





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


_lowerCAmelCase 				=     {
    "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    _UpperCAmelCase							(		__UpperCAmelCase     ):
      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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):







         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_     :				int			      =	self.lang_to_code[src_lang]
         A_     :				Union[str, Any]			      =	self.lang_to_code[tgt_lang]
         return self.pre_processor._build_translation_inputs(
             lowerCAmelCase_      ,       return_tensors="""pt"""      ,       src_lang=lowerCAmelCase_      ,       tgt_lang=lowerCAmelCase_       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.model.generate(**lowerCAmelCase_       )






      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.post_processor.decode(outputs[0].tolist()      ,       skip_special_tokens=lowerCAmelCase_       )













import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase 				=     logging.get_logger(__name__)



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				List[Any]			      =	[]
   for i in range(config.num_hidden_layers     ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")     )
      rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")     )
      rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")     )
      rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")     )
      rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")     )
      rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")     )
      rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")     )

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ("""cls_token""", """deit.embeddings.cls_token"""),
           ("""dist_token""", """deit.embeddings.distillation_token"""),
           ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
           ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
           ("""pos_embed""", """deit.embeddings.position_embeddings"""),
       ]     )

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
              ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
              ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
          ]     )

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A_     :				Tuple			      =	[(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""     ) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ("""norm.weight""", """deit.layernorm.weight"""),
              ("""norm.bias""", """deit.layernorm.bias"""),
              ("""head.weight""", """cls_classifier.weight"""),
              ("""head.bias""", """cls_classifier.bias"""),
              ("""head_dist.weight""", """distillation_classifier.weight"""),
              ("""head_dist.bias""", """distillation_classifier.bias"""),
          ]     )

   return rename_keys



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   for i in range(config.num_hidden_layers     ):
      if base_model:
         A_     :				str			      =	""""""
      else:
         A_     :				List[str]			      =	"""deit."""
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A_     :				Tuple			      =	state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""     )
      A_     :				str			      =	state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      A_     :				Optional[Any]			      =	in_proj_weight[
          : config.hidden_size, :
      ]
      A_     :				Optional[Any]			      =	in_proj_bias[: config.hidden_size]
      A_     :				Optional[Any]			      =	in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A_     :				int			      =	in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A_     :				str			      =	in_proj_weight[
          -config.hidden_size :, :
      ]
      A_     :				Any			      =	in_proj_bias[-config.hidden_size :]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	dct.pop(_lowerCAmelCase     )
   A_     :				Any			      =	val



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   A_     :				Union[str, Any]			      =	Image.open(requests.get(_lowerCAmelCase  ,stream=_lowerCAmelCase     ).raw     )
   return im



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	DeiTConfig()
   # all deit models have fine-tuned heads
   A_     :				Optional[Any]			      =	False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A_     :				Optional[int]			      =	1_0_0_0
   A_     :				List[Any]			      =	"""huggingface/label-files"""
   A_     :				str			      =	"""imagenet-1k-id2label.json"""
   A_     :				Union[str, Any]			      =	json.load(open(hf_hub_download(_lowerCAmelCase  ,_lowerCAmelCase  ,repo_type="""dataset"""     )  ,"""r"""     )     )
   A_     :				Optional[Any]			      =	{int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
   A_     :				Union[str, Any]			      =	idalabel
   A_     :				Optional[int]			      =	{v: k for k, v in idalabel.items()}
   A_     :				Dict			      =	int(deit_name[-6:-4]     )
   A_     :				Any			      =	int(deit_name[-3:]     )
   # size of the architecture
   if deit_name[9:].startswith("""tiny"""     ):
      A_     :				str			      =	1_9_2
      A_     :				Dict			      =	7_6_8
      A_     :				List[Any]			      =	1_2
      A_     :				Dict			      =	3
   elif deit_name[9:].startswith("""small"""     ):
      A_     :				Optional[int]			      =	3_8_4
      A_     :				List[Any]			      =	1_5_3_6
      A_     :				Optional[Any]			      =	1_2
      A_     :				List[str]			      =	6
   if deit_name[9:].startswith("""base"""     ):
      pass
   elif deit_name[4:].startswith("""large"""     ):
      A_     :				Dict			      =	1_0_2_4
      A_     :				int			      =	4_0_9_6
      A_     :				Any			      =	2_4
      A_     :				str			      =	1_6

   # load original model from timm
   A_     :				int			      =	timm.create_model(_lowerCAmelCase  ,pretrained=_lowerCAmelCase     )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A_     :				Union[str, Any]			      =	timm_model.state_dict()
   A_     :				Optional[Any]			      =	create_rename_keys(_lowerCAmelCase  ,_lowerCAmelCase     )
   for src, dest in rename_keys:
      rename_key(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
   read_in_q_k_v(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   # load HuggingFace model
   A_     :				List[str]			      =	DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
   model.load_state_dict(_lowerCAmelCase     )

   # Check outputs on an image, prepared by DeiTImageProcessor
   A_     :				List[Any]			      =	int(
       (2_5_6 / 2_2_4) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A_     :				Any			      =	DeiTImageProcessor(size=_lowerCAmelCase  ,crop_size=config.image_size     )
   A_     :				Optional[int]			      =	image_processor(images=prepare_img()  ,return_tensors="""pt"""     )
   A_     :				List[str]			      =	encoding["""pixel_values"""]
   A_     :				List[Any]			      =	model(_lowerCAmelCase     )

   A_     :				Tuple			      =	timm_model(_lowerCAmelCase     )
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(_lowerCAmelCase  ,outputs.logits  ,atol=1e-3     )

   Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
   print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(_lowerCAmelCase     )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--deit_name""",
          default="""vit_deit_base_distilled_patch16_224""",
          type=str,
          help="""Name of the DeiT timm 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."""
      )

      _lowerCAmelCase 				=     parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






