




'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_nllb import NllbTokenizer
else:
   A     =None


A     =logging.get_logger(__name__)


A     ={'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A     ={
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

A     ={
    'facebook/nllb-large-en-ro': 10_24,
    'facebook/nllb-200-distilled-600M': 10_24,
}

# fmt: off
A     =['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']


class 			_a  (	_UpperCAmelCase ):
       __a				:     List[str]   				=					VOCAB_FILES_NAMES
       __a				:     Any   				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __a				:     List[str]   				=					PRETRAINED_VOCAB_FILES_MAP
       __a				:     Optional[Any]   				=					["""input_ids""", """attention_mask"""]
       __a				:     Optional[int]   				=					NllbTokenizer

       __a				:     Dict   				=					[]
       __a				:     str   				=					[]
       def __init__(	self						:	Optional[Any]   ,				lowercase						:	List[str]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	List[str]="<s>"   ,				lowercase						:	Tuple="</s>"   ,				lowercase						:	List[Any]="</s>"   ,				lowercase						:	Dict="<s>"   ,				lowercase						:	Union[str, Any]="<unk>"   ,				lowercase						:	Union[str, Any]="<pad>"   ,				lowercase						:	int="<mask>"   ,				lowercase						:	Tuple=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	int=False   ,				**lowercase						:	List[str]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_				) if isinstance(lowercase_   ,				lowercase_				) else mask_token
           UpperCAmelCase			       =		legacy_behaviour
           super().__init__(
               vocab_file=lowercase_   ,				tokenizer_file=lowercase_   ,				bos_token=lowercase_   ,				eos_token=lowercase_   ,				sep_token=lowercase_   ,				cls_token=lowercase_   ,				unk_token=lowercase_   ,				pad_token=lowercase_   ,				mask_token=lowercase_   ,				src_lang=lowercase_   ,				tgt_lang=lowercase_   ,				additional_special_tokens=lowercase_   ,				legacy_behaviour=lowercase_   ,				**lowercase_   ,				)

           UpperCAmelCase			       =		vocab_file
           UpperCAmelCase			       =		False if not self.vocab_file else True

           UpperCAmelCase			       =		FAIRSEQ_LANGUAGE_CODES.copy()

           if additional_special_tokens is not None:
               # Only add those special tokens if they are not already there.
               _additional_special_tokens.extend(
                   [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

           self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}				)
           UpperCAmelCase			       =		{
               lang_code: self.convert_tokens_to_ids(lowercase_				) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           UpperCAmelCase			       =		src_lang if src_lang is not None else '''eng_Latn'''
           UpperCAmelCase			       =		self.convert_tokens_to_ids(self._src_lang				)
           UpperCAmelCase			       =		tgt_lang
           self.set_src_lang_special_tokens(self._src_lang				)
       @property
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           return self._src_lang
       @src_lang.setter
       def     A      (	self						:	List[Any]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		new_src_lang
           self.set_src_lang_special_tokens(self._src_lang				)
       def     A      (	self						:	Any   ,				lowercase						:	List[int]   ,				lowercase						:	Optional[List[int]] = None				):



           '''simple docstring'''

           if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
       def     A      (	self						:	str   ,				lowercase						:	List[int]   ,				lowercase						:	Optional[List[int]] = None				):



           '''simple docstring'''

           UpperCAmelCase			       =		[self.sep_token_id]
           UpperCAmelCase			       =		[self.cls_token_id]

           if token_ids_a is None:
               return len(cls + token_ids_a + sep				) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Dict   ,				lowercase						:	str   ,				lowercase						:	Optional[str]   ,				lowercase						:	Optional[str]   ,				**lowercase						:	Tuple				):



           '''simple docstring'''

           if src_lang is None or tgt_lang is None:
               raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''				)
           UpperCAmelCase			       =		src_lang
           UpperCAmelCase			       =		self(lowercase_   ,				add_special_tokens=lowercase_   ,				return_tensors=lowercase_   ,				**lowercase_				)
           UpperCAmelCase			       =		self.convert_tokens_to_ids(lowercase_				)
           UpperCAmelCase			       =		tgt_lang_id
           return inputs
       def     A      (	self						:	Dict   ,				lowercase						:	List[str]   ,				lowercase						:	str = "eng_Latn"   ,				lowercase						:	Optional[List[str]] = None   ,				lowercase						:	str = "fra_Latn"   ,				**lowercase						:	Tuple   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		src_lang
           UpperCAmelCase			       =		tgt_lang
           return super().prepare_seqaseq_batch(lowercase_   ,				lowercase_   ,				**lowercase_				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang				)
       def     A      (	self						:	str   ,				lowercase						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.convert_tokens_to_ids(lowercase_				)

           if self.legacy_behaviour:
               UpperCAmelCase			       =		[]
               UpperCAmelCase			       =		[self.eos_token_id, self.cur_lang_code]
           else:
               UpperCAmelCase			       =		[self.cur_lang_code]
               UpperCAmelCase			       =		[self.eos_token_id]

