
'''simple docstring'''







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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Dict													=	384
						if "tiny" in model_name:
												lowercase_     :					Tuple													=	[3, 3, 9, 3]
												lowercase_     :					int													=	[96, 192, 384, 768]
						if "small" in model_name:
												lowercase_     :					List[Any]													=	[3, 3, 27, 3]
												lowercase_     :					List[Any]													=	[96, 192, 384, 768]
						if "base" in model_name:
												lowercase_     :					Optional[int]													=	[3, 3, 27, 3]
												lowercase_     :					Optional[Any]													=	[128, 256, 512, 1024]
												lowercase_     :					List[Any]													=	512
						if "large" in model_name:
												lowercase_     :					Union[str, Any]													=	[3, 3, 27, 3]
												lowercase_     :					Union[str, Any]													=	[192, 384, 768, 1536]
												lowercase_     :					Optional[int]													=	768
						if "xlarge" in model_name:
												lowercase_     :					str													=	[3, 3, 27, 3]
												lowercase_     :					Union[str, Any]													=	[256, 512, 1024, 2048]
												lowercase_     :					Union[str, Any]													=	1024

						# set label information
						lowercase_     :					Tuple													=	150
						lowercase_     :					Tuple													=	'''huggingface/label-files'''
						lowercase_     :					List[str]													=	'''ade20k-id2label.json'''
						lowercase_     :					Tuple													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''							)				,				'''r'''							)							)
						lowercase_     :					Tuple													=	{int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
						lowercase_     :					List[str]													=	{v: k for k, v in idalabel.items()}

						lowercase_     :					Any													=	ConvNextConfig(
						    depths=__SCREAMING_SNAKE_CASE				,				hidden_sizes=__SCREAMING_SNAKE_CASE				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
						lowercase_     :					Tuple													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	[]

						# 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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	dct.pop(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					List[Any]													=	val
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	{
						    '''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''',
						}
						lowercase_     :					Any													=	model_name_to_url[model_name]
						lowercase_     :					Union[str, Any]													=	torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,				map_location='''cpu'''							)['''state_dict''']

						lowercase_     :					Union[str, Any]													=	get_upernet_config(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	UperNetForSemanticSegmentation(__SCREAMING_SNAKE_CASE							)
						model.eval()

						# replace "bn" => "batch_norm"
						for key in state_dict.copy().keys():
												lowercase_     :					Union[str, Any]													=	state_dict.pop(__SCREAMING_SNAKE_CASE							)
												if "bn" in key:
																		lowercase_     :					List[str]													=	key.replace('''bn'''				,				'''batch_norm'''							)
												lowercase_     :					List[Any]													=	val

						# rename keys
						lowercase_     :					int													=	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
						lowercase_     :					str													=	'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
						lowercase_     :					Optional[Any]													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE				,				stream=__SCREAMING_SNAKE_CASE							).raw							).convert('''RGB'''							)

						lowercase_     :					Union[str, Any]													=	SegformerImageProcessor()
						lowercase_     :					Any													=	processor(__SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''							).pixel_values

						with torch.no_grad():
												lowercase_     :					Tuple													=	model(__SCREAMING_SNAKE_CASE							)

						if model_name == "upernet-convnext-tiny":
												lowercase_     :					Any													=	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":
												lowercase_     :					Union[str, Any]													=	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":
												lowercase_     :					Any													=	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":
												lowercase_     :					Optional[Any]													=	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":
												lowercase_     :					Optional[Any]													=	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__":
							_lowercase			:  List[Any]   							=	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			:  Optional[int]   							=	parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	flatten_dict(__SCREAMING_SNAKE_CASE							)
						return flax_params
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	{}

						lowercase_     :					Any													=	{
						    '''token_embedder''': '''embeddings''',
						    '''encoder_norm''': '''layernorm''',
						    '''kernel''': '''weight''',
						    '''.out''': '''.output''',
						    '''scale''': '''weight''',
						    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
						    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
						}

						lowercase_     :					Tuple													=	{
						    '''query''': '''attention.query''',
						    '''key''': '''attention.key''',
						    '''value''': '''attention.value''',
						    '''output.dense''': '''output''',
						    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
						    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
						    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
						    '''mlp.''': '''mlp.DenseReluDense.''',
						    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
						    '''self_attention.o''': '''self_attention.attention.o''',
						    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
						    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
						    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
						    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase_     :					Tuple													=	'''.'''.join(key[1:]							)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					List[Any]													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)
																								lowercase_     :					Union[str, Any]													=	new_key.replace('''encoder'''				,				'''encoder.encoder'''							)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					str													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)

																		lowercase_     :					Dict													=	flax_dict[key]

						lowercase_     :					Any													=	{}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key].T							)
												else:
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key]							)

						return converted_torch_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False				,				__SCREAMING_SNAKE_CASE :			List[Any]=False							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	get_flax_param(__SCREAMING_SNAKE_CASE							)

						if not use_large:
												lowercase_     :					List[str]													=	PixaStructVisionConfig()
												lowercase_     :					Optional[Any]													=	PixaStructTextConfig()
						else:
												lowercase_     :					Optional[int]													=	PixaStructVisionConfig(
												    hidden_size=1536				,				d_ff=3968				,				num_attention_heads=24				,				num_hidden_layers=18							)
												lowercase_     :					Dict													=	PixaStructTextConfig(hidden_size=1536				,				d_ff=3968				,				num_heads=24				,				num_layers=18							)
						lowercase_     :					str													=	PixaStructConfig(
						    vision_config=encoder_config.to_dict()				,				text_config=decoder_config.to_dict()				,				is_vqa=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					int													=	rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
						lowercase_     :					List[Any]													=	PixaStructImageProcessor()
						lowercase_     :					int													=	PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE				,				tokenizer=__SCREAMING_SNAKE_CASE							)

						if use_large:
												lowercase_     :					Tuple													=	4096

						lowercase_     :					Optional[int]													=	True

						# mkdir if needed
						os.makedirs(__SCREAMING_SNAKE_CASE				,				exist_ok=__SCREAMING_SNAKE_CASE							)

						model.save_pretrained(__SCREAMING_SNAKE_CASE							)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							_lowercase			:  str   							=	argparse.ArgumentParser()
							parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--use_large", action="store_true", help="Use large model.")
							parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
							_lowercase			:  Tuple   							=	parser.parse_args()

							convert_pixastruct_original_pytorch_checkpoint_to_hf(
							    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
							)



