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__       :							int					):
					SCREAMING_SNAKE_CASE           =    3_8_4
					if "tiny" in model_name:
										SCREAMING_SNAKE_CASE           =    [3, 3, 9, 3]
										SCREAMING_SNAKE_CASE           =    [9_6, 1_9_2, 3_8_4, 7_6_8]
					if "small" in model_name:
										SCREAMING_SNAKE_CASE           =    [3, 3, 2_7, 3]
										SCREAMING_SNAKE_CASE           =    [9_6, 1_9_2, 3_8_4, 7_6_8]
					if "base" in model_name:
										SCREAMING_SNAKE_CASE           =    [3, 3, 2_7, 3]
										SCREAMING_SNAKE_CASE           =    [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
										SCREAMING_SNAKE_CASE           =    5_1_2
					if "large" in model_name:
										SCREAMING_SNAKE_CASE           =    [3, 3, 2_7, 3]
										SCREAMING_SNAKE_CASE           =    [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
										SCREAMING_SNAKE_CASE           =    7_6_8
					if "xlarge" in model_name:
										SCREAMING_SNAKE_CASE           =    [3, 3, 2_7, 3]
										SCREAMING_SNAKE_CASE           =    [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
										SCREAMING_SNAKE_CASE           =    1_0_2_4

					# set label information
					SCREAMING_SNAKE_CASE           =    1_5_0
					SCREAMING_SNAKE_CASE           =    "huggingface/label-files"
					SCREAMING_SNAKE_CASE           =    "ade20k-id2label.json"
					SCREAMING_SNAKE_CASE           =    json.load(open(hf_hub_download(UpperCAmelCase__ ,	UpperCAmelCase__ ,	repo_type="dataset"					) ,	"r"					)					)
					SCREAMING_SNAKE_CASE           =    {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE           =    {v: k for k, v in idalabel.items()}

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

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    []

					# 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__       :							List[str] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    dct.pop(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    val





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    {
					    "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",
					}
					SCREAMING_SNAKE_CASE           =    model_name_to_url[model_name]
					SCREAMING_SNAKE_CASE           =    torch.hub.load_state_dict_from_url(UpperCAmelCase__ ,	map_location="cpu"					)["state_dict"]

					SCREAMING_SNAKE_CASE           =    get_upernet_config(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    UperNetForSemanticSegmentation(UpperCAmelCase__					)
					model.eval()

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

					# rename keys
					SCREAMING_SNAKE_CASE           =    create_rename_keys(UpperCAmelCase__					)
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					model.load_state_dict(UpperCAmelCase__					)

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

					SCREAMING_SNAKE_CASE           =    SegformerImageProcessor()
					SCREAMING_SNAKE_CASE           =    processor(UpperCAmelCase__ ,	return_tensors="pt"					).pixel_values

					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =    model(UpperCAmelCase__					)

					if model_name == "upernet-convnext-tiny":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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] ,	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__":
							_lowerCamelCase   :				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.'''
							)

							_lowerCamelCase   :				Tuple     			=		parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Dict     			=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				List[Any]     			=						[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

_lowerCamelCase   :				Optional[Any]     			=						{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
_lowerCamelCase   :				Any     			=						{f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowerCamelCase   :				Optional[Any]     			=						{f"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							VOCAB_FILES_NAMES
							lowercase__  :    Optional[Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Union[str, Any]									=							FunnelTokenizer
							lowercase__  :    List[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    int									=							2




							def __init__(  self				:   str		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   str="<unk>"		,							_UpperCamelCase				:   Optional[Any]="<sep>"		,							_UpperCamelCase				:   Optional[int]="<pad>"		,							_UpperCamelCase				:   int="<cls>"		,							_UpperCamelCase				:   Dict="<mask>"		,							_UpperCamelCase				:   Union[str, Any]="<s>"		,							_UpperCamelCase				:   Optional[int]="</s>"		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Dict="##"		,							**_UpperCamelCase				:   Dict		,							)   ->			Optional[int]:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							do_lower_case=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							clean_text=_UpperCamelCase		,							tokenize_chinese_chars=_UpperCamelCase		,							strip_accents=_UpperCamelCase		,							wordpieces_prefix=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
												if (
												    normalizer_state.get("lowercase"		,							_UpperCamelCase ) != do_lower_case
												    or normalizer_state.get("strip_accents"		,							_UpperCamelCase ) != strip_accents
												    or normalizer_state.get("handle_chinese_chars"		,							_UpperCamelCase ) != tokenize_chinese_chars
												):
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							normalizer_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    do_lower_case
																	SCREAMING_SNAKE_CASE           =    strip_accents
																	SCREAMING_SNAKE_CASE           =    tokenize_chinese_chars
																	SCREAMING_SNAKE_CASE           =    normalizer_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_lower_case




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Dict=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												if token_ids_a is None:
																	return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
												return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




