

# 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


__UpperCAmelCase	:       List[str]    	=							{
    "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    __snake_case	(							snake_case_      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""facebook/nllb-200-distilled-600M"""
       lowerCAmelCase__						=					(
           """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`."""
       )
       lowerCAmelCase__						=					"""translator"""
       lowerCAmelCase__						=					AutoTokenizer
       lowerCAmelCase__						=					AutoModelForSeqaSeqLM
       lowerCAmelCase__						=					LANGUAGE_CODES

       lowerCAmelCase__						=					["""text""", """text""", """text"""]
       lowerCAmelCase__						=					["""text"""]




       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Tuple							,    A			:   Union[str, Any]							,    A			:   List[Any]		):
         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.'''		)
         __snake_case:						List[str]			  =						self.lang_to_code[src_lang]
         __snake_case:						List[str]			  =						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			:   Any							,    A			:   Tuple		):
         return self.model.generate(**A		)






       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   List[str]		):
         return self.post_processor.decode(outputs[0].tolist()							,    skip_special_tokens=A		)













from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCAmelCase	:       str    	=							logging.get_logger(__name__)







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   Any							,    A			:   int							,    A			:   int							,    A			:   float							,    **A			:   Optional[int]		):
         __snake_case:						List[str]			  =						feature_size
         __snake_case:						Optional[int]			  =						sampling_rate
         __snake_case:						Any			  =						padding_value

         __snake_case:						Dict			  =						kwargs.pop("""padding_side"""							,    """right"""		)
         __snake_case:						Union[str, Any]			  =						kwargs.pop("""return_attention_mask"""							,    A		)

         super().__init__(**A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[
               BatchFeature,
               List[BatchFeature],
               Dict[str, BatchFeature],
               Dict[str, List[BatchFeature]],
               List[Dict[str, BatchFeature]],
           ]							,    A			:   Union[bool, str, PaddingStrategy] = True							,    A			:   Optional[int] = None							,    A			:   bool = False							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    A			:   Optional[Union[str, TensorType]] = None							,    ):
         # If we have a list of dicts, let's convert it in a dict of lists
         # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
         if isinstance(A							,    (list, tuple)		) and isinstance(processed_features[0]							,    (dict, BatchFeature)		):
           __snake_case:						Optional[int]			  =						{
               key: [example[key] for example in processed_features] for key in processed_features[0].keys()
           }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
           raise ValueError(
               """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
               f''' to this method that includes {self.model_input_names[0]}, but you provided'''
               f''' {list(processed_features.keys()		)}'''		)

         __snake_case:						List[str]			  =						processed_features[self.model_input_names[0]]
         __snake_case:						Any			  =						(
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(A		) == 0:
           if return_attention_mask:
             __snake_case:						Union[str, Any]			  =						[]
           return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         __snake_case:						int			  =						required_input[0]
         if isinstance(A							,    (list, tuple)		):
           # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
           __snake_case:						Optional[int]			  =						0
           while len(required_input[index]		) == 0:
             index += 1
           if index < len(A		):
             __snake_case:						Optional[int]			  =						required_input[index][0]

         if return_tensors is None:
           if is_tf_tensor(A		):
             __snake_case:						str			  =						"""tf"""
           elif is_torch_tensor(A		):
             __snake_case:						str			  =						"""pt"""
           elif isinstance(A							,    (int, float, list, tuple, np.ndarray)		):
             __snake_case:						List[str]			  =						"""np"""
           else:
             raise ValueError(
                 f'''type of {first_element} unknown: {type(A		)}. '''
                 """Should be one of a python, numpy, pytorch or tensorflow object."""		)

         for key, value in processed_features.items():
           if isinstance(value[0]							,    (int, float)		):
             __snake_case:						List[Any]			  =						to_numpy(A		)
           else:
             __snake_case:						Union[str, Any]			  =						[to_numpy(A		) for v in value]

        # Convert padding_strategy in PaddingStrategy
         __snake_case:						Union[str, Any]			  =						self._get_padding_strategies(padding=A							,    max_length=A		)

