


import argparse

import torch

from diffusers import UNetaDConditionModel





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: str=None	,	SCREAMING_SNAKE_CASE_				: Tuple=None	,	SCREAMING_SNAKE_CASE_				: str=None      ):






							"""simple docstring"""



							assert isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ), "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():
																					a_       :   Optional[Any]			=	old_checkpoint[path]
																					a_       :   List[Any]			=	old_tensor.shape[0] // 3

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

																					a_       :   List[Any]			=	old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

																					a_       :   Optional[int]			=	query.reshape(SCREAMING_SNAKE_CASE_      )
																					a_       :   Optional[int]			=	key.reshape(SCREAMING_SNAKE_CASE_      )
																					a_       :   List[Any]			=	value.reshape(SCREAMING_SNAKE_CASE_      )

							for path in paths:
														a_       :   Union[str, Any]			=	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:
																												a_       :   Any			=	new_path.replace(replacement["""old"""]	,	replacement["""new"""]      )

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





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: List[str]=0      ):






							"""simple docstring"""



							a_       :   Optional[Any]			=	[]
							for old_item in old_list:
														a_       :   Tuple			=	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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]	,	SCREAMING_SNAKE_CASE_				: List[str]=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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: Optional[int]=0      ):






							"""simple docstring"""



							a_       :   Optional[Any]			=	[]
							for old_item in old_list:
														mapping.append({"""old""": old_item, """new""": old_item}      )

							return mapping





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Optional[int]=0      ):






							"""simple docstring"""



							a_       :   Optional[int]			=	[]
							for old_item in old_list:
														a_       :   int			=	old_item.replace("""in_layers.0"""	,	"""norm1"""      )
														a_       :   Union[str, Any]			=	new_item.replace("""in_layers.2"""	,	"""conv1"""      )

														a_       :   Optional[int]			=	new_item.replace("""out_layers.0"""	,	"""norm2"""      )
														a_       :   Dict			=	new_item.replace("""out_layers.3"""	,	"""conv2"""      )

														a_       :   Any			=	new_item.replace("""emb_layers.1"""	,	"""time_emb_proj"""      )
														a_       :   Union[str, Any]			=	new_item.replace("""skip_connection"""	,	"""conv_shortcut"""      )

														a_       :   Union[str, Any]			=	shave_segments(SCREAMING_SNAKE_CASE_	,	n_shave_prefix_segments=SCREAMING_SNAKE_CASE_      )

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

							return mapping





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: List[str]	,	SCREAMING_SNAKE_CASE_				: str=None	,	SCREAMING_SNAKE_CASE_				: int=False      ):






							"""simple docstring"""



							a_       :   Union[str, Any]			=	{}
							a_       :   Optional[int]			=	list(checkpoint.keys()      )

							a_       :   Dict			=	"""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      ) > 1_00 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"""      ):
																												a_       :   Optional[int]			=	"""model_ema.""" + """""".join(key.split("""."""      )[1:]      )
																												a_       :   Any			=	checkpoint.pop(SCREAMING_SNAKE_CASE_      )
							else:
														if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 1_00:
																					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:
																					a_       :   str			=	checkpoint.pop(SCREAMING_SNAKE_CASE_      )

							a_       :   Any			=	{}

							a_       :   Dict			=	unet_state_dict["""time_embed.0.weight"""]
							a_       :   Dict			=	unet_state_dict["""time_embed.0.bias"""]
							a_       :   Optional[Any]			=	unet_state_dict["""time_embed.2.weight"""]
							a_       :   Optional[int]			=	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":
														a_       :   str			=	unet_state_dict["""label_emb.0.0.weight"""]
														a_       :   Union[str, Any]			=	unet_state_dict["""label_emb.0.0.bias"""]
														a_       :   Dict			=	unet_state_dict["""label_emb.0.2.weight"""]
														a_       :   List[str]			=	unet_state_dict["""label_emb.0.2.bias"""]
							else:
														raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""      )

							a_       :   Optional[int]			=	unet_state_dict["""input_blocks.0.0.weight"""]
							a_       :   str			=	unet_state_dict["""input_blocks.0.0.bias"""]

