




'''simple docstring'''


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

UpperCAmelCase_					=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def     SCREAMING_SNAKE_CASE							(   a_						:				int      ):






  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      __a								=       model_type_to_module_name(a_      )

      __a								=       importlib.import_module(f".{module_name}"					,       'transformers.models'      )
      try:
        return getattr(a_					,       a_      )
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(a_					,       '__name__'					,       a_      ) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  __a								=       importlib.import_module('transformers'      )
  if hasattr(a_					,       a_      ):
    return getattr(a_					,       a_      )

  return None


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[Any]					,       a_						:				str = None					,       a_						:				Any = False					,       a_						:				Any = False					,       a_						:				Any = None					,       a_						:				Optional[int] = None					,       a_						:				str = None					,       a_						:				List[Any] = False					,       **a_						:				Tuple					,       ):
  __a								=       get_file_from_repo(
      a_					,       a_					,       cache_dir=a_					,       force_download=a_					,       resume_download=a_					,       proxies=a_					,       use_auth_token=a_					,       revision=a_					,       local_files_only=a_					,       )
  if resolved_config_file is None:
    logger.info(
        'Could not locate the image processor configuration file, will try to use the model config instead.'      )
    return {}

  with open(a_					,       encoding='utf-8'      ) as reader:
    return json.load(a_      )





class  __lowercase    :







   def __init__(						self   )					->     Union[str, Any]:
     raise EnvironmentError(
         'AutoImageProcessor is designed to be instantiated '
         'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'   )







   @classmethod
   @replace_list_option_in_docstrings(UpperCamelCase   )
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     List[str]:
     __a								=       kwargs.pop('config'   ,				UpperCamelCase   )
     __a								=       kwargs.pop('trust_remote_code'   ,				UpperCamelCase   )
     __a								=       True

     __a						,							__a								=       ImageProcessingMixin.get_image_processor_dict(UpperCamelCase   ,				**UpperCamelCase   )
     __a								=       config_dict.get('image_processor_type'   ,				UpperCamelCase   )
     __a								=       None
     if "AutoImageProcessor" in config_dict.get('auto_map'   ,				{}   ):
       __a								=       config_dict['auto_map']['AutoImageProcessor']

     # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
     # and if so, infer the image processor class from there.
     if image_processor_class is None and image_processor_auto_map is None:
       __a								=       config_dict.pop('feature_extractor_type'   ,				UpperCamelCase   )
       if feature_extractor_class is not None:
         logger.warning(
             'Could not find image processor class in the image processor config or the model config. Loading'
             ' based on pattern matching with the model\'s feature extractor configuration.'   )
         __a								=       feature_extractor_class.replace('FeatureExtractor'   ,				'ImageProcessor'   )
       if "AutoFeatureExtractor" in config_dict.get('auto_map'   ,				{}   ):
         __a								=       config_dict['auto_map']['AutoFeatureExtractor']
         __a								=       feature_extractor_auto_map.replace('FeatureExtractor'   ,				'ImageProcessor'   )
         logger.warning(
             'Could not find image processor auto map in the image processor config or the model config.'
             ' Loading based on pattern matching with the model\'s feature extractor configuration.'   )

        # If we don't find the image processor class in the image processor config, let's try the model config.
     if image_processor_class is None and image_processor_auto_map is None:
       if not isinstance(UpperCamelCase   ,				UpperCamelCase   ):
         __a								=       AutoConfig.from_pretrained(UpperCamelCase   ,				**UpperCamelCase   )
       # It could be in `config.image_processor_type``
       __a								=       getattr(UpperCamelCase   ,				'image_processor_type'   ,				UpperCamelCase   )
       if hasattr(UpperCamelCase   ,				'auto_map'   ) and "AutoImageProcessor" in config.auto_map:
         __a								=       config.auto_map['AutoImageProcessor']

     if image_processor_class is not None:
       __a								=       image_processor_class_from_name(UpperCamelCase   )

     __a								=       image_processor_auto_map is not None
     __a								=       image_processor_class is not None or type(UpperCamelCase   ) in IMAGE_PROCESSOR_MAPPING
     __a								=       resolve_trust_remote_code(
         UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )

     if has_remote_code and trust_remote_code:
       __a								=       get_class_from_dynamic_module(
           UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )
       __a								=       kwargs.pop('code_revision'   ,				UpperCamelCase   )
       if os.path.isdir(UpperCamelCase   ):
         image_processor_class.register_for_auto_class()
       return image_processor_class.from_dict(UpperCamelCase   ,				**UpperCamelCase   )
     elif image_processor_class is not None:
       return image_processor_class.from_dict(UpperCamelCase   ,				**UpperCamelCase   )
     # Last try: we use the IMAGE_PROCESSOR_MAPPING.
     elif type(UpperCamelCase   ) in IMAGE_PROCESSOR_MAPPING:
       __a								=       IMAGE_PROCESSOR_MAPPING[type(UpperCamelCase   )]
       return image_processor_class.from_dict(UpperCamelCase   ,				**UpperCamelCase   )

     raise ValueError(
         f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
         f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
         f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()   )}"   )







   @staticmethod
   def 		UpperCamelCase__      (						UpperCamelCase   ,				UpperCamelCase   )					->     Optional[int]:
     IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase   ,				UpperCamelCase   )





# 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					)



