


'''simple docstring'''







from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


A				:					Any    						=			{
    """Acehnese Arabic""": """ace_Arab""",
    """Acehnese Latin""": """ace_Latn""",
    """Mesopotamian Arabic""": """acm_Arab""",
    """Ta'izzi-Adeni Arabic""": """acq_Arab""",
    """Tunisian Arabic""": """aeb_Arab""",
    """Afrikaans""": """afr_Latn""",
    """South Levantine Arabic""": """ajp_Arab""",
    """Akan""": """aka_Latn""",
    """Amharic""": """amh_Ethi""",
    """North Levantine Arabic""": """apc_Arab""",
    """Modern Standard Arabic""": """arb_Arab""",
    """Modern Standard Arabic Romanized""": """arb_Latn""",
    """Najdi Arabic""": """ars_Arab""",
    """Moroccan Arabic""": """ary_Arab""",
    """Egyptian Arabic""": """arz_Arab""",
    """Assamese""": """asm_Beng""",
    """Asturian""": """ast_Latn""",
    """Awadhi""": """awa_Deva""",
    """Central Aymara""": """ayr_Latn""",
    """South Azerbaijani""": """azb_Arab""",
    """North Azerbaijani""": """azj_Latn""",
    """Bashkir""": """bak_Cyrl""",
    """Bambara""": """bam_Latn""",
    """Balinese""": """ban_Latn""",
    """Belarusian""": """bel_Cyrl""",
    """Bemba""": """bem_Latn""",
    """Bengali""": """ben_Beng""",
    """Bhojpuri""": """bho_Deva""",
    """Banjar Arabic""": """bjn_Arab""",
    """Banjar Latin""": """bjn_Latn""",
    """Standard Tibetan""": """bod_Tibt""",
    """Bosnian""": """bos_Latn""",
    """Buginese""": """bug_Latn""",
    """Bulgarian""": """bul_Cyrl""",
    """Catalan""": """cat_Latn""",
    """Cebuano""": """ceb_Latn""",
    """Czech""": """ces_Latn""",
    """Chokwe""": """cjk_Latn""",
    """Central Kurdish""": """ckb_Arab""",
    """Crimean Tatar""": """crh_Latn""",
    """Welsh""": """cym_Latn""",
    """Danish""": """dan_Latn""",
    """German""": """deu_Latn""",
    """Southwestern Dinka""": """dik_Latn""",
    """Dyula""": """dyu_Latn""",
    """Dzongkha""": """dzo_Tibt""",
    """Greek""": """ell_Grek""",
    """English""": """eng_Latn""",
    """Esperanto""": """epo_Latn""",
    """Estonian""": """est_Latn""",
    """Basque""": """eus_Latn""",
    """Ewe""": """ewe_Latn""",
    """Faroese""": """fao_Latn""",
    """Fijian""": """fij_Latn""",
    """Finnish""": """fin_Latn""",
    """Fon""": """fon_Latn""",
    """French""": """fra_Latn""",
    """Friulian""": """fur_Latn""",
    """Nigerian Fulfulde""": """fuv_Latn""",
    """Scottish Gaelic""": """gla_Latn""",
    """Irish""": """gle_Latn""",
    """Galician""": """glg_Latn""",
    """Guarani""": """grn_Latn""",
    """Gujarati""": """guj_Gujr""",
    """Haitian Creole""": """hat_Latn""",
    """Hausa""": """hau_Latn""",
    """Hebrew""": """heb_Hebr""",
    """Hindi""": """hin_Deva""",
    """Chhattisgarhi""": """hne_Deva""",
    """Croatian""": """hrv_Latn""",
    """Hungarian""": """hun_Latn""",
    """Armenian""": """hye_Armn""",
    """Igbo""": """ibo_Latn""",
    """Ilocano""": """ilo_Latn""",
    """Indonesian""": """ind_Latn""",
    """Icelandic""": """isl_Latn""",
    """Italian""": """ita_Latn""",
    """Javanese""": """jav_Latn""",
    """Japanese""": """jpn_Jpan""",
    """Kabyle""": """kab_Latn""",
    """Jingpho""": """kac_Latn""",
    """Kamba""": """kam_Latn""",
    """Kannada""": """kan_Knda""",
    """Kashmiri Arabic""": """kas_Arab""",
    """Kashmiri Devanagari""": """kas_Deva""",
    """Georgian""": """kat_Geor""",
    """Central Kanuri Arabic""": """knc_Arab""",
    """Central Kanuri Latin""": """knc_Latn""",
    """Kazakh""": """kaz_Cyrl""",
    """Kabiyè""": """kbp_Latn""",
    """Kabuverdianu""": """kea_Latn""",
    """Khmer""": """khm_Khmr""",
    """Kikuyu""": """kik_Latn""",
    """Kinyarwanda""": """kin_Latn""",
    """Kyrgyz""": """kir_Cyrl""",
    """Kimbundu""": """kmb_Latn""",
    """Northern Kurdish""": """kmr_Latn""",
    """Kikongo""": """kon_Latn""",
    """Korean""": """kor_Hang""",
    """Lao""": """lao_Laoo""",
    """Ligurian""": """lij_Latn""",
    """Limburgish""": """lim_Latn""",
    """Lingala""": """lin_Latn""",
    """Lithuanian""": """lit_Latn""",
    """Lombard""": """lmo_Latn""",
    """Latgalian""": """ltg_Latn""",
    """Luxembourgish""": """ltz_Latn""",
    """Luba-Kasai""": """lua_Latn""",
    """Ganda""": """lug_Latn""",
