
import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.generation import (
						    FlaxForcedBOSTokenLogitsProcessor,
						    FlaxForcedEOSTokenLogitsProcessor,
						    FlaxLogitsProcessorList,
						    FlaxMinLengthLogitsProcessor,
						    FlaxTemperatureLogitsWarper,
						    FlaxTopKLogitsWarper,
						    FlaxTopPLogitsWarper,
						)
@require_flax
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Tuple:
					lowercase_       =					jnp.ones((batch_size, length)       ) / length
					return scores
	def 						_lowercase  (       self  :			Tuple       )       ->     Optional[int]:
					lowercase_       =					None
					lowercase_       =					2_0

					lowercase_       =					self._get_uniform_logits(batch_size=2       ,							length=SCREAMING_SNAKE_CASE_       )

					# tweak scores to not be uniform anymore
					lowercase_       =					scores.at[1, 5].set((1 / length) + 0.1       )  # peak, 1st batch
					lowercase_       =					scores.at[1, 1_0].set((1 / length) - 0.4       )  # valley, 1st batch

					# compute softmax
					lowercase_       =					jax.nn.softmax(SCREAMING_SNAKE_CASE_       ,							axis=-1       )

					lowercase_       =					FlaxTemperatureLogitsWarper(temperature=0.5       )
					lowercase_       =					FlaxTemperatureLogitsWarper(temperature=1.3       )

					lowercase_       =					jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE_       ,							scores.copy()       ,							cur_len=SCREAMING_SNAKE_CASE_       )       ,							axis=-1       )
					lowercase_       =					jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE_       ,							scores.copy()       ,							cur_len=SCREAMING_SNAKE_CASE_       )       ,							axis=-1       )

					# uniform distribution stays uniform
					self.assertTrue(jnp.allclose(probs[0, :]       ,							warped_prob_sharp[0, :]       ,							atol=1e-3       )       )
					self.assertTrue(jnp.allclose(probs[0, :]       ,							warped_prob_smooth[0, :]       ,							atol=1e-3       )       )

					# sharp peaks get higher, valleys get lower
					self.assertLess(probs[1, :].max()       ,							warped_prob_sharp[1, :].max()       )
					self.assertGreater(probs[1, :].min()       ,							warped_prob_sharp[1, :].min()       )

					# smooth peaks get lower, valleys get higher
					self.assertGreater(probs[1, :].max()       ,							warped_prob_smooth[1, :].max()       )
					self.assertLess(probs[1, :].min()       ,							warped_prob_smooth[1, :].min()       )
	def 						_lowercase  (       self  :			str       )       ->     Union[str, Any]:
					lowercase_       =					None
					lowercase_       =					1_0
					lowercase_       =					2

					# create ramp distribution
					lowercase_       =					np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_       )[None, :]       ,							(batch_size, vocab_size)       ).copy()
					lowercase_       =					ramp_logits[1:, : vocab_size // 2] + vocab_size

					lowercase_       =					FlaxTopKLogitsWarper(3       )

					lowercase_       =					top_k_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )

					# check that correct tokens are filtered
					self.assertListEqual(jnp.isinf(scores[0]       ).tolist()       ,							7 * [True] + 3 * [False]       )
					self.assertListEqual(jnp.isinf(scores[1]       ).tolist()       ,							2 * [True] + 3 * [False] + 5 * [True]       )

					# check special case
					lowercase_       =					5
					lowercase_       =					FlaxTopKLogitsWarper(top_k=1       ,							filter_value=0.0       ,							min_tokens_to_keep=3       )

					lowercase_       =					np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_       )[None, :]       ,							(batch_size, length)       ).copy()
					lowercase_       =					top_k_warp_safety_check(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )

					# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
					self.assertListEqual((scores == 0.0).sum(axis=-1       ).tolist()       ,							[2, 2]       )
	def 						_lowercase  (       self  :			Dict       )       ->     List[str]:
					lowercase_       =					None
					lowercase_       =					1_0
					lowercase_       =					2

					# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
					lowercase_       =					np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]       )       )

					lowercase_       =					FlaxTopPLogitsWarper(0.8       )
					lowercase_       =					np.exp(top_p_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )       )

