




'''simple docstring'''






import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
      from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class        lowercase_      :





     def __init__(     self     :					List[str]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					int=13     ,							__lowerCamelCase     :					Tuple=32     ,							__lowerCamelCase     :					Any=3     ,							__lowerCamelCase     :					int=4     ,							__lowerCamelCase     :					List[Any]=[10, 20, 30, 40]     ,							__lowerCamelCase     :					Optional[int]=[2, 2, 3, 2]     ,							__lowerCamelCase     :					Tuple=True     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					List[str]=37     ,							__lowerCamelCase     :					List[Any]="gelu"     ,							__lowerCamelCase     :					Tuple=10     ,							__lowerCamelCase     :					List[str]=0.0_2     ,							__lowerCamelCase     :					Dict=["stage2", "stage3", "stage4"]     ,							__lowerCamelCase     :					str=[2, 3, 4]     ,							__lowerCamelCase     :					Dict=None     ,							):
           snake_case__       :   int	      =    parent
           snake_case__       :   Any	      =    batch_size
           snake_case__       :   Optional[int]	      =    image_size
           snake_case__       :   Optional[int]	      =    num_channels
           snake_case__       :   List[Any]	      =    num_stages
           snake_case__       :   Optional[int]	      =    hidden_sizes
           snake_case__       :   int	      =    depths
           snake_case__       :   str	      =    is_training
           snake_case__       :   Union[str, Any]	      =    use_labels
           snake_case__       :   Optional[Any]	      =    intermediate_size
           snake_case__       :   Any	      =    hidden_act
           snake_case__       :   List[str]	      =    num_labels
           snake_case__       :   Any	      =    initializer_range
           snake_case__       :   Tuple	      =    out_features
           snake_case__       :   Optional[Any]	      =    out_indices
           snake_case__       :   Union[str, Any]	      =    scope





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   str	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           snake_case__       :   str	      =    None
           if self.use_labels:
                 snake_case__       :   List[Any]	      =    ids_tensor([self.batch_size]     ,							self.num_labels			)

           snake_case__       :   Tuple	      =    self.get_config()

           return config, pixel_values, labels





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return ConvNextVaConfig(
               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=snake_case__     ,							initializer_range=self.initializer_range     ,							out_features=self.out_features     ,							out_indices=self.out_indices     ,							num_labels=self.num_labels     ,							)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   Tuple	      =    ConvNextVaModel(config=snake_case__			)
           model.to(snake_case__			)
           model.eval()
           snake_case__       :   int	      =    model(snake_case__			)
           # 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 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					int			):
           snake_case__       :   List[Any]	      =    ConvNextVaForImageClassification(snake_case__			)
           model.to(snake_case__			)
           model.eval()
           snake_case__       :   Dict	      =    model(snake_case__     ,							labels=snake_case__			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					List[Any]			):
           snake_case__       :   Any	      =    ConvNextVaBackbone(config=snake_case__			)
           model.to(snake_case__			)
           model.eval()
           snake_case__       :   Optional[Any]	      =    model(snake_case__			)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps			)     ,							len(config.out_features			)			)
           self.parent.assertListEqual(list(result.feature_maps[0].shape			)     ,							[self.batch_size, self.hidden_sizes[1], 4, 4]			)

           # verify channels
           self.parent.assertEqual(len(model.channels			)     ,							len(config.out_features			)			)
           self.parent.assertListEqual(model.channels     ,							config.hidden_sizes[1:]			)

           # verify backbone works with out_features=None
           snake_case__       :   List[Any]	      =    None
           snake_case__       :   List[Any]	      =    ConvNextVaBackbone(config=snake_case__			)
           model.to(snake_case__			)
           model.eval()
           snake_case__       :   Optional[Any]	      =    model(snake_case__			)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps			)     ,							1			)
           self.parent.assertListEqual(list(result.feature_maps[0].shape			)     ,							[self.batch_size, self.hidden_sizes[-1], 1, 1]			)

           # verify channels
           self.parent.assertEqual(len(model.channels			)     ,							1			)
           self.parent.assertListEqual(model.channels     ,							[config.hidden_sizes[-1]]			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[str]	      =    self.prepare_config_and_inputs()
           snake_case__       :   Tuple	      =    config_and_inputs
           snake_case__       :   Optional[int]	      =    {"pixel_values": pixel_values}
           return config, inputs_dict





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Dict	      =    self.prepare_config_and_inputs()
           snake_case__       :   int	      =    config_and_inputs
           snake_case__       :   Union[str, Any]	      =    {"pixel_values": pixel_values, "labels": labels}
           return config, inputs_dict






@require_torch
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (
             ConvNextVaModel,
             ConvNextVaForImageClassification,
             ConvNextVaBackbone,
         )
         if is_torch_available()
         else ()
     )
     A_											=			(
         {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
         if is_torch_available()
         else {}
     )

     A_											=			False
     A_											=			False
     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Union[str, Any]	      =    ConvNextVaModelTester(self			)
           snake_case__       :   List[str]	      =    ConfigTester(self     ,							config_class=snake_case__     ,							has_text_modality=snake_case__     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return





     @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'			)
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           pass





