




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
   from PIL import Image

   from transformers import ConvNextImageProcessor

class   UpperCAmelCase    :






   def __init__(						self :							Union[str, Any]    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							Union[str, Any]=13    ,       UpperCamelCase_ :							List[str]=32    ,       UpperCamelCase_ :							int=3    ,       UpperCamelCase_ :							Optional[int]=4    ,       UpperCamelCase_ :							Union[str, Any]=[10, 20, 30, 40]    ,       UpperCamelCase_ :							Any=[2, 2, 3, 2]    ,       UpperCamelCase_ :							List[str]=True    ,       UpperCamelCase_ :							Any=True    ,       UpperCamelCase_ :							int=37    ,       UpperCamelCase_ :							Dict="gelu"    ,       UpperCamelCase_ :							Tuple=10    ,       UpperCamelCase_ :							List[str]=0.02    ,       UpperCamelCase_ :							Optional[int]=3    ,       UpperCamelCase_ :							Optional[Any]=None    ,       )->      Any:
     _lowercase:			Optional[int]      =     parent
     _lowercase:			Optional[Any]      =     batch_size
     _lowercase:			List[str]      =     image_size
     _lowercase:			Optional[Any]      =     num_channels
     _lowercase:			int      =     num_stages
     _lowercase:			List[Any]      =     hidden_sizes
     _lowercase:			Optional[Any]      =     depths
     _lowercase:			List[str]      =     is_training
     _lowercase:			Tuple      =     use_labels
     _lowercase:			Optional[Any]      =     intermediate_size
     _lowercase:			Dict      =     hidden_act
     _lowercase:			List[str]      =     type_sequence_label_size
     _lowercase:			Union[str, Any]      =     initializer_range
     _lowercase:			Optional[Any]      =     scope






   def 			_a    (						self :							Optional[Any]  )->      Optional[Any]:
     _lowercase:			List[Any]      =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     _lowercase:			str      =     None
     if self.use_labels:
       _lowercase:			Optional[Any]      =     ids_tensor([self.batch_size]    ,       self.type_sequence_label_size  )

     _lowercase:			Optional[int]      =     self.get_config()

     return config, pixel_values, labels






   def 			_a    (						self :							int  )->      Optional[Any]:
     return ConvNextConfig(
         num_channels=self.num_channels    ,       hidden_sizes=self.hidden_sizes    ,       depths=self.depths    ,       num_stages=self.num_stages    ,       hidden_act=self.hidden_act    ,       is_decoder=__UpperCAmelCase    ,       initializer_range=self.initializer_range    ,       )






   def 			_a    (						self :							Dict    ,       UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							Optional[int]  )->      List[Any]:
     _lowercase:			List[Any]      =     TFConvNextModel(config=__UpperCAmelCase  )
     _lowercase:			List[Any]      =     model(__UpperCAmelCase    ,       training=__UpperCAmelCase  )
     # expected last hidden states: B, C, H // 32, W // 32
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,       )






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							List[Any]    ,       UpperCamelCase_ :							Optional[int]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     self.type_sequence_label_size
     _lowercase:			List[Any]      =     TFConvNextForImageClassification(__UpperCAmelCase  )
     _lowercase:			List[str]      =     model(__UpperCAmelCase    ,       labels=__UpperCAmelCase    ,       training=__UpperCAmelCase  )
     self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.type_sequence_label_size)  )






   def 			_a    (						self :							Tuple  )->      Union[str, Any]:
     _lowercase:			List[str]      =     self.prepare_config_and_inputs()
     _lowercase:			Optional[int]      =     config_and_inputs
     _lowercase:			Union[str, Any]      =     {"""pixel_values""": pixel_values}
     return config, inputs_dict

@require_tf
class   UpperCAmelCase    (      __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				Dict      					=       (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
   __SCREAMING_SNAKE_CASE		:				Optional[Any]      					=       (
       {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
       if is_tf_available()
       else {}
   )

   __SCREAMING_SNAKE_CASE		:				Tuple      					=       False
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       False
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       False
   __SCREAMING_SNAKE_CASE		:				Optional[Any]      					=       False
   __SCREAMING_SNAKE_CASE		:				str      					=       False