					# dist should be filtered to keep min num values so that sum is >= top_p
					# exp (-inf) => 0
					lowercase_       =					np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]       )
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							atol=1e-3       )       )

					# check edge cases with negative and extreme logits
					lowercase_       =					np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_       )[None, :]       ,							(batch_size, vocab_size)       ).copy() - (
					    vocab_size // 2
					)

					# make ramp_logits more extreme
					lowercase_       =					ramp_logits[1] * 1_00.0

					# make sure at least 2 tokens are kept
					lowercase_       =					FlaxTopPLogitsWarper(0.9       ,							min_tokens_to_keep=2       ,							filter_value=0.0       )
					lowercase_       =					top_p_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )

					# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
					self.assertListEqual((filtered_dist != 0.0).sum(axis=-1       ).tolist()       ,							[3, 2]       )
	def 						_lowercase  (       self  :			Any       )       ->     List[str]:
					lowercase_       =					2_0
					lowercase_       =					4
					lowercase_       =					0

					lowercase_       =					FlaxMinLengthLogitsProcessor(min_length=1_0       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )

					# check that min length is applied at length 5
					lowercase_       =					ids_tensor((batch_size, 2_0)       ,							vocab_size=2_0       )
					lowercase_       =					5
					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					min_dist_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()       ,							4 * [-float('''inf'''       )]       )

					# check that min length is not applied anymore at length 15
					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					1_5
					lowercase_       =					min_dist_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_       ).any()       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     int:
					lowercase_       =					2_0
					lowercase_       =					4
					lowercase_       =					0

					lowercase_       =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_       )

					# check that all scores are -inf except the bos_token_id score
					lowercase_       =					ids_tensor((batch_size, 1)       ,							vocab_size=2_0       )
					lowercase_       =					1
					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					logits_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]       ).all()       )
					self.assertListEqual(scores[:, bos_token_id].tolist()       ,							4 * [0]       )  # score for bos_token_id shold be zero

					# check that bos_token_id is not forced if current length is greater than 1
					lowercase_       =					3
					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					logits_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_       ).any()       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[Any]:
					lowercase_       =					2_0
					lowercase_       =					4
					lowercase_       =					0
					lowercase_       =					5

					lowercase_       =					FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )

					# check that all scores are -inf except the eos_token_id when max_length is reached
					lowercase_       =					ids_tensor((batch_size, 4)       ,							vocab_size=2_0       )
					lowercase_       =					4
					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					logits_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]       ).all()       )
					self.assertListEqual(scores[:, eos_token_id].tolist()       ,							4 * [0]       )  # score for eos_token_id should be zero

					# check that eos_token_id is not forced if max_length is not reached
					lowercase_       =					3
					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					logits_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_       ).any()       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[str]:
					lowercase_       =					4
					lowercase_       =					1_0
					lowercase_       =					1_5
					lowercase_       =					2
					lowercase_       =					1
					lowercase_       =					1_5

					# dummy input_ids and scores
					lowercase_       =					ids_tensor((batch_size, sequence_length)       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					input_ids.copy()

					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					scores.copy()

					# instantiate all dist processors
					lowercase_       =					FlaxTemperatureLogitsWarper(temperature=0.5       )
					lowercase_       =					FlaxTopKLogitsWarper(3       )
					lowercase_       =					FlaxTopPLogitsWarper(0.8       )

					# instantiate all logits processors
					lowercase_       =					FlaxMinLengthLogitsProcessor(min_length=1_0       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					1_0

					# no processor list
					lowercase_       =					temp_dist_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					top_k_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					top_p_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					min_dist_proc(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					bos_dist_proc(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					eos_dist_proc(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )

					# with processor list
					lowercase_       =					FlaxLogitsProcessorList(
					    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
					lowercase_       =					processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )

					# scores should be equal
					self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							atol=1e-3       )       )

					# input_ids should never be changed
					self.assertListEqual(input_ids.tolist()       ,							input_ids_comp.tolist()       )




	def 						_lowercase  (       self  :			List[str]       )       ->     int:
					lowercase_       =					4
					lowercase_       =					1_0
					lowercase_       =					1_5
					lowercase_       =					2
					lowercase_       =					1
					lowercase_       =					1_5

