



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


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  List[str]								=					{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}



class 				__snake_case						(     A__						):
							_a			      = '''layoutlmv3'''




							def __init__(       self     :       str				,				A_     :       Tuple=5_0_2_6_5				,				A_     :       str=7_6_8				,				A_     :       int=1_2				,				A_     :       List[Any]=1_2				,				A_     :       int=3_0_7_2				,				A_     :       List[str]="gelu"				,				A_     :       List[str]=0.1				,				A_     :       Any=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       str=2				,				A_     :       Dict=0.02				,				A_     :       Any=1e-5				,				A_     :       Optional[int]=1				,				A_     :       List[str]=0				,				A_     :       List[Any]=2				,				A_     :       Dict=1_0_2_4				,				A_     :       List[str]=1_2_8				,				A_     :       Optional[int]=1_2_8				,				A_     :       Any=True				,				A_     :       Union[str, Any]=3_2				,				A_     :       Dict=1_2_8				,				A_     :       Optional[Any]=6_4				,				A_     :       Union[str, Any]=2_5_6				,				A_     :       Dict=True				,				A_     :       Tuple=True				,				A_     :       Any=True				,				A_     :       Optional[int]=2_2_4				,				A_     :       Tuple=3				,				A_     :       int=1_6				,				A_     :       Any=None				,				**A_     :       str				,				):
									super().__init__(
									    vocab_size=UpperCamelCase_				,				hidden_size=UpperCamelCase_				,				num_hidden_layers=UpperCamelCase_				,				num_attention_heads=UpperCamelCase_				,				intermediate_size=UpperCamelCase_				,				hidden_act=UpperCamelCase_				,				hidden_dropout_prob=UpperCamelCase_				,				attention_probs_dropout_prob=UpperCamelCase_				,				max_position_embeddings=UpperCamelCase_				,				type_vocab_size=UpperCamelCase_				,				initializer_range=UpperCamelCase_				,				layer_norm_eps=UpperCamelCase_				,				pad_token_id=UpperCamelCase_				,				bos_token_id=UpperCamelCase_				,				eos_token_id=UpperCamelCase_				,				**UpperCamelCase_				,				)
									lowerCAmelCase_			:     Tuple									= max_ad_position_embeddings
									lowerCAmelCase_			:     List[Any]									= coordinate_size
									lowerCAmelCase_			:     Union[str, Any]									= shape_size
									lowerCAmelCase_			:     int									= has_relative_attention_bias
									lowerCAmelCase_			:     Any									= rel_pos_bins
									lowerCAmelCase_			:     Tuple									= max_rel_pos
									lowerCAmelCase_			:     Optional[Any]									= has_spatial_attention_bias
									lowerCAmelCase_			:     Union[str, Any]									= rel_ad_pos_bins
									lowerCAmelCase_			:     Union[str, Any]									= max_rel_ad_pos
									lowerCAmelCase_			:     Dict									= text_embed
									lowerCAmelCase_			:     Optional[Any]									= visual_embed
									lowerCAmelCase_			:     Dict									= input_size
									lowerCAmelCase_			:     List[Any]									= num_channels
									lowerCAmelCase_			:     Optional[int]									= patch_size
									lowerCAmelCase_			:     str									= classifier_dropout





class 				__snake_case						(     A__						):
							_a			      = version.parse('''1.12'''						)




							@property
							def    UpperCAmelCase__			(       self     :       Any):


									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    UpperCAmelCase__			(       self     :       str):
									return 1e-5




							@property
							def    UpperCAmelCase__			(       self     :       Dict):
									return 1_2







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       List[str]				,				A_     :       Union[str, Any] = -1				,				A_     :       Tuple = -1				,				A_     :       str = False				,				A_     :       Dict = None				,				A_     :       List[str] = 3				,				A_     :       Any = 4_0				,				A_     :       Union[str, Any] = 4_0				,				):
									setattr(processor.image_processor				,				'''apply_ocr'''				,				UpperCamelCase_)

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

									# Generate dummy bounding boxes
									lowerCAmelCase_			:     str									= [[[4_8, 8_4, 7_3, 1_2_8]]] * 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[Any]									= self._generate_dummy_images(UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)

									lowerCAmelCase_			:     Any									= dict(
									    processor(
									        UpperCamelCase_				,				text=UpperCamelCase_				,				boxes=UpperCamelCase_				,				return_tensors=UpperCamelCase_				,				))

									return inputs









'''simple docstring'''
import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				ComputeEnvironment.AMAZON_SAGEMAKER
		_a     =				True
		_a     =				'ml.p3.2xlarge'
		_a     =				'accelerate_sagemaker_execution_role'
		_a     =				'hf-sm'
		_a     =				'us-east-1'
		_a     =				1
		_a     =				'accelerate-sagemaker-1'
		_a     =				'1.6'
		_a     =				'4.4'
		_a     =				'train.py'
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    'False',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    '--do_test',
		    'False',
		    '--do_predict',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]



class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
							assert isinstance(converted_args['''model_name_or_path''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''do_train''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''epochs''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''learning_rate''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''max_steps''']    ,   UpperCamelCase_							)

							with pytest.raises(UpperCamelCase_							):
												_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)