"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase__			=    {"""LayoutLMv2Config""", """LayoutLMv3Config"""}


@is_pipeline_test
class 							a					(		unittest.TestCase ):
    _snake_case  :	str  				=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    _snake_case  :	Union[str, Any]  				=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        _snake_case  :	int  				=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        _snake_case  :	Union[str, Any]  				=				{
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[str]       ):
      _UpperCAmelCase    			=   ZeroShotClassificationPipeline(
          model=SCREAMING_SNAKE_CASE__ ,					tokenizer=SCREAMING_SNAKE_CASE__ ,					candidate_labels=["""polics""", """health"""]       )
      return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Any       ):
      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels="""politics"""       )
      self.assertEqual(SCREAMING_SNAKE_CASE__ ,					{"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       )]}       )

      # No kwarg
      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					["""politics"""]       )
      self.assertEqual(SCREAMING_SNAKE_CASE__ ,					{"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       )]}       )

      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels=["""politics"""]       )
      self.assertEqual(SCREAMING_SNAKE_CASE__ ,					{"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       )]}       )

      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels="""politics, public health"""       )
      self.assertEqual(
          SCREAMING_SNAKE_CASE__ ,					{"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )]}       )
      self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       ) ,					1.0       )

      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health"""]       )
      self.assertEqual(
          SCREAMING_SNAKE_CASE__ ,					{"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )]}       )
      self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       ) ,					1.0       )

      _UpperCAmelCase    			=   classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels="""politics""" ,					hypothesis_template="""This text is about {}"""       )
      self.assertEqual(SCREAMING_SNAKE_CASE__ ,					{"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       )]}       )

      # https://github.com/huggingface/transformers/issues/13846
      _UpperCAmelCase    			=   classifier(["""I am happy"""] ,					["""positive""", """negative"""]       )
      self.assertEqual(
          SCREAMING_SNAKE_CASE__ ,					[
              {"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )]}
              for i in range(1       )
          ] ,					)
      _UpperCAmelCase    			=   classifier(["""I am happy""", """I am sad"""] ,					["""positive""", """negative"""]       )
      self.assertEqual(
          SCREAMING_SNAKE_CASE__ ,					[
              {"""sequence""": ANY(SCREAMING_SNAKE_CASE__       ), """labels""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )], """scores""": [ANY(SCREAMING_SNAKE_CASE__       ), ANY(SCREAMING_SNAKE_CASE__       )]}
              for i in range(2       )
          ] ,					)

      with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
        classifier("""""" ,					candidate_labels="""politics"""       )

      with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
        classifier(SCREAMING_SNAKE_CASE__ ,					candidate_labels="""politics"""       )

      with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
        classifier("""Who are you voting for in 2020?""" ,					candidate_labels=""""""       )

      with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
        classifier("""Who are you voting for in 2020?""" ,					candidate_labels=SCREAMING_SNAKE_CASE__       )

      with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
        classifier(
            """Who are you voting for in 2020?""" ,					candidate_labels="""politics""" ,					hypothesis_template="""Not formatting template""" ,					)

      with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
        classifier(
            """Who are you voting for in 2020?""" ,					candidate_labels="""politics""" ,					hypothesis_template=SCREAMING_SNAKE_CASE__ ,					)

      self.run_entailment_id(SCREAMING_SNAKE_CASE__       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   zero_shot_classifier.model.config
      _UpperCAmelCase    			=   config.labelaid
      _UpperCAmelCase    			=   zero_shot_classifier.entailment_id

      _UpperCAmelCase    			=   {"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
      self.assertEqual(zero_shot_classifier.entailment_id ,					-1       )

      _UpperCAmelCase    			=   {"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
      self.assertEqual(zero_shot_classifier.entailment_id ,					0       )

      _UpperCAmelCase    			=   {"""ENTAIL""": 0, """NON-ENTAIL""": 1}
      self.assertEqual(zero_shot_classifier.entailment_id ,					0       )

      _UpperCAmelCase    			=   {"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
      self.assertEqual(zero_shot_classifier.entailment_id ,					2       )

      _UpperCAmelCase    			=   original_labelaid
      self.assertEqual(SCREAMING_SNAKE_CASE__ ,					zero_shot_classifier.entailment_id       )


    @require_torch
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   pipeline(
          """zero-shot-classification""" ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,					framework="""pt""" ,					)
      # There was a regression in 4.10 for this
      # Adding a test so we don't make the mistake again.
      # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
      zero_shot_classifier(
          """Who are you voting for in 2020?""" * 100 ,					candidate_labels=["""politics""", """public health""", """science"""]       )