					# dummy input_ids and scores
					lowercase_       =					ids_tensor((batch_size, sequence_length)       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					input_ids.copy()

					lowercase_       =					self._get_uniform_logits(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					scores.copy()

					# instantiate all dist processors
					lowercase_       =					FlaxTemperatureLogitsWarper(temperature=0.5       )
					lowercase_       =					FlaxTopKLogitsWarper(3       )
					lowercase_       =					FlaxTopPLogitsWarper(0.8       )

					# instantiate all logits processors
					lowercase_       =					FlaxMinLengthLogitsProcessor(min_length=1_0       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					1_0

					# no processor list
					def run_no_processor_list(SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ):
									lowercase_       =					temp_dist_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									lowercase_       =					top_k_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									lowercase_       =					top_p_warp(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									lowercase_       =					min_dist_proc(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									lowercase_       =					bos_dist_proc(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									lowercase_       =					eos_dist_proc(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									return scores

					# with processor list
					def run_processor_list(SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ):
									lowercase_       =					FlaxLogitsProcessorList(
									    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
									lowercase_       =					processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							cur_len=SCREAMING_SNAKE_CASE_       )
									return scores

					lowercase_       =					jax.jit(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					jax.jit(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					jitted_run_no_processor_list(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					jitted_run_processor_list(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					# scores should be equal
					self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							atol=1e-3       )       )

					# input_ids should never be changed
					self.assertListEqual(input_ids.tolist()       ,							input_ids_comp.tolist()       )








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        _lowercase      :






										'''simple docstring'''



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




						def        _UpperCamelCase							(      lowercase__      ):
												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	: str  				=(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Tuple						=		MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


				@require_pytesseract
				@require_vision
				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Any	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		pipeline(
										    '''document-question-answering'''			,			model=lowerCAmelCase__			,			tokenizer=lowerCAmelCase__			,			image_processor=lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:Tuple	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Any					=		dqa_pipeline(lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(
										    lowerCAmelCase__			,			[
										        [
										            {'''score''': ANY(lowerCAmelCase__	), '''answer''': ANY(lowerCAmelCase__	), '''start''': ANY(lowerCAmelCase__	), '''end''': ANY(lowerCAmelCase__	)},
										            {'''score''': ANY(lowerCAmelCase__	), '''answer''': ANY(lowerCAmelCase__	), '''start''': ANY(lowerCAmelCase__	), '''end''': ANY(lowerCAmelCase__	)},
										        ]
										    ]
										    * 3			,			)


				@require_torch
				@require_detectrona
				@require_pytesseract
				def 		__magic_name__(       self			:Dict	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		pipeline('''document-question-answering'''			,			model='''hf-internal-testing/tiny-random-layoutlmv2'''	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		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						:     Tuple					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		dqa_pipeline({'''image''': image, '''question''': question}			,			top_k=2	)
										self.assertEqual(nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			lowerCAmelCase__	)

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

										# We can optionnally pass directly the words and bounding boxes
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[]
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[]
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			words=lowerCAmelCase__			,			boxes=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(lowerCAmelCase__			,			[]	)


				@slow
				@require_torch
				@require_detectrona
				@require_pytesseract
				def 		__magic_name__(       self			:int	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		pipeline(
										    '''document-question-answering'''			,			model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''			,			revision='''9977165'''			,			)
										__SCREAMING_SNAKE_CASE						:     Dict					=		INVOICE_URL
										__SCREAMING_SNAKE_CASE						:     Any					=		'''What is the invoice number?'''

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

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

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

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

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

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			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						:     Optional[int]					=		dqa_pipeline(
										    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			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						:     List[str]					=		list(zip(*apply_tesseract(load_image(lowerCAmelCase__	)			,			lowerCAmelCase__			,			''''''	)	)	)

										# 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(lowerCAmelCase__			,			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			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     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						:     Union[str, Any]					=		INVOICE_URL
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''What is the invoice number?'''
										__SCREAMING_SNAKE_CASE						:     Tuple					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[{'''answer''': '''us-001'''}]	)



				@require_tf
				@unittest.skip('''Document question answering not implemented in TF'''	)
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										pass