           UpperCAmelCase			       =		self.convert_ids_to_tokens(self.prefix_tokens				)
           UpperCAmelCase			       =		self.convert_ids_to_tokens(self.suffix_tokens				)

           UpperCAmelCase			       =		processors.TemplateProcessing(
               single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str   ,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str   ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,				self.prefix_tokens + self.suffix_tokens				)				)   ,				)
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.convert_tokens_to_ids(lowercase_				)
           if self.legacy_behaviour:
               UpperCAmelCase			       =		[]
               UpperCAmelCase			       =		[self.eos_token_id, self.cur_lang_code]
           else:
               UpperCAmelCase			       =		[self.cur_lang_code]
               UpperCAmelCase			       =		[self.eos_token_id]

           UpperCAmelCase			       =		self.convert_ids_to_tokens(self.prefix_tokens				)
           UpperCAmelCase			       =		self.convert_ids_to_tokens(self.suffix_tokens				)

           UpperCAmelCase			       =		processors.TemplateProcessing(
               single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str   ,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str   ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,				self.prefix_tokens + self.suffix_tokens				)				)   ,				)







       def     A      (	self						:	Dict   ,				lowercase						:	str   ,				lowercase						:	Optional[str] = None				):



           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
               raise ValueError(
                   '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                   '''tokenizer.'''				)

           if not os.path.isdir(lowercase_				):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory."				)
               return
           UpperCAmelCase			       =		os.path.join(
               lowercase_   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

           if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				):
               copyfile(self.vocab_file   ,				lowercase_				)

           return (out_vocab_file,)







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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      int:





    '''simple docstring'''






    A__          =   384
    A__          =   7
    if "tiny" in model_name:
        A__          =   96
        A__          =   (2, 2, 6, 2)
        A__          =   (3, 6, 12, 24)
    elif "small" in model_name:
        A__          =   96
        A__          =   (2, 2, 18, 2)
        A__          =   (3, 6, 12, 24)
    elif "base" in model_name:
        A__          =   128
        A__          =   (2, 2, 18, 2)
        A__          =   (4, 8, 16, 32)
        A__          =   12
        A__          =   512
    elif "large" in model_name:
        A__          =   192
        A__          =   (2, 2, 18, 2)
        A__          =   (6, 12, 24, 48)
        A__          =   12
        A__          =   768

    # set label information
    A__          =   150
    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__          =   SwinConfig(
        embed_dim=SCREAMING_SNAKE_CASE__    ,			depths=SCREAMING_SNAKE_CASE__    ,			num_heads=SCREAMING_SNAKE_CASE__    ,			window_size=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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Dict:





    '''simple docstring'''






    A__          =   []

    # 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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   dct.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Any:





    '''simple docstring'''






    A__          =   [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
    for i in range(len(backbone_config.depths      )      ):
        A__          =   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__          =   state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'      )
            A__          =   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__          =   in_proj_weight[:dim, :]
            A__          =   in_proj_bias[: dim]
            A__          =   in_proj_weight[
                dim : dim * 2, :
            ]
            A__          =   in_proj_bias[
                dim : dim * 2
            ]
            A__          =   in_proj_weight[
                -dim :, :
            ]
            A__          =   in_proj_bias[-dim :]
            # fmt: on
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Optional[Any]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			4    ,			in_channel // 4      )
    A__          =   x[:, [0, 2, 1, 3], :].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      List[str]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			in_channel // 4    ,			4      )
    A__          =   x[:, :, [0, 2, 1, 3]].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(4    ,			in_channel // 4      )
    A__          =   x[[0, 2, 1, 3], :].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      List[Any]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(in_channel // 4    ,			4      )
    A__          =   x[:, [0, 2, 1, 3]].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   {
        '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__          =   model_name_to_url[model_name]
    A__          =   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'    ,			file_name=SCREAMING_SNAKE_CASE__      )[
        'state_dict'
    ]

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

    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__      )
    read_in_q_k_v(SCREAMING_SNAKE_CASE__    ,			config.backbone_config      )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                A__          =   reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE__      )
            if "norm" in key:
                A__          =   reverse_correct_unfold_norm_order(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__      )
        A__          =   outputs.logits

    print(logits.shape      )
    print('First values of logits:'    ,			logits[0, 0, :3, :3]      )
    # assert values
    if model_name == "upernet-swin-tiny":
        A__          =   torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]      )
    elif model_name == "upernet-swin-small":
        A__          =   torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]      )
    elif model_name == "upernet-swin-base":
        A__          =   torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]      )
    elif model_name == "upernet-swin-large":
        A__          =   torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]      )
    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_       		=  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."
     )

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

