


"""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


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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'facebook/nllb-200-distilled-600M'
		__UpperCAmelCase		:			int										=				(
		    '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		:			Tuple										=				'translator'
		__UpperCAmelCase		:			int										=				AutoTokenizer
		__UpperCAmelCase		:			Dict										=				AutoModelForSeqaSeqLM
		__UpperCAmelCase		:			List[str]										=				LANGUAGE_CODES

		__UpperCAmelCase		:			Dict										=				['text', 'text', 'text']
		__UpperCAmelCase		:			Dict										=				['text']

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				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(
				    _a							, return_tensors='''pt'''							, src_lang=_a							, tgt_lang=_a      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.model.generate(**_a      )





		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.post_processor.decode(outputs[0].tolist()							, skip_special_tokens=_a      )













import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image

    from transformers.image_utils import load_image
else:

    class 		SCREAMING_SNAKE_CASE__				:


          @staticmethod
          def    a (*a__       :      List[str]				,			**a__       :      List[str]      ):





             """simple docstring"""



             pass






    def lowerCamelCase__    (							snake_case_	:  int       )      ->				Optional[int]:
       return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
snake_case_				=			(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)

@is_pipeline_test
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):
      A_						:				Optional[Any]							=    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


      @require_pytesseract
      @require_vision
      def    a (self       :      List[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model=a__				,			tokenizer=a__				,			image_processor=a__      )

         __snake_case							       =   INVOICE_URL
         __snake_case							       =   list(zip(*apply_tesseract(load_image(a__      )				,			a__				,			''''''      )      )      )
         __snake_case							       =   '''What is the placebo?'''
         __snake_case							       =   [
             {
                 '''image''': load_image(a__      ),
                 '''question''': question,
             },
             {
                 '''image''': image,
                 '''question''': question,
             },
             {
                 '''image''': image,
                 '''question''': question,
                 '''word_boxes''': word_boxes,
             },
         ]
         return dqa_pipeline, examples


      def    a (self       :      Union[str, Any]				,			a__       :      Optional[int]				,			a__       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   dqa_pipeline(a__				,			top_k=2      )
         self.assertEqual(
             a__				,			[
                 [
                     {'''score''': ANY(a__      ), '''answer''': ANY(a__      ), '''start''': ANY(a__      ), '''end''': ANY(a__      )},
                     {'''score''': ANY(a__      ), '''answer''': ANY(a__      ), '''start''': ANY(a__      ), '''end''': ANY(a__      )},
                 ]
             ]
             * 3				,			)


      @require_torch
      @require_detectrona
      @require_pytesseract
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   pipeline('''document-question-answering'''				,			model='''hf-internal-testing/tiny-random-layoutlmv2'''      )
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''How many cats are there?'''

         __snake_case							       =   [
             {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
             {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
         ]
         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(nested_simplify(a__				,			decimals=4      )				,			a__      )

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(nested_simplify(a__				,			decimals=4      )				,			a__      )

         # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
         # Empty answer probably
         __snake_case							       =   '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(a__				,			[]      )

         # We can optionnally pass directly the words and bounding boxes
         __snake_case							       =   '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
         __snake_case							       =   []
         __snake_case							       =   []
         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			words=a__				,			boxes=a__				,			top_k=2      )
         self.assertEqual(a__				,			[]      )


      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,			revision='''9977165'''				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                     {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 ],
             ]
             * 2				,			)


      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,			revision='''9977165'''				,			max_seq_len=50				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                     {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 ]
             ]
             * 2				,			)


      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained(
             '''impira/layoutlm-document-qa'''				,			revision='''3dc6de3'''				,			add_prefix_space=a__      )
         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''impira/layoutlm-document-qa'''				,			tokenizer=a__				,			revision='''3dc6de3'''				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]				,			)

         __snake_case							       =   dqa_pipeline({'''image''': image, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                     {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                 ]
             ]
             * 2				,			)

         __snake_case							       =   list(zip(*apply_tesseract(load_image(a__      )				,			a__				,			''''''      )      )      )

         # This model should also work if `image` is set to None
         __snake_case							       =   dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
             ]				,			)


      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained(
             '''impira/layoutlm-document-qa'''				,			revision='''3dc6de3'''				,			add_prefix_space=a__      )
         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''impira/layoutlm-document-qa'''				,			tokenizer=a__				,			revision='''3dc6de3'''				,			max_seq_len=50				,			)
         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''

         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)

         __snake_case							       =   dqa_pipeline(
             [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 [
                     {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                     {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 ]
             ]
             * 2				,			)

         __snake_case							       =   list(zip(*apply_tesseract(load_image(a__      )				,			a__				,			''''''      )      )      )

         # This model should also work if `image` is set to None
         __snake_case							       =   dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,			top_k=2      )
         self.assertEqual(
             nested_simplify(a__				,			decimals=4      )				,			[
                 {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                 {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
             ]				,			)


      @slow
      @require_torch
      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   pipeline(
             '''document-question-answering'''				,			model='''naver-clova-ix/donut-base-finetuned-docvqa'''				,			tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa'''      )				,			feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa'''				,			)

         __snake_case							       =   INVOICE_URL
         __snake_case							       =   '''What is the invoice number?'''
         __snake_case							       =   dqa_pipeline(image=a__				,			question=a__				,			top_k=2      )
         self.assertEqual(nested_simplify(a__				,			decimals=4      )				,			[{'''answer''': '''us-001'''}]      )


      @require_tf
      @unittest.skip('''Document question answering not implemented in TF'''      )
      def    a (self       :      List[str]      ):





         """simple docstring"""



         pass

