






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase_		  =						logging.get_logger(__name__)

lowerCamelCase_		  =						{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}




class 							a_				(		a_	):


							'''simple docstring'''






							__a:			str									=					'''bigbird_pegasus'''
							__a:			str									=					['''past_key_values''']
							__a:			int									=					{
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''hidden_size''': '''d_model''',
							    '''attention_probs_dropout_prob''': '''attention_dropout''',
							}

							def __init__(	self    ,    lowercase_=9_6_1_0_3    ,    lowercase_=4_0_9_6    ,    lowercase_=1_6    ,    lowercase_=4_0_9_6    ,    lowercase_=1_6    ,    lowercase_=1_6    ,    lowercase_=4_0_9_6    ,    lowercase_=1_6    ,    lowercase_=0.0    ,    lowercase_=0.0    ,    lowercase_=True    ,    lowercase_=True    ,    lowercase_="gelu_new"    ,    lowercase_=1_0_2_4    ,    lowercase_=0.1    ,    lowercase_=0.0    ,    lowercase_=0.0    ,    lowercase_=0.02    ,    lowercase_=2    ,    lowercase_=0.0    ,    lowercase_=True    ,    lowercase_=0    ,    lowercase_=2    ,    lowercase_=1    ,    lowercase_="block_sparse"    ,    lowercase_=6_4    ,    lowercase_=3    ,    lowercase_=False    ,    **lowercase_    ,    )      ->      Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  vocab_size
														lowerCAmelCase_       		=  max_position_embeddings
														lowerCAmelCase_       		=  d_model
														lowerCAmelCase_       		=  encoder_ffn_dim
														lowerCAmelCase_       		=  encoder_layers
														lowerCAmelCase_       		=  encoder_attention_heads
														lowerCAmelCase_       		=  decoder_ffn_dim
														lowerCAmelCase_       		=  decoder_layers
														lowerCAmelCase_       		=  decoder_attention_heads
														lowerCAmelCase_       		=  dropout
														lowerCAmelCase_       		=  attention_dropout
														lowerCAmelCase_       		=  activation_dropout
														lowerCAmelCase_       		=  activation_function
														lowerCAmelCase_       		=  init_std
														lowerCAmelCase_       		=  encoder_layerdrop
														lowerCAmelCase_       		=  decoder_layerdrop
														lowerCAmelCase_       		=  classifier_dropout
														lowerCAmelCase_       		=  use_cache
														lowerCAmelCase_       		=  encoder_layers
														lowerCAmelCase_       		=  scale_embedding  # scale factor will be sqrt(d_model) if True

														# extra config
														lowerCAmelCase_       		=  attention_type
														lowerCAmelCase_       		=  block_size
														lowerCAmelCase_       		=  num_random_blocks
														lowerCAmelCase_       		=  use_bias

														super().__init__(
														    pad_token_id=lowercase_    ,    bos_token_id=lowercase_    ,    eos_token_id=lowercase_    ,    is_encoder_decoder=lowercase_    ,    decoder_start_token_id=lowercase_    ,    **lowercase_    ,    )




class 							a_				(		a_	):


							'''simple docstring'''






							@property
							def   _lowercase (	self     )      ->      Mapping[str, Mapping[int, str]]:
														'''simple docstring'''

														if self.task in ["default", "seq2seq-lm"]:
																					lowerCAmelCase_       		=  OrderedDict(
																					    [
																					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																					    ]     )

																					if self.use_past:
																												lowerCAmelCase_       		=  {0: 'batch'}
																												lowerCAmelCase_       		=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																					else:
																												lowerCAmelCase_       		=  {0: 'batch', 1: 'decoder_sequence'}
																												lowerCAmelCase_       		=  {0: 'batch', 1: 'decoder_sequence'}

																					if self.use_past:
																												self.fill_with_past_key_values_(lowercase_    ,    direction='inputs'     )
														elif self.task == "causal-lm":
																					# TODO: figure this case out.
																					lowerCAmelCase_       		=  OrderedDict(
																					    [
																					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																					    ]     )
																					if self.use_past:
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_layers
																												for i in range(lowercase_     ):
																																			lowerCAmelCase_       		=  {0: 'batch', 2: 'past_sequence + sequence'}
																																			lowerCAmelCase_       		=  {0: 'batch', 2: 'past_sequence + sequence'}
														else:
																					lowerCAmelCase_       		=  OrderedDict(
																					    [
																					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																					        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
																					        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
																					    ]     )

														return common_inputs

							@property
							def   _lowercase (	self     )      ->      Mapping[str, Mapping[int, str]]:
														'''simple docstring'''

														if self.task in ["default", "seq2seq-lm"]:
																					lowerCAmelCase_       		=  super().outputs
														else:
																					lowerCAmelCase_       		=  super(lowercase_    ,    self     ).outputs
																					if self.use_past:
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_layers
																												for i in range(lowercase_     ):
																																			lowerCAmelCase_       		=  {0: 'batch', 2: 'past_sequence + sequence'}
																																			lowerCAmelCase_       		=  {0: 'batch', 2: 'past_sequence + sequence'}
														return common_outputs