    @require_torch
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   pipeline(
          """zero-shot-classification""" ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,					framework="""pt""" ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""science""", """public health""", """politics"""],
              """scores""": [0.333, 0.333, 0.333],
          } ,					)


    @require_tf
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   pipeline(
          """zero-shot-classification""" ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,					framework="""tf""" ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""science""", """public health""", """politics"""],
              """scores""": [0.333, 0.333, 0.333],
          } ,					)


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   pipeline("""zero-shot-classification""" ,					model="""roberta-large-mnli""" ,					framework="""pt"""       )
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""politics""", """public health""", """science"""],
              """scores""": [0.976, 0.015, 0.009],
          } ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
          """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
          """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
          """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
          """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
          """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
          """ English-to-German translation task, improving over the existing best results, including ensembles by"""
          """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
          """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
          """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
          """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
          """ large and limited training data.""" ,					candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,					multi_label=SCREAMING_SNAKE_CASE__ ,					)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__       ) ,					{
              """sequence""": (
                  """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                  """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                  """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                  """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                  """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                  """ superior in quality while being more parallelizable and requiring significantly less time to"""
                  """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                  """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                  """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                  """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                  """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                  """ other tasks by applying it successfully to English constituency parsing both with large and"""
                  """ limited training data."""
              ),
              """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
              """scores""": [0.817, 0.713, 0.018, 0.018],
          } ,					)


    @slow
    @require_tf
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   pipeline("""zero-shot-classification""" ,					model="""roberta-large-mnli""" ,					framework="""tf"""       )
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""politics""", """public health""", """science"""],
              """scores""": [0.976, 0.015, 0.009],
          } ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
          """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
          """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
          """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
          """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
          """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
          """ English-to-German translation task, improving over the existing best results, including ensembles by"""
          """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
          """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
          """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
          """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
          """ large and limited training data.""" ,					candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,					multi_label=SCREAMING_SNAKE_CASE__ ,					)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__       ) ,					{
              """sequence""": (
                  """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                  """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                  """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                  """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                  """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                  """ superior in quality while being more parallelizable and requiring significantly less time to"""
                  """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                  """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                  """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                  """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                  """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                  """ other tasks by applying it successfully to English constituency parsing both with large and"""
                  """ limited training data."""
              ),
              """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
              """scores""": [0.817, 0.713, 0.018, 0.018],
          } ,					)





"""simple docstring"""







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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50265						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=224						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Any:
				super().__init__(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	hidden_size=SCREAMING_SNAKE_CASE__						,	num_hidden_layers=SCREAMING_SNAKE_CASE__						,	num_attention_heads=SCREAMING_SNAKE_CASE__						,	intermediate_size=SCREAMING_SNAKE_CASE__						,	hidden_act=SCREAMING_SNAKE_CASE__						,	hidden_dropout_prob=SCREAMING_SNAKE_CASE__						,	attention_probs_dropout_prob=SCREAMING_SNAKE_CASE__						,	max_position_embeddings=SCREAMING_SNAKE_CASE__						,	type_vocab_size=SCREAMING_SNAKE_CASE__						,	initializer_range=SCREAMING_SNAKE_CASE__						,	layer_norm_eps=SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)
				A__											=					max_ad_position_embeddings
				A__											=					coordinate_size
				A__											=					shape_size
				A__											=					has_relative_attention_bias
				A__											=					rel_pos_bins
				A__											=					max_rel_pos
				A__											=					has_spatial_attention_bias
				A__											=					rel_ad_pos_bins
				A__											=					max_rel_ad_pos
				A__											=					text_embed
				A__											=					visual_embed
				A__											=					input_size
				A__											=					num_channels
				A__											=					patch_size
				A__											=					classifier_dropout






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				# The order of inputs is different for question answering and sequence classification
				if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]		)
				else:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels"}),
					    ]		)


			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-5


			@property
			def   snake_case__	(      self		)      ->	int:
				return 12







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				setattr(processor.image_processor						,	"apply_ocr"						,	SCREAMING_SNAKE_CASE__		)

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				A__											=					compute_effective_axis_dimension(
				    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch						,	num_token_to_add=0		)
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				A__											=					processor.tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
				A__											=					compute_effective_axis_dimension(
				    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__		)
				# Generate dummy inputs according to compute batch and sequence
				A__											=					[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				A__											=					[[[48, 84, 73, 128]]] * batch_size

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