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			Dict      =     TFConvNextModelTester(self  )
     _lowercase:			Optional[int]      =     ConfigTester(
         self    ,       config_class=__UpperCAmelCase    ,       has_text_modality=__UpperCAmelCase    ,       hidden_size=37    ,       )






   @unittest.skip(reason='ConvNext does not use inputs_embeds'  )
   def 			_a    (						self :							Optional[int]  )->      str:
     pass






   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU'  )  ) == 0    ,       reason='TF does not support backprop for grouped convolutions on CPU.'    ,       )
   @slow
   def 			_a    (						self :							int  )->      Optional[int]:
     super().test_keras_fit()






   @unittest.skip(reason='ConvNext does not support input and output embeddings'  )
   def 			_a    (						self :							int  )->      Dict:
     pass






   def 			_a    (						self :							List[str]  )->      str:
     _lowercase:			List[str]      =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _lowercase:			List[Any]      =     model_class(__UpperCAmelCase  )
       _lowercase:			str      =     inspect.signature(model.call  )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _lowercase:			Tuple      =     [*signature.parameters.keys()]

       _lowercase:			Tuple      =     ["""pixel_values"""]
       self.assertListEqual(arg_names[:1]    ,       __UpperCAmelCase  )






   def 			_a    (						self :							Optional[Any]  )->      Dict:
     _lowercase:			Dict      =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__UpperCAmelCase  )






   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU'  )  ) == 0    ,       reason='TF does not support backprop for grouped convolutions on CPU.'    ,       )
   def 			_a    (						self :							int  )->      Union[str, Any]:
     super().test_dataset_conversion()






   def 			_a    (						self :							Dict  )->      Optional[int]:
     def check_hidden_states_output(UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							Tuple  ):
       _lowercase:			Optional[int]      =     model_class(__UpperCAmelCase  )

       _lowercase:			str      =     model(**self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase  )  )
       _lowercase:			List[str]      =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       _lowercase:			Optional[int]      =     self.model_tester.num_stages
       self.assertEqual(len(__UpperCAmelCase  )    ,       expected_num_stages + 1  )

       # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]  )    ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,       )

     _lowercase:			Union[str, Any]      =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _lowercase:			List[str]      =     True
       check_hidden_states_output(__UpperCAmelCase    ,       __UpperCAmelCase    ,       __UpperCAmelCase  )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _lowercase:			Optional[Any]      =     True

       check_hidden_states_output(__UpperCAmelCase    ,       __UpperCAmelCase    ,       __UpperCAmelCase  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			Any      =     self.model_tester.prepare_config_and_inputs_for_common()

     def check_equivalence(UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Any    ,       UpperCamelCase_ :							Optional[Any]={}  ):
       _lowercase:			Optional[Any]      =     model(__UpperCAmelCase    ,       return_dict=__UpperCAmelCase    ,       **__UpperCAmelCase  )
       _lowercase:			Optional[int]      =     model(__UpperCAmelCase    ,       return_dict=__UpperCAmelCase    ,       **__UpperCAmelCase  ).to_tuple()

       def recursive_check(UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							Optional[Any]  ):
         if isinstance(__UpperCAmelCase    ,       (List, Tuple)  ):
           for tuple_iterable_value, dict_iterable_value in zip(__UpperCAmelCase    ,       __UpperCAmelCase  ):
             recursive_check(__UpperCAmelCase    ,       __UpperCAmelCase  )
         elif tuple_object is None:
           return
         else:
           self.assertTrue(
               all(tf.equal(__UpperCAmelCase    ,       __UpperCAmelCase  )  )    ,       msg=(
                   'Tuple and dict output are not equal. Difference:'
                   f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object  )  )}'
               )    ,       )

         recursive_check(__UpperCAmelCase    ,       __UpperCAmelCase  )

     for model_class in self.all_model_classes:
       _lowercase:			Dict      =     model_class(__UpperCAmelCase  )

       _lowercase:			Optional[int]      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase  )
       _lowercase:			Tuple      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase  )
       check_equivalence(__UpperCAmelCase    ,       __UpperCAmelCase    ,       __UpperCAmelCase  )

