





'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
  from ...feature_extraction_utils import FeatureExtractionMixin
  from ...tokenization_utils_base import PreTrainedTokenizerBase
  from ...utils import TensorType

__lowerCAmelCase				:      Any						      =logging.get_logger(__name__)

__lowerCAmelCase				:      Optional[Any]						      ={
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
__lowerCAmelCase				:      List[str]						      =[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
__lowerCAmelCase				:      List[str]						      =[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""whisper"""
 __lowercase  						=							["""past_key_values"""]
 __lowercase  						=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(							self :Optional[int]		,			lowercase_ :List[Any]=5_18_65		,			lowercase_ :Any=80		,			lowercase_ :Union[str, Any]=6		,			lowercase_ :Any=4		,			lowercase_ :List[str]=6		,			lowercase_ :Dict=4		,			lowercase_ :List[str]=15_36		,			lowercase_ :List[str]=15_36		,			lowercase_ :Any=0.0		,			lowercase_ :int=0.0		,			lowercase_ :int=5_02_57		,			lowercase_ :int=True		,			lowercase_ :Optional[Any]=True		,			lowercase_ :Dict="gelu"		,			lowercase_ :int=2_56		,			lowercase_ :Dict=0.0		,			lowercase_ :Dict=0.0		,			lowercase_ :Optional[int]=0.0		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :Tuple=False		,			lowercase_ :List[str]=15_00		,			lowercase_ :Dict=4_48		,			lowercase_ :List[Any]=5_02_56		,			lowercase_ :Tuple=5_02_56		,			lowercase_ :str=5_02_56		,			lowercase_ :Optional[Any]=None		,			lowercase_ :List[str]=[2_20, 5_02_56]		,			lowercase_ :Optional[Any]=False		,			lowercase_ :List[str]=2_56		,			lowercase_ :int=False		,			lowercase_ :Optional[Any]=0.0_5		,			lowercase_ :Dict=10		,			lowercase_ :str=2		,			lowercase_ :Any=0.0		,			lowercase_ :Dict=10		,			lowercase_ :List[Any]=0		,			lowercase_ :Any=7		,			**lowercase_ :Tuple		,			)->				Union[str, Any]:
     A__							=     vocab_size
     A__							=     num_mel_bins
     A__							=     d_model
     A__							=     encoder_layers
     A__							=     encoder_attention_heads
     A__							=     decoder_layers
     A__							=     decoder_attention_heads
     A__							=     decoder_ffn_dim
     A__							=     encoder_ffn_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation_dropout
     A__							=     activation_function
     A__							=     init_std
     A__							=     encoder_layerdrop
     A__							=     decoder_layerdrop
     A__							=     use_cache
     A__							=     encoder_layers
     A__							=     scale_embedding  # scale factor will be sqrt(d_model) if True
     A__							=     max_source_positions
     A__							=     max_target_positions

     # Audio Classification-specific parameters. Feel free to ignore for other classes.
     A__							=     classifier_proj_size
     A__							=     use_weighted_layer_sum

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     A__							=     apply_spec_augment
     A__							=     mask_time_prob
     A__							=     mask_time_length
     A__							=     mask_time_min_masks
     A__							=     mask_feature_prob
     A__							=     mask_feature_length
     A__							=     mask_feature_min_masks

     A__							=     median_filter_width

     super().__init__(
         pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			is_encoder_decoder=lowercase_		,			decoder_start_token_id=lowercase_		,			suppress_tokens=lowercase_		,			begin_suppress_tokens=lowercase_		,			**lowercase_		,			)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Mapping[str, Mapping[int, str]]:
     A__							=     OrderedDict(
         [
             ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
         ]				)
     if self.use_past:
         A__							=     {0: "batch"}
     else:
         A__							=     {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(lowercase_		,			direction="inputs"				)

     return common_inputs
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,			lowercase_ :int = -1		,			lowercase_ :int = -1		,			lowercase_ :bool = False		,			lowercase_ :Optional["TensorType"] = None		,			lowercase_ :int = 2_20_50		,			lowercase_ :float = 5.0		,			lowercase_ :int = 2_20		,			)->				Mapping[str, Any]:
     A__							=     OrderedDict()
     A__							=     OnnxConfig.generate_dummy_inputs(
         self		,			preprocessor=preprocessor.feature_extractor		,			batch_size=lowercase_		,			framework=lowercase_		,			sampling_rate=lowercase_		,			time_duration=lowercase_		,			frequency=lowercase_		,			)
     A__							=     encoder_inputs["input_features"].shape[2]
     A__							=     encoder_sequence_length // 2 if self.use_past else seq_length

     A__							=     super().generate_dummy_inputs(
         preprocessor.tokenizer		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				)

     A__							=     encoder_inputs.pop("input_features"				)
     A__							=     decoder_inputs.pop("decoder_input_ids"				)

     if "past_key_values" in decoder_inputs:
         A__							=     decoder_inputs.pop("past_key_values"				)

     return dummy_inputs



 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				float:
     return 1E-3










'''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



