





# 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


lowercase_          =		{
    """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     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		List[str]			 =	'facebook/nllb-200-distilled-600M'
   _UpperCamelCase   :		Tuple			 =	(
       '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   :		Union[str, Any]			 =	'translator'
   _UpperCamelCase   :		Tuple			 =	AutoTokenizer
   _UpperCamelCase   :		Any			 =	AutoModelForSeqaSeqLM
   _UpperCamelCase   :		List[str]			 =	LANGUAGE_CODES

   _UpperCamelCase   :		Dict			 =	['text', 'text', 'text']
   _UpperCamelCase   :		Optional[Any]			 =	['text']


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    List[Any]      ,					a      :    List[str]      ,					a      :    int	)->       List[Any]:





     """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."""	)
     lowercase__           =  self.lang_to_code[src_lang]
     lowercase__           =  self.lang_to_code[tgt_lang]
     return self.pre_processor._build_translation_inputs(
         a      ,					return_tensors='pt'      ,					src_lang=a      ,					tgt_lang=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    Optional[Any]	)->       Union[str, Any]:





     """simple docstring"""







     return self.model.generate(**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    int	)->       str:





     """simple docstring"""







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









import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple[int, int]:
  def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=0		,						_SCREAMING_SNAKE_CASE=None		):
    lowercase__           =  round(val / multiple		) * multiple

    if max_val is not None and x > max_val:
      lowercase__           =  math.floor(val / multiple		) * multiple

    if x < min_val:
      lowercase__           =  math.ceil(val / multiple		) * multiple

    return x

  lowercase__           =  (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) else output_size

  lowercase__      ,   lowercase__           =  get_image_size(_SCREAMING_SNAKE_CASE		)
  lowercase__      ,   lowercase__           =  output_size

  # determine new height and width
  lowercase__           =  output_height / input_height
  lowercase__           =  output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width		) < abs(1 - scale_height		):
      # fit width
      lowercase__           =  scale_width
    else:
      # fit height
      lowercase__           =  scale_height

  lowercase__           =  constraint_to_multiple_of(scale_height * input_height		,						multiple=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  constraint_to_multiple_of(scale_width * input_width		,						multiple=_SCREAMING_SNAKE_CASE		)

  return (new_height, new_width)


class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Dict			 =	['pixel_values']


   def __init__(   self      :    Any      ,					a      :    bool = True      ,					a      :    Dict[str, int] = None      ,					a      :    PILImageResampling = PILImageResampling.BILINEAR      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    bool = True      ,					a      :    Union[int, float] = 1 / 255      ,					a      :    bool = True      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					**a      :    Tuple      ,					)->       None:





     """simple docstring"""







     super().__init__(**a	)
     lowercase__           =  size if size is not None else {'height': 384, 'width': 384}
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  do_resize
     lowercase__           =  size
     lowercase__           =  keep_aspect_ratio
     lowercase__           =  ensure_multiple_of
     lowercase__           =  resample
     lowercase__           =  do_rescale
     lowercase__           =  rescale_factor
     lowercase__           =  do_normalize
     lowercase__           =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowercase__           =  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    np.ndarray      ,					a      :    Dict[str, int]      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    PILImageResampling = PILImageResampling.BICUBIC      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[Any]      ,					)->       np.ndarray:





     """simple docstring"""







     lowercase__           =  get_size_dict(a	)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""	)
     lowercase__           =  get_resize_output_image_size(
         a      ,					output_size=(size['height'], size['width'])      ,					keep_aspect_ratio=a      ,					multiple=a      ,					)
     return resize(a      ,					size=a      ,					resample=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    np.ndarray      ,					a      :    Union[int, float]      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Dict      ,					)->       str:





     """simple docstring"""







     return rescale(a      ,					scale=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    np.ndarray      ,					a      :    Union[float, List[float]]      ,					a      :    Union[float, List[float]]      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[int]      ,					)->       np.ndarray:





     """simple docstring"""







     return normalize(a      ,					mean=a      ,					std=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    ImageInput      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    PILImageResampling = None      ,					a      :    bool = None      ,					a      :    float = None      ,					a      :    bool = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[str, TensorType]] = None      ,					a      :    ChannelDimension = ChannelDimension.FIRST      ,					**a      :    str      ,					)->       PIL.Image.Image:





     """simple docstring"""







     lowercase__           =  do_resize if do_resize is not None else self.do_resize
     lowercase__           =  size if size is not None else self.size
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowercase__           =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowercase__           =  resample if resample is not None else self.resample
     lowercase__           =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__           =  rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__           =  do_normalize if do_normalize is not None else self.do_normalize
     lowercase__           =  image_mean if image_mean is not None else self.image_mean
     lowercase__           =  image_std if image_std is not None else self.image_std

     lowercase__           =  make_list_of_images(a	)

     if not valid_images(a	):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'	)

     if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

     # All transformations expect numpy arrays.
     lowercase__           =  [to_numpy_array(a	) for image in images]

     if do_resize:
       lowercase__           =  [self.resize(image=a      ,					size=a      ,					resample=a	) for image in images]

     if do_rescale:
       lowercase__           =  [self.rescale(image=a      ,					scale=a	) for image in images]

     if do_normalize:
       lowercase__           =  [self.normalize(image=a      ,					mean=a      ,					std=a	) for image in images]

     lowercase__           =  [to_channel_dimension_format(a      ,					a	) for image in images]

     lowercase__           =  {'pixel_values': images}
     return BatchFeature(data=a      ,					tensor_type=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    str      ,					a      :    List[Tuple] = None	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(a	) != len(a	):
         raise ValueError(
             'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

       if is_torch_tensor(a	):
         lowercase__           =  target_sizes.numpy()

       lowercase__           =  []

       for idx in range(len(a	)	):
         lowercase__           =  torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0	)      ,					size=target_sizes[idx]      ,					mode='bilinear'      ,					align_corners=a	)
         lowercase__           =  resized_logits[0].argmax(dim=0	)
         semantic_segmentation.append(a	)
     else:
       lowercase__           =  logits.argmax(dim=1	)
       lowercase__           =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation


