import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
      from .features import FeatureType


UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__											=					False, False, False





@dataclass
class      A							:
   __UpperCAmelCase : Optional[int]      =			None
   __UpperCAmelCase : bool      =			True
   __UpperCAmelCase : bool      =			True
   __UpperCAmelCase : Optional[str]      =			None
   # Automatically constructed
   __UpperCAmelCase : ClassVar[str]      =			"dict"
   __UpperCAmelCase : ClassVar[Any]      =			pa.struct({'bytes': pa.binary(), 'path': pa.string()}     )
   __UpperCAmelCase : str      =			field(default='Audio'		,				init=UpperCAmelCase_		,				repr=UpperCAmelCase_     )


   def __call__(self     :						int		)						->				Dict:
         """simple docstring"""



         return self.pa_type


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Union[str, bytes, dict]		)						->				dict:
         """simple docstring"""



         try:
               import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
         except ImportError as err:
               raise ImportError("To support encoding audio data, please install 'soundfile'."		) from err
         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               return {"bytes": None, "path": value}
         elif isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               return {"bytes": value, "path": None}
         elif "array" in value:
               # convert the audio array to wav bytes
               UpperCAmelCase__								=  BytesIO()
               sf.write(__UpperCAmelCase       ,  value["array"]       ,  value["sampling_rate"]       ,  format="wav"		)
               return {"bytes": buffer.getvalue(), "path": None}
         elif value.get("path"		) is not None and os.path.isfile(value["path"]		):
               # we set "bytes": None to not duplicate the data if they're already available locally
               if value["path"].endswith("pcm"		):
                     # "PCM" only has raw audio bytes
                     if value.get("sampling_rate"		) is None:
                           # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
                           raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"		)
                     if value.get("bytes"		):
                           # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
                           UpperCAmelCase__								=  np.frombuffer(value["bytes"]       ,  dtype=np.intaa		).astype(np.floataa		) / 3_2_7_6_7
                     else:
                           UpperCAmelCase__								=  np.memmap(value["path"]       ,  dtype="h"       ,  mode="r"		).astype(np.floataa		) / 3_2_7_6_7

                     UpperCAmelCase__								=  BytesIO(bytes()		)
                     sf.write(__UpperCAmelCase       ,  __UpperCAmelCase       ,  value["sampling_rate"]       ,  format="wav"		)
                     return {"bytes": buffer.getvalue(), "path": None}
               else:
                     return {"bytes": None, "path": value.get("path"		)}
         elif value.get("bytes"		) is not None or value.get("path"		) is not None:
               # store the audio bytes, and path is used to infer the audio format using the file extension
               return {"bytes": value.get("bytes"		), "path": value.get("path"		)}
         else:
               raise ValueError(
                   f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""		)


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						dict       ,  __UpperCAmelCase     :						Optional[Dict[str, Union[str, bool, None]]] = None		)						->				dict:
         """simple docstring"""



         if not self.decode:
               raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."		)

         UpperCAmelCase__		,      UpperCAmelCase__								=  (value["path"], BytesIO(value["bytes"]		)) if value["bytes"] is not None else (value["path"], None)
         if path is None and file is None:
               raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""		)

         try:
               import librosa
               import soundfile as sf
         except ImportError as err:
               raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."		) from err

         UpperCAmelCase__								=  xsplitext(__UpperCAmelCase		)[1][1:].lower() if path is not None else None
         if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
               raise RuntimeError(
                   "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
                   "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "		)
         elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
               raise RuntimeError(
                   "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
                   "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "		)

         if file is None:
               UpperCAmelCase__								=  token_per_repo_id or {}
               UpperCAmelCase__								=  path.split("::"		)[-1]
               try:
                     UpperCAmelCase__								=  string_to_dict(__UpperCAmelCase       ,  config.HUB_DATASETS_URL		)["repo_id"]
                     UpperCAmelCase__								=  token_per_repo_id[repo_id]
               except (ValueError, KeyError):
                     UpperCAmelCase__								=  None

               with xopen(__UpperCAmelCase       ,  "rb"       ,  use_auth_token=__UpperCAmelCase		) as f:
                     UpperCAmelCase__		,      UpperCAmelCase__								=  sf.read(__UpperCAmelCase		)

         else:
               UpperCAmelCase__		,      UpperCAmelCase__								=  sf.read(__UpperCAmelCase		)

