





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							:			int        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Optional[int]        =							{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}

class   lowerCAmelCase				(   _a     ):
 UpperCAmelCase__						=						"""layoutlmv3"""
 def __init__( self    : Any     ,	UpperCAmelCase    : Dict=50265     ,	UpperCAmelCase    : Union[str, Any]=768     ,	UpperCAmelCase    : Union[str, Any]=12     ,	UpperCAmelCase    : Optional[int]=12     ,	UpperCAmelCase    : Union[str, Any]=3072     ,	UpperCAmelCase    : List[str]="gelu"     ,	UpperCAmelCase    : List[Any]=0.1     ,	UpperCAmelCase    : Union[str, Any]=0.1     ,	UpperCAmelCase    : Optional[int]=512     ,	UpperCAmelCase    : List[str]=2     ,	UpperCAmelCase    : List[str]=0.0_2     ,	UpperCAmelCase    : List[Any]=1e-5     ,	UpperCAmelCase    : Any=1     ,	UpperCAmelCase    : Any=0     ,	UpperCAmelCase    : int=2     ,	UpperCAmelCase    : Optional[Any]=1024     ,	UpperCAmelCase    : int=128     ,	UpperCAmelCase    : Union[str, Any]=128     ,	UpperCAmelCase    : int=True     ,	UpperCAmelCase    : Tuple=32     ,	UpperCAmelCase    : str=128     ,	UpperCAmelCase    : Optional[int]=64     ,	UpperCAmelCase    : Optional[Any]=256     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : str=True     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : int=224     ,	UpperCAmelCase    : Any=3     ,	UpperCAmelCase    : int=16     ,	UpperCAmelCase    : Tuple=None     ,	**UpperCAmelCase    : Optional[Any]     ,	)     ->      str:
     super().__init__(
         vocab_size=snake_case_     ,	hidden_size=snake_case_     ,	num_hidden_layers=snake_case_     ,	num_attention_heads=snake_case_     ,	intermediate_size=snake_case_     ,	hidden_act=snake_case_     ,	hidden_dropout_prob=snake_case_     ,	attention_probs_dropout_prob=snake_case_     ,	max_position_embeddings=snake_case_     ,	type_vocab_size=snake_case_     ,	initializer_range=snake_case_     ,	layer_norm_eps=snake_case_     ,	pad_token_id=snake_case_     ,	bos_token_id=snake_case_     ,	eos_token_id=snake_case_     ,	**snake_case_     ,	)
     lowerCamelCase__       : List[Any]   					=						max_ad_position_embeddings
     lowerCamelCase__       : Optional[int]   					=						coordinate_size
     lowerCamelCase__       : int   					=						shape_size
     lowerCamelCase__       : Optional[int]   					=						has_relative_attention_bias
     lowerCamelCase__       : Dict   					=						rel_pos_bins
     lowerCamelCase__       : List[Any]   					=						max_rel_pos
     lowerCamelCase__       : str   					=						has_spatial_attention_bias
     lowerCamelCase__       : Optional[Any]   					=						rel_ad_pos_bins
     lowerCamelCase__       : Optional[int]   					=						max_rel_ad_pos
     lowerCamelCase__       : Union[str, Any]   					=						text_embed
     lowerCamelCase__       : List[Any]   					=						visual_embed
     lowerCamelCase__       : Union[str, Any]   					=						input_size
     lowerCamelCase__       : int   					=						num_channels
     lowerCamelCase__       : Optional[Any]   					=						patch_size
     lowerCamelCase__       : Dict   					=						classifier_dropout


class   lowerCAmelCase				(   _a     ):
 UpperCAmelCase__						=						version.parse("""1.12"""     )
 @property
 def 	A_      ( self    : Tuple      )     ->      Tuple:
     # 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 	A_      ( self    : str      )     ->      Optional[int]:
     return 1e-5
 @property
 def 	A_      ( self    : List[Any]      )     ->      Optional[int]:
     return 12







 def 	A_      ( self    : str     ,	UpperCAmelCase    : "ProcessorMixin"     ,	UpperCAmelCase    : int = -1     ,	UpperCAmelCase    : int = -1     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : Optional["TensorType"] = None     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 40     ,	UpperCAmelCase    : int = 40     ,	)     ->      Tuple:
     setattr(processor.image_processor     ,	'apply_ocr'     ,	snake_case_      )

