






'''simple docstring'''



import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




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


       '''simple docstring'''

       lowercase					:   List[Any]				  ='''ylacombe/bark-small'''
       lowercase					:   int				  =tempfile.mkdtemp()
       lowercase					:   Any				  ='''en_speaker_1'''
       lowercase					:   Union[str, Any]				  ='''This is a test string'''
       lowercase					:   Union[str, Any]				  ='''speaker_embeddings_path.json'''
       lowercase					:   Optional[Any]				  ='''speaker_embeddings'''






      def 				A__      ( self			:		Tuple     ,      **UpperCAmelCase			:		Any		)  ->  Tuple:


       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  str:


       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)






      def 				A__      ( self			:		Tuple		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.get_tokenizer()

       lowercase					:   List[str]				  =BarkProcessor(tokenizer=UpperCAmelCase		)

       processor.save_pretrained(self.tmpdirname		)
       lowercase					:   List[Any]				  =BarkProcessor.from_pretrained(self.tmpdirname		)

       self.assertEqual(processor.tokenizer.get_vocab()     ,      tokenizer.get_vocab()		)






      @slow
      def 				A__      ( self			:		List[Any]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint     ,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,      )
       processor.save_pretrained(
           self.tmpdirname     ,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,      speaker_embeddings_directory=self.speaker_embeddings_directory     ,      )

       lowercase					:   str				  =self.get_tokenizer(bos_token='''(BOS)'''     ,      eos_token='''(EOS)'''		)

       lowercase					:   Optional[Any]				  =BarkProcessor.from_pretrained(
           self.tmpdirname     ,      self.speaker_embeddings_dict_path     ,      bos_token='''(BOS)'''     ,      eos_token='''(EOS)'''     ,      )

       self.assertEqual(processor.tokenizer.get_vocab()     ,      tokenizer_add_kwargs.get_vocab()		)






      def 				A__      ( self			:		List[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Any				  =BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint     ,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,      )

       lowercase					:   Any				  =35
       lowercase					:   Optional[int]				  =2
       lowercase					:   Dict				  =8

       lowercase					:   List[str]				  ={
           '''semantic_prompt''': np.ones(UpperCAmelCase		),
           '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)		),
           '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)		),
       }

       # test providing already loaded voice_preset
       lowercase					:   Tuple				  =processor(text=self.input_string     ,      voice_preset=UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =inputs['''history_prompt''']
       for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()     ,      processed_voice_preset.get(UpperCAmelCase     ,      np.array([]		)		).tolist()		)

       # test loading voice preset from npz file
       lowercase					:   Optional[Any]				  =os.path.join(self.tmpdirname     ,      '''file.npz'''		)
       np.savez(UpperCAmelCase     ,      **UpperCAmelCase		)
       lowercase					:   str				  =processor(text=self.input_string     ,      voice_preset=UpperCAmelCase		)
       lowercase					:   List[str]				  =inputs['''history_prompt''']

       for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()     ,      processed_voice_preset.get(UpperCAmelCase     ,      np.array([]		)		).tolist()		)

       # test loading voice preset from the hub
       lowercase					:   Optional[int]				  =processor(text=self.input_string     ,      voice_preset=self.voice_preset		)






      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.get_tokenizer()

       lowercase					:   Dict				  =BarkProcessor(tokenizer=UpperCAmelCase		)

       lowercase					:   List[str]				  =processor(text=self.input_string		)

       lowercase					:   Any				  =tokenizer(
           self.input_string     ,      padding='''max_length'''     ,      max_length=256     ,      add_special_tokens=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      return_token_type_ids=UpperCAmelCase     ,      )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]     ,      encoded_processor[key].squeeze().tolist()		)











'''simple docstring'''



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 UpperCAmelCase_					:




            """simple docstring"""




            @staticmethod
            def 				A__      ( *UpperCAmelCase			:		Union[str, Any]     ,      **UpperCAmelCase			:		Optional[int]		)  ->  Any:


             '''simple docstring'''

             pass





      def 							lowercase_				(    __A :       Union[str, Any]			)       ->      List[Any]:



       """simple docstring"""




       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.
SCREAMING_SNAKE_CASE						=	(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING






      @require_pytesseract
      @require_vision
      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		List[str]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Dict				  =pipeline(
           '''document-question-answering'''     ,      model=UpperCAmelCase     ,      tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =INVOICE_URL
       lowercase					:   Any				  =list(zip(*apply_tesseract(load_image(UpperCAmelCase		)     ,      UpperCAmelCase     ,      ''''''		)		)		)
       lowercase					:   Dict				  ='''What is the placebo?'''
       lowercase					:   Optional[Any]				  =[
           {
               '''image''': load_image(UpperCAmelCase		),
               '''question''': question,
           },
           {
               '''image''': image,
               '''question''': question,
           },
           {
               '''image''': image,
               '''question''': question,
               '''word_boxes''': word_boxes,
           },
       ]
       return dqa_pipeline, examples






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		int		)  ->  Optional[int]:


       '''simple docstring'''

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






      @require_torch
      @require_detectrona
      @require_pytesseract
      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Dict				  =pipeline('''document-question-answering'''     ,      model='''hf-internal-testing/tiny-random-layoutlmv2'''		)
       lowercase					:   Union[str, Any]				  =INVOICE_URL
       lowercase					:   Tuple				  ='''How many cats are there?'''

       lowercase					:   Optional[int]				  =[
           {'''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},
       ]
       lowercase					:   Optional[Any]				  =dqa_pipeline(image=UpperCAmelCase     ,      question=UpperCAmelCase     ,      top_k=2		)
       self.assertEqual(nested_simplify(UpperCAmelCase     ,      decimals=4		)     ,      UpperCAmelCase		)

       lowercase					:   List[str]				  =dqa_pipeline({'''image''': image, '''question''': question}     ,      top_k=2		)
       self.assertEqual(nested_simplify(UpperCAmelCase     ,      decimals=4		)     ,      UpperCAmelCase		)

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

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






      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =pipeline(
           '''document-question-answering'''     ,      model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''     ,      revision='''9977165'''     ,      )
       lowercase					:   Dict				  =INVOICE_URL
       lowercase					:   str				  ='''What is the invoice number?'''

       lowercase					:   List[str]				  =dqa_pipeline(image=UpperCAmelCase     ,      question=UpperCAmelCase     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   List[Any]				  =dqa_pipeline({'''image''': image, '''question''': question}     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   List[Any]				  =dqa_pipeline(
           [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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			:		Dict		)  ->  List[str]:


       '''simple docstring'''

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

       lowercase					:   Union[str, Any]				  =dqa_pipeline(image=UpperCAmelCase     ,      question=UpperCAmelCase     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   int				  =dqa_pipeline({'''image''': image, '''question''': question}     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   Any				  =dqa_pipeline(
           [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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			:		str		)  ->  Dict:


       '''simple docstring'''

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

       lowercase					:   Dict				  =dqa_pipeline(image=UpperCAmelCase     ,      question=UpperCAmelCase     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   Any				  =dqa_pipeline({'''image''': image, '''question''': question}     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   str				  =dqa_pipeline(
           [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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     ,      )

       lowercase					:   Tuple				  =list(zip(*apply_tesseract(load_image(UpperCAmelCase		)     ,      UpperCAmelCase     ,      ''''''		)		)		)

       # This model should also work if `image` is set to None
       lowercase					:   Dict				  =dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Dict				  =AutoTokenizer.from_pretrained(
           '''impira/layoutlm-document-qa'''     ,      revision='''3dc6de3'''     ,      add_prefix_space=UpperCAmelCase		)
       lowercase					:   List[Any]				  =pipeline(
           '''document-question-answering'''     ,      model='''impira/layoutlm-document-qa'''     ,      tokenizer=UpperCAmelCase     ,      revision='''3dc6de3'''     ,      max_seq_len=50     ,      )
       lowercase					:   str				  =INVOICE_URL
       lowercase					:   int				  ='''What is the invoice number?'''

       lowercase					:   Tuple				  =dqa_pipeline(image=UpperCAmelCase     ,      question=UpperCAmelCase     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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},
           ]     ,      )

       lowercase					:   Union[str, Any]				  =dqa_pipeline(
           [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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     ,      )

       lowercase					:   List[str]				  =list(zip(*apply_tesseract(load_image(UpperCAmelCase		)     ,      UpperCAmelCase     ,      ''''''		)		)		)

       # This model should also work if `image` is set to None
       lowercase					:   Union[str, Any]				  =dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}     ,      top_k=2		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase     ,      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			:		List[Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   str				  =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'''     ,      )

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






      @require_tf
      @unittest.skip('''Document question answering not implemented in TF'''		)
      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       pass