         UpperCAmelCase__								=  array.T
         if self.mono:
               UpperCAmelCase__								=  librosa.to_mono(__UpperCAmelCase		)
         if self.sampling_rate and self.sampling_rate != sampling_rate:
               UpperCAmelCase__								=  librosa.resample(__UpperCAmelCase       ,  orig_sr=__UpperCAmelCase       ,  target_sr=self.sampling_rate		)
               UpperCAmelCase__								=  self.sampling_rate

         return {"path": path, "array": array, "sampling_rate": sampling_rate}


   def 			lowercase_      (self     :						Optional[int]		)						->				Union["FeatureType", Dict[str, "FeatureType"]]:
         """simple docstring"""



         from .features import Value

         if self.decode:
               raise ValueError("Cannot flatten a decoded Audio feature."		)
         return {
             "bytes": Value("binary"		),
             "path": Value("string"		),
         }


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Union[pa.StringArray, pa.StructArray]		)						->				pa.StructArray:
         """simple docstring"""



         if pa.types.is_string(storage.type		):
               UpperCAmelCase__								=  pa.array([None] * len(__UpperCAmelCase		)       ,  type=pa.binary()		)
               UpperCAmelCase__								=  pa.StructArray.from_arrays([bytes_array, storage]       ,  ["bytes", "path"]       ,  mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
               UpperCAmelCase__								=  pa.array([None] * len(__UpperCAmelCase		)       ,  type=pa.string()		)
               UpperCAmelCase__								=  pa.StructArray.from_arrays([storage, path_array]       ,  ["bytes", "path"]       ,  mask=storage.is_null()		)
         elif pa.types.is_struct(storage.type		) and storage.type.get_all_field_indices("array"		):
               UpperCAmelCase__								=  pa.array([Audio().encode_example(__UpperCAmelCase		) if x is not None else None for x in storage.to_pylist()]		)
         elif pa.types.is_struct(storage.type		):
               if storage.type.get_field_index("bytes"		) >= 0:
                     UpperCAmelCase__								=  storage.field("bytes"		)
               else:
                     UpperCAmelCase__								=  pa.array([None] * len(__UpperCAmelCase		)       ,  type=pa.binary()		)
               if storage.type.get_field_index("path"		) >= 0:
                     UpperCAmelCase__								=  storage.field("path"		)
               else:
                     UpperCAmelCase__								=  pa.array([None] * len(__UpperCAmelCase		)       ,  type=pa.string()		)
               UpperCAmelCase__								=  pa.StructArray.from_arrays([bytes_array, path_array]       ,  ["bytes", "path"]       ,  mask=storage.is_null()		)
         return array_cast(__UpperCAmelCase       ,  self.pa_type		)





   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						pa.StructArray		)						->				pa.StructArray:
         """simple docstring"""



         @no_op_if_value_is_null
         def path_to_bytes(__UpperCAmelCase     :						Any		):
               with xopen(__UpperCAmelCase       ,  "rb"		) as f:
                     UpperCAmelCase__								=  f.read()
               return bytes_

         UpperCAmelCase__								=  pa.array(
             [
                 (path_to_bytes(x["path"]		) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]       ,  type=pa.binary()       ,  )
         UpperCAmelCase__								=  pa.array(
             [os.path.basename(__UpperCAmelCase		) if path is not None else None for path in storage.field("path"		).to_pylist()]       ,  type=pa.string()       ,  )
         UpperCAmelCase__								=  pa.StructArray.from_arrays([bytes_array, path_array]       ,  ["bytes", "path"]       ,  mask=bytes_array.is_null()		)
         return array_cast(__UpperCAmelCase       ,  self.pa_type		)










'''simple docstring'''

# 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							:	Optional[Any]			={
    "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    UpperCAmelCase	(       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      :List[Any]						,       lowercase_      :Any						,       lowercase_      :List[str]						,       lowercase_      :int							)->							str:
     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."							)
     A__				=			self.lang_to_code[src_lang]
     A__				=			self.lang_to_code[tgt_lang]
     return self.pre_processor._build_translation_inputs(
         lowercase_						,       return_tensors="pt"						,       src_lang=lowercase_						,       tgt_lang=lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Any							)->							int:
     return self.model.generate(**lowercase_							)



 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Optional[Any]							)->							str:
     return self.post_processor.decode(outputs[0].tolist()						,       skip_special_tokens=lowercase_							)



