
"""simple docstring"""




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__   :					Tuple												=				logging.get_logger(__name__)

UpperCAmelCase__   :					Optional[Any]												=				{
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[Any]       =     '''wavlm'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=30_72     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1E-5     , SCREAMING_SNAKE_CASE__="group"     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     , SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)     , SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1_28     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=3_20     , SCREAMING_SNAKE_CASE__=8_00     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=0.05     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=3_20     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__="mean"     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=(5_12, 5_12, 5_12, 5_12, 15_00)     , SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1)     , SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1)     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=80     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=None     , **SCREAMING_SNAKE_CASE__     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__     , pad_token_id=SCREAMING_SNAKE_CASE__     , bos_token_id=SCREAMING_SNAKE_CASE__     , eos_token_id=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     feat_extract_norm
											SCREAMING_SNAKE_CASE__		: int     =     feat_extract_activation
											SCREAMING_SNAKE_CASE__		: List[Any]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     conv_bias
											SCREAMING_SNAKE_CASE__		: Any     =     num_buckets
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     max_bucket_distance
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_conv_pos_embeddings
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_conv_pos_embedding_groups
											SCREAMING_SNAKE_CASE__		: Any     =     len(self.conv_dim					)
											SCREAMING_SNAKE_CASE__		: Any     =     num_hidden_layers
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     intermediate_size
											SCREAMING_SNAKE_CASE__		: Any     =     hidden_act
											SCREAMING_SNAKE_CASE__		: Dict     =     num_attention_heads
											SCREAMING_SNAKE_CASE__		: Tuple     =     hidden_dropout
											SCREAMING_SNAKE_CASE__		: List[str]     =     attention_dropout
											SCREAMING_SNAKE_CASE__		: List[Any]     =     activation_dropout
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     feat_proj_dropout
											SCREAMING_SNAKE_CASE__		: List[str]     =     final_dropout
											SCREAMING_SNAKE_CASE__		: Any     =     layerdrop
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     layer_norm_eps
											SCREAMING_SNAKE_CASE__		: str     =     initializer_range
											SCREAMING_SNAKE_CASE__		: List[str]     =     num_ctc_classes
											SCREAMING_SNAKE_CASE__		: Tuple     =     vocab_size
											SCREAMING_SNAKE_CASE__		: int     =     do_stable_layer_norm
											SCREAMING_SNAKE_CASE__		: List[str]     =     use_weighted_layer_sum
											SCREAMING_SNAKE_CASE__		: Dict     =     classifier_proj_size

											if (
											    (len(self.conv_stride					) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel					) != self.num_feat_extract_layers)
											    or (len(self.conv_dim					) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																		    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																		    F''' {len(self.conv_dim					)}`, `len(config.conv_stride) = {len(self.conv_stride					)}`,'''
																		    F''' `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'''					)

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     apply_spec_augment
											SCREAMING_SNAKE_CASE__		: Any     =     mask_time_prob
											SCREAMING_SNAKE_CASE__		: str     =     mask_time_length
											SCREAMING_SNAKE_CASE__		: Dict     =     mask_time_min_masks
											SCREAMING_SNAKE_CASE__		: List[str]     =     mask_feature_prob
											SCREAMING_SNAKE_CASE__		: Tuple     =     mask_feature_length

											# parameters for pretraining with codevector quantized representations
											SCREAMING_SNAKE_CASE__		: int     =     num_codevectors_per_group
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_codevector_groups
											SCREAMING_SNAKE_CASE__		: List[str]     =     contrastive_logits_temperature
											SCREAMING_SNAKE_CASE__		: List[str]     =     num_negatives
											SCREAMING_SNAKE_CASE__		: Tuple     =     codevector_dim
											SCREAMING_SNAKE_CASE__		: Any     =     proj_codevector_dim
											SCREAMING_SNAKE_CASE__		: str     =     diversity_loss_weight

											# ctc loss
											SCREAMING_SNAKE_CASE__		: int     =     ctc_loss_reduction
											SCREAMING_SNAKE_CASE__		: Dict     =     ctc_zero_infinity

											# adapter
											SCREAMING_SNAKE_CASE__		: str     =     add_adapter
											SCREAMING_SNAKE_CASE__		: Dict     =     adapter_kernel_size
											SCREAMING_SNAKE_CASE__		: Dict     =     adapter_stride
											SCREAMING_SNAKE_CASE__		: str     =     num_adapter_layers
											SCREAMING_SNAKE_CASE__		: Any     =     output_hidden_size or hidden_size

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											SCREAMING_SNAKE_CASE__		: str     =     classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     xvector_output_dim



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return functools.reduce(operator.mul     , self.conv_stride     , 1					)






"""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 			lowerCAmelCase_		:


									"""simple docstring"""



									@staticmethod
									def 			__magic_name__					(*SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



																"""simple docstring"""




																pass






					def lowercase_						(     _snake_case		):
												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.
UpperCAmelCase__   :					str												=				(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)



@is_pipeline_test
@require_torch
@require_vision
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING



				@require_pytesseract
				@require_vision
				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     pipeline(
											    """document-question-answering"""     , model=SCREAMING_SNAKE_CASE__     , tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)

