
import argparse

import torch

from diffusers import UNetaDConditionModel



def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  str	,__UpperCamelCase       :  Union[str, Any]=None	,__UpperCamelCase       :  Dict=None	,__UpperCamelCase       :  List[Any]=None    ):
   assert isinstance(__UpperCamelCase	,__UpperCamelCase    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         lowerCAmelCase__ :    List[Any]				      =			old_checkpoint[path]
         lowerCAmelCase__ :    List[Any]				      =			old_tensor.shape[0] // 3

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

         lowerCAmelCase__ :    Optional[Any]				      =			old_tensor.shape[0] // config["num_head_channels"] // 3

         lowerCAmelCase__ :    Union[str, Any]				      =			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
         lowerCAmelCase__ :    List[str]				      =			old_tensor.split(channels // num_heads	,dim=1    )

         lowerCAmelCase__ :    Optional[Any]				      =			query.reshape(__UpperCamelCase    )
         lowerCAmelCase__ :    int				      =			key.reshape(__UpperCamelCase    )
         lowerCAmelCase__ :    Any				      =			value.reshape(__UpperCamelCase    )

   for path in paths:
      lowerCAmelCase__ :    List[str]				      =			path["new"]

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

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

        # proj_attn.weight has to be converted from conv 1D to linear
      lowerCAmelCase__ :    str				      =			old_checkpoint[path["old"]]
      lowerCAmelCase__ :    Dict				      =			["proj_attn.weight"]
      lowerCAmelCase__ :    Union[str, Any]				      =			["proj_out.weight", "proj_in.weight"]
      if any(k in new_path for k in names    ):
         lowerCAmelCase__ :    Tuple				      =			weight[:, :, 0]
      elif any(k in new_path for k in names_a    ) and len(weight.shape    ) > 2 and ".attentions." not in new_path:
         lowerCAmelCase__ :    Optional[int]				      =			weight[:, :, 0]
      else:
         lowerCAmelCase__ :    Union[str, Any]				      =			weight



def      _a     (   __UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  str=0    ):
   lowerCAmelCase__ :    List[str]				      =			[]
   for old_item in old_list:
      lowerCAmelCase__ :    Optional[Any]				      =			old_item

      #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
      #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

      #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
      #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

      #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

   return mapping



def      _a     (   __UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  List[Any]=1    ):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split('''.'''    )[n_shave_prefix_segments:]    )
   else:
      return ".".join(path.split('''.'''    )[:n_shave_prefix_segments]    )



def      _a     (   __UpperCamelCase       :  Tuple	,__UpperCamelCase       :  Optional[Any]=0    ):
   lowerCAmelCase__ :    Optional[int]				      =			[]
   for old_item in old_list:
      mapping.append({'''old''': old_item, '''new''': old_item}    )

   return mapping



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  List[str]=0    ):
   lowerCAmelCase__ :    Union[str, Any]				      =			[]
   for old_item in old_list:
      lowerCAmelCase__ :    Tuple				      =			old_item.replace('''in_layers.0'''	,'''norm1'''    )
      lowerCAmelCase__ :    Dict				      =			new_item.replace('''in_layers.2'''	,'''conv1'''    )

      lowerCAmelCase__ :    Optional[int]				      =			new_item.replace('''out_layers.0'''	,'''norm2'''    )
      lowerCAmelCase__ :    List[str]				      =			new_item.replace('''out_layers.3'''	,'''conv2'''    )

      lowerCAmelCase__ :    List[Any]				      =			new_item.replace('''emb_layers.1'''	,'''time_emb_proj'''    )
      lowerCAmelCase__ :    Optional[int]				      =			new_item.replace('''skip_connection'''	,'''conv_shortcut'''    )

      lowerCAmelCase__ :    Tuple				      =			shave_segments(__UpperCamelCase	,n_shave_prefix_segments=__UpperCamelCase    )

      if "temopral_conv" not in old_item:
         mapping.append({'''old''': old_item, '''new''': new_item}    )

   return mapping



def      _a     (   __UpperCamelCase       :  str	,__UpperCamelCase       :  str	,__UpperCamelCase       :  Optional[int]=None	,__UpperCamelCase       :  str=False    ):
   lowerCAmelCase__ :    Tuple				      =			{}
   lowerCAmelCase__ :    Any				      =			list(checkpoint.keys()    )

   lowerCAmelCase__ :    str				      =			"model.diffusion_model."

   # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
   if sum(k.startswith('''model_ema'''    ) for k in keys    ) > 100 and extract_ema:
      print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''    )
      print(
          '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
          ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''    )
      for key in keys:
         if key.startswith('''model.diffusion_model'''    ):
            lowerCAmelCase__ :    List[str]				      =			"model_ema." + "".join(key.split('''.'''    )[1:]    )
            lowerCAmelCase__ :    Tuple				      =			checkpoint.pop(__UpperCamelCase    )
   else:
      if sum(k.startswith('''model_ema'''    ) for k in keys    ) > 100:
         print(
             '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
             ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''    )

      for key in keys:
         lowerCAmelCase__ :    str				      =			checkpoint.pop(__UpperCamelCase    )

   lowerCAmelCase__ :    Optional[Any]				      =			{}

   lowerCAmelCase__ :    str				      =			unet_state_dict["time_embed.0.weight"]
   lowerCAmelCase__ :    str				      =			unet_state_dict["time_embed.0.bias"]
   lowerCAmelCase__ :    Union[str, Any]				      =			unet_state_dict["time_embed.2.weight"]
   lowerCAmelCase__ :    Tuple				      =			unet_state_dict["time_embed.2.bias"]

   if config["class_embed_type"] is None:
      # No parameters to port
      ...
   elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
      lowerCAmelCase__ :    str				      =			unet_state_dict["label_emb.0.0.weight"]
      lowerCAmelCase__ :    int				      =			unet_state_dict["label_emb.0.0.bias"]
      lowerCAmelCase__ :    int				      =			unet_state_dict["label_emb.0.2.weight"]
      lowerCAmelCase__ :    List[str]				      =			unet_state_dict["label_emb.0.2.bias"]
   else:
      raise NotImplementedError(f'''Not implemented `class_embed_type`: {config["class_embed_type"]}'''    )

   lowerCAmelCase__ :    Optional[Any]				      =			unet_state_dict["input_blocks.0.0.weight"]
   lowerCAmelCase__ :    Tuple				      =			unet_state_dict["input_blocks.0.0.bias"]

   lowerCAmelCase__ :    List[Any]				      =			[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''    )]
   lowerCAmelCase__ :    int				      =			renew_attention_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    Optional[Any]				      =			{"old": "input_blocks.0.1", "new": "transformer_in"}
   assign_to_checkpoint(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

   lowerCAmelCase__ :    List[Any]				      =			unet_state_dict["out.0.weight"]
   lowerCAmelCase__ :    str				      =			unet_state_dict["out.0.bias"]
   lowerCAmelCase__ :    int				      =			unet_state_dict["out.2.weight"]
   lowerCAmelCase__ :    int				      =			unet_state_dict["out.2.bias"]

   # Retrieves the keys for the input blocks only
   lowerCAmelCase__ :    List[str]				      =			len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in unet_state_dict if '''input_blocks''' in layer}    )
   lowerCAmelCase__ :    Dict				      =			{
       layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
       for layer_id in range(__UpperCamelCase    )
   }

   # Retrieves the keys for the middle blocks only
   lowerCAmelCase__ :    Union[str, Any]				      =			len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in unet_state_dict if '''middle_block''' in layer}    )
   lowerCAmelCase__ :    Optional[int]				      =			{
       layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
       for layer_id in range(__UpperCamelCase    )
   }

   # Retrieves the keys for the output blocks only
   lowerCAmelCase__ :    List[str]				      =			len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in unet_state_dict if '''output_blocks''' in layer}    )
   lowerCAmelCase__ :    int				      =			{
       layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
       for layer_id in range(__UpperCamelCase    )
   }

   for i in range(1	,__UpperCamelCase    ):
      lowerCAmelCase__ :    Optional[int]				      =			(i - 1) // (config["layers_per_block"] + 1)
      lowerCAmelCase__ :    Tuple				      =			(i - 1) % (config["layers_per_block"] + 1)

      lowerCAmelCase__ :    Optional[int]				      =			[
          key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
      ]
      lowerCAmelCase__ :    Union[str, Any]				      =			[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
      lowerCAmelCase__ :    Dict				      =			[key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]

      if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
         lowerCAmelCase__ :    Any				      =			unet_state_dict.pop(
             f'''input_blocks.{i}.op.weight'''    )
         lowerCAmelCase__ :    List[Any]				      =			unet_state_dict.pop(
             f'''input_blocks.{i}.op.bias'''    )

      lowerCAmelCase__ :    List[str]				      =			renew_resnet_paths(__UpperCamelCase    )
      lowerCAmelCase__ :    int				      =			{"old": f'''input_blocks.{i}.0''', "new": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
      assign_to_checkpoint(
          __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

      lowerCAmelCase__ :    Tuple				      =			[key for key in resnets if "temopral_conv" in key]
      lowerCAmelCase__ :    Dict				      =			renew_temp_conv_paths(__UpperCamelCase    )
      lowerCAmelCase__ :    Optional[Any]				      =			{
          "old": f'''input_blocks.{i}.0.temopral_conv''',
          "new": f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
      }
      assign_to_checkpoint(
          __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

      if len(__UpperCamelCase    ):
         lowerCAmelCase__ :    Union[str, Any]				      =			renew_attention_paths(__UpperCamelCase    )
         lowerCAmelCase__ :    Tuple				      =			{"old": f'''input_blocks.{i}.1''', "new": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
         assign_to_checkpoint(
             __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

      if len(__UpperCamelCase    ):
         lowerCAmelCase__ :    Tuple				      =			renew_attention_paths(__UpperCamelCase    )
         lowerCAmelCase__ :    Union[str, Any]				      =			{
             "old": f'''input_blocks.{i}.2''',
             "new": f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
         }
         assign_to_checkpoint(
             __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

   lowerCAmelCase__ :    List[Any]				      =			middle_blocks[0]
   lowerCAmelCase__ :    Any				      =			[key for key in resnet_a if "temopral_conv" in key]
   lowerCAmelCase__ :    int				      =			middle_blocks[1]
   lowerCAmelCase__ :    str				      =			middle_blocks[2]
   lowerCAmelCase__ :    List[Any]				      =			middle_blocks[3]
   lowerCAmelCase__ :    Dict				      =			[key for key in resnet_a if "temopral_conv" in key]

   lowerCAmelCase__ :    Tuple				      =			renew_resnet_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    Tuple				      =			{"old": "middle_block.0", "new": "mid_block.resnets.0"}
   assign_to_checkpoint(
       __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,config=__UpperCamelCase	,additional_replacements=[meta_path]    )

   lowerCAmelCase__ :    Optional[int]				      =			renew_temp_conv_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    List[str]				      =			{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
   assign_to_checkpoint(
       __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,config=__UpperCamelCase	,additional_replacements=[meta_path]    )

   lowerCAmelCase__ :    Optional[Any]				      =			renew_resnet_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    int				      =			{"old": "middle_block.3", "new": "mid_block.resnets.1"}
   assign_to_checkpoint(
       __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,config=__UpperCamelCase	,additional_replacements=[meta_path]    )

   lowerCAmelCase__ :    Any				      =			renew_temp_conv_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    Optional[Any]				      =			{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
   assign_to_checkpoint(
       __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,config=__UpperCamelCase	,additional_replacements=[meta_path]    )

   lowerCAmelCase__ :    Optional[int]				      =			renew_attention_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    Union[str, Any]				      =			{"old": "middle_block.1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(
       __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

   lowerCAmelCase__ :    List[str]				      =			renew_attention_paths(__UpperCamelCase    )
   lowerCAmelCase__ :    Union[str, Any]				      =			{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
   assign_to_checkpoint(
       __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

   for i in range(__UpperCamelCase    ):
      lowerCAmelCase__ :    int				      =			i // (config["layers_per_block"] + 1)
      lowerCAmelCase__ :    Dict				      =			i % (config["layers_per_block"] + 1)
      lowerCAmelCase__ :    Dict				      =			[shave_segments(__UpperCamelCase	,2    ) for name in output_blocks[i]]
      lowerCAmelCase__ :    Union[str, Any]				      =			{}

      for layer in output_block_layers:
         lowerCAmelCase__ :    str				      =			layer.split('''.'''    )[0], shave_segments(__UpperCamelCase	,1    )
         if layer_id in output_block_list:
            output_block_list[layer_id].append(__UpperCamelCase    )
         else:
            lowerCAmelCase__ :    List[str]				      =			[layer_name]