     @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'			)
     def 			_lowerCAmelCase      (     self     :					Any			):
           pass





     @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'			)
     def 			_lowerCAmelCase      (     self     :					int			):
           pass





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           if not self.model_tester.is_training:
                 return

           for model_class in self.all_model_classes:
                 snake_case__       :   List[Any]	      =    self.model_tester.prepare_config_and_inputs_with_labels()
                 snake_case__       :   Tuple	      =    True

                 if model_class.__name__ in [
                     *get_values(snake_case__			),
                     *get_values(snake_case__			),
                 ]:
                       continue

                 snake_case__       :   Optional[int]	      =    model_class(snake_case__			)
                 model.to(snake_case__			)
                 model.train()
                 snake_case__       :   Dict	      =    self._prepare_for_class(snake_case__     ,							snake_case__     ,							return_labels=snake_case__			)
                 snake_case__       :   Union[str, Any]	      =    model(**snake_case__			).loss
                 loss.backward()





     def 			_lowerCAmelCase      (     self     :					int			):
           if not self.model_tester.is_training:
                 return

           for model_class in self.all_model_classes:
                 snake_case__       :   Optional[int]	      =    self.model_tester.prepare_config_and_inputs_with_labels()
                 snake_case__       :   List[str]	      =    False
                 snake_case__       :   Any	      =    True

                 if (
                     model_class.__name__
                     in [*get_values(snake_case__			), *get_values(snake_case__			)]
                     or not model_class.supports_gradient_checkpointing
                 ):
                       continue

                 snake_case__       :   List[Any]	      =    model_class(snake_case__			)
                 model.to(snake_case__			)
                 model.gradient_checkpointing_enable()
                 model.train()
                 snake_case__       :   Optional[Any]	      =    self._prepare_for_class(snake_case__     ,							snake_case__     ,							return_labels=snake_case__			)
                 snake_case__       :   Tuple	      =    model(**snake_case__			).loss
                 loss.backward()





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case__       :   str	      =    ["pixel_values"]
                 self.assertListEqual(arg_names[:1]     ,							snake_case__			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case__			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           def check_hidden_states_output(__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Union[str, Any]			):
                 snake_case__       :   Optional[int]	      =    model_class(snake_case__			)
                 model.to(snake_case__			)
                 model.eval()

                 with torch.no_grad():
                       snake_case__       :   Union[str, Any]	      =    model(**self._prepare_for_class(snake_case__     ,							snake_case__			)			)

                 snake_case__       :   int	      =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 snake_case__       :   Any	      =    self.model_tester.num_stages
                 self.assertEqual(len(snake_case__			)     ,							expected_num_stages + 1			)

                 # ConvNextV2'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]     ,							)

           snake_case__       :   List[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   Optional[int]	      =    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__       :   Dict	      =    True

                 check_hidden_states_output(snake_case__     ,							snake_case__     ,							snake_case__			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   List[str]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case__			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   Optional[Any]	      =    ConvNextVaModel.from_pretrained(snake_case__			)
                 self.assertIsNotNone(snake_case__			)






def 		UpperCamelCase__      (		)    ->   Optional[Any]:
      snake_case__       :   List[Any]	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






@require_torch
@require_vision
class        lowercase_      (						unittest.TestCase							):





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'			) if is_vision_available() else None





     @slow
     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[Any]	      =    ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'			).to(snake_case__			)

           snake_case__       :   Tuple	      =    self.default_image_processor
           snake_case__       :   Any	      =    prepare_img()
           snake_case__       :   List[Any]	      =    preprocessor(images=snake_case__     ,							return_tensors='pt'			).to(snake_case__			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(**snake_case__			)

           # verify the logits
           snake_case__       :   Optional[int]	      =    torch.Size((1, 1000)			)
           self.assertEqual(outputs.logits.shape     ,							snake_case__			)

           snake_case__       :   List[str]	      =    torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]			).to(snake_case__			)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,							snake_case__     ,							atol=1E-4			)			)








"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	str				=      {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	int				=      {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str				)->		str:






     '''simple docstring'''



     if isinstance(snake_case			,					snake_case				):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError("boolean value expected"				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Optional[Any]			,					snake_case       : List[str]			,					snake_case       : Any			,					snake_case       : str=False				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.0.bias']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.2.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.2.bias']
     UpperCAmelCase__						:    Optional[int]							=							checkpoint[f'{old_prefix}.emb_layers.1.weight']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.emb_layers.1.bias']
     UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.out_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.out_layers.0.bias']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.weight']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.bias']

     if has_skip:
          UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.skip_connection.weight']
          UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.skip_connection.bias']

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Union[str, Any]			,					snake_case       : Any			,					snake_case       : Tuple			,					snake_case       : Dict=None				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							checkpoint[f'{old_prefix}.qkv.weight'].chunk(3			,					dim=0				)
     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.qkv.bias'].chunk(3			,					dim=0				)

     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.norm.weight']
     UpperCAmelCase__						:    Tuple							=							checkpoint[f'{old_prefix}.norm.bias']

