


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


_lowerCAmelCase:						Union[str, Any]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='layoutlmv3'





				def __init__(				self       ,  lowercase_=50265       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-5       ,  lowercase_=1       ,  lowercase_=0       ,  lowercase_=2       ,  lowercase_=1024       ,  lowercase_=128       ,  lowercase_=128       ,  lowercase_=True       ,  lowercase_=32       ,  lowercase_=128       ,  lowercase_=64       ,  lowercase_=256       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=224       ,  lowercase_=3       ,  lowercase_=16       ,  lowercase_=None       ,  **lowercase_       ,  )  -> int:
											super().__init__(
											    vocab_size=lowercase_       ,  hidden_size=lowercase_       ,  num_hidden_layers=lowercase_       ,  num_attention_heads=lowercase_       ,  intermediate_size=lowercase_       ,  hidden_act=lowercase_       ,  hidden_dropout_prob=lowercase_       ,  attention_probs_dropout_prob=lowercase_       ,  max_position_embeddings=lowercase_       ,  type_vocab_size=lowercase_       ,  initializer_range=lowercase_       ,  layer_norm_eps=lowercase_       ,  pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  **lowercase_       ,  )
											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       lowercase_					(lowercase__   ):
				snake_case	    =version.parse('1.12'   )





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





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





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

											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											a__      =compute_effective_axis_dimension(
											    lowercase_       ,  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(lowercase_)
											a__      =compute_effective_axis_dimension(
											    lowercase_       ,  fixed_dimension=OnnxConfig.default_fixed_sequence       ,  num_token_to_add=lowercase_)
											# 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(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)

											a__      =dict(
											    processor(
											        lowercase_       ,  text=lowercase_       ,  boxes=lowercase_       ,  return_tensors=lowercase_       ,  ))

											return inputs






"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


a_							=    logging.get_logger(__name__)

a_							=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a_							=    {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

a_							=    {
    """squeezebert/squeezebert-uncased""": 5_12,
    """squeezebert/squeezebert-mnli""": 5_12,
    """squeezebert/squeezebert-mnli