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     lowerCamelCase__       : Optional[Any]   					=						compute_effective_axis_dimension(
         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
     lowerCamelCase__       : List[Any]   					=						processor.tokenizer.num_special_tokens_to_add(snake_case_      )
     lowerCamelCase__       : Tuple   					=						compute_effective_axis_dimension(
         snake_case_     ,	fixed_dimension=OnnxConfig.default_fixed_sequence     ,	num_token_to_add=snake_case_      )
     # Generate dummy inputs according to compute batch and sequence
     lowerCamelCase__       : int   					=						[[""" """.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

     # Generate dummy bounding boxes
     lowerCamelCase__       : int   					=						[[[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)
     lowerCamelCase__       : Optional[int]   					=						self._generate_dummy_images(snake_case_     ,	snake_case_     ,	snake_case_     ,	snake_case_      )

     lowerCamelCase__       : Optional[int]   					=						dict(
         processor(
             snake_case_     ,	text=snake_case_     ,	boxes=snake_case_     ,	return_tensors=snake_case_     ,	)      )

     return inputs










'''simple docstring'''





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       int       		= logging.get_logger()

          # the current default level is logging.WARNING
          snake_case__    :       str       		= logging.get_verbosity()

          logging.set_verbosity_error()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          logging.set_verbosity_warning()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          logging.set_verbosity_info()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          logging.set_verbosity_debug()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          # restore to the original level
          logging.set_verbosity(snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       List[Any]       		= logging.get_verbosity()

          snake_case__    :       Any       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          snake_case__    :       int       		= """Testing 1, 2, 3"""

          # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
          if level_origin <= logging.WARNING:
                with CaptureLogger(snake_case_		) as cl:
                      logger.warning(snake_case_		)
                self.assertEqual(cl.out					,	msg + """\n"""		)

          # this is setting the level for all of `transformers.*` loggers
          logging.set_verbosity_error()

          # should not be able to log warnings
          with CaptureLogger(snake_case_		) as cl:
                logger.warning(snake_case_		)
          self.assertEqual(cl.out					,	""""""		)

          # should be able to log warnings again
          logging.set_verbosity_warning()
          with CaptureLogger(snake_case_		) as cl:
                logger.warning(snake_case_		)
          self.assertEqual(cl.out					,	msg + """\n"""		)

          # restore to the original level
          logging.set_verbosity(snake_case_		)




    @mockenv(TRANSFORMERS_VERBOSITY="""error"""		)
    def lowerCamelCase			(       self   :	Any		):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          # this action activates the env var
          snake_case__    :       Union[str, Any]       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)

          snake_case__    :       int       		= os.getenv("""TRANSFORMERS_VERBOSITY"""					,	snake_case_		)
          snake_case__    :       List[Any]       		= logging.log_levels[env_level_str]

          snake_case__    :       str       		= logging.get_verbosity()
          self.assertEqual(
              snake_case_					,	snake_case_					,	f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"					,	)

          # restore to the original level
          snake_case__    :       List[Any]       		= """"""
          transformers.utils.logging._reset_library_root_logger()




    @mockenv(TRANSFORMERS_VERBOSITY="""super-error"""		)
    def lowerCamelCase			(       self   :	int		):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          snake_case__    :       List[Any]       		= logging.logging.getLogger()
          with CaptureLogger(snake_case_		) as cl:
                # this action activates the env var
                logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""					,	cl.out		)

          # no need to restore as nothing was changed




    def lowerCamelCase			(       self   :	int		):
          # testing `logger.warning_advice()`
          transformers.utils.logging._reset_library_root_logger()

          snake_case__    :       Optional[int]       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          snake_case__    :       Tuple       		= """Testing 1, 2, 3"""

          with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""		):
                # nothing should be logged as env var disables this method
                with CaptureLogger(snake_case_		) as cl:
                      logger.warning_advice(snake_case_		)
                self.assertEqual(cl.out					,	""""""		)

          with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""		):
                # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
                with CaptureLogger(snake_case_		) as cl:
                      logger.warning_advice(snake_case_		)
                self.assertEqual(cl.out					,	msg + """\n"""		)





def 		__snake_case(							)						->					List[str]:
      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()