      if len(__UpperCamelCase    ) > 1:
         lowerCAmelCase__ :    Tuple				      =			[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
         lowerCAmelCase__ :    List[Any]				      =			[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
         lowerCAmelCase__ :    Any				      =			[key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

         lowerCAmelCase__ :    str				      =			renew_resnet_paths(__UpperCamelCase    )
         lowerCAmelCase__ :    Tuple				      =			renew_resnet_paths(__UpperCamelCase    )

         lowerCAmelCase__ :    str				      =			{"old": f'''output_blocks.{i}.0''', "new": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
         assign_to_checkpoint(
             __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

         lowerCAmelCase__ :    List[str]				      =			[key for key in resnets if "temopral_conv" in key]
         lowerCAmelCase__ :    List[str]				      =			renew_temp_conv_paths(__UpperCamelCase    )
         lowerCAmelCase__ :    Optional[Any]				      =			{
             "old": f'''output_blocks.{i}.0.temopral_conv''',
             "new": f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
         }
         assign_to_checkpoint(
             __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

         lowerCAmelCase__ :    str				      =			{k: sorted(__UpperCamelCase    ) for k, v in output_block_list.items()}
         if ["conv.bias", "conv.weight"] in output_block_list.values():
            lowerCAmelCase__ :    List[Any]				      =			list(output_block_list.values()    ).index(['''conv.bias''', '''conv.weight''']    )
            lowerCAmelCase__ :    Dict				      =			unet_state_dict[
                f'''output_blocks.{i}.{index}.conv.weight'''
            ]
            lowerCAmelCase__ :    List[str]				      =			unet_state_dict[
                f'''output_blocks.{i}.{index}.conv.bias'''
            ]

            # Clear attentions as they have been attributed above.
            if len(__UpperCamelCase    ) == 2:
               lowerCAmelCase__ :    Dict				      =			[]

         if len(__UpperCamelCase    ):
            lowerCAmelCase__ :    int				      =			renew_attention_paths(__UpperCamelCase    )
            lowerCAmelCase__ :    Tuple				      =			{
                "old": f'''output_blocks.{i}.1''',
                "new": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
            }
            assign_to_checkpoint(
                __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )

         if len(__UpperCamelCase    ):
            lowerCAmelCase__ :    Any				      =			renew_attention_paths(__UpperCamelCase    )
            lowerCAmelCase__ :    Dict				      =			{
                "old": f'''output_blocks.{i}.2''',
                "new": f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
            }
            assign_to_checkpoint(
                __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,additional_replacements=[meta_path]	,config=__UpperCamelCase    )
      else:
         lowerCAmelCase__ :    Optional[int]				      =			renew_resnet_paths(__UpperCamelCase	,n_shave_prefix_segments=1    )
         for path in resnet_0_paths:
            lowerCAmelCase__ :    List[str]				      =			".".join(['''output_blocks''', str(__UpperCamelCase    ), path['''old''']]    )
            lowerCAmelCase__ :    Dict				      =			".".join(['''up_blocks''', str(__UpperCamelCase    ), '''resnets''', str(__UpperCamelCase    ), path['''new''']]    )
            lowerCAmelCase__ :    Union[str, Any]				      =			unet_state_dict[old_path]

         lowerCAmelCase__ :    str				      =			[l for l in output_block_layers if "temopral_conv" in l]
         for path in temopral_conv_paths:
            lowerCAmelCase__ :    int				      =			path.split('''temopral_conv.'''    )[-1]
            lowerCAmelCase__ :    Union[str, Any]				      =			".".join(['''output_blocks''', str(__UpperCamelCase    ), str(__UpperCamelCase    ), '''temopral_conv''', pruned_path]    )
            lowerCAmelCase__ :    Any				      =			".".join(['''up_blocks''', str(__UpperCamelCase    ), '''temp_convs''', str(__UpperCamelCase    ), pruned_path]    )
            lowerCAmelCase__ :    Union[str, Any]				      =			unet_state_dict[old_path]

   return new_checkpoint


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

       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
       )
       parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
       A__       :   List[str]   						=					parser.parse_args()

       A__       :   Union[str, Any]   						=					torch.load(args.checkpoint_path, map_location="""cpu""")
       A__       :   int   						=					UNetaDConditionModel()

       A__       :   Tuple   						=					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       A__       :   Any   						=					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       A__       :   List[str]   						=					set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



