





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


A       :   Optional[Any]					= logging.get_logger(__name__)

A       :   Optional[int]					= OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

A       :   Dict					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def   __lowerCamelCase   (				__a  :str       )						->							Any:






		"""simple docstring"""

		for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
				if class_name in extractors:
						A__     =  model_type_to_module_name(__a       )

						A__     =  importlib.import_module(F'.{module_name}'       ,	"""transformers.models"""       )
						try:
								return getattr(__a       ,	__a       )
						except AttributeError:
								continue

		for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
				if getattr(__a       ,	"""__name__"""       ,	__a       ) == class_name:
						return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
		A__     =  importlib.import_module("""transformers"""       )
		if hasattr(__a       ,	__a       ):
				return getattr(__a       ,	__a       )

		return None







def   __lowerCamelCase   (				__a  :Union[str, os.PathLike]       ,	__a  :Optional[Union[str, os.PathLike]] = None       ,	__a  :bool = False       ,	__a  :bool = False       ,	__a  :Optional[Dict[str, str]] = None       ,	__a  :Optional[Union[bool, str]] = None       ,	__a  :Optional[str] = None       ,	__a  :bool = False       ,	**__a  :Optional[Any]       ,	)						->							Union[str, Any]:






		"""simple docstring"""

		A__     =  get_file_from_repo(
		    __a       ,	__a       ,	cache_dir=__a       ,	force_download=__a       ,	resume_download=__a       ,	proxies=__a       ,	use_auth_token=__a       ,	revision=__a       ,	local_files_only=__a       ,	)
		if resolved_config_file is None:
				logger.info(
				    """Could not locate the feature extractor configuration file, will try to use the model config instead."""       )
				return {}

		with open(__a       ,	encoding="""utf-8"""       ) as reader:
				return json.load(__a       )






class     A     :
			'''simple docstring'''




			def __init__(     self  :       Any  )      ->      Optional[int]:







					"""simple docstring"""






					raise EnvironmentError(
					    """AutoFeatureExtractor is designed to be instantiated """
					    """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""  )






			@classmethod
			@replace_list_option_in_docstrings(__lowerCAmelCase  )
			def   a_			(     cls  :       List[str]				,      __lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      Any:







					"""simple docstring"""






					A__     =  kwargs.pop("""config"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""trust_remote_code"""				,      __lowerCAmelCase  )
					A__     =  True

					A__ ,     A__     =  FeatureExtractionMixin.get_feature_extractor_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )
					A__     =  config_dict.get("""feature_extractor_type"""				,      __lowerCAmelCase  )
					A__     =  None
					if "AutoFeatureExtractor" in config_dict.get("""auto_map"""				,      {}  ):
							A__     =  config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

					# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
					if feature_extractor_class is None and feature_extractor_auto_map is None:
							if not isinstance(__lowerCAmelCase				,      __lowerCAmelCase  ):
									A__     =  AutoConfig.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
							# It could be in `config.feature_extractor_type``
							A__     =  getattr(__lowerCAmelCase				,      """feature_extractor_type"""				,      __lowerCAmelCase  )
							if hasattr(__lowerCAmelCase				,      """auto_map"""  ) and "AutoFeatureExtractor" in config.auto_map:
									A__     =  config.auto_map["""AutoFeatureExtractor"""]

					if feature_extractor_class is not None:
							A__     =  feature_extractor_class_from_name(__lowerCAmelCase  )

					A__     =  feature_extractor_auto_map is not None
					A__     =  feature_extractor_class is not None or type(__lowerCAmelCase  ) in FEATURE_EXTRACTOR_MAPPING
					A__     =  resolve_trust_remote_code(
					    __lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase  )

					if has_remote_code and trust_remote_code:
							A__     =  get_class_from_dynamic_module(
							    __lowerCAmelCase				,      __lowerCAmelCase				,      **__lowerCAmelCase  )
							A__     =  kwargs.pop("""code_revision"""				,      __lowerCAmelCase  )
							if os.path.isdir(__lowerCAmelCase  ):
									feature_extractor_class.register_for_auto_class()
							return feature_extractor_class.from_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )
					elif feature_extractor_class is not None:
							return feature_extractor_class.from_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )
					# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
					elif type(__lowerCAmelCase  ) in FEATURE_EXTRACTOR_MAPPING:
							A__     =  FEATURE_EXTRACTOR_MAPPING[type(__lowerCAmelCase  )]
							return feature_extractor_class.from_dict(__lowerCAmelCase				,      **__lowerCAmelCase  )

					raise ValueError(
					    f'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
					    f'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
					    f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()  )}'  )







			@staticmethod
			def   a_			(     __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       List[str]  )      ->      Union[str, Any]:







					"""simple docstring"""






