

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 							__UpperCamelCase (			*a							: Any      ,      **a							: Optional[int]	)							->       Optional[Any]:

							"""simple docstring"""

							pass

			def  lowerCamelCase__ (					_a):
				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.
a_						     =    (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)






@is_pipeline_test
@require_torch
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Union[str, Any]      ,      a							: Any      ,      a							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       pipeline(
				    "document-question-answering"      ,      model=a      ,      tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the placebo?"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [
				    {
				        "image": load_image(a	),
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				        "word_boxes": word_boxes,
				    },
				]
				return dqa_pipeline, examples
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: List[Any]      ,      a							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [
				    {"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   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       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
				SCREAMING_SNAKE_CASE   :      Tuple				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)

				# We can optionnally pass directly the words and bounding boxes
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       []
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      words=a      ,      boxes=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Union[str, Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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[Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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   :      Any				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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   :      int				     =       "What is the invoice number?"

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

				SCREAMING_SNAKE_CASE   :      Tuple				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "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(a      ,      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 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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(a      ,      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   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# 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(a      ,      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 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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   :      int				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      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 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[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"      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the invoice number?"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				pass


import functools
import operator

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    '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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='wavlm'
			def __init__(			self							: Optional[int]      ,      a							: Optional[Any]=32      ,      a							: int=768      ,      a							: Tuple=12      ,      a							: List[str]=12      ,      a							: str=3072      ,      a							: Any="gelu"      ,      a							: Dict=0.1      ,      a							: int=0.1      ,      a							: str=0.1      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.1      ,      a							: Any=0.1      ,      a							: List[str]=0.02      ,      a							: List[Any]=1e-5      ,      a							: Any="group"      ,      a							: Optional[int]="gelu"      ,      a							: List[str]=(512, 512, 512, 512, 512, 512, 512)      ,      a							: Any=(5, 2, 2, 2, 2, 2, 2)      ,      a							: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)      ,      a							: Optional[Any]=False      ,      a							: Dict=128      ,      a							: Optional[Any]=16      ,      a							: Optional[Any]=320      ,      a							: str=800      ,      a							: Optional[int]=False      ,      a							: Tuple=True      ,      a							: Optional[Any]=0.05      ,      a							: Any=10      ,      a							: Optional[int]=2      ,      a							: Dict=0.0      ,      a							: str=10      ,      a							: Tuple=320      ,      a							: Optional[int]=2      ,      a							: int=0.1      ,      a							: List[str]=100      ,      a							: Tuple=256      ,      a							: str=256      ,      a							: Tuple=0.1      ,      a							: str="mean"      ,      a							: int=False      ,      a							: int=False      ,      a							: Optional[Any]=256      ,      a							: Any=(512, 512, 512, 512, 1500)      ,      a							: Tuple=(5, 3, 3, 1, 1)      ,      a							: str=(1, 2, 3, 1, 1)      ,      a							: Optional[Any]=512      ,      a							: Optional[Any]=80      ,      a							: Tuple=0      ,      a							: Any=1      ,      a							: Optional[Any]=2      ,      a							: int=False      ,      a							: Dict=3      ,      a							: Any=2      ,      a							: List[Any]=3      ,      a							: int=None      ,      **a							: Any      ,      )							->       Dict:

				"""simple docstring"""

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

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

				# ctc loss
				SCREAMING_SNAKE_CASE   :      Any				     =       ctc_loss_reduction
				SCREAMING_SNAKE_CASE   :      Dict				     =       ctc_zero_infinity

				# adapter
				SCREAMING_SNAKE_CASE   :      Any				     =       add_adapter
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       adapter_kernel_size
				SCREAMING_SNAKE_CASE   :      Any				     =       adapter_stride
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_adapter_layers
				SCREAMING_SNAKE_CASE   :      int				     =       output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_size

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






			@property
			def 							__UpperCamelCase (			self							: int	)							->       Union[str, Any]:

				"""simple docstring"""

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