

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__							(	SCREAMING_SNAKE_CASE_:							List[Any]      )			->      str:
				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(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       repo_type="dataset"      )	,       "r"      )      )
				A__        =			{int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}
				A__        =			{v: k for k, v in idalabel.items()}

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

				return config



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Union[str, Any]:
				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__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Optional[Any]      )			->      str:
				A__        =			dct.pop(SCREAMING_SNAKE_CASE_      )
				A__        =			val



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Optional[Any]:
				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(SCREAMING_SNAKE_CASE_	,       map_location="cpu"      )["state_dict"]

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

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

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

				model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

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

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

				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]	,       SCREAMING_SNAKE_CASE_	,       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(SCREAMING_SNAKE_CASE_      )
								print(F'Saving processor to {pytorch_dump_folder_path}'      )
								processor.save_pretrained(SCREAMING_SNAKE_CASE_      )

				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__":
	lowerCAmelCase__	   = 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."""
	)

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




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			tf.convert_to_tensor(
							    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,      dtype=tf.floataa      ,      )
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,      expected_slice.numpy()      ,      atol=1e-4       )       )