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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     dqa_pipeline(SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(
											    SCREAMING_SNAKE_CASE__     , [
											        [
											            {"""score""": ANY(SCREAMING_SNAKE_CASE__					), """answer""": ANY(SCREAMING_SNAKE_CASE__					), """start""": ANY(SCREAMING_SNAKE_CASE__					), """end""": ANY(SCREAMING_SNAKE_CASE__					)},
											            {"""score""": ANY(SCREAMING_SNAKE_CASE__					), """answer""": ANY(SCREAMING_SNAKE_CASE__					), """start""": ANY(SCREAMING_SNAKE_CASE__					), """end""": ANY(SCREAMING_SNAKE_CASE__					)},
											        ]
											    ]
											    * 3     , )



				@require_torch
				@require_detectrona
				@require_pytesseract
				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     pipeline("""document-question-answering"""     , model="""hf-internal-testing/tiny-random-layoutlmv2"""					)
											SCREAMING_SNAKE_CASE__		: str     =     INVOICE_URL
											SCREAMING_SNAKE_CASE__		: Any     =     """How many cats are there?"""

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [
											    {"""score""": 0.0001, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
											    {"""score""": 0.0001, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
											]
											SCREAMING_SNAKE_CASE__		: Any     =     dqa_pipeline(image=SCREAMING_SNAKE_CASE__     , question=SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , SCREAMING_SNAKE_CASE__					)

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

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

											# We can optionnally pass directly the words and bounding boxes
											SCREAMING_SNAKE_CASE__		: Tuple     =     """./tests/fixtures/tests_samples/COCO/000000039769.png"""
											SCREAMING_SNAKE_CASE__		: Dict     =     []
											SCREAMING_SNAKE_CASE__		: Any     =     []
											SCREAMING_SNAKE_CASE__		: Dict     =     dqa_pipeline(image=SCREAMING_SNAKE_CASE__     , question=SCREAMING_SNAKE_CASE__     , words=SCREAMING_SNAKE_CASE__     , boxes=SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , []					)



				@slow
				@require_torch
				@require_detectrona
				@require_pytesseract
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     pipeline(
											    """document-question-answering"""     , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""     , revision="""9977165"""     , )
											SCREAMING_SNAKE_CASE__		: str     =     INVOICE_URL
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """What is the invoice number?"""

											SCREAMING_SNAKE_CASE__		: List[str]     =     dqa_pipeline(image=SCREAMING_SNAKE_CASE__     , question=SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: Tuple     =     dqa_pipeline({"""image""": image, """question""": question}     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     dqa_pipeline(
											    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        [
											            {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
											            {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
											        ],
											    ]
											    * 2     , )



				@slow
				@require_torch
				@require_detectrona
				@require_pytesseract
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     pipeline(
											    """document-question-answering"""     , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""     , revision="""9977165"""     , max_seq_len=50     , )
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     INVOICE_URL
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """What is the invoice number?"""

											SCREAMING_SNAKE_CASE__		: int     =     dqa_pipeline(image=SCREAMING_SNAKE_CASE__     , question=SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
											        {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: str     =     dqa_pipeline({"""image""": image, """question""": question}     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
											        {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: List[str]     =     dqa_pipeline(
											    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        [
											            {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
											            {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
											        ]
											    ]
											    * 2     , )



				@slow
				@require_torch
				@require_pytesseract
				@require_vision
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: int     =     dqa_pipeline(image=SCREAMING_SNAKE_CASE__     , question=SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: str     =     dqa_pipeline({"""image""": image, """question""": question}     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: Dict     =     dqa_pipeline(
											    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        [
											            {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
											            {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
											        ]
											    ]
											    * 2     , )

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__     , """"""					)					)					)

											# This model should also work if `image` is set to None
											SCREAMING_SNAKE_CASE__		: Dict     =     dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
											    ]     , )



				@slow
				@require_torch
				@require_pytesseract
				@require_vision
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     AutoTokenizer.from_pretrained(
											    """impira/layoutlm-document-qa"""     , revision="""3dc6de3"""     , add_prefix_space=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     pipeline(
											    """document-question-answering"""     , model="""impira/layoutlm-document-qa"""     , tokenizer=SCREAMING_SNAKE_CASE__     , revision="""3dc6de3"""     , max_seq_len=50     , )
											SCREAMING_SNAKE_CASE__		: str     =     INVOICE_URL
											SCREAMING_SNAKE_CASE__		: Dict     =     """What is the invoice number?"""

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     dqa_pipeline(image=SCREAMING_SNAKE_CASE__     , question=SCREAMING_SNAKE_CASE__     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
											    ]     , )

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dqa_pipeline(
											    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        [
											            {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
											            {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
											        ]
											    ]
											    * 2     , )

											SCREAMING_SNAKE_CASE__		: Any     =     list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__     , """"""					)					)					)

											# This model should also work if `image` is set to None
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}     , top_k=2					)
											self.assertEqual(
											    nested_simplify(SCREAMING_SNAKE_CASE__     , decimals=4					)     , [
											        {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
											        {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
											    ]     , )



				@slow
				@require_torch
				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     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"""     , )

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



				@require_tf
				@unittest.skip("""Document question answering not implemented in TF"""					)
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											pass




