





'''simple docstring'''

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase				:      Optional[int]						      =logging.get_logger(__name__)






@dataclass
class   UpperCAmelCase      :
 __lowercase  						=							field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()							)}							)
 __lowercase  						=							field(
     metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}							)
 __lowercase  						=							field(
     default=128     ,			metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     }     ,			)
 __lowercase  						=							field(
     default=UpperCamelCase__     ,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)
 def 	UpperCAmelCase_				(							self :Dict				)->				Union[str, Any]:
     A__							=     self.task_name.lower()






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""train"""
 __lowercase  						=							"""dev"""
 __lowercase  						=							"""test"""






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42
 __lowercase  						=							42
 __lowercase  						=							42
 def __init__(							self :List[Any]		,			lowercase_ :GlueDataTrainingArguments		,			lowercase_ :PreTrainedTokenizerBase		,			lowercase_ :Optional[int] = None		,			lowercase_ :Union[str, Split] = Split.train		,			lowercase_ :Optional[str] = None		,			)->				Tuple:
     warnings.warn(
         "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
         "library. You can have a look at this example script for pointers: "
         "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"		,			lowercase_		,			)
     A__							=     args
     A__							=     glue_processors[args.task_name]()
     A__							=     glue_output_modes[args.task_name]
     if isinstance(lowercase_		,			lowercase_				):
         try:
             A__							=     Split[mode]
         except KeyError:
             raise KeyError("mode is not a valid split name"				)
        # Load data features from cache or dataset file
     A__							=     os.path.join(
         cache_dir if cache_dir is not None else args.data_dir		,			F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"		,			)
     A__							=     self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         A__,      A__							=     label_list[2], label_list[1]
     A__							=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     A__							=     cached_features_file + ".lock"
     with FileLock(lowercase_				):
         if os.path.exists(lowercase_				) and not args.overwrite_cache:
             A__							=     time.time()
             A__							=     torch.load(lowercase_				)
             logger.info(
                 F"Loading features from cached file {cached_features_file} [took %.3f s]"		,			time.time() - start				)
         else:
             logger.info(F"Creating features from dataset file at {args.data_dir}"				)

             if mode == Split.dev:
                 A__							=     self.processor.get_dev_examples(args.data_dir				)
             elif mode == Split.test:
                 A__							=     self.processor.get_test_examples(args.data_dir				)
             else:
                 A__							=     self.processor.get_train_examples(args.data_dir				)
             if limit_length is not None:
                 A__							=     examples[:limit_length]
             A__							=     glue_convert_examples_to_features(
                 lowercase_		,			lowercase_		,			max_length=args.max_seq_length		,			label_list=lowercase_		,			output_mode=self.output_mode		,			)
             A__							=     time.time()
             torch.save(self.features		,			lowercase_				)
             # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
             logger.info(
                 F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"				)
 def __len__(							self :List[str]				)->				Any:
     return len(self.features				)
 def __getitem__(							self :List[Any]		,			lowercase_ :List[Any]				)->				InputFeatures:
     return self.features[i]



 def 	UpperCAmelCase_				(							self :str				)->				Optional[int]:
     return self.label_list










'''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      :
   @staticmethod
   def 	UpperCAmelCase_				(							*lowercase_ :Optional[Any]		,			**lowercase_ :List[Any]				)->				Optional[int]:
       pass





  def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]			):
      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.
__lowerCAmelCase				:      int						      =(
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)






@is_pipeline_test
@require_torch
@require_vision
class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
 @require_pytesseract
 @require_vision
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Optional[Any]		,			lowercase_ :Optional[Any]		,			lowercase_ :Dict				)->				int:
     A__							=     pipeline(
         "document-question-answering"		,			model=lowercase_		,			tokenizer=lowercase_		,			image_processor=lowercase_				)

     A__							=     INVOICE_URL
     A__							=     list(zip(*apply_tesseract(load_image(lowercase_				)		,			lowercase_		,			""				)				)				)
     A__							=     "What is the placebo?"
     A__							=     [
         {
             "image": load_image(lowercase_				),
             "question": question,
         },
         {
             "image": image,
             "question": question,
         },
         {
             "image": image,
             "question": question,
             "word_boxes": word_boxes,
         },
     ]
     return dqa_pipeline, examples
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :List[Any]		,			lowercase_ :List[str]				)->				List[str]:
     A__							=     dqa_pipeline(lowercase_		,			top_k=2				)
     self.assertEqual(
         lowercase_		,			[
             [
                 {"score": ANY(lowercase_				), "answer": ANY(lowercase_				), "start": ANY(lowercase_				), "end": ANY(lowercase_				)},
                 {"score": ANY(lowercase_				), "answer": ANY(lowercase_				), "start": ANY(lowercase_				), "end": ANY(lowercase_				)},
             ]
         ]
         * 3		,			)
 @require_torch
 @require_detectrona
 @require_pytesseract
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[Any]:
     A__							=     pipeline("document-question-answering"		,			model="hf-internal-testing/tiny-random-layoutlmv2"				)
     A__							=     INVOICE_URL
     A__							=     "How many cats are there?"

