

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

    from transformers import RegNetForImageClassification, RegNetModel
    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Dict=3     ,       __lowerCamelCase						:		str=32     ,       __lowerCamelCase						:		List[str]=3     ,       __lowerCamelCase						:		Dict=10     ,       __lowerCamelCase						:		Any=[10, 20, 30, 40]     ,       __lowerCamelCase						:		List[Any]=[1, 1, 2, 1]     ,       __lowerCamelCase						:		List[Any]=True     ,       __lowerCamelCase						:		str=True     ,       __lowerCamelCase						:		Tuple="relu"     ,       __lowerCamelCase						:		List[str]=3     ,       __lowerCamelCase						:		Optional[Any]=None     ,       )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   parent
       SCREAMING_SNAKE_CASE__   =   batch_size
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   embeddings_size
       SCREAMING_SNAKE_CASE__   =   hidden_sizes
       SCREAMING_SNAKE_CASE__   =   depths
       SCREAMING_SNAKE_CASE__   =   is_training
       SCREAMING_SNAKE_CASE__   =   use_labels
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   num_labels
       SCREAMING_SNAKE_CASE__   =   scope
       SCREAMING_SNAKE_CASE__   =   len(__snake_case )






  def 				lowercase_		(							self						:		int )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

       SCREAMING_SNAKE_CASE__   =   self.get_config()

       return config, pixel_values, labels






  def 				lowercase_		(							self						:		Optional[Any] )		->							List[str]:
       return RegNetConfig(
           num_channels=self.num_channels     ,       embeddings_size=self.embeddings_size     ,       hidden_sizes=self.hidden_sizes     ,       depths=self.depths     ,       hidden_act=self.hidden_act     ,       num_labels=self.num_labels     ,       )






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Dict )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   RegNetModel(config=__snake_case )
       model.to(__snake_case )
       model.eval()
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Union[str, Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   self.num_labels
       SCREAMING_SNAKE_CASE__   =   RegNetForImageClassification(__snake_case )
       model.to(__snake_case )
       model.eval()
       SCREAMING_SNAKE_CASE__   =   model(__snake_case     ,       labels=__snake_case )
       self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels) )







  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   config_and_inputs
       SCREAMING_SNAKE_CASE__   =   {'''pixel_values''': pixel_values}
       return config, inputs_dict






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






  """simple docstring"""


  a									= (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
  a									= (
      {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
      if is_torch_available()
      else {}
  )

  a									= False
  a									= False
  a									= False
  a									= False






  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   RegNetModelTester(self )
       SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=__snake_case     ,       has_text_modality=__snake_case )






  def 				lowercase_		(							self						:		List[str] )		->							Tuple:
       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 				lowercase_		(							self						:		List[str] )		->							Dict:
       return






  @unittest.skip(reason='''RegNet does not use inputs_embeds''' )
  def 				lowercase_		(							self						:		Dict )		->							Dict:
       pass






  @unittest.skip(reason='''RegNet does not support input and output embeddings''' )
  def 				lowercase_		(							self						:		Union[str, Any] )		->							Any:
       pass






  def 				lowercase_		(							self						:		Union[str, Any] )		->							List[Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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

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






  def 				lowercase_		(							self						:		List[str] )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__snake_case )






  def 				lowercase_		(							self						:		Any )		->							Dict:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__   =   model_class(config=__snake_case )
            for name, module in model.named_modules():
                 if isinstance(__snake_case     ,       (nn.BatchNormad, nn.GroupNorm) ):
                      self.assertTrue(
                          torch.all(module.weight == 1 )     ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,       )
                      self.assertTrue(
                          torch.all(module.bias == 0 )     ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,       )






  def 				lowercase_		(							self						:		List[str] )		->							Optional[Any]:
       def check_hidden_states_output(__lowerCamelCase						:		int     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		str ):
            SCREAMING_SNAKE_CASE__   =   model_class(__snake_case )
            model.to(__snake_case )
            model.eval()

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

            SCREAMING_SNAKE_CASE__   =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            SCREAMING_SNAKE_CASE__   =   self.model_tester.num_stages
            self.assertEqual(len(__snake_case )     ,       expected_num_stages + 1 )

            # RegNet'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 // 2, self.model_tester.image_size // 2]     ,       )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__   =   ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 SCREAMING_SNAKE_CASE__   =   layer_type
                 SCREAMING_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"]
                 SCREAMING_SNAKE_CASE__   =   True

                 check_hidden_states_output(__snake_case     ,       __snake_case     ,       __snake_case )






  def 				lowercase_		(							self						:		str )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__snake_case )







  @slow
  def 				lowercase_		(							self						:		Tuple )		->							Any:
       for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__   =   RegNetModel.from_pretrained(__snake_case )
            self.assertIsNotNone(__snake_case )



def     UpperCAmelCase_       (						):





     '''simple docstring'''




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







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






  """simple docstring"""


  @cached_property
  def 				lowercase_		(							self						:		List[str] )		->							Dict:
       return (
           AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
           if is_vision_available()
           else None
       )







  @slow
  def 				lowercase_		(							self						:		Optional[Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__snake_case )

       SCREAMING_SNAKE_CASE__   =   self.default_image_processor
       SCREAMING_SNAKE_CASE__   =   prepare_img()
       SCREAMING_SNAKE_CASE__   =   image_processor(images=__snake_case     ,       return_tensors='''pt''' ).to(__snake_case )

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

       # verify the logits
       SCREAMING_SNAKE_CASE__   =   torch.Size((1, 1000) )
       self.assertEqual(outputs.logits.shape     ,       __snake_case )

       SCREAMING_SNAKE_CASE__   =   torch.tensor([-0.4180, -1.5051, -3.4836] ).to(__snake_case )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,       __snake_case     ,       atol=1e-4 ) )





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_SCREAMING_SNAKE_CASE					:  str             =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  Dict             =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

_SCREAMING_SNAKE_CASE					:  str             =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if isinstance(_A				,		_A    ):
          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     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.skip_connection.weight''']
          SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=None    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3				,		dim=0    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3				,		dim=0    )

     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.norm.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.norm.bias''']

     SCREAMING_SNAKE_CASE__   =   weight_q.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   bias_q.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   weight_k.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   bias_k.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   weight_v.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   bias_v.squeeze(-1    ).squeeze(-1    )

     SCREAMING_SNAKE_CASE__   =   (
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1    ).squeeze(-1    )
     )
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1    ).squeeze(-1    )

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )
     SCREAMING_SNAKE_CASE__   =   {}

     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.2.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.2.bias''']

     if unet_config["num_class_embeds"] is not None:
          SCREAMING_SNAKE_CASE__   =   checkpoint['''label_emb.weight''']

     SCREAMING_SNAKE_CASE__   =   checkpoint['''input_blocks.0.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''input_blocks.0.0.bias''']

     SCREAMING_SNAKE_CASE__   =   unet_config['''down_block_types''']
     SCREAMING_SNAKE_CASE__   =   unet_config['''layers_per_block''']
     SCREAMING_SNAKE_CASE__   =   unet_config['''attention_head_dim''']
     SCREAMING_SNAKE_CASE__   =   unet_config['''block_out_channels''']
     SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   channels_list[0]

     for i, layer_type in enumerate(_A    ):
          SCREAMING_SNAKE_CASE__   =   channels_list[i]
          SCREAMING_SNAKE_CASE__   =   current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(_A    ):
                    SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   True if j == 0 and downsample_block_has_skip else False
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(_A    ):
                    SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   True if j == 0 and downsample_block_has_skip else False
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.attentions.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.1'''
                    SCREAMING_SNAKE_CASE__   =   convert_attention(
                        _A				,		_A				,		_A				,		_A				,		_A    )
                    current_layer += 1

          if i != len(_A    ) - 1:
               SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.downsamplers.0'''
               SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.0'''
               SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )
               current_layer += 1

          SCREAMING_SNAKE_CASE__   =   current_channels

     # hardcoded the mid-block for now
     SCREAMING_SNAKE_CASE__   =   '''mid_block.resnets.0'''
     SCREAMING_SNAKE_CASE__   =   '''middle_block.0'''
     SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   '''mid_block.attentions.0'''
     SCREAMING_SNAKE_CASE__   =   '''middle_block.1'''
     SCREAMING_SNAKE_CASE__   =   convert_attention(_A				,		_A				,		_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   '''mid_block.resnets.1'''
     SCREAMING_SNAKE_CASE__   =   '''middle_block.2'''
     SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   unet_config['''up_block_types''']

     for i, layer_type in enumerate(_A    ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1    ):
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    current_layer += 1

               if i != len(_A    ) - 1:
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.upsamplers.0'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer-1}.1'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1    ):
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.attentions.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer}.1'''
                    SCREAMING_SNAKE_CASE__   =   convert_attention(
                        _A				,		_A				,		_A				,		_A				,		_A    )
                    current_layer += 1

               if i != len(_A    ) - 1:
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.upsamplers.0'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer-1}.2'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )

     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.2.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.2.bias''']

     return new_checkpoint


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      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.''')

       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()
       _SCREAMING_SNAKE_CASE					:  List[str]             =      strabool(args.class_cond)

       _SCREAMING_SNAKE_CASE					:  int             =      os.path.basename(args.unet_path)
       print(F"Checkpoint: {ckpt_name}")

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              _SCREAMING_SNAKE_CASE					:  int             =      LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      TEST_UNET_CONFIG
       else:
              raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

       if not args.class_cond:
              _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      None

       _SCREAMING_SNAKE_CASE					:  int             =      con_pt_to_diffuser(args.unet_path, unet_config)

       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

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

       _SCREAMING_SNAKE_CASE					:  int             =      CMStochasticIterativeScheduler(**scheduler_config)

       _SCREAMING_SNAKE_CASE					:  str             =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)


