


import argparse

import torch

from diffusers import UNetaDConditionModel






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=None			,  __lowerCamelCase=None			,  __lowerCamelCase=None							)		->List[Any]:


 '''simple docstring'''
 assert isinstance(__snake_case			,  __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():
   snake_case :			str							=   old_checkpoint[path]
   snake_case :			str							=   old_tensor.shape[0] // 3

   snake_case :			List[str]							=   (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

   snake_case :			Optional[int]							=   old_tensor.shape[0] // config["num_head_channels"] // 3

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

   snake_case :			Union[str, Any]							=   query.reshape(__snake_case							)
   snake_case :			Tuple							=   key.reshape(__snake_case							)
   snake_case :			Union[str, Any]							=   value.reshape(__snake_case							)

 for path in paths:
  snake_case :			int							=   path["new"]

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
  snake_case :			Optional[int]							=   old_checkpoint[path["old"]]
  snake_case :			Optional[Any]							=   ["proj_attn.weight"]
  snake_case :			int							=   ["proj_out.weight", "proj_in.weight"]
  if any(k in new_path for k in names							):
   snake_case :			Optional[int]							=   weight[:, :, 0]
  elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
   snake_case :			Dict							=   weight[:, :, 0]
  else:
   snake_case :			List[str]							=   weight






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=0							)		->Union[str, Any]:


 '''simple docstring'''
 snake_case :			Tuple							=   []
 for old_item in old_list:
  snake_case :			Dict							=   old_item

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

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

  #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

 return mapping






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=1							)		->Dict:


 '''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  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=0							)		->Tuple:


 '''simple docstring'''
 snake_case :			List[Any]							=   []
 for old_item in old_list:
  mapping.append({'''old''': old_item, '''new''': old_item}							)

 return mapping






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=0							)		->Any:


 '''simple docstring'''
 snake_case :			Any							=   []
 for old_item in old_list:
  snake_case :			Optional[Any]							=   old_item.replace('''in_layers.0'''			,  '''norm1'''							)
  snake_case :			List[str]							=   new_item.replace('''in_layers.2'''			,  '''conv1'''							)

  snake_case :			Optional[int]							=   new_item.replace('''out_layers.0'''			,  '''norm2'''							)
  snake_case :			List[str]							=   new_item.replace('''out_layers.3'''			,  '''conv2'''							)

  snake_case :			str							=   new_item.replace('''emb_layers.1'''			,  '''time_emb_proj'''							)
  snake_case :			Tuple							=   new_item.replace('''skip_connection'''			,  '''conv_shortcut'''							)

  snake_case :			List[Any]							=   shave_segments(__snake_case			,  n_shave_prefix_segments=__snake_case							)

  if "temopral_conv" not in old_item:
   mapping.append({'''old''': old_item, '''new''': new_item}							)

 return mapping






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=None			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 snake_case :			List[Any]							=   {}
 snake_case :			List[Any]							=   list(checkpoint.keys()							)

 snake_case :			Optional[int]							=   "model.diffusion_model."

 # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
 if sum(k.startswith('''model_ema'''							) for k in keys							) > 100 and extract_ema:
  print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''							)
  print(
      '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
      ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''							)
  for key in keys:
   if key.startswith('''model.diffusion_model'''							):
    snake_case :			Any							=   "model_ema." + "".join(key.split('''.'''							)[1:]							)
    snake_case :			Tuple							=   checkpoint.pop(__snake_case							)
 else:
  if sum(k.startswith('''model_ema'''							) for k in keys							) > 100:
   print(
       '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
       ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''							)

  for key in keys:
   snake_case :			int							=   checkpoint.pop(__snake_case							)

 snake_case :			Union[str, Any]							=   {}

 snake_case :			int							=   unet_state_dict["time_embed.0.weight"]
 snake_case :			Dict							=   unet_state_dict["time_embed.0.bias"]
 snake_case :			Optional[Any]							=   unet_state_dict["time_embed.2.weight"]
 snake_case :			Union[str, Any]							=   unet_state_dict["time_embed.2.bias"]