     A__							=     [
         {"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},
     ]
     A__							=     dqa_pipeline(image=lowercase_		,			question=lowercase_		,			top_k=2				)
     self.assertEqual(nested_simplify(lowercase_		,			decimals=4				)		,			lowercase_				)

     A__							=     dqa_pipeline({"image": image, "question": question}		,			top_k=2				)
     self.assertEqual(nested_simplify(lowercase_		,			decimals=4				)		,			lowercase_				)

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

     # We can optionnally pass directly the words and bounding boxes
     A__							=     "./tests/fixtures/tests_samples/COCO/000000039769.png"
     A__							=     []
     A__							=     []
     A__							=     dqa_pipeline(image=lowercase_		,			question=lowercase_		,			words=lowercase_		,			boxes=lowercase_		,			top_k=2				)
     self.assertEqual(lowercase_		,			[]				)
 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Tuple:
     A__							=     pipeline(
         "document-question-answering"		,			model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"		,			revision="9977165"		,			)
     A__							=     INVOICE_URL
     A__							=     "What is the invoice number?"

     A__							=     dqa_pipeline(image=lowercase_		,			question=lowercase_		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline({"image": image, "question": question}		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline(
         [{"image": image, "question": question}, {"image": image, "question": question}]		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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 	UpperCAmelCase_				(							self :Tuple				)->				Optional[Any]:
     A__							=     pipeline(
         "document-question-answering"		,			model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"		,			revision="9977165"		,			max_seq_len=50		,			)
     A__							=     INVOICE_URL
     A__							=     "What is the invoice number?"

     A__							=     dqa_pipeline(image=lowercase_		,			question=lowercase_		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline({"image": image, "question": question}		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline(
         [{"image": image, "question": question}, {"image": image, "question": question}]		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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 	UpperCAmelCase_				(							self :List[Any]				)->				Any:
     A__							=     AutoTokenizer.from_pretrained(
         "impira/layoutlm-document-qa"		,			revision="3dc6de3"		,			add_prefix_space=lowercase_				)
     A__							=     pipeline(
         "document-question-answering"		,			model="impira/layoutlm-document-qa"		,			tokenizer=lowercase_		,			revision="3dc6de3"		,			)
     A__							=     INVOICE_URL
     A__							=     "What is the invoice number?"

     A__							=     dqa_pipeline(image=lowercase_		,			question=lowercase_		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline({"image": image, "question": question}		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline(
         [{"image": image, "question": question}, {"image": image, "question": question}]		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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		,			)

     A__							=     list(zip(*apply_tesseract(load_image(lowercase_				)		,			lowercase_		,			""				)				)				)

     # This model should also work if `image` is set to None
     A__							=     dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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 	UpperCAmelCase_				(							self :Optional[int]				)->				Union[str, Any]:
     A__							=     AutoTokenizer.from_pretrained(
         "impira/layoutlm-document-qa"		,			revision="3dc6de3"		,			add_prefix_space=lowercase_				)
     A__							=     pipeline(
         "document-question-answering"		,			model="impira/layoutlm-document-qa"		,			tokenizer=lowercase_		,			revision="3dc6de3"		,			max_seq_len=50		,			)
     A__							=     INVOICE_URL
     A__							=     "What is the invoice number?"

     A__							=     dqa_pipeline(image=lowercase_		,			question=lowercase_		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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},
         ]		,			)

     A__							=     dqa_pipeline(
         [{"image": image, "question": question}, {"image": image, "question": question}]		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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		,			)

     A__							=     list(zip(*apply_tesseract(load_image(lowercase_				)		,			lowercase_		,			""				)				)				)

     # This model should also work if `image` is set to None
     A__							=     dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}		,			top_k=2				)
     self.assertEqual(
         nested_simplify(lowercase_		,			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 	UpperCAmelCase_				(							self :Optional[int]				)->				Tuple:
     A__							=     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"		,			)

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



 @require_tf
 @unittest.skip("Document question answering not implemented in TF"				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				List[str]:
     pass