       _lowercase:			Union[str, Any]      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase    ,       return_labels=__UpperCAmelCase  )
       _lowercase:			Union[str, Any]      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase    ,       return_labels=__UpperCAmelCase  )
       check_equivalence(__UpperCAmelCase    ,       __UpperCAmelCase    ,       __UpperCAmelCase  )

       _lowercase:			Optional[Any]      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase  )
       _lowercase:			List[str]      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase  )
       check_equivalence(__UpperCAmelCase    ,       __UpperCAmelCase    ,       __UpperCAmelCase    ,       {'output_hidden_states': True}  )

       _lowercase:			List[Any]      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase    ,       return_labels=__UpperCAmelCase  )
       _lowercase:			Dict      =     self._prepare_for_class(__UpperCAmelCase    ,       __UpperCAmelCase    ,       return_labels=__UpperCAmelCase  )
       check_equivalence(__UpperCAmelCase    ,       __UpperCAmelCase    ,       __UpperCAmelCase    ,       {'output_hidden_states': True}  )






   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			int      =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase  )






   @slow
   def 			_a    (						self :							Union[str, Any]  )->      Tuple:
     _lowercase:			Union[str, Any]      =     TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'  )
     self.assertIsNotNone(__UpperCAmelCase  )


def 		_lowerCamelCase					(       )      ->		int:





  """simple docstring"""





  _lowercase:			str      =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
  return image





@require_tf
@require_vision
class   UpperCAmelCase    (      unittest.TestCase ):






   @cached_property
   def 			_a    (						self :							Optional[Any]  )->      Any:
     return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'  ) if is_vision_available() else None






   @slow
   def 			_a    (						self :							str  )->      int:
     _lowercase:			Tuple      =     TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'  )

     _lowercase:			List[Any]      =     self.default_image_processor
     _lowercase:			int      =     prepare_img()
     _lowercase:			Optional[Any]      =     image_processor(images=__UpperCAmelCase    ,       return_tensors='tf'  )

     # forward pass
     _lowercase:			List[Any]      =     model(**__UpperCAmelCase  )

     # verify the logits
     _lowercase:			List[str]      =     tf.TensorShape((1, 10_00)  )
     self.assertEqual(outputs.logits.shape    ,       __UpperCAmelCase  )

     _lowercase:			str      =     tf.constant([-0.0_260, -0.4_739, 0.1_911]  )

     tf.debugging.assert_near(outputs.logits[0, :3]    ,       __UpperCAmelCase    ,       atol=1E-4  )





'''simple docstring'''
import argparse
import json

import torch

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


def 	lowercase_						( lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Optional[Any]=1			):

			"""simple docstring"""





			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
			else:
						return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)


def 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	str=0			):

			"""simple docstring"""





			__UpperCAmelCase :   int    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   str    					=  old_item
						__UpperCAmelCase :   Dict    					=  new_item.replace("""block."""      ,				"""resnets."""			)
						__UpperCAmelCase :   Union[str, Any]    					=  new_item.replace("""conv_shorcut"""      ,				"""conv1"""			)
						__UpperCAmelCase :   List[str]    					=  new_item.replace("""in_shortcut"""      ,				"""conv_shortcut"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""temb_proj"""      ,				"""time_emb_proj"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)

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

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	Any=0      ,				lowerCAmelCase__						:	Optional[Any]=False			):

			"""simple docstring"""





			__UpperCAmelCase :   Any    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   List[Any]    					=  old_item

						# In `model.mid`, the layer is called `attn`.
						if not in_mid:
									__UpperCAmelCase :   List[Any]    					=  new_item.replace("""attn"""      ,				"""attentions"""			)
						__UpperCAmelCase :   str    					=  new_item.replace(""".k."""      ,				""".key."""			)
						__UpperCAmelCase :   Dict    					=  new_item.replace(""".v."""      ,				""".value."""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace(""".q."""      ,				""".query."""			)

						__UpperCAmelCase :   str    					=  new_item.replace("""proj_out"""      ,				"""proj_attn"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""norm"""      ,				"""group_norm"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)
						mapping.append({"""old""": old_item, """new""": new_item}			)

