






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
	from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor




class 			UpperCamelCase_				(      A__    ):



	'''simple docstring'''




	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								snake_case_           =							self.config_class(**self.inputs_dict )
								self.parent.assertTrue(hasattr(snake_case_	,      'hidden_sizes' ) )
								self.parent.assertTrue(hasattr(snake_case_	,      'num_attention_heads' ) )
								self.parent.assertTrue(hasattr(snake_case_	,      'num_encoder_blocks' ) )






class 			UpperCamelCase_				:



	'''simple docstring'''




	def __init__(	self	,      a	,      a=13	,      a=64	,      a=3	,      a=4	,      a=[2, 2, 2, 2]	,      a=[8, 4, 2, 1]	,      a=[16, 32, 64, 1_28]	,      a=[1, 4, 8, 16]	,      a=[1, 2, 4, 8]	,      a=True	,      a=True	,      a="gelu"	,      a=0.1	,      a=0.1	,      a=0.02	,      a=16	,      a=3	,      a=None	,      )	->   Any:
								snake_case_           =							parent
								snake_case_           =							batch_size
								snake_case_           =							image_size
								snake_case_           =							num_channels
								snake_case_           =							num_encoder_blocks
								snake_case_           =							sr_ratios
								snake_case_           =							depths
								snake_case_           =							hidden_sizes
								snake_case_           =							downsampling_rates
								snake_case_           =							num_attention_heads
								snake_case_           =							is_training
								snake_case_           =							use_labels
								snake_case_           =							hidden_act
								snake_case_           =							hidden_dropout_prob
								snake_case_           =							attention_probs_dropout_prob
								snake_case_           =							initializer_range
								snake_case_           =							decoder_hidden_size
								snake_case_           =							num_labels
								snake_case_           =							scope




	def 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_           =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								snake_case_           =							None
								if self.use_labels:
															snake_case_           =							ids_tensor([self.batch_size, self.image_size, self.image_size]	,      self.num_labels )

								snake_case_           =							self.get_config()
								return config, pixel_values, labels




	def 							_UpperCamelCase      (	self )	->   Any:
								return GLPNConfig(
								    image_size=self.image_size	,      num_channels=self.num_channels	,      num_encoder_blocks=self.num_encoder_blocks	,      depths=self.depths	,      hidden_sizes=self.hidden_sizes	,      num_attention_heads=self.num_attention_heads	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      initializer_range=self.initializer_range	,      decoder_hidden_size=self.decoder_hidden_size	,      )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   str:
								snake_case_           =							GLPNModel(config=snake_case_ )
								model.to(snake_case_ )
								model.eval()
								snake_case_           =							model(snake_case_ )
								snake_case_           =							snake_case_           =							self.image_size // (self.downsampling_rates[-1] * 2)
								self.parent.assertEqual(
								    result.last_hidden_state.shape	,      (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   Dict:
								snake_case_           =							self.num_labels
								snake_case_           =							GLPNForDepthEstimation(snake_case_ )
								model.to(snake_case_ )
								model.eval()
								snake_case_           =							model(snake_case_ )
								self.parent.assertEqual(result.predicted_depth.shape	,      (self.batch_size, self.image_size, self.image_size) )
								snake_case_           =							model(snake_case_	,      labels=snake_case_ )
								self.parent.assertEqual(result.predicted_depth.shape	,      (self.batch_size, self.image_size, self.image_size) )






	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							self.prepare_config_and_inputs()
								snake_case_      ,		snake_case_      ,		snake_case_           =							config_and_inputs
								snake_case_           =							{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 			UpperCamelCase_				(      A__	, A__	, unittest.TestCase    ):



	'''simple docstring'''




	lowerCAmelCase													=			(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
	lowerCAmelCase													=			(
	    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
	)

	lowerCAmelCase													=			False
	lowerCAmelCase													=			False
	lowerCAmelCase													=			False




	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							GLPNModelTester(self )
								snake_case_           =							GLPNConfigTester(self	,      config_class=snake_case_ )




	def 							_UpperCamelCase      (	self )	->   Any:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase      (	self )	->   Tuple:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_ )




	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*snake_case_ )




	@unittest.skip('GLPN does not use inputs_embeds' )
	def 							_UpperCamelCase      (	self )	->   int:
								pass




	@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods' )
	def 							_UpperCamelCase      (	self )	->   Any:
								pass




	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															snake_case_           =							model_class(snake_case_ )
															snake_case_           =							inspect.signature(model.forward )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case_           =							[*signature.parameters.keys()]

															snake_case_           =							['pixel_values']
															self.assertListEqual(arg_names[:1]	,      snake_case_ )




	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_           =							True

								for model_class in self.all_model_classes:
															snake_case_           =							True
															snake_case_           =							False
															snake_case_           =							True
															snake_case_           =							model_class(snake_case_ )
															model.to(snake_case_ )
															model.eval()
															with torch.no_grad():
																						snake_case_           =							model(**self._prepare_for_class(snake_case_	,      snake_case_ ) )
															snake_case_           =							outputs.attentions

															snake_case_           =							sum(self.model_tester.depths )
															self.assertEqual(len(snake_case_ )	,      snake_case_ )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															snake_case_           =							True
															snake_case_           =							model_class(snake_case_ )
															model.to(snake_case_ )
															model.eval()
															with torch.no_grad():
																						snake_case_           =							model(**self._prepare_for_class(snake_case_	,      snake_case_ ) )
															snake_case_           =							outputs.attentions

															self.assertEqual(len(snake_case_ )	,      snake_case_ )

