


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A  :     Any    					=		{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     List[Any]    					=		["RoFormerTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     Tuple    					=		[
	    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "RoFormerForCausalLM",
	    "RoFormerForMaskedLM",
	    "RoFormerForMultipleChoice",
	    "RoFormerForQuestionAnswering",
	    "RoFormerForSequenceClassification",
	    "RoFormerForTokenClassification",
	    "RoFormerLayer",
	    "RoFormerModel",
	    "RoFormerPreTrainedModel",
	    "load_tf_weights_in_roformer",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     Any    					=		[
	    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFRoFormerForCausalLM",
	    "TFRoFormerForMaskedLM",
	    "TFRoFormerForMultipleChoice",
	    "TFRoFormerForQuestionAnswering",
	    "TFRoFormerForSequenceClassification",
	    "TFRoFormerForTokenClassification",
	    "TFRoFormerLayer",
	    "TFRoFormerModel",
	    "TFRoFormerPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     List[str]    					=		[
	    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "FlaxRoFormerForMaskedLM",
	    "FlaxRoFormerForMultipleChoice",
	    "FlaxRoFormerForQuestionAnswering",
	    "FlaxRoFormerForSequenceClassification",
	    "FlaxRoFormerForTokenClassification",
	    "FlaxRoFormerModel",
	    "FlaxRoFormerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	A  :     Union[str, Any]    					=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""


import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    384
			__lowerCAmelCase       					=    7
			if "tiny" in model_name:
						__lowerCAmelCase       					=    96
						__lowerCAmelCase       					=    (2, 2, 6, 2)
						__lowerCAmelCase       					=    (3, 6, 12, 24)
			elif "small" in model_name:
						__lowerCAmelCase       					=    96
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (3, 6, 12, 24)
			elif "base" in model_name:
						__lowerCAmelCase       					=    128
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (4, 8, 16, 32)
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    512
			elif "large" in model_name:
						__lowerCAmelCase       					=    192
						__lowerCAmelCase       					=    (2, 2, 18, 2)
						__lowerCAmelCase       					=    (6, 12, 24, 48)
						__lowerCAmelCase       					=    12
						__lowerCAmelCase       					=    768

			# set label information
			__lowerCAmelCase       					=    150
			__lowerCAmelCase       					=    "huggingface/label-files"
			__lowerCAmelCase       					=    "ade20k-id2label.json"
			__lowerCAmelCase       					=    json.load(open(hf_hub_download(_UpperCamelCase					,				_UpperCamelCase					,				repo_type="dataset"   )					,				"r"   )   )
			__lowerCAmelCase       					=    {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
			__lowerCAmelCase       					=    {v: k for k, v in idalabel.items()}

			__lowerCAmelCase       					=    SwinConfig(
			    embed_dim=_UpperCamelCase					,				depths=_UpperCamelCase					,				num_heads=_UpperCamelCase					,				window_size=_UpperCamelCase					,				out_features=["stage1", "stage2", "stage3", "stage4"]					,				)
			__lowerCAmelCase       					=    UperNetConfig(
			    backbone_config=_UpperCamelCase					,				auxiliary_in_channels=_UpperCamelCase					,				num_labels=_UpperCamelCase					,				idalabel=_UpperCamelCase					,				labelaid=_UpperCamelCase					,				)

			return config


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

			# fmt: off
			# stem
			rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")   )
			rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")   )
			rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")   )
			rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.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}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")   )
									rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")   )

						if i < 3:
									rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")   )
									rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")   )
									rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.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			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    dct.pop(_UpperCamelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
			for i in range(len(backbone_config.depths   )   ):
						__lowerCAmelCase       					=    num_features[i]
						for j in range(backbone_config.depths[i]   ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowerCAmelCase       					=    state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"   )
									__lowerCAmelCase       					=    state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"   )
									# next, add query, keys and values (in that order) to the state dict
									__lowerCAmelCase       					=    in_proj_weight[:dim, :]
									__lowerCAmelCase       					=    in_proj_bias[: dim]
									__lowerCAmelCase       					=    in_proj_weight[
									    dim : dim * 2, :
									]
									__lowerCAmelCase       					=    in_proj_bias[
									    dim : dim * 2
									]
									__lowerCAmelCase       					=    in_proj_weight[
									    -dim :, :
									]
									__lowerCAmelCase       					=    in_proj_bias[-dim :]
									# fmt: on


