



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 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					int              =						384
			A							:					Optional[int]              =						7
			if "tiny" in model_name:
						A							:					str              =						96
						A							:					str              =						(2, 2, 6, 2)
						A							:					Any              =						(3, 6, 12, 24)
			elif "small" in model_name:
						A							:					Tuple              =						96
						A							:					str              =						(2, 2, 18, 2)
						A							:					Tuple              =						(3, 6, 12, 24)
			elif "base" in model_name:
						A							:					int              =						128
						A							:					Optional[Any]              =						(2, 2, 18, 2)
						A							:					Dict              =						(4, 8, 16, 32)
						A							:					List[str]              =						12
						A							:					List[str]              =						512
			elif "large" in model_name:
						A							:					Any              =						192
						A							:					Optional[Any]              =						(2, 2, 18, 2)
						A							:					str              =						(6, 12, 24, 48)
						A							:					List[Any]              =						12
						A							:					List[Any]              =						768

			# set label information
			A							:					List[Any]              =						150
			A							:					Dict              =						"huggingface/label-files"
			A							:					Optional[int]              =						"ade20k-id2label.json"
			A							:					Optional[Any]              =						json.load(open(hf_hub_download(A_ ,    A_ ,    repo_type="dataset"    ) ,    "r"    )    )
			A							:					Any              =						{int(A_    ): v for k, v in idalabel.items()}
			A							:					Dict              =						{v: k for k, v in idalabel.items()}

			A							:					Union[str, Any]              =						SwinConfig(
			    embed_dim=A_ ,    depths=A_ ,    num_heads=A_ ,    window_size=A_ ,    out_features=["stage1", "stage2", "stage3", "stage4"] ,    )
			A							:					Any              =						UperNetConfig(
			    backbone_config=A_ ,    auxiliary_in_channels=A_ ,    num_labels=A_ ,    idalabel=A_ ,    labelaid=A_ ,    )

			return config




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Tuple              =						[]

			# 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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						dct.pop(A_    )
			A							:					Union[str, Any]              =						val




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[str]              =						[int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
			for i in range(len(backbone_config.depths    )    ):
						A							:					Optional[int]              =						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)
									A							:					Tuple              =						state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""    )
									A							:					Tuple              =						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
									A							:					Dict              =						in_proj_weight[:dim, :]
									A							:					List[str]              =						in_proj_bias[: dim]
									A							:					Any              =						in_proj_weight[
									    dim : dim * 2, :
									]
									A							:					List[Any]              =						in_proj_bias[
									    dim : dim * 2
									]
									A							:					Dict              =						in_proj_weight[
									    -dim :, :
									]
									A							:					Union[str, Any]              =						in_proj_bias[-dim :]
									# fmt: on




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A       , A							:					int              =						x.shape
			A							:					Union[str, Any]              =						x.reshape(A_ ,    4 ,    in_channel // 4    )
			A							:					List[Any]              =						x[:, [0, 2, 1, 3], :].transpose(1 ,    2    ).reshape(A_ ,    A_    )
			return x




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A       , A							:					Union[str, Any]              =						x.shape
			A							:					List[Any]              =						x.reshape(A_ ,    in_channel // 4 ,    4    )
			A							:					List[Any]              =						x[:, :, [0, 2, 1, 3]].transpose(1 ,    2    ).reshape(A_ ,    A_    )

			return x




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					int              =						x.shape[0]
			A							:					List[str]              =						x.reshape(4 ,    in_channel // 4    )
			A							:					Union[str, Any]              =						x[[0, 2, 1, 3], :].transpose(0 ,    1    ).reshape(A_    )
			return x




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[str]              =						x.shape[0]
			A							:					List[str]              =						x.reshape(in_channel // 4 ,    4    )
			A							:					List[str]              =						x[:, [0, 2, 1, 3]].transpose(0 ,    1    ).reshape(A_    )
			return x




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					List[Any]              =						{
			    "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",
			}
			A							:					str              =						model_name_to_url[model_name]
			A							:					Optional[int]              =						torch.hub.load_state_dict_from_url(A_ ,    map_location="cpu" ,    file_name=A_    )[
			    "state_dict"
			]

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

			A							:					Any              =						get_upernet_config(A_    )
			A							:					Optional[int]              =						UperNetForSemanticSegmentation(A_    )
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						A							:					Any              =						state_dict.pop(A_    )
						if "bn" in key:
									A							:					Optional[int]              =						key.replace("bn" ,    "batch_norm"    )
						A							:					Optional[Any]              =						val

			# rename keys
			A							:					Any              =						create_rename_keys(A_    )
			for src, dest in rename_keys:
						rename_key(A_ ,    A_ ,    A_    )
			read_in_q_k_v(A_ ,    config.backbone_config    )

			# fix downsample parameters
			for key, value in state_dict.items():
						if "downsample" in key:
									if "reduction" in key:
												A							:					Any              =						reverse_correct_unfold_reduction_order(A_    )
									if "norm" in key:
												A							:					int              =						reverse_correct_unfold_norm_order(A_    )

			model.load_state_dict(A_    )

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

			A							:					List[str]              =						SegformerImageProcessor()
			A							:					int              =						processor(A_ ,    return_tensors="pt"    ).pixel_values

			with torch.no_grad():
						A							:					Dict              =						model(A_    )
						A							:					Tuple              =						outputs.logits

			print(logits.shape    )
			print("First values of logits:" ,    logits[0, 0, :3, :3]    )
			# assert values
			if model_name == "upernet-swin-tiny":
						A							:					Union[str, Any]              =						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":
						A							:					int              =						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":
						A							:					List[str]              =						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":
						A							:					Union[str, Any]              =						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] ,    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__":
	__SCREAMING_SNAKE_CASE           =  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."""
	)

	__SCREAMING_SNAKE_CASE           =  parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from sklearn.metrics import recall_score

import datasets


__SCREAMING_SNAKE_CASE           =  """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


__SCREAMING_SNAKE_CASE           =  """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


__SCREAMING_SNAKE_CASE           =  """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION						)
class 	lowerCamelCase_    ( datasets.Metric						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				str:
									return datasets.MetricInfo(
									    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("int32" ) ),
									            "references": datasets.Sequence(datasets.Value("int32" ) ),
									        }
									        if self.config_name == "multilabel"
									        else {
									            "predictions": datasets.Value("int32" ),
									            "references": datasets.Value("int32" ),
									        } )	,	reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"]	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			List[Any]=1	,	__lowerCamelCase      :			Tuple="binary"	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			Tuple="warn"	,	)      ->				Optional[Any]:
									A							:					str              =						recall_score(
									    __lowerCamelCase	,	__lowerCamelCase	,	labels=__lowerCamelCase	,	pos_label=__lowerCamelCase	,	average=__lowerCamelCase	,	sample_weight=__lowerCamelCase	,	zero_division=__lowerCamelCase	,	)
									return {"recall": float(__lowerCamelCase ) if score.size == 1 else score}