


from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_lowercase							:   List[Any]						=						"\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

_lowercase							:   Any						=						"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

_lowercase							:   int						=						"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"





def 	_lowerCAmelCase					( UpperCamelCase__:  Dict				,       UpperCamelCase__:  Any    )			-> Optional[Any]:





	"""simple docstring"""







	return float((preds == labels).mean()    )





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  int    )			-> int:





	"""simple docstring"""







	A             =					simple_accuracy(UpperCamelCase__				,       UpperCamelCase__    )
	A             =					float(fa_score(y_true=UpperCamelCase__				,       y_pred=UpperCamelCase__    )    )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  str    )			-> Any:





	"""simple docstring"""







	A             =					float(pearsonr(UpperCamelCase__				,       UpperCamelCase__    )[0]    )
	A             =					float(spearmanr(UpperCamelCase__				,       UpperCamelCase__    )[0]    )
	return {
	    "pearson": pearson_corr,
	    "spearmanr": spearman_corr,
	}






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

				"""simple docstring"""







				def   _UpperCAmelCase       (    self )						->					int:
					if self.config_name not in [
					    "sst2",
					    "mnli",
					    "mnli_mismatched",
					    "mnli_matched",
					    "cola",
					    "stsb",
					    "mrpc",
					    "qqp",
					    "qnli",
					    "rte",
					    "wnli",
					    "hans",
					]:
						raise KeyError(
						    """You should supply a configuration name selected in """
						    """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
						    """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
					return datasets.MetricInfo(
					    description=_DESCRIPTION					, citation=_CITATION					, inputs_description=_KWARGS_DESCRIPTION					, features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
					            """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
					        } )					, codebase_urls=[]					, reference_urls=[]					, format="""numpy"""					, )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					List[str]:
					if self.config_name == "cola":
						return {"matthews_correlation": matthews_corrcoef(a__					, a__ )}
					elif self.config_name == "stsb":
						return pearson_and_spearman(a__					, a__ )
					elif self.config_name in ["mrpc", "qqp"]:
						return acc_and_fa(a__					, a__ )
					elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
						return {"accuracy": simple_accuracy(a__					, a__ )}
					else:
						raise KeyError(
						    """You should supply a configuration name selected in """
						    """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
						    """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )







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					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					3_84
	if "tiny" in model_name:
		A             =					[3, 3, 9, 3]
		A             =					[96, 1_92, 3_84, 7_68]
	if "small" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[96, 1_92, 3_84, 7_68]
	if "base" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[1_28, 2_56, 5_12, 10_24]
		A             =					5_12
	if "large" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[1_92, 3_84, 7_68, 15_36]
		A             =					7_68
	if "xlarge" in model_name:
		A             =					[3, 3, 27, 3]
		A             =					[2_56, 5_12, 10_24, 20_48]
		A             =					10_24

	# set label information
	A             =					1_50
	A             =					"""huggingface/label-files"""
	A             =					"""ade20k-id2label.json"""
	A             =					json.load(open(hf_hub_download(UpperCamelCase__				,       UpperCamelCase__				,       repo_type="""dataset"""    )				,       """r"""    )    )
	A             =					{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
	A             =					{v: k for k, v in idalabel.items()}

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

	return config





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> Dict:





	"""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					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  int    )			-> Optional[Any]:





	"""simple docstring"""







	A             =					dct.pop(UpperCamelCase__    )
	A             =					val





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  int    )			-> 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(UpperCamelCase__				,       map_location="""cpu"""    )["""state_dict"""]

	A             =					get_upernet_config(UpperCamelCase__    )
	A             =					UperNetForSemanticSegmentation(UpperCamelCase__    )
	model.eval()

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

	# rename keys
	A             =					create_rename_keys(UpperCamelCase__    )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )

	model.load_state_dict(UpperCamelCase__    )

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

	A             =					SegformerImageProcessor()
	A             =					processor(UpperCamelCase__				,       return_tensors="""pt"""    ).pixel_values

	with torch.no_grad():
		A             =					model(UpperCamelCase__    )

	if model_name == "upernet-convnext-tiny":
		A             =					torch.tensor(
		    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]    )
	elif model_name == "upernet-convnext-small":
		A             =					torch.tensor(
		    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]    )
	elif model_name == "upernet-convnext-base":
		A             =					torch.tensor(
		    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]    )
	elif model_name == "upernet-convnext-large":
		A             =					torch.tensor(
		    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]    )
	elif model_name == "upernet-convnext-xlarge":
		A             =					torch.tensor(
		    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]    )
	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__":
				_lowercase							:   List[str]						=						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."
				)

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