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    x.shape
			__lowerCAmelCase       					=    x.reshape(_UpperCamelCase					,				4					,				in_channel // 4   )
			__lowerCAmelCase       					=    x[:, [0, 2, 1, 3], :].transpose(1					,				2   ).reshape(_UpperCamelCase					,				_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    x.shape
			__lowerCAmelCase       					=    x.reshape(_UpperCamelCase					,				in_channel // 4					,				4   )
			__lowerCAmelCase       					=    x[:, :, [0, 2, 1, 3]].transpose(1					,				2   ).reshape(_UpperCamelCase					,				_UpperCamelCase   )

			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.shape[0]
			__lowerCAmelCase       					=    x.reshape(4					,				in_channel // 4   )
			__lowerCAmelCase       					=    x[[0, 2, 1, 3], :].transpose(0					,				1   ).reshape(_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    x.shape[0]
			__lowerCAmelCase       					=    x.reshape(in_channel // 4					,				4   )
			__lowerCAmelCase       					=    x[:, [0, 2, 1, 3]].transpose(0					,				1   ).reshape(_UpperCamelCase   )
			return x


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {
			    "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
			    "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
			    "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
			    "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
			}
			__lowerCAmelCase       					=    model_name_to_url[model_name]
			__lowerCAmelCase       					=    torch.hub.load_state_dict_from_url(_UpperCamelCase					,				map_location="cpu"					,				file_name=_UpperCamelCase   )[
			    "state_dict"
			]

			for name, param in state_dict.items():
						print(_UpperCamelCase					,				param.shape   )

			__lowerCAmelCase       					=    get_upernet_config(_UpperCamelCase   )
			__lowerCAmelCase       					=    UperNetForSemanticSegmentation(_UpperCamelCase   )
			model.eval()

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

			# rename keys
			__lowerCAmelCase       					=    create_rename_keys(_UpperCamelCase   )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
			read_in_q_k_v(_UpperCamelCase					,				config.backbone_config   )

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_reduction_order(_UpperCamelCase   )
									if "norm" in key:
												__lowerCAmelCase       					=    reverse_correct_unfold_norm_order(_UpperCamelCase   )

			model.load_state_dict(_UpperCamelCase   )

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

			__lowerCAmelCase       					=    SegformerImageProcessor()
			__lowerCAmelCase       					=    processor(_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

			with torch.no_grad():
						__lowerCAmelCase       					=    model(_UpperCamelCase   )
						__lowerCAmelCase       					=    outputs.logits

			print(logits.shape   )
			print("First values of logits:"					,				logits[0, 0, :3, :3]   )
			# assert values
			if model_name == "upernet-swin-tiny":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]   )
			elif model_name == "upernet-swin-small":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]   )
			elif model_name == "upernet-swin-base":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]   )
			elif model_name == "upernet-swin-large":
						__lowerCAmelCase       					=    torch.tensor(
						    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]   )
			print("Logits:"					,				outputs.logits[0, 0, :3, :3]   )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]					,				_UpperCamelCase					,				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(_UpperCamelCase   )
						print(f"Saving processor to {pytorch_dump_folder_path}"   )
						processor.save_pretrained(_UpperCamelCase   )

			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  :     int    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="upernet-swin-tiny",
	    type=str,
	    choices=[f'''upernet-swin-{size}''' for size in ["tiny", "small", "base", "large"]],
	    help="Name of the Swin + 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  :     List[Any]    					=    parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