    """Luo""": """luo_Latn""",
    """Mizo""": """lus_Latn""",
    """Standard Latvian""": """lvs_Latn""",
    """Magahi""": """mag_Deva""",
    """Maithili""": """mai_Deva""",
    """Malayalam""": """mal_Mlym""",
    """Marathi""": """mar_Deva""",
    """Minangkabau Arabic """: """min_Arab""",
    """Minangkabau Latin""": """min_Latn""",
    """Macedonian""": """mkd_Cyrl""",
    """Plateau Malagasy""": """plt_Latn""",
    """Maltese""": """mlt_Latn""",
    """Meitei Bengali""": """mni_Beng""",
    """Halh Mongolian""": """khk_Cyrl""",
    """Mossi""": """mos_Latn""",
    """Maori""": """mri_Latn""",
    """Burmese""": """mya_Mymr""",
    """Dutch""": """nld_Latn""",
    """Norwegian Nynorsk""": """nno_Latn""",
    """Norwegian Bokmål""": """nob_Latn""",
    """Nepali""": """npi_Deva""",
    """Northern Sotho""": """nso_Latn""",
    """Nuer""": """nus_Latn""",
    """Nyanja""": """nya_Latn""",
    """Occitan""": """oci_Latn""",
    """West Central Oromo""": """gaz_Latn""",
    """Odia""": """ory_Orya""",
    """Pangasinan""": """pag_Latn""",
    """Eastern Panjabi""": """pan_Guru""",
    """Papiamento""": """pap_Latn""",
    """Western Persian""": """pes_Arab""",
    """Polish""": """pol_Latn""",
    """Portuguese""": """por_Latn""",
    """Dari""": """prs_Arab""",
    """Southern Pashto""": """pbt_Arab""",
    """Ayacucho Quechua""": """quy_Latn""",
    """Romanian""": """ron_Latn""",
    """Rundi""": """run_Latn""",
    """Russian""": """rus_Cyrl""",
    """Sango""": """sag_Latn""",
    """Sanskrit""": """san_Deva""",
    """Santali""": """sat_Olck""",
    """Sicilian""": """scn_Latn""",
    """Shan""": """shn_Mymr""",
    """Sinhala""": """sin_Sinh""",
    """Slovak""": """slk_Latn""",
    """Slovenian""": """slv_Latn""",
    """Samoan""": """smo_Latn""",
    """Shona""": """sna_Latn""",
    """Sindhi""": """snd_Arab""",
    """Somali""": """som_Latn""",
    """Southern Sotho""": """sot_Latn""",
    """Spanish""": """spa_Latn""",
    """Tosk Albanian""": """als_Latn""",
    """Sardinian""": """srd_Latn""",
    """Serbian""": """srp_Cyrl""",
    """Swati""": """ssw_Latn""",
    """Sundanese""": """sun_Latn""",
    """Swedish""": """swe_Latn""",
    """Swahili""": """swh_Latn""",
    """Silesian""": """szl_Latn""",
    """Tamil""": """tam_Taml""",
    """Tatar""": """tat_Cyrl""",
    """Telugu""": """tel_Telu""",
    """Tajik""": """tgk_Cyrl""",
    """Tagalog""": """tgl_Latn""",
    """Thai""": """tha_Thai""",
    """Tigrinya""": """tir_Ethi""",
    """Tamasheq Latin""": """taq_Latn""",
    """Tamasheq Tifinagh""": """taq_Tfng""",
    """Tok Pisin""": """tpi_Latn""",
    """Tswana""": """tsn_Latn""",
    """Tsonga""": """tso_Latn""",
    """Turkmen""": """tuk_Latn""",
    """Tumbuka""": """tum_Latn""",
    """Turkish""": """tur_Latn""",
    """Twi""": """twi_Latn""",
    """Central Atlas Tamazight""": """tzm_Tfng""",
    """Uyghur""": """uig_Arab""",
    """Ukrainian""": """ukr_Cyrl""",
    """Umbundu""": """umb_Latn""",
    """Urdu""": """urd_Arab""",
    """Northern Uzbek""": """uzn_Latn""",
    """Venetian""": """vec_Latn""",
    """Vietnamese""": """vie_Latn""",
    """Waray""": """war_Latn""",
    """Wolof""": """wol_Latn""",
    """Xhosa""": """xho_Latn""",
    """Eastern Yiddish""": """ydd_Hebr""",
    """Yoruba""": """yor_Latn""",
    """Yue Chinese""": """yue_Hant""",
    """Chinese Simplified""": """zho_Hans""",
    """Chinese Traditional""": """zho_Hant""",
    """Standard Malay""": """zsm_Latn""",
    """Zulu""": """zul_Latn""",
}


class 			__lowerCamelCase			(	__snake_case	):
    """simple docstring"""
    a													=   "facebook/nllb-200-distilled-600M"
    a													=   (
        "This is a tool that translates text from a language to another. It takes three inputs: `text`, which should "
        "be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, "
        "which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in "
        "plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`."
    )
    a													=   "translator"
    a													=   AutoTokenizer
    a													=   AutoModelForSeqaSeqLM
    a													=   LANGUAGE_CODES

    a													=   ["text", "text", "text"]
    a													=   ["text"]


