






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer




@require_torch
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							@slow
							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'     )
														model.to(lowercase_     )

														# encoder-decoder outputs
														lowerCAmelCase_       		=  torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowercase_     )
														lowerCAmelCase_       		=  torch.tensor(
														    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]     ).to(lowercase_     )
														lowerCAmelCase_       		=  model(
														    input_ids=lowercase_    ,    attention_mask=lowercase_    ,    encoder_outputs=lowercase_    ,    decoder_input_ids=lowercase_     )
														lowerCAmelCase_       		=  output[0]
														lowerCAmelCase_       		=  torch.Size((1, 1_4, 2_5_0_0_1_2)     )
														self.assertEqual(output_predited_logis.shape    ,    lowercase_     )
														lowerCAmelCase_       		=  torch.tensor(
														    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]     ).to(lowercase_     )
														self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,    lowercase_    ,    atol=1e-4     )     )

														# encoder outputs
														lowerCAmelCase_       		=  model.prophetnet.encoder(lowercase_     )[0]
														lowerCAmelCase_       		=  torch.tensor(
														    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]     ).to(lowercase_     )
														lowerCAmelCase_       		=  torch.Size((1, 4, 1_0_2_4)     )
														self.assertEqual(encoder_outputs.shape    ,    lowercase_     )
														self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]    ,    lowercase_    ,    atol=1e-4     )     )

														# decoder outputs
														lowerCAmelCase_       		=  model.prophetnet.decoder(
														    lowercase_    ,    encoder_hidden_states=lowercase_    ,    )
														lowerCAmelCase_       		=  decoder_outputs[1].view(1    ,    model.config.ngram    ,    1_4    ,    -1     )
														lowerCAmelCase_       		=  model.lm_head(lowercase_     )
														lowerCAmelCase_       		=  predicting_streams_logits[:, 0]
														self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]    ,    lowercase_    ,    atol=1e-4     )     )

							@slow
							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  XLMProphetNetForConditionalGeneration.from_pretrained(
														    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'     )
														model.to(lowercase_     )

														lowerCAmelCase_       		=  torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowercase_     )
														lowerCAmelCase_       		=  torch.tensor(
														    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]     ).to(lowercase_     )
														lowerCAmelCase_       		=  model(
														    input_ids=lowercase_    ,    attention_mask=lowercase_    ,    encoder_outputs=lowercase_    ,    decoder_input_ids=lowercase_     )
														lowerCAmelCase_       		=  output[0]
														lowerCAmelCase_       		=  torch.Size((1, 1_4, 2_5_0_0_1_2)     )
														self.assertEqual(output_predited_logis.shape    ,    lowercase_     )
														# compare the actual values for a slice.
														lowerCAmelCase_       		=  torch.tensor(
														    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]     ).to(lowercase_     )

														self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,    lowercase_    ,    atol=1e-4     )     )






							@slow
							def   _lowercase (	self     )      ->      str:
														'''simple docstring'''

														lowerCAmelCase_       		=  XLMProphetNetForConditionalGeneration.from_pretrained(
														    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'     )
														model.to(lowercase_     )
														lowerCAmelCase_       		=  5_1_2

														lowerCAmelCase_       		=  XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'     )

														lowerCAmelCase_       		=  (
														    'Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'
														    ' January 14, 2020, according to the official portal of the organization. From that day, users of this'
														    ' system will not be able to receive security updates, which could make their computers vulnerable to'
														    ' cyber attacks.'
														)
														lowerCAmelCase_       		=  (
														    'орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'
														    ' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'
														    ' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'
														    ' к кибератакам.'
														)
														lowerCAmelCase_       		=  (
														    '根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'
														)

														lowerCAmelCase_       		=  tokenizer(
														    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]    ,    padding=lowercase_    ,    max_length=2_5_5    ,    return_tensors='pt'     ).input_ids
														lowerCAmelCase_       		=  input_ids.to(lowercase_     )

														lowerCAmelCase_       		=  model.generate(
														    lowercase_    ,    num_beams=1_0    ,    length_penalty=1.0    ,    no_repeat_ngram_size=3    ,    early_stopping=lowercase_     )
														lowerCAmelCase_       		=  [tokenizer.decode(lowercase_    ,    skip_special_tokens=lowercase_     ) for g in summary_ids]
														lowerCAmelCase_       		=  'Microsoft to end Windows 7 free support after January 14, 2020'
														lowerCAmelCase_       		=  'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
														lowerCAmelCase_       		=  '微软打算终止对Windows 7操作系统的免费支持'
														self.assertListEqual(
														    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]    ,    lowercase_    ,    )

														lowerCAmelCase_       		=  model.generate(
														    lowercase_    ,    num_beams=1    ,    length_penalty=1.0    ,    no_repeat_ngram_size=3    ,    early_stopping=lowercase_     )
														lowerCAmelCase_       		=  [
														    tokenizer.convert_ids_to_tokens(lowercase_    ,    skip_special_tokens=lowercase_     ) for g in summary_ids_beama
														]
														lowerCAmelCase_       		=  '▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '     )
														lowerCAmelCase_       		=  '▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
														    ' '     )
														lowerCAmelCase_       		=  '微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'.split(' '     )
														self.assertListEqual(
														    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]    ,    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)



