelCase     =    f"down_blocks.{i}.resnets.{j}"
								lowerCAmelCase     =    f"input_blocks.{current_layer}.0"
								lowerCAmelCase     =    True if j == 0 and downsample_block_has_skip else False
								lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  has_skip=_lowerCAmelCase					)
								lowerCAmelCase     =    f"down_blocks.{i}.attentions.{j}"
								lowerCAmelCase     =    f"input_blocks.{current_layer}.1"
								lowerCAmelCase     =    convert_attention(
								    _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)
								current_layer += 1

				if i != len(_lowerCAmelCase					) - 1:
						lowerCAmelCase     =    f"down_blocks.{i}.downsamplers.0"
						lowerCAmelCase     =    f"input_blocks.{current_layer}.0"
						lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)
						current_layer += 1

				lowerCAmelCase     =    current_channels

		# hardcoded the mid-block for now
		lowerCAmelCase     =    "mid_block.resnets.0"
		lowerCAmelCase     =    "middle_block.0"
		lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)
		lowerCAmelCase     =    "mid_block.attentions.0"
		lowerCAmelCase     =    "middle_block.1"
		lowerCAmelCase     =    convert_attention(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)
		lowerCAmelCase     =    "mid_block.resnets.1"
		lowerCAmelCase     =    "middle_block.2"
		lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)

		lowerCAmelCase     =    0
		lowerCAmelCase     =    unet_config["up_block_types"]

		for i, layer_type in enumerate(_lowerCAmelCase					):
				if layer_type == "ResnetUpsampleBlock2D":
						for j in range(layers_per_block + 1					):
								lowerCAmelCase     =    f"up_blocks.{i}.resnets.{j}"
								lowerCAmelCase     =    f"output_blocks.{current_layer}.0"
								lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  has_skip=_lowerCAmelCase					)
								current_layer += 1

						if i != len(_lowerCAmelCase					) - 1:
								lowerCAmelCase     =    f"up_blocks.{i}.upsamplers.0"
								lowerCAmelCase     =    f"output_blocks.{current_layer-1}.1"
								lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)
				elif layer_type == "AttnUpBlock2D":
						for j in range(layers_per_block + 1					):
								lowerCAmelCase     =    f"up_blocks.{i}.resnets.{j}"
								lowerCAmelCase     =    f"output_blocks.{current_layer}.0"
								lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  has_skip=_lowerCAmelCase					)
								lowerCAmelCase     =    f"up_blocks.{i}.attentions.{j}"
								lowerCAmelCase     =    f"output_blocks.{current_layer}.1"
								lowerCAmelCase     =    convert_attention(
								    _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)
								current_layer += 1

						if i != len(_lowerCAmelCase					) - 1:
								lowerCAmelCase     =    f"up_blocks.{i}.upsamplers.0"
								lowerCAmelCase     =    f"output_blocks.{current_layer-1}.2"
								lowerCAmelCase     =    convert_resnet(_lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase					)

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

		return new_checkpoint


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

						parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
						parser.add_argument(
						    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
						)
						parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

						__lowercase        =    parser.parse_args()
						__lowercase        =    strabool(args.class_cond)

						__lowercase        =    os.path.basename(args.unet_path)
						print(f'Checkpoint: {ckpt_name}')

						# Get U-Net config
						if "imagenet64" in ckpt_name:
												__lowercase        =    IMAGENET_64_UNET_CONFIG
						elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												__lowercase        =    LSUN_256_UNET_CONFIG
						elif "test" in ckpt_name:
												__lowercase        =    TEST_UNET_CONFIG
						else:
												raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

						if not args.class_cond:
												__lowercase        =    None

						__lowercase        =    con_pt_to_diffuser(args.unet_path, unet_config)

						__lowercase        =    UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												__lowercase        =    CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												__lowercase        =    CT_IMAGENET_64_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												__lowercase        =    CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

						__lowercase        =    CMStochasticIterativeScheduler(**scheduler_config)

						__lowercase        =    ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)











from random import shuffle

import tensorflow as tf
from numpy import array



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[Any]:
 UpperCamelCase      :   List[Any]			      =  int(_lowerCAmelCase	)
 assert noofclusters < len(_lowerCAmelCase	)

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

 # Will help select random centroids from among the available vectors
 UpperCamelCase      :   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.

 UpperCamelCase      :   Any			      =  tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

  UpperCamelCase      :   Union[str, 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
  UpperCamelCase      :   Optional[int]			      =  [
      tf.Variable(vectors[vector_indices[i]]	) for i in range(_lowerCAmelCase	)
  ]
  ##These nodes will assign the centroid Variables the appropriate
  ##values
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float64"				,  [dim]	)
  UpperCamelCase      :   str			      =  []
  for centroid in centroids:
   cent_assigns.append(tf.assign(_lowerCAmelCase				,  _lowerCAmelCase	)	)

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  UpperCamelCase      :   List[str]			      =  [tf.Variable(0	) for i in range(len(_lowerCAmelCase	)	)]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  UpperCamelCase      :   Any			      =  tf.placeholder("int32"	)
  UpperCamelCase      :   Optional[int]			      =  []
  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
  UpperCamelCase      :   Tuple			      =  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
  UpperCamelCase      :   List[str]			      =  tf.reduce_mean(_lowerCAmelCase				,  0	)

  ##Node for computing Euclidean distances
  # Placeholders for input
  UpperCamelCase      :   Optional[int]			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   Any			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   List[str]			      =  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
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float"				,  [noofclusters]	)
  UpperCamelCase      :   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.
  UpperCamelCase      :   Optional[Any]			      =  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.
  UpperCamelCase      :   Any			      =  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	)	):
    UpperCamelCase      :   Dict			      =  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.
    UpperCamelCase      :   Optional[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
    UpperCamelCase      :   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
    UpperCamelCase      :   str			      =  [
        vectors[i]
        for i in range(len(_lowerCAmelCase	)	)
        if sess.run(assignments[i]	) == cluster_n
    ]
    # Compute new centroid location
    UpperCamelCase      :   Optional[int]			      =  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
  UpperCamelCase      :   Union[str, Any]			      =  sess.run(_lowerCAmelCase	)
  UpperCamelCase      :   Tuple			      =  sess.run(_lowerCAmelCase	)
  return centroids, assignments