     UpperCAmelCase__						:    Tuple							=							weight_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    int							=							bias_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							weight_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[int]							=							bias_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Tuple							=							weight_v.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							bias_v.squeeze(-1				).squeeze(-1				)

     UpperCAmelCase__						:    Union[str, Any]							=							(
         checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1				).squeeze(-1				)
     )
     UpperCAmelCase__						:    List[str]							=							checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1				).squeeze(-1				)

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Union[str, Any]				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Any							=							{}

     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.0.weight"]
     UpperCAmelCase__						:    Optional[int]							=							checkpoint["time_embed.0.bias"]
     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.2.weight"]
     UpperCAmelCase__						:    Optional[Any]							=							checkpoint["time_embed.2.bias"]

     if unet_config["num_class_embeds"] is not None:
          UpperCAmelCase__						:    List[Any]							=							checkpoint["label_emb.weight"]

     UpperCAmelCase__						:    Dict							=							checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase__						:    List[Any]							=							checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase__						:    Any							=							unet_config["down_block_types"]
     UpperCAmelCase__						:    Tuple							=							unet_config["layers_per_block"]
     UpperCAmelCase__						:    str							=							unet_config["attention_head_dim"]
     UpperCAmelCase__						:    str							=							unet_config["block_out_channels"]
     UpperCAmelCase__						:    Optional[int]							=							1
     UpperCAmelCase__						:    Tuple							=							channels_list[0]

     for i, layer_type in enumerate(snake_case				):
          UpperCAmelCase__						:    List[str]							=							channels_list[i]
          UpperCAmelCase__						:    Optional[int]							=							current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(snake_case				):
                    UpperCAmelCase__						:    int							=							f'down_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Union[str, Any]							=							f'input_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    List[str]							=							True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__						:    Optional[Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(snake_case				):
                    UpperCAmelCase__						:    List[str]							=							f'down_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Optional[Any]							=							f'input_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    Optional[int]							=							True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.attentions.{j}'
                    UpperCAmelCase__						:    List[Any]							=							f'input_blocks.{current_layer}.1'
                    UpperCAmelCase__						:    Optional[Any]							=							convert_attention(
                        snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
                    current_layer += 1

          if i != len(snake_case				) - 1:
               UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.downsamplers.0'
               UpperCAmelCase__						:    Optional[int]							=							f'input_blocks.{current_layer}.0'
               UpperCAmelCase__						:    Tuple							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
               current_layer += 1

          UpperCAmelCase__						:    Dict							=							current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase__						:    int							=							"mid_block.resnets.0"
     UpperCAmelCase__						:    Optional[int]							=							"middle_block.0"
     UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.attentions.0"
     UpperCAmelCase__						:    Tuple							=							"middle_block.1"
     UpperCAmelCase__						:    Any							=							convert_attention(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.resnets.1"
     UpperCAmelCase__						:    Union[str, Any]							=							"middle_block.2"
     UpperCAmelCase__						:    Optional[int]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							0
     UpperCAmelCase__						:    int							=							unet_config["up_block_types"]

     for i, layer_type in enumerate(snake_case				):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1				):
                    UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    List[str]							=							f'output_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    List[str]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    current_layer += 1

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Optional[Any]							=							f'output_blocks.{current_layer-1}.1'
                    UpperCAmelCase__						:    Optional[Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1				):
                    UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Tuple							=							f'output_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    Union[str, Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    UpperCAmelCase__						:    Optional[int]							=							f'up_blocks.{i}.attentions.{j}'
                    UpperCAmelCase__						:    List[Any]							=							f'output_blocks.{current_layer}.1'
                    UpperCAmelCase__						:    Any							=							convert_attention(
                        snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
                    current_layer += 1

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Any							=							f'output_blocks.{current_layer-1}.2'
                    UpperCAmelCase__						:    int							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							checkpoint["out.0.weight"]
     UpperCAmelCase__						:    Any							=							checkpoint["out.0.bias"]
     UpperCAmelCase__						:    int							=							checkpoint["out.2.weight"]
     UpperCAmelCase__						:    str							=							checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase	:	str				=      argparse.ArgumentParser()

 parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
 parser.add_argument(
     """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
 )
 parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

 _lowerCAmelCase	:	int				=      parser.parse_args()
 _lowerCAmelCase	:	Optional[int]				=      strabool(args.class_cond)

 _lowerCAmelCase	:	Optional[Any]				=      os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	Dict				=      IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _lowerCAmelCase	:	Dict				=      LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  _lowerCAmelCase	:	str				=      TEST_UNET_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 if not args.class_cond:
  _lowerCAmelCase	:	int				=      None

 _lowerCAmelCase	:	Optional[int]				=      con_pt_to_diffuser(args.unet_path, unet_config)

 _lowerCAmelCase	:	Optional[Any]				=      UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

 # Get scheduler config
 if "cd" in ckpt_name or "test" in ckpt_name:
  _lowerCAmelCase	:	Optional[int]				=      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	str				=      CT_IMAGENET_64_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _lowerCAmelCase	:	Any				=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 _lowerCAmelCase	:	Dict				=      CMStochasticIterativeScheduler(**scheduler_config)

 _lowerCAmelCase	:	Tuple				=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)