         __snake_case:						Any			  =						processed_features[self.model_input_names[0]]

         __snake_case:						int			  =						len(A		)
         if not all(len(A		) == batch_size for v in processed_features.values()		):
           raise ValueError("""Some items in the output dictionary have a different batch size than others."""		)

         __snake_case:						Union[str, Any]			  =						[]
         for i in range(A		):
           __snake_case:						List[Any]			  =						{k: v[i] for k, v in processed_features.items()}
           # truncation
           __snake_case:						Tuple			  =						self._truncate(
               A							,    max_length=A							,    pad_to_multiple_of=A							,    truncation=A							,    )
           truncated_inputs.append(A		)

         if padding_strategy == PaddingStrategy.LONGEST:
           # make sure that `max_length` cannot be longer than the longest truncated length
           __snake_case:						Optional[Any]			  =						max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
           __snake_case:						List[str]			  =						PaddingStrategy.MAX_LENGTH

         __snake_case:						List[Any]			  =						{}
         for i in range(A		):
           # padding
           __snake_case:						Any			  =						self._pad(
               truncated_inputs[i]							,    max_length=A							,    padding_strategy=A							,    pad_to_multiple_of=A							,    return_attention_mask=A							,    )

           for key, value in outputs.items():
             if key not in batch_outputs:
               __snake_case:						Optional[Any]			  =						[]
             if value.dtype is np.dtype(np.floataa		):
               __snake_case:						str			  =						value.astype(np.floataa		)
             batch_outputs[key].append(A		)

         return BatchFeature(A							,    tensor_type=A		)




       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Union[Dict[str, np.ndarray], BatchFeature]							,    A			:   Optional[int] = None							,    A			:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    ):
         __snake_case:						List[Any]			  =						processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
           __snake_case:						List[str]			  =						len(A		)

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           __snake_case:						List[Any]			  =						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __snake_case:						Dict			  =						padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A		) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
           __snake_case:						List[str]			  =						np.ones(len(A		)							,    dtype=np.intaa		)

         if needs_to_be_padded:
           __snake_case:						Any			  =						max_length - len(A		)
           if self.padding_side == "right":
             if return_attention_mask:
               __snake_case:						Optional[int]			  =						np.pad(
                   processed_features["""attention_mask"""]							,    (0, difference)		)
             __snake_case:						Any			  =						((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
             __snake_case:						Union[str, Any]			  =						np.pad(
                 A							,    A							,    """constant"""							,    constant_values=self.padding_value		)
           elif self.padding_side == "left":
             if return_attention_mask:
               __snake_case:						Dict			  =						np.pad(
                   processed_features["""attention_mask"""]							,    (difference, 0)		)
             __snake_case:						Union[str, Any]			  =						((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
             __snake_case:						str			  =						np.pad(
                 A							,    A							,    """constant"""							,    constant_values=self.padding_value		)
           else:
             raise ValueError("""Invalid padding strategy:""" + str(self.padding_side		)		)

         return processed_features




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[Dict[str, np.ndarray], BatchFeature]							,    A			:   Optional[int] = None							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    ):
         if not truncation:
           return processed_features
         elif truncation and max_length is None:
           raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""		)

         __snake_case:						List[str]			  =						processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           __snake_case:						List[Any]			  =						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __snake_case:						Tuple			  =						len(A		) > max_length

         if needs_to_be_truncated:
           __snake_case:						List[Any]			  =						processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
             __snake_case:						int			  =						processed_features["""attention_mask"""][:max_length]

         return processed_features






       def 							UpperCAmelCase__			(  self			:   int							,    A			:   int=False							,    A			:   int=None		):

         # Get padding strategy
         if padding is not False:
           if padding is True:
             __snake_case:						Optional[int]			  =						PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(A							,    A		):
             __snake_case:						Optional[int]			  =						PaddingStrategy(A		)
           elif isinstance(A							,    A		):
             __snake_case:						Any			  =						padding
         else:
           __snake_case:						Any			  =						PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
           if padding_strategy == PaddingStrategy.MAX_LENGTH:
             raise ValueError(
                 f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''		)

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
           raise ValueError(
               """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
               """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""		)

         return padding_strategy