 if config["class_embed_type"] is None:
  # No parameters to port
  ...
 elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  snake_case :			Union[str, Any]							=   unet_state_dict["label_emb.0.0.weight"]
  snake_case :			int							=   unet_state_dict["label_emb.0.0.bias"]
  snake_case :			Optional[int]							=   unet_state_dict["label_emb.0.2.weight"]
  snake_case :			Dict							=   unet_state_dict["label_emb.0.2.bias"]
 else:
  raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''							)

 snake_case :			int							=   unet_state_dict["input_blocks.0.0.weight"]
 snake_case :			Any							=   unet_state_dict["input_blocks.0.0.bias"]

 snake_case :			List[str]							=   [v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''							)]
 snake_case :			str							=   renew_attention_paths(__snake_case							)
 snake_case :			List[str]							=   {"old": "input_blocks.0.1", "new": "transformer_in"}
 assign_to_checkpoint(__snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

 snake_case :			Optional[int]							=   unet_state_dict["out.0.weight"]
 snake_case :			str							=   unet_state_dict["out.0.bias"]
 snake_case :			Any							=   unet_state_dict["out.2.weight"]
 snake_case :			List[Any]							=   unet_state_dict["out.2.bias"]

 # Retrieves the keys for the input blocks only
 snake_case :			Any							=   len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''input_blocks''' in layer}							)
 snake_case :			int							=   {
     layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
     for layer_id in range(__snake_case							)
 }

 # Retrieves the keys for the middle blocks only
 snake_case :			Optional[int]							=   len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''middle_block''' in layer}							)
 snake_case :			str							=   {
     layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
     for layer_id in range(__snake_case							)
 }

 # Retrieves the keys for the output blocks only
 snake_case :			Tuple							=   len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''output_blocks''' in layer}							)
 snake_case :			Union[str, Any]							=   {
     layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
     for layer_id in range(__snake_case							)
 }

 for i in range(1			,  __snake_case							):
  snake_case :			List[Any]							=   (i - 1) // (config["layers_per_block"] + 1)
  snake_case :			List[str]							=   (i - 1) % (config["layers_per_block"] + 1)

  snake_case :			List[str]							=   [
      key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
  ]
  snake_case :			Tuple							=   [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
  snake_case :			List[Any]							=   [key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]

  if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
   snake_case :			Dict							=   unet_state_dict.pop(
       f'''input_blocks.{i}.op.weight'''							)
   snake_case :			Optional[int]							=   unet_state_dict.pop(
       f'''input_blocks.{i}.op.bias'''							)

  snake_case :			Optional[int]							=   renew_resnet_paths(__snake_case							)
  snake_case :			Optional[Any]							=   {"old": f'''input_blocks.{i}.0''', "new": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
  assign_to_checkpoint(
      __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

  snake_case :			List[str]							=   [key for key in resnets if "temopral_conv" in key]
  snake_case :			Any							=   renew_temp_conv_paths(__snake_case							)
  snake_case :			Any							=   {
      "old": f'''input_blocks.{i}.0.temopral_conv''',
      "new": f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
  }
  assign_to_checkpoint(
      __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

  if len(__snake_case							):
   snake_case :			Any							=   renew_attention_paths(__snake_case							)
   snake_case :			List[Any]							=   {"old": f'''input_blocks.{i}.1''', "new": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
   assign_to_checkpoint(
       __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

  if len(__snake_case							):
   snake_case :			Tuple							=   renew_attention_paths(__snake_case							)
   snake_case :			int							=   {
       "old": f'''input_blocks.{i}.2''',
       "new": f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

 snake_case :			Dict							=   middle_blocks[0]
 snake_case :			Optional[int]							=   [key for key in resnet_a if "temopral_conv" in key]
 snake_case :			List[str]							=   middle_blocks[1]
 snake_case :			Dict							=   middle_blocks[2]
 snake_case :			Dict							=   middle_blocks[3]
 snake_case :			Optional[Any]							=   [key for key in resnet_a if "temopral_conv" in key]