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = -1    ,    lowercase_ = -1    ,    lowercase_ = False    ,    lowercase_ = None    ,    )      ->      Mapping[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )

														# Generate decoder inputs
														lowerCAmelCase_       		=  seq_length if not self.use_past else 1
														lowerCAmelCase_       		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )
														lowerCAmelCase_       		=  {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
														lowerCAmelCase_       		=  dict(**lowercase_    ,    **lowercase_     )

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
																					else:
																												import torch
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  common_inputs['input_ids'].shape
																					lowerCAmelCase_       		=  common_inputs['decoder_input_ids'].shape[1]
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_attention_heads
																					lowerCAmelCase_       		=  (
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					lowerCAmelCase_       		=  decoder_seq_length + 3
																					lowerCAmelCase_       		=  (
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					lowerCAmelCase_       		=  torch.cat(
																					    [common_inputs['decoder_attention_mask'], torch.ones(lowercase_    ,    lowercase_     )]    ,    dim=1     )

																					lowerCAmelCase_       		=  []
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_layers
																					lowerCAmelCase_       		=  min(lowercase_    ,    lowercase_     )
																					lowerCAmelCase_       		=  max(lowercase_    ,    lowercase_     ) - min_num_layers
																					lowerCAmelCase_       		=  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																					for _ in range(lowercase_     ):
																												common_inputs["past_key_values"].append(
																												    (
																												        torch.zeros(lowercase_     ),
																												        torch.zeros(lowercase_     ),
																												        torch.zeros(lowercase_     ),
																												        torch.zeros(lowercase_     ),
																												    )     )
																					# TODO: test this.
																					lowerCAmelCase_       		=  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																					for _ in range(lowercase_    ,    lowercase_     ):
																												common_inputs["past_key_values"].append((torch.zeros(lowercase_     ), torch.zeros(lowercase_     ))     )
														return common_inputs

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = -1    ,    lowercase_ = -1    ,    lowercase_ = False    ,    lowercase_ = None    ,    )      ->      Mapping[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
																					else:
																												import torch
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  common_inputs['input_ids'].shape
																					# Not using the same length for past_key_values
																					lowerCAmelCase_       		=  seqlen + 2
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_layers
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  self.num_attention_heads
																					lowerCAmelCase_       		=  (
																					    batch,
																					    num_encoder_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)

																					lowerCAmelCase_       		=  common_inputs['attention_mask'].dtype
																					lowerCAmelCase_       		=  torch.cat(
																					    [common_inputs['attention_mask'], torch.ones(lowercase_    ,    lowercase_    ,    dtype=lowercase_     )]    ,    dim=1     )
																					lowerCAmelCase_       		=  [
																					    (torch.zeros(lowercase_     ), torch.zeros(lowercase_     )) for _ in range(lowercase_     )
																					]
														return common_inputs

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = -1    ,    lowercase_ = -1    ,    lowercase_ = False    ,    lowercase_ = None    ,    )      ->      Mapping[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  compute_effective_axis_dimension(
														    lowercase_    ,    fixed_dimension=OnnxConfig.default_fixed_batch    ,    num_token_to_add=0     )

														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														lowerCAmelCase_       		=  tokenizer.num_special_tokens_to_add(lowercase_     )
														lowerCAmelCase_       		=  compute_effective_axis_dimension(
														    lowercase_    ,    fixed_dimension=OnnxConfig.default_fixed_sequence    ,    num_token_to_add=lowercase_     )

														# Generate dummy inputs according to compute batch and sequence
														lowerCAmelCase_       		=  [' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
														lowerCAmelCase_       		=  dict(tokenizer(lowercase_    ,    return_tensors=lowercase_     )     )
														return common_inputs

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = -1    ,    lowercase_ = -1    ,    lowercase_ = False    ,    lowercase_ = None    ,    )      ->      Mapping[str, Any]:
														'''simple docstring'''

														if self.task in ["default", "seq2seq-lm"]:
																					lowerCAmelCase_       		=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																					    lowercase_    ,    batch_size=lowercase_    ,    seq_length=lowercase_    ,    is_pair=lowercase_    ,    framework=lowercase_     )

														elif self.task == "causal-lm":
																					lowerCAmelCase_       		=  self._generate_dummy_inputs_for_causal_lm(
																					    lowercase_    ,    batch_size=lowercase_    ,    seq_length=lowercase_    ,    is_pair=lowercase_    ,    framework=lowercase_     )
														else:
																					lowerCAmelCase_       		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																					    lowercase_    ,    batch_size=lowercase_    ,    seq_length=lowercase_    ,    is_pair=lowercase_    ,    framework=lowercase_     )

														return common_inputs






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      List[Any]:
														'''simple docstring'''

														if self.task in ["default", "seq2seq-lm"]:
																					lowerCAmelCase_       		=  super()._flatten_past_key_values_(lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )
														else:
																					lowerCAmelCase_       		=  super(lowercase_    ,    self     )._flatten_past_key_values_(
																					    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )