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):

           if src_lang not in self.lang_to_code:
                  raise ValueError(F'{src_lang} is not a supported language.')
           if tgt_lang not in self.lang_to_code:
                  raise ValueError(F'{tgt_lang} is not a supported language.')
           _A					:     Dict     = self.lang_to_code[src_lang]
           _A					:     str     = self.lang_to_code[tgt_lang]
           return self.pre_processor._build_translation_inputs(
               a_				,							return_tensors='pt'				,							src_lang=a_				,							tgt_lang=a_)


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str):
           return self.model.generate(**a_)


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Any):
           return self.post_processor.decode(outputs[0].tolist()				,							skip_special_tokens=a_)




'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A				:					Optional[int]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			torch.device('''cpu''')






def 				lowerCAmelCase__	(    ):
       _A					:     Tuple     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     Dict     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )
       return im






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]       )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]       )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]       )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
       _A					:     List[str]     = val






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]       ):
       _A					:     Optional[Any]     = []
       for k in state_dict.keys():
              _A					:     Optional[int]     = k
              if ".pwconv" in k:
                     _A					:     str     = k_new.replace('.pwconv'  ,'.point_wise_conv'       )
              if ".dwconv" in k:
                     _A					:     Any     = k_new.replace('.dwconv'  ,'.depth_wise_conv'       )
              if ".Proj." in k:
                     _A					:     Optional[Any]     = k_new.replace('.Proj.'  ,'.proj.'       )
              if "patch_embed" in k_new:
                     _A					:     Optional[int]     = k_new.replace('patch_embed'  ,'swiftformer.patch_embed.patch_embedding'       )
              if "network" in k_new:
                     _A					:     Tuple     = k_new.split('.'       )
                     if ls[2].isdigit():
                            _A					:     List[Any]     = 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
                     else:
                            _A					:     List[str]     = k_new.replace('network'  ,'swiftformer.encoder.network'       )
              rename_keys.append((k, k_new)       )
       return rename_keys






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _A					:     Any     = 1000
       _A					:     int     = 'huggingface/label-files'
       _A					:     List[Any]     = 'imagenet-1k-id2label.json'
       _A					:     Optional[int]     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
       _A					:     Dict     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}
       _A					:     Optional[int]     = idalabel
       _A					:     Any     = {v: k for k, v in idalabel.items()}

       # size of the architecture
       if swiftformer_name == "swiftformer_xs":
              _A					:     Optional[Any]     = [3, 3, 6, 4]
              _A					:     Optional[int]     = [48, 56, 112, 220]

       elif swiftformer_name == "swiftformer_s":
              _A					:     List[Any]     = [3, 3, 9, 6]
              _A					:     Tuple     = [48, 64, 168, 224]

       elif swiftformer_name == "swiftformer_l1":
              _A					:     int     = [4, 3, 10, 5]
              _A					:     int     = [48, 96, 192, 384]

       elif swiftformer_name == "swiftformer_l3":
              _A					:     Optional[Any]     = [4, 4, 12, 6]
              _A					:     Any     = [64, 128, 320, 512]

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith('https'       ):
                     _A					:     Optional[Any]     = torch.hub.load_state_dict_from_url(lowerCamelCase  ,map_location='cpu'  ,check_hash=lowerCamelCase       )
              else:
                     _A					:     Union[str, Any]     = torch.load(lowerCamelCase  ,map_location='cpu'       )
       _A					:     Union[str, Any]     = checkpoint

       _A					:     List[str]     = create_rename_keys(lowerCamelCase       )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # load HuggingFace model
       _A					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
       hf_model.load_state_dict(lowerCamelCase       )

       # prepare test inputs
       _A					:     Any     = prepare_img()
       _A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

       # compare outputs from both models
       _A					:     int     = get_expected_output(lowerCamelCase       )
       _A					:     Optional[int]     = hf_model(inputs['pixel_values']       ).logits

       assert hf_logits.shape == torch.Size([1, 1000]       )
       assert torch.allclose(hf_logits[0, 0:5]  ,lowerCamelCase  ,atol=1E-3       )

       Path(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
       print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
       hf_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
   A				:					Dict    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--swiftformer_name''',
       default='''swiftformer_xs''',
       choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
       type=str,
       help='''Name of the SwiftFormer model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default='''./converted_outputs/''',
       type=str,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

   A				:					List[str]    						=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