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

							a_       :   str			=	unet_state_dict["""out.0.weight"""]
							a_       :   Any			=	unet_state_dict["""out.0.bias"""]
							a_       :   Optional[int]			=	unet_state_dict["""out.2.weight"""]
							a_       :   Tuple			=	unet_state_dict["""out.2.bias"""]

							# Retrieves the keys for the input blocks only
							a_       :   str			=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """input_blocks""" in layer}      )
							a_       :   Union[str, Any]			=	{
							    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
							    for layer_id in range(SCREAMING_SNAKE_CASE_      )
							}

							# Retrieves the keys for the middle blocks only
							a_       :   Tuple			=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """middle_block""" in layer}      )
							a_       :   List[Any]			=	{
							    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
							    for layer_id in range(SCREAMING_SNAKE_CASE_      )
							}

							# Retrieves the keys for the output blocks only
							a_       :   Optional[int]			=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """output_blocks""" in layer}      )
							a_       :   str			=	{
							    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
							    for layer_id in range(SCREAMING_SNAKE_CASE_      )
							}

							for i in range(1	,	SCREAMING_SNAKE_CASE_      ):
														a_       :   Dict			=	(i - 1) // (config["""layers_per_block"""] + 1)
														a_       :   Optional[int]			=	(i - 1) % (config["""layers_per_block"""] + 1)

														a_       :   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
														]
														a_       :   List[str]			=	[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
														a_       :   List[str]			=	[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:
																					a_       :   str			=	unet_state_dict.pop(
																					    F"""input_blocks.{i}.op.weight"""      )
																					a_       :   Tuple			=	unet_state_dict.pop(
																					    F"""input_blocks.{i}.op.bias"""      )

														a_       :   Any			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
														a_       :   Optional[Any]			=	{"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														assign_to_checkpoint(
														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

														a_       :   List[str]			=	[key for key in resnets if """temopral_conv""" in key]
														a_       :   List[Any]			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
														a_       :   str			=	{
														    """old""": F"""input_blocks.{i}.0.temopral_conv""",
														    """new""": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
														}
														assign_to_checkpoint(
														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

														if len(SCREAMING_SNAKE_CASE_      ):
																					a_       :   Optional[Any]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
																					a_       :   Optional[Any]			=	{"""old""": F"""input_blocks.{i}.1""", """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
																					assign_to_checkpoint(
																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

														if len(SCREAMING_SNAKE_CASE_      ):
																					a_       :   str			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
																					a_       :   Optional[Any]			=	{
																					    """old""": F"""input_blocks.{i}.2""",
																					    """new""": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																					}
																					assign_to_checkpoint(
																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

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

							a_       :   int			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
							a_       :   Union[str, Any]			=	{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
							assign_to_checkpoint(
							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )

							a_       :   List[Any]			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
							a_       :   Optional[Any]			=	{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
							assign_to_checkpoint(
							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )

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

							a_       :   List[Any]			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
							a_       :   List[str]			=	{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
							assign_to_checkpoint(
							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )

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

							a_       :   List[str]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
							a_       :   Optional[int]			=	{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
							assign_to_checkpoint(
							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

							for i in range(SCREAMING_SNAKE_CASE_      ):
														a_       :   Optional[Any]			=	i // (config["""layers_per_block"""] + 1)
														a_       :   Optional[Any]			=	i % (config["""layers_per_block"""] + 1)
														a_       :   str			=	[shave_segments(SCREAMING_SNAKE_CASE_	,	2      ) for name in output_blocks[i]]
														a_       :   Optional[int]			=	{}

														for layer in output_block_layers:
																					a_		,				a_       :   List[str]			=	layer.split("""."""      )[0], shave_segments(SCREAMING_SNAKE_CASE_	,	1      )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_      )
																					else:
																												a_       :   Union[str, Any]			=	[layer_name]

														if len(SCREAMING_SNAKE_CASE_      ) > 1:
																					a_       :   Any			=	[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																					a_       :   Dict			=	[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
																					a_       :   Optional[Any]			=	[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

																					a_       :   Dict			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
																					a_       :   Optional[int]			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )

																					a_       :   Tuple			=	{"""old""": F"""output_blocks.{i}.0""", """new""": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																					assign_to_checkpoint(
																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