					FEATURE_EXTRACTOR_MAPPING.register(__lowerCAmelCase				,      __lowerCAmelCase  )










import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation







def   __lowerCamelCase   (				__a  :int       )						->							int:






		"""simple docstring"""

		A__     =  3_8_4
		if "tiny" in model_name:
				A__     =  [3, 3, 9, 3]
				A__     =  [9_6, 1_9_2, 3_8_4, 7_6_8]
		if "small" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [9_6, 1_9_2, 3_8_4, 7_6_8]
		if "base" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
				A__     =  5_1_2
		if "large" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
				A__     =  7_6_8
		if "xlarge" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
				A__     =  1_0_2_4

		# set label information
		A__     =  1_5_0
		A__     =  """huggingface/label-files"""
		A__     =  """ade20k-id2label.json"""
		A__     =  json.load(open(hf_hub_download(__a       ,	__a       ,	repo_type="""dataset"""       )       ,	"""r"""       )       )
		A__     =  {int(__a       ): v for k, v in idalabel.items()}
		A__     =  {v: k for k, v in idalabel.items()}

		A__     =  ConvNextConfig(
		    depths=__a       ,	hidden_sizes=__a       ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
		A__     =  UperNetConfig(
		    backbone_config=__a       ,	auxiliary_in_channels=__a       ,	num_labels=__a       ,	idalabel=__a       ,	labelaid=__a       ,	)

		return config







def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							List[Any]:






		"""simple docstring"""

		A__     =  []

		# fmt: off
		# stem
		rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")       )
		rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")       )
		rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")       )
		rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")       )
		# stages
		for i in range(len(config.backbone_config.depths       )       ):
				for j in range(config.backbone_config.depths[i]       ):
						rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')       )
				if i > 0:
						rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')       )
						rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')       )
						rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')       )
						rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')       )

				rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')       )
				rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')       )

		# decode head
		rename_keys.extend(
		    [
		        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
		        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
		        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
		        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
		    ]       )
		# fmt: on

		return rename_keys







def   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :List[str]       ,	__a  :str       )						->							str:






		"""simple docstring"""

		A__     =  dct.pop(__a       )
		A__     =  val







def   __lowerCamelCase   (				__a  :Any       ,	__a  :int       ,	__a  :Any       )						->							List[Any]:






		"""simple docstring"""

		A__     =  {
		    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
		    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
		    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
		    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
		    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
		}
		A__     =  model_name_to_url[model_name]
		A__     =  torch.hub.load_state_dict_from_url(__a       ,	map_location="""cpu"""       )["""state_dict"""]

		A__     =  get_upernet_config(__a       )
		A__     =  UperNetForSemanticSegmentation(__a       )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				A__     =  state_dict.pop(__a       )
				if "bn" in key:
						A__     =  key.replace("""bn"""       ,	"""batch_norm"""       )
				A__     =  val

		# rename keys
		A__     =  create_rename_keys(__a       )
		for src, dest in rename_keys:
				rename_key(__a       ,	__a       ,	__a       )

		model.load_state_dict(__a       )

		# verify on image
		A__     =  """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
		A__     =  Image.open(requests.get(__a       ,	stream=__a       ).raw       ).convert("""RGB"""       )

		A__     =  SegformerImageProcessor()
		A__     =  processor(__a       ,	return_tensors="""pt"""       ).pixel_values

		with torch.no_grad():
				A__     =  model(__a       )

		if model_name == "upernet-convnext-tiny":
				A__     =  torch.tensor(
				    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]       )
		elif model_name == "upernet-convnext-small":
				A__     =  torch.tensor(
				    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]       )
		elif model_name == "upernet-convnext-base":
				A__     =  torch.tensor(
				    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]       )
		elif model_name == "upernet-convnext-large":
				A__     =  torch.tensor(
				    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]       )
		elif model_name == "upernet-convnext-xlarge":
				A__     =  torch.tensor(
				    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]       )
		print("""Logits:"""       ,	outputs.logits[0, 0, :3, :3]       )
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,	__a       ,	atol=1E-4       )
		print("""Looks ok!"""       )

		if pytorch_dump_folder_path is not None:
				print(F'Saving model {model_name} to {pytorch_dump_folder_path}'       )
				model.save_pretrained(__a       )
				print(F'Saving processor to {pytorch_dump_folder_path}'       )
				processor.save_pretrained(__a       )

		if push_to_hub:
				print(F'Pushing model and processor for {model_name} to hub'       )
				model.push_to_hub(F'openmmlab/{model_name}'       )
				processor.push_to_hub(F'openmmlab/{model_name}'       )


if __name__ == "__main__":
					A       :   Optional[int]					= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''upernet-convnext-tiny''',
					    type=str,
					    choices=[F'''upernet-convnext-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
					    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					A       :   str					= parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



