

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,
)


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = 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'),
    ]
)

SCREAMING_SNAKE_CASE_            = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Tuple:
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												_UpperCAmelCase    :						Union[str, Any]							=      model_type_to_module_name(lowerCAmelCase	)

												_UpperCAmelCase    :						Optional[int]							=      importlib.import_module(F'.{module_name}'					, "transformers.models"	)
												try:
																return getattr(lowerCAmelCase					, lowerCAmelCase	)
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowerCAmelCase					, "__name__"					, lowerCAmelCase	) == 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.
				_UpperCAmelCase    :						Any							=      importlib.import_module("transformers"	)
				if hasattr(lowerCAmelCase					, lowerCAmelCase	):
								return getattr(lowerCAmelCase					, lowerCAmelCase	)

				return None

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, os.PathLike]					, lowerCAmelCase:					Optional[Union[str, os.PathLike]] = None					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					Optional[Dict[str, str]] = None					, lowerCAmelCase:					Optional[Union[bool, str]] = None					, lowerCAmelCase:					Optional[str] = None					, lowerCAmelCase:					bool = False					, **lowerCAmelCase:					List[Any]					, )				->    Any:
				_UpperCAmelCase    :						List[Any]							=      get_file_from_repo(
				    lowerCAmelCase					, lowerCAmelCase					, cache_dir=lowerCAmelCase					, force_download=lowerCAmelCase					, resume_download=lowerCAmelCase					, proxies=lowerCAmelCase					, use_auth_token=lowerCAmelCase					, revision=lowerCAmelCase					, local_files_only=lowerCAmelCase					, )
				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(lowerCAmelCase					, encoding="utf-8"	) as reader:
								return json.load(lowerCAmelCase	)





class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										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(A_		)
						def     _UpperCAmelCase			(    cls  ,   A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      kwargs.pop("config"  ,   A_		)
										_UpperCAmelCase    :						Dict							=      kwargs.pop("trust_remote_code"  ,   A_		)
										_UpperCAmelCase    :						str							=      True

										_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      ImageProcessingMixin.get_image_processor_dict(A_  ,   **A_		)
										_UpperCAmelCase    :						Any							=      config_dict.get("image_processor_type"  ,   A_		)
										_UpperCAmelCase    :						str							=      None
										if "AutoImageProcessor" in config_dict.get("auto_map"  ,   {}		):
														_UpperCAmelCase    :						int							=      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:
														_UpperCAmelCase    :						Any							=      config_dict.pop("feature_extractor_type"  ,   A_		)
														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."		)
																		_UpperCAmelCase    :						Optional[Any]							=      feature_extractor_class.replace("FeatureExtractor"  ,   "ImageProcessor"		)
														if "AutoFeatureExtractor" in config_dict.get("auto_map"  ,   {}		):
																		_UpperCAmelCase    :						Optional[Any]							=      config_dict["auto_map"]["AutoFeatureExtractor"]
																		_UpperCAmelCase    :						List[Any]							=      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(A_  ,   A_		):
																		_UpperCAmelCase    :						Any							=      AutoConfig.from_pretrained(A_  ,   **A_		)
														# It could be in `config.image_processor_type``
														_UpperCAmelCase    :						Optional[Any]							=      getattr(A_  ,   "image_processor_type"  ,   A_		)
														if hasattr(A_  ,   "auto_map"		) and "AutoImageProcessor" in config.auto_map:
																		_UpperCAmelCase    :						Any							=      config.auto_map["AutoImageProcessor"]

										if image_processor_class is not None:
														_UpperCAmelCase    :						List[str]							=      image_processor_class_from_name(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      image_processor_auto_map is not None
										_UpperCAmelCase    :						Any							=      image_processor_class is not None or type(A_		) in IMAGE_PROCESSOR_MAPPING
										_UpperCAmelCase    :						List[Any]							=      resolve_trust_remote_code(
										    A_  ,   A_  ,   A_  ,   A_		)

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

										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			(    A_  ,   A_		):







										'''simple docstring'''


										IMAGE_PROCESSOR_MAPPING.register(A_  ,   A_		)





# 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


SCREAMING_SNAKE_CASE_            = {
    '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 			a					(		UpperCAmelCase			):
						_lowercase        =     "facebook/nllb-200-distilled-600M"
						_lowercase        =     (
						    "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`."
						)
						_lowercase        =     "translator"
						_lowercase        =     AutoTokenizer
						_lowercase        =     AutoModelForSeqaSeqLM
						_lowercase        =     LANGUAGE_CODES

						_lowercase        =     ["text", "text", "text"]
						_lowercase        =     ["text"]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''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    :						int							=      self.lang_to_code[src_lang]
										_UpperCAmelCase    :						int							=      self.lang_to_code[tgt_lang]
										return self.pre_processor._build_translation_inputs(
										    A_  ,   return_tensors="pt"  ,   src_lang=A_  ,   tgt_lang=A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.model.generate(**A_		)





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


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