import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def   lowerCamelCase      (		a_				,		a_=1			)			->    str:
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.'			)[n_shave_prefix_segments:]			)
							else:
														return ".".join(path.split('.'			)[:n_shave_prefix_segments]			)
def   lowerCamelCase      (		a_				,		a_=0			)			->    List[Any]:
							lowerCAmelCase_       		=  []
							for old_item in old_list:
														lowerCAmelCase_       		=  old_item
														lowerCAmelCase_       		=  new_item.replace('block.'				,		'resnets.'			)
														lowerCAmelCase_       		=  new_item.replace('conv_shorcut'				,		'conv1'			)
														lowerCAmelCase_       		=  new_item.replace('in_shortcut'				,		'conv_shortcut'			)
														lowerCAmelCase_       		=  new_item.replace('temb_proj'				,		'time_emb_proj'			)

														lowerCAmelCase_       		=  shave_segments(a_				,		n_shave_prefix_segments=a_			)

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

							return mapping
def   lowerCamelCase      (		a_				,		a_=0				,		a_=False			)			->    Dict:
							lowerCAmelCase_       		=  []
							for old_item in old_list:
														lowerCAmelCase_       		=  old_item

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

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

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

							return mapping
def   lowerCamelCase      (		a_				,		a_				,		a_				,		a_=None				,		a_=None				,		a_=None			)			->    Tuple:
							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():
																					lowerCAmelCase_       		=  old_checkpoint[path]
																					lowerCAmelCase_       		=  old_tensor.shape[0] // 3

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

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

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

																					lowerCAmelCase_       		=  query.reshape(a_			).squeeze()
																					lowerCAmelCase_       		=  key.reshape(a_			).squeeze()
																					lowerCAmelCase_       		=  value.reshape(a_			).squeeze()

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

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

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

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

														if "attentions" in new_path:
																					lowerCAmelCase_       		=  old_checkpoint[path['old']].squeeze()
														else:
																					lowerCAmelCase_       		=  old_checkpoint[path['old']]
def   lowerCamelCase      (		a_				,		a_			)			->    List[str]:
							lowerCAmelCase_       		=  {}

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

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

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

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'down' in layer}			)
							lowerCAmelCase_       		=  {
							    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_			)
							}

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'up' in layer}			)
							lowerCAmelCase_       		=  {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_			):
														lowerCAmelCase_       		=  (i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]			):
																					lowerCAmelCase_       		=  checkpoint[
																					    F'''down.{i}.downsample.op.weight'''
																					]
																					lowerCAmelCase_       		=  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]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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']			):
																																			lowerCAmelCase_       		=  renew_resnet_paths(blocks[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_			)

														if any('attn' in layer for layer in down_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'attn' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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']			):
																																			lowerCAmelCase_       		=  renew_attention_paths(attns[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_				,		config=a_			)

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

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

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

							lowerCAmelCase_       		=  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_			):
														lowerCAmelCase_       		=  num_up_blocks - 1 - i

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

														if any('block' in layer for layer in up_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  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]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'attn' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  renew_attention_paths(attns[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_				,		additional_replacements=[replace_indices]			)

							lowerCAmelCase_       		=  {k.replace('mid_new_2'				,		'mid_block'			): v for k, v in new_checkpoint.items()}
							return new_checkpoint
def   lowerCamelCase      (		a_				,		a_			)			->    Any:
							lowerCAmelCase_       		=  {}

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

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

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

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

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'down' in layer}			)
							lowerCAmelCase_       		=  {
							    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_			)
							}

							lowerCAmelCase_       		=  len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'up' in layer}			)
							lowerCAmelCase_       		=  {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_			):
														lowerCAmelCase_       		=  (i - 1) // (config['layers_per_block'] + 1)

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

														if any('block' in layer for layer in down_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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']			):
																																			lowerCAmelCase_       		=  renew_resnet_paths(blocks[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_			)

														if any('attn' in layer for layer in down_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'attn' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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']			):
																																			lowerCAmelCase_       		=  renew_attention_paths(attns[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_				,		config=a_			)

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

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

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

							lowerCAmelCase_       		=  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_			):
														lowerCAmelCase_       		=  num_up_blocks - 1 - i

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

														if any('block' in layer for layer in up_blocks[i]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'block' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  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]			):
																					lowerCAmelCase_       		=  len(
																					    {'.'.join(shave_segments(a_				,		3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'attn' in layer}			)
																					lowerCAmelCase_       		=  {
																					    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			):
																																			lowerCAmelCase_       		=  {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			lowerCAmelCase_       		=  renew_attention_paths(attns[j]			)
																																			assign_to_checkpoint(a_				,		a_				,		a_				,		additional_replacements=[replace_indices]			)

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

							return new_checkpoint


if __name__ == "__main__":
		lowerCamelCase_		  =						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_		  =						parser.parse_args()
		lowerCamelCase_		  =						torch.load(args.checkpoint_path)

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

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

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

		if config["_class_name"] == "VQModel":
				lowerCamelCase_		  =						VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				lowerCamelCase_		  =						AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				lowerCamelCase_		  =						UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				lowerCamelCase_		  =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

				lowerCamelCase_		  =						DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)



