






import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			a_						     =    'platform'
			import jax
			import jax.numpy as jnp
			import numpy as np

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =PegasusConfig
			lowerCamelCase__           ={}
			lowerCamelCase__           ='gelu'
			def __init__(			self							: List[Any]      ,      a							: Dict      ,      a							: Optional[int]=13      ,      a							: List[str]=7      ,      a							: Union[str, Any]=True      ,      a							: str=False      ,      a							: Tuple=99      ,      a							: Tuple=32      ,      a							: Dict=5      ,      a							: Optional[Any]=4      ,      a							: List[str]=37      ,      a							: Optional[int]=0.1      ,      a							: List[Any]=0.1      ,      a							: Dict=20      ,      a							: List[Any]=2      ,      a							: Tuple=1      ,      a							: Union[str, Any]=0      ,      )							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       parent
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       seq_length
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       is_training
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_labels
				SCREAMING_SNAKE_CASE   :      Any				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       intermediate_size

				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      Dict				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      str				     =       eos_token_id
				SCREAMING_SNAKE_CASE   :      Dict				     =       pad_token_id
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       bos_token_id
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ids_tensor([self.batch_size, self.seq_length - 1]      ,      self.vocab_size	).clip(3      ,      self.vocab_size	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)      ,      1	)
				SCREAMING_SNAKE_CASE   :      str				     =       np.concatenate([input_ids, eos_tensor]      ,      axis=1	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.config_cls(
				    vocab_size=self.vocab_size      ,      d_model=self.hidden_size      ,      encoder_layers=self.num_hidden_layers      ,      decoder_layers=self.num_hidden_layers      ,      encoder_attention_heads=self.num_attention_heads      ,      decoder_attention_heads=self.num_attention_heads      ,      encoder_ffn_dim=self.intermediate_size      ,      decoder_ffn_dim=self.intermediate_size      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      eos_token_ids=[2]      ,      bos_token_id=self.bos_token_id      ,      pad_token_id=self.pad_token_id      ,      decoder_start_token_id=self.pad_token_id      ,      **self.config_updates      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       prepare_pegasus_inputs_dict(a      ,      a      ,      a	)
				return config, inputs_dict
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       20
				SCREAMING_SNAKE_CASE   :      str				     =       model_class_name(a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model.encode(inputs_dict["input_ids"]	)

				SCREAMING_SNAKE_CASE   :      int				     =       (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       model.init_cache(decoder_input_ids.shape[0]      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,      dtype="i4"	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]      ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       model.decode(
				    decoder_input_ids[:, :-1]      ,      a      ,      decoder_attention_mask=a      ,      past_key_values=a      ,      decoder_position_ids=a      ,      )

				SCREAMING_SNAKE_CASE   :      str				     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,      dtype="i4"	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model.decode(
				    decoder_input_ids[:, -1:]      ,      a      ,      decoder_attention_mask=a      ,      past_key_values=outputs_cache.past_key_values      ,      decoder_position_ids=a      ,      )

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model.decode(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
				self.parent.assertTrue(diff < 1e-3      ,      msg=F"Max diff is {diff}"	)






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: Optional[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       20
				SCREAMING_SNAKE_CASE   :      Dict				     =       model_class_name(a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model.encode(inputs_dict["input_ids"]	)

				SCREAMING_SNAKE_CASE   :      str				     =       (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				SCREAMING_SNAKE_CASE   :      str				     =       jnp.concatenate(
				    [
				        decoder_attention_mask,
				        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
				    ]      ,      axis=-1      ,      )

				SCREAMING_SNAKE_CASE   :      Any				     =       model.init_cache(decoder_input_ids.shape[0]      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]      ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model.decode(
				    decoder_input_ids[:, :-1]      ,      a      ,      decoder_attention_mask=a      ,      past_key_values=a      ,      decoder_position_ids=a      ,      )
				SCREAMING_SNAKE_CASE   :      List[str]				     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,      dtype="i4"	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       model.decode(
				    decoder_input_ids[:, -1:]      ,      a      ,      past_key_values=outputs_cache.past_key_values      ,      decoder_attention_mask=a      ,      decoder_position_ids=a      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model.decode(a      ,      a      ,      decoder_attention_mask=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
				self.parent.assertTrue(diff < 1e-3      ,      msg=F"Max diff is {diff}"	)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a=None							,			_a=None							,			):
	if attention_mask is None:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       np.not_equal(_a							,			config.pad_token_id).astype(np.inta)
	if decoder_attention_mask is None:
		SCREAMING_SNAKE_CASE   :      str				     =       np.concatenate(
		    [
		        np.ones(decoder_input_ids[:, :1].shape							,			dtype=np.inta),
		        np.not_equal(decoder_input_ids[:, 1:]							,			config.pad_token_id).astype(np.inta),
		    ]							,			axis=-1							,			)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	}






@require_flax
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        FlaxPegasusForConditionalGeneration,
			        FlaxPegasusModel,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase__           =(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
			lowerCamelCase__           =True
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       FlaxPegasusModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Any				     =       ConfigTester(self      ,      config_class=a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase (			self							: List[str]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Any	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self._prepare_for_class(a      ,      a	)
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model_class(a	)

						@jax.jit
						def encode_jitted(a							: str      ,      a							: Optional[Any]=None      ,      **a							: str	):
							return model.encode(input_ids=a      ,      attention_mask=a	)

						with self.subTest("JIT Enabled"	):
							SCREAMING_SNAKE_CASE   :      Any				     =       encode_jitted(**a	).to_tuple()

						with self.subTest("JIT Disabled"	):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE   :      Dict				     =       encode_jitted(**a	).to_tuple()

						self.assertEqual(len(a	)      ,      len(a	)	)
						for jitted_output, output in zip(a      ,      a	):
							self.assertEqual(jitted_output.shape      ,      output.shape	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_class(a	)
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model.encode(inputs_dict["input_ids"]      ,      inputs_dict["attention_mask"]	)

						SCREAMING_SNAKE_CASE   :      List[Any]				     =       {
						    "decoder_input_ids": inputs_dict["decoder_input_ids"],
						    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
						    "encoder_outputs": encoder_outputs,
						}

						@jax.jit
						def decode_jitted(a							: List[str]      ,      a							: int      ,      a							: int	):
							return model.decode(
							    decoder_input_ids=a      ,      decoder_attention_mask=a      ,      encoder_outputs=a      ,      )

						with self.subTest("JIT Enabled"	):
							SCREAMING_SNAKE_CASE   :      str				     =       decode_jitted(**a	).to_tuple()

						with self.subTest("JIT Disabled"	):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE   :      Dict				     =       decode_jitted(**a	).to_tuple()

						self.assertEqual(len(a	)      ,      len(a	)	)
						for jitted_output, output in zip(a      ,      a	):
							self.assertEqual(jitted_output.shape      ,      output.shape	)
			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       int:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class_name.from_pretrained("google/pegasus-large"      ,      from_pt=a	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       np.ones((1, 1)	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       model(a	)
					self.assertIsNotNone(a	)






			@slow
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       PegasusTokenizer.from_pretrained("google/pegasus-xsum"	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       [
				    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
				    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
				]

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
				    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
				]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer(a      ,      return_tensors="np"      ,      truncation=a      ,      max_length=512      ,      padding=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       model.generate(**a      ,      num_beams=2	).sequences
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer.batch_decode(a      ,      skip_special_tokens=a	)
				assert tgt_text == decoded


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