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	Optional[Any]      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	int=None      ,				lowerCAmelCase__						:	List[str]=None      ,				lowerCAmelCase__						:	int=None			):

			"""simple docstring"""





			assert isinstance(lowerCAmelCase__      ,				lowerCAmelCase__			), "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():
									__UpperCAmelCase :   Optional[int]    					=  old_checkpoint[path]
									__UpperCAmelCase :   Union[str, Any]    					=  old_tensor.shape[0] // 3

									__UpperCAmelCase :   Optional[int]    					=  (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

									__UpperCAmelCase :   Optional[int]    					=  old_tensor.shape[0] // config.get("""num_head_channels"""      ,				1			) // 3

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

									__UpperCAmelCase :   Optional[Any]    					=  query.reshape(lowerCAmelCase__			).squeeze()
									__UpperCAmelCase :   Union[str, Any]    					=  key.reshape(lowerCAmelCase__			).squeeze()
									__UpperCAmelCase :   str    					=  value.reshape(lowerCAmelCase__			).squeeze()

			for path in paths:
						__UpperCAmelCase :   Optional[Any]    					=  path["""new"""]

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						__UpperCAmelCase :   List[str]    					=  new_path.replace("""down."""      ,				"""down_blocks."""			)
						__UpperCAmelCase :   List[str]    					=  new_path.replace("""up."""      ,				"""up_blocks."""			)

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

						if "attentions" in new_path:
									__UpperCAmelCase :   int    					=  old_checkpoint[path["""old"""]].squeeze()
						else:
									__UpperCAmelCase :   str    					=  old_checkpoint[path["""old"""]]


def 	lowercase_						( lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Tuple			):

			"""simple docstring"""





			__UpperCAmelCase :   Optional[int]    					=  {}

			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.0.weight"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""temb.dense.0.bias"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.1.weight"""]
			__UpperCAmelCase :   Any    					=  checkpoint["""temb.dense.1.bias"""]

			__UpperCAmelCase :   Any    					=  checkpoint["""norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""norm_out.bias"""]

			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""conv_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""conv_out.bias"""]

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   str    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   Union[str, Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Dict    					=  checkpoint[
									    f'down.{i}.downsample.op.weight'
									]
									__UpperCAmelCase :   int    					=  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]			):
									__UpperCAmelCase :   str    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Tuple    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[str]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Tuple    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Dict    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   Union[str, Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.attn_1""" in key]

			# Mid new 2
			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,				)

			__UpperCAmelCase :   Union[str, Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

			__UpperCAmelCase :   Dict    					=  renew_attention_paths(lowerCAmelCase__      ,				in_mid=lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,				)

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   List[Any]    					=  num_up_blocks - 1 - i

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

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   Any    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   Any    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Tuple    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

			__UpperCAmelCase :   Optional[int]    					=  {k.replace("""mid_new_2"""      ,				"""mid_block"""			): v for k, v in new_checkpoint.items()}
			return new_checkpoint


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	Optional[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   Tuple    					=  {}

			__UpperCAmelCase :   str    					=  checkpoint["""encoder.norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""encoder.norm_out.bias"""]

			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.weight"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_out.weight"""]
			__UpperCAmelCase :   int    					=  checkpoint["""encoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  checkpoint["""decoder.norm_out.weight"""]
			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""decoder.norm_out.bias"""]

			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.bias"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""decoder.conv_out.weight"""]
			__UpperCAmelCase :   str    					=  checkpoint["""decoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   Tuple    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   List[Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Optional[Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.weight'
									]
									__UpperCAmelCase :   Union[str, Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.bias'
									]

						if any("""block""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[Any]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[Any]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Optional[int]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Dict    					=  [key for key in checkpoint if """mid.attn_1""" in key]

			# Mid new 2
			__UpperCAmelCase :   Any    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,				)

			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

			__UpperCAmelCase :   Tuple    					=  renew_attention_paths(lowerCAmelCase__      ,				in_mid=lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,				)

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Union[str, Any]    					=  num_up_blocks - 1 - i

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

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Union[str, Any]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Optional[int]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   int    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   List[str]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Union[str, Any]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

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

			return new_checkpoint


if __name__ == "__main__":
					_UpperCamelCase									=      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.''')

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

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

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

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

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

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

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




