




# 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								=	{
    '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			(       UpperCamelCase__       ):




    """simple docstring"""

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

    snake_case__   = ["text", "text", "text"]
    snake_case__   = ["text"]


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ) ->			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.'  )
         lowerCAmelCase__            =					self.lang_to_code[src_lang]
         lowerCAmelCase__            =					self.lang_to_code[tgt_lang]
         return self.pre_processor._build_translation_inputs(
             SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ,      src_lang=SCREAMING_SNAKE_CASE__  ,      tgt_lang=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ) ->			List[Any]:
         return self.model.generate(**SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]  ) ->			List[str]:
         return self.post_processor.decode(outputs[0].tolist()  ,      skip_special_tokens=SCREAMING_SNAKE_CASE__  )







from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
  )







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case=13       ,      snake_case=7       ,      snake_case=True       ,      snake_case=True       ,      snake_case=True       ,      snake_case=True       ,      snake_case=99       ,      snake_case=32       ,      snake_case=2       ,      snake_case=4       ,      snake_case=37       ,      snake_case="gelu"       ,      snake_case=0.1       ,      snake_case=0.1       ,      snake_case=512       ,      snake_case=16       ,      snake_case=2       ,      snake_case=0.02       ,      snake_case=False       ,      snake_case=True       ,      snake_case="None"       ,      snake_case=3       ,      snake_case=4       ,      snake_case=None       ,      )					->	int:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	parent
      a__						:    Dict   		=	batch_size
      a__						:    List[Any]   		=	seq_length
      a__						:    List[str]   		=	is_training
      a__						:    str   		=	use_input_mask
      a__						:    Union[str, Any]   		=	use_token_type_ids
      a__						:    Optional[int]   		=	use_labels
      a__						:    List[Any]   		=	vocab_size
      a__						:    Optional[Any]   		=	hidden_size
      a__						:    Union[str, Any]   		=	num_hidden_layers
      a__						:    Optional[int]   		=	num_attention_heads
      a__						:    Dict   		=	intermediate_size
      a__						:    Union[str, Any]   		=	hidden_act
      a__						:    str   		=	hidden_dropout_prob
      a__						:    Dict   		=	attention_probs_dropout_prob
      a__						:    str   		=	max_position_embeddings
      a__						:    int   		=	type_vocab_size
      a__						:    Any   		=	type_sequence_label_size
      a__						:    Optional[Any]   		=	initializer_range
      a__						:    List[Any]   		=	num_labels
      a__						:    Optional[int]   		=	num_choices
      a__						:    int   		=	relative_attention
      a__						:    str   		=	position_biased_input
      a__						:    List[Any]   		=	pos_att_type
      a__						:    Union[str, Any]   		=	scope





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size   )

      a__						:    Union[str, Any]   		=	None
      if self.use_input_mask:
         a__						:    Any   		=	random_attention_mask([self.batch_size, self.seq_length]   )

      a__						:    Optional[Any]   		=	None
      if self.use_token_type_ids:
         a__						:    str   		=	ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size   )

      a__						:    Dict   		=	None
      a__						:    List[Any]   		=	None
      a__						:    Dict   		=	None
      if self.use_labels:
         a__						:    List[Any]   		=	ids_tensor([self.batch_size]       ,      self.type_sequence_label_size   )
         a__						:    List[str]   		=	ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels   )

      a__						:    List[str]   		=	DebertaVaConfig(
          vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      type_vocab_size=self.type_vocab_size       ,      relative_attention=self.relative_attention       ,      position_biased_input=self.position_biased_input       ,      initializer_range=self.initializer_range       ,      return_dict=snake_case       ,      )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      a__						:    Dict   		=	TFDebertaVaModel(config=snake_case   )
      a__						:    Union[str, Any]   		=	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

      a__						:    Union[str, Any]   		=	[input_ids, input_mask]
      a__						:    Any   		=	model(snake_case   )

      a__						:    Optional[Any]   		=	model(snake_case   )

      self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    Optional[Any]   		=	TFDebertaVaForMaskedLM(config=snake_case   )
      a__						:    Optional[Any]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      a__						:    List[str]   		=	model(snake_case   )
      self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	self.num_labels
      a__						:    int   		=	TFDebertaVaForSequenceClassification(config=snake_case   )
      a__						:    Optional[Any]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      a__						:    int   		=	model(snake_case   )
      self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      a__						:    Dict   		=	self.num_labels
      a__						:    str   		=	TFDebertaVaForTokenClassification(config=snake_case   )
      a__						:    Dict   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      a__						:    Tuple   		=	model(snake_case   )
      self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	TFDebertaVaForQuestionAnswering(config=snake_case   )
      a__						:    Optional[int]   		=	{
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      a__						:    List[Any]   		=	model(snake_case   )
      self.parent.assertEqual(result.start_logits.shape       ,      (self.batch_size, self.seq_length)   )
      self.parent.assertEqual(result.end_logits.shape       ,      (self.batch_size, self.seq_length)   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    List[str]   		=	self.prepare_config_and_inputs()
      (
          (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  (
          a__
      )		,  
      )						:    int   		=	config_and_inputs
      a__						:    Any   		=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_tf
class 	__lowerCAmelCase      (       _UpperCamelCase		,_UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    int  		=					(
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _UpperCamelCase			:    List[str]  		=					(
       {
           """feature-extraction""": TFDebertaVaModel,
           """fill-mask""": TFDebertaVaForMaskedLM,
           """question-answering""": TFDebertaVaForQuestionAnswering,
           """text-classification""": TFDebertaVaForSequenceClassification,
           """token-classification""": TFDebertaVaForTokenClassification,
           """zero-shot""": TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _UpperCamelCase			:    str  		=					False
   _UpperCamelCase			:    List[str]  		=					False





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Any   		=	TFDebertaVaModelTester(self   )
      a__						:    int   		=	ConfigTester(self       ,      config_class=snake_case       ,      hidden_size=37   )





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Dict   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    List[str]   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Tuple   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    int   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case   )





   @slow
   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      a__						:    Tuple   		=	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
      self.assertIsNotNone(snake_case   )







@require_tf
class 	__lowerCAmelCase      (       unittest.TestCase	):





   @unittest.skip(reason="Model not available yet"   )
   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      pass





   @slow
   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
      a__						:    Any   		=	tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
      a__						:    List[str]   		=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
      a__						:    Tuple   		=	model(snake_case       ,      attention_mask=snake_case   )[0]

      a__						:    List[Any]   		=	tf.constant(
          [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]   )
      tf.debugging.assert_near(output[:, 1:4, 1:4]       ,      snake_case       ,      atol=1E-4   )

