




'''simple docstring'''


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()
UpperCAmelCase_					=   logging.get_logger(__name__)


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[Any]					,       a_						:				int=False      ):
  __a								=       []
  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								=       [(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     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				str					,       a_						:				List[Any]=False      ):






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


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]					,       a_						:				int					,       a_						:				List[str]      ):
  __a								=       dct.pop(a_      )
  __a								=       val


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       'http://images.cocodataset.org/val2017/000000039769.jpg'
  __a								=       Image.open(requests.get(a_					,       stream=a_      ).raw      )
  return im


@torch.no_grad()
def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[Any]					,       a_						:				Optional[int]      ):
  __a								=       DeiTConfig()
  # all deit models have fine-tuned heads
  __a								=       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  __a								=       1000
  __a								=       'huggingface/label-files'
  __a								=       'imagenet-1k-id2label.json'
  __a								=       json.load(open(hf_hub_download(a_					,       a_					,       repo_type='dataset'      )					,       'r'      )      )
  __a								=       {int(a_      ): v for k, v in idalabel.items()}
  __a								=       idalabel
  __a								=       {v: k for k, v in idalabel.items()}
  __a								=       int(deit_name[-6:-4]      )
  __a								=       int(deit_name[-3:]      )
  # size of the architecture
  if deit_name[9:].startswith('tiny'      ):
    __a								=       192
    __a								=       768
    __a								=       12
    __a								=       3
  elif deit_name[9:].startswith('small'      ):
    __a								=       384
    __a								=       1536
    __a								=       12
    __a								=       6
  if deit_name[9:].startswith('base'      ):
    pass
  elif deit_name[4:].startswith('large'      ):
    __a								=       1024
    __a								=       4096
    __a								=       24
    __a								=       16

  # load original model from timm
  __a								=       timm.create_model(a_					,       pretrained=a_      )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  __a								=       timm_model.state_dict()
  __a								=       create_rename_keys(a_					,       a_      )
  for src, dest in rename_keys:
    rename_key(a_					,       a_					,       a_      )
  read_in_q_k_v(a_					,       a_					,       a_      )

  # load HuggingFace model
  __a								=       DeiTForImageClassificationWithTeacher(a_      ).eval()
  model.load_state_dict(a_      )

  # Check outputs on an image, prepared by DeiTImageProcessor
  __a								=       int(
      (256 / 224) * config.image_size      )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  __a								=       DeiTImageProcessor(size=a_					,       crop_size=config.image_size      )
  __a								=       image_processor(images=prepare_img()					,       return_tensors='pt'      )
  __a								=       encoding['pixel_values']
  __a								=       model(a_      )

  __a								=       timm_model(a_      )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(a_					,       outputs.logits					,       atol=1e-3      )

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


if __name__ == "__main__":
  UpperCAmelCase_					=   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."
  )

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





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Tuple						=		"facebook/nllb-200-distilled-600M"
						UpperCamelCase_				:			Optional[Any]						=		(
						    "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`."
						)
						UpperCamelCase_				:			Any						=		"translator"
						UpperCamelCase_				:			Union[str, Any]						=		AutoTokenizer
						UpperCamelCase_				:			Union[str, Any]						=		AutoModelForSeqaSeqLM
						UpperCamelCase_				:			Any						=		LANGUAGE_CODES

						UpperCamelCase_				:			Optional[int]						=		["text", "text", "text"]
						UpperCamelCase_				:			Optional[Any]						=		["text"]

						def 			A_					(      self							,				a							,				a							,				a					)       ->   str:


								'''simple docstring'''

								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.'					)
								_UpperCamelCase         =						self.lang_to_code[src_lang]
								_UpperCamelCase         =						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							,				a					)       ->   Any:


								'''simple docstring'''

								return self.model.generate(**a					)







						def 			A_					(      self							,				a					)       ->   Optional[Any]:


								'''simple docstring'''

								return self.post_processor.decode(outputs[0].tolist()							,				skip_special_tokens=a					)