 snake_case :			Any							=   renew_resnet_paths(__snake_case							)
 snake_case :			Union[str, Any]							=   {"old": "middle_block.0", "new": "mid_block.resnets.0"}
 assign_to_checkpoint(
     __snake_case			,  __snake_case			,  __snake_case			,  config=__snake_case			,  additional_replacements=[meta_path]							)

 snake_case :			Optional[Any]							=   renew_temp_conv_paths(__snake_case							)
 snake_case :			Any							=   {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
 assign_to_checkpoint(
     __snake_case			,  __snake_case			,  __snake_case			,  config=__snake_case			,  additional_replacements=[meta_path]							)

 snake_case :			Union[str, Any]							=   renew_resnet_paths(__snake_case							)
 snake_case :			Union[str, Any]							=   {"old": "middle_block.3", "new": "mid_block.resnets.1"}
 assign_to_checkpoint(
     __snake_case			,  __snake_case			,  __snake_case			,  config=__snake_case			,  additional_replacements=[meta_path]							)

 snake_case :			Optional[Any]							=   renew_temp_conv_paths(__snake_case							)
 snake_case :			List[Any]							=   {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
 assign_to_checkpoint(
     __snake_case			,  __snake_case			,  __snake_case			,  config=__snake_case			,  additional_replacements=[meta_path]							)

 snake_case :			str							=   renew_attention_paths(__snake_case							)
 snake_case :			Any							=   {"old": "middle_block.1", "new": "mid_block.attentions.0"}
 assign_to_checkpoint(
     __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

 snake_case :			Union[str, Any]							=   renew_attention_paths(__snake_case							)
 snake_case :			int							=   {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
 assign_to_checkpoint(
     __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

 for i in range(__snake_case							):
  snake_case :			Any							=   i // (config["layers_per_block"] + 1)
  snake_case :			Optional[Any]							=   i % (config["layers_per_block"] + 1)
  snake_case :			List[str]							=   [shave_segments(__snake_case			,  2							) for name in output_blocks[i]]
  snake_case :			List[str]							=   {}

  for layer in output_block_layers:
   snake_case :			Optional[Any]							=   layer.split('''.'''							)[0], shave_segments(__snake_case			,  1							)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__snake_case							)
   else:
    snake_case :			Dict							=   [layer_name]

  if len(__snake_case							) > 1:
   snake_case :			int							=   [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
   snake_case :			str							=   [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
   snake_case :			Union[str, Any]							=   [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

   snake_case :			Union[str, Any]							=   renew_resnet_paths(__snake_case							)
   snake_case :			Any							=   renew_resnet_paths(__snake_case							)

   snake_case :			Optional[Any]							=   {"old": f'''output_blocks.{i}.0''', "new": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
   assign_to_checkpoint(
       __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

   snake_case :			List[str]							=   [key for key in resnets if "temopral_conv" in key]
   snake_case :			Union[str, Any]							=   renew_temp_conv_paths(__snake_case							)
   snake_case :			Optional[Any]							=   {
       "old": f'''output_blocks.{i}.0.temopral_conv''',
       "new": f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

   snake_case :			Dict							=   {k: sorted(__snake_case							) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    snake_case :			Union[str, Any]							=   list(output_block_list.values()							).index(['''conv.bias''', '''conv.weight''']							)
    snake_case :			Dict							=   unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.weight'''
    ]
    snake_case :			List[Any]							=   unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.bias'''
    ]

    # Clear attentions as they have been attributed above.
    if len(__snake_case							) == 2:
     snake_case :			Union[str, Any]							=   []

   if len(__snake_case							):
    snake_case :			str							=   renew_attention_paths(__snake_case							)
    snake_case :			Optional[int]							=   {
        "old": f'''output_blocks.{i}.1''',
        "new": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)

   if len(__snake_case							):
    snake_case :			str							=   renew_attention_paths(__snake_case							)
    snake_case :			Dict							=   {
        "old": f'''output_blocks.{i}.2''',
        "new": f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        __snake_case			,  __snake_case			,  __snake_case			,  additional_replacements=[meta_path]			,  config=__snake_case							)
  else:
   snake_case :			Union[str, Any]							=   renew_resnet_paths(__snake_case			,  n_shave_prefix_segments=1							)
   for path in resnet_0_paths:
    snake_case :			Tuple							=   ".".join(['''output_blocks''', str(__snake_case							), path['''old''']]							)
    snake_case :			Any							=   ".".join(['''up_blocks''', str(__snake_case							), '''resnets''', str(__snake_case							), path['''new''']]							)
    snake_case :			str							=   unet_state_dict[old_path]

   snake_case :			Union[str, Any]							=   [l for l in output_block_layers if "temopral_conv" in l]
   for path in temopral_conv_paths:
    snake_case :			Optional[int]							=   path.split('''temopral_conv.'''							)[-1]
    snake_case :			Tuple							=   ".".join(['''output_blocks''', str(__snake_case							), str(__snake_case							), '''temopral_conv''', pruned_path]							)
    snake_case :			Optional[Any]							=   ".".join(['''up_blocks''', str(__snake_case							), '''temp_convs''', str(__snake_case							), pruned_path]							)
    snake_case :			Tuple							=   unet_state_dict[old_path]

 return new_checkpoint


if __name__ == "__main__":
  a					:				Dict       =      argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  )
  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  a					:				Union[str, Any]       =      parser.parse_args()

  a					:				int       =      torch.load(args.checkpoint_path, map_location='cpu')
  a					:				Union[str, Any]       =      UNetaDConditionModel()

  a					:				Optional[int]       =      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  a					:				int       =      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  a					:				Union[str, Any]       =      set(converted_ckpt.keys()) - set(unet.state_dict().keys())

  assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

  # load state_dict
  unet.load_state_dict(converted_ckpt)

  unet.save_pretrained(args.dump_path)

  # -- finish converting the unet --











import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def  _snake_case			(							__snake_case = 1       ,	__snake_case = 1     )  ->  qiskit.result.counts.Counts:






      '''simple docstring'''







      if isinstance(__snake_case       ,	__snake_case     ) or isinstance(__snake_case       ,	__snake_case     ):
            raise TypeError("inputs must be integers."     )
      if (bit_a < 0) or (bit_a < 0):
            raise ValueError("inputs must be positive."     )
      if (math.floor(__snake_case     ) != bit_a) or (math.floor(__snake_case     ) != bit_a):
            raise ValueError("inputs must be exact integers."     )
      if (bit_a > 1) or (bit_a > 1):
            raise ValueError("inputs must be less or equal to 1."     )

      # build registers
      UpperCAmelCase_	:						Union[str, Any]					   =		QuantumRegister(2       ,	"qr"     )
      UpperCAmelCase_	:						Tuple					   =		ClassicalRegister(2       ,	"cr"     )

      UpperCAmelCase_	:						Union[str, Any]					   =		QuantumCircuit(__snake_case       ,	__snake_case     )

      # entanglement the qubits
      quantum_circuit.h(1     )
      quantum_circuit.cx(1       ,	0     )

      # send the information
      UpperCAmelCase_	:						Any					   =		str(__snake_case     ) + str(__snake_case     )

      if c_information == "11":
            quantum_circuit.x(1     )
            quantum_circuit.z(1     )
      elif c_information == "10":
            quantum_circuit.z(1     )
      elif c_information == "01":
            quantum_circuit.x(1     )
      else:
            quantum_circuit.i(1     )

      # unentangled the circuit
      quantum_circuit.cx(1       ,	0     )
      quantum_circuit.h(1     )

      # measure the circuit
      quantum_circuit.measure(__snake_case       ,	__snake_case     )

      UpperCAmelCase_	:						Union[str, Any]					   =		Aer.get_backend("aer_simulator"     )
      UpperCAmelCase_	:						Dict					   =		execute(__snake_case       ,	__snake_case       ,	shots=1_0_0_0     )

      return job.result().get_counts(__snake_case     )


if __name__ == "__main__":
   print(F'''Counts for classical state send: {superdense_coding(1,1)}''')