																					a_       :   Optional[int]			=	[key for key in resnets if """temopral_conv""" in key]
																					a_       :   Any			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
																					a_       :   Dict			=	{
																					    """old""": F"""output_blocks.{i}.0.temopral_conv""",
																					    """new""": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
																					}
																					assign_to_checkpoint(
																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

																					a_       :   Tuple			=	{k: sorted(SCREAMING_SNAKE_CASE_      ) for k, v in output_block_list.items()}
																					if ["conv.bias", "conv.weight"] in output_block_list.values():
																												a_       :   int			=	list(output_block_list.values()      ).index(["""conv.bias""", """conv.weight"""]      )
																												a_       :   Optional[int]			=	unet_state_dict[
																												    F"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												a_       :   List[str]			=	unet_state_dict[
																												    F"""output_blocks.{i}.{index}.conv.bias"""
																												]

																												# Clear attentions as they have been attributed above.
																												if len(SCREAMING_SNAKE_CASE_      ) == 2:
																																			a_       :   Tuple			=	[]

																					if len(SCREAMING_SNAKE_CASE_      ):
																												a_       :   Optional[Any]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
																												a_       :   Optional[Any]			=	{
																												    """old""": F"""output_blocks.{i}.1""",
																												    """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												assign_to_checkpoint(
																												    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )

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

																					a_       :   str			=	[l for l in output_block_layers if """temopral_conv""" in l]
																					for path in temopral_conv_paths:
																												a_       :   Any			=	path.split("""temopral_conv."""      )[-1]
																												a_       :   Tuple			=	""".""".join(["""output_blocks""", str(SCREAMING_SNAKE_CASE_      ), str(SCREAMING_SNAKE_CASE_      ), """temopral_conv""", pruned_path]      )
																												a_       :   Tuple			=	""".""".join(["""up_blocks""", str(SCREAMING_SNAKE_CASE_      ), """temp_convs""", str(SCREAMING_SNAKE_CASE_      ), pruned_path]      )
																												a_       :   Tuple			=	unet_state_dict[old_path]

							return new_checkpoint


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE : Any          =    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.")
	SCREAMING_SNAKE_CASE : int          =    parser.parse_args()

	SCREAMING_SNAKE_CASE : Optional[int]          =    torch.load(args.checkpoint_path, map_location="cpu")
	SCREAMING_SNAKE_CASE : Tuple          =    UNetaDConditionModel()

	SCREAMING_SNAKE_CASE : int          =    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	SCREAMING_SNAKE_CASE : Dict          =    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	SCREAMING_SNAKE_CASE : List[Any]          =    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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : str          =    {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							List[str]         = """mobilevit"""





	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
								"""simple docstring"""






								super().__init__(**UpperCamelCase_							)

								a_       :   Optional[int]			=	num_channels
								a_       :   Optional[Any]			=	image_size
								a_       :   int			=	patch_size
								a_       :   List[str]			=	hidden_sizes
								a_       :   Optional[int]			=	neck_hidden_sizes
								a_       :   List[Any]			=	num_attention_heads
								a_       :   Union[str, Any]			=	mlp_ratio
								a_       :   List[str]			=	expand_ratio
								a_       :   str			=	hidden_act
								a_       :   Union[str, Any]			=	conv_kernel_size
								a_       :   Optional[Any]			=	output_stride
								a_       :   Union[str, Any]			=	hidden_dropout_prob
								a_       :   List[Any]			=	attention_probs_dropout_prob
								a_       :   List[Any]			=	classifier_dropout_prob
								a_       :   Union[str, Any]			=	initializer_range
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   str			=	qkv_bias

								# decode head attributes for semantic segmentation
								a_       :   Optional[Any]			=	aspp_out_channels
								a_       :   Any			=	atrous_rates
								a_       :   Union[str, Any]			=	aspp_dropout_prob
								a_       :   Any			=	semantic_loss_ignore_index





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								if self.task == "image-classification":
															return OrderedDict([("""logits""", {0: """batch"""})]							)
								else:
															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





	@property
	def     A				(  self							)		->			float:
								"""simple docstring"""






								return 1e-4