															# verify the first attentions (first block, first layer)
															snake_case_           =							(self.model_tester.image_size // 4) ** 2
															snake_case_           =							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
															self.assertListEqual(
															    list(attentions[0].shape[-3:] )	,      [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,      )

															# verify the last attentions (last block, last layer)
															snake_case_           =							(self.model_tester.image_size // 32) ** 2
															snake_case_           =							(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
															self.assertListEqual(
															    list(attentions[-1].shape[-3:] )	,      [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,      )
															snake_case_           =							len(snake_case_ )

															# Check attention is always last and order is fine
															snake_case_           =							True
															snake_case_           =							True
															snake_case_           =							model_class(snake_case_ )
															model.to(snake_case_ )
															model.eval()
															with torch.no_grad():
																						snake_case_           =							model(**self._prepare_for_class(snake_case_	,      snake_case_ ) )

															self.assertEqual(out_len + 1	,      len(snake_case_ ) )

															snake_case_           =							outputs.attentions

															self.assertEqual(len(snake_case_ )	,      snake_case_ )
															# verify the first attentions (first block, first layer)
															snake_case_           =							(self.model_tester.image_size // 4) ** 2
															snake_case_           =							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:] )	,      [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,      )




	def 							_UpperCamelCase      (	self )	->   List[Any]:
								def check_hidden_states_output(a	,      a	,      a ):
															snake_case_           =							model_class(snake_case_ )
															model.to(snake_case_ )
															model.eval()

															with torch.no_grad():
																						snake_case_           =							model(**self._prepare_for_class(snake_case_	,      snake_case_ ) )

															snake_case_           =							outputs.hidden_states

															snake_case_           =							self.model_tester.num_encoder_blocks
															self.assertEqual(len(snake_case_ )	,      snake_case_ )

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:] )	,      [
															        self.model_tester.hidden_sizes[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]	,      )

								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															snake_case_           =							True
															check_hidden_states_output(snake_case_	,      snake_case_	,      snake_case_ )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_           =							True

															check_hidden_states_output(snake_case_	,      snake_case_	,      snake_case_ )




	def 							_UpperCamelCase      (	self )	->   List[Any]:
								if not self.model_tester.is_training:
															return

								snake_case_      ,		snake_case_           =							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_           =							True

								for model_class in self.all_model_classes:
															if model_class in get_values(snake_case_ ):
																						continue
															# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
															# this can then be incorporated into _prepare_for_class in test_modeling_common.py
															if model_class.__name__ == "GLPNForDepthEstimation":
																						snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_           =							inputs_dict['pixel_values'].shape
																						snake_case_           =							torch.zeros(
																						    [self.model_tester.batch_size, height, width]	,      device=snake_case_ ).long()
															snake_case_           =							model_class(snake_case_ )
															model.to(snake_case_ )
															model.train()
															snake_case_           =							self._prepare_for_class(snake_case_	,      snake_case_	,      return_labels=snake_case_ )
															snake_case_           =							model(**snake_case_ ).loss
															loss.backward()






	@slow
	def 							_UpperCamelCase      (	self )	->   List[str]:
								for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_           =							GLPNModel.from_pretrained(snake_case_ )
															self.assertIsNotNone(snake_case_ )






def 						__UpperCAmelCase      (					):
							snake_case_           =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
							return image




@require_torch
@require_vision
@slow
class 			UpperCamelCase_				(      unittest.TestCase    ):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase      (	self )	->   Any:
								snake_case_           =							GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
								snake_case_           =							GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case_ )

								snake_case_           =							prepare_img()
								snake_case_           =							image_processor(images=snake_case_	,      return_tensors='pt' ).to(snake_case_ )

								# forward pass
								with torch.no_grad():
															snake_case_           =							model(**snake_case_ )

								# verify the predicted depth
								snake_case_           =							torch.Size([1, 4_80, 6_40] )
								self.assertEqual(outputs.predicted_depth.shape	,      snake_case_ )

								snake_case_           =							torch.tensor(
								    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] ).to(snake_case_ )

								self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,      snake_case_	,      atol=1E-4 ) )













import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__UpperCAmelCase      (					a_							,					a_=1):
							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      (					a_							,					a_=0):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item
														snake_case_           =							new_item.replace('block.'							,					'resnets.')
														snake_case_           =							new_item.replace('conv_shorcut'							,					'conv1')
														snake_case_           =							new_item.replace('in_shortcut'							,					'conv_shortcut')
														snake_case_           =							new_item.replace('temb_proj'							,					'time_emb_proj')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)

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

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_=0							,					a_=False):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item

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

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

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

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=None							,					a_=None							,					a_=None):
							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():
																					snake_case_           =							old_checkpoint[path]
																					snake_case_           =							old_tensor.shape[0] // 3

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

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

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

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

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

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

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

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

														if "attentions" in new_path:
																					snake_case_           =							old_checkpoint[path['old']].squeeze()
														else:
																					snake_case_           =							old_checkpoint[path['old']]





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

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

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

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

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

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

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

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

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

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

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

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

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

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

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

							return new_checkpoint


if __name__ == "__main__":
	lowercase  							= 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.")

	lowercase  							= parser.parse_args()
	lowercase  							= torch.load(args.checkpoint_path)

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

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

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

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

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

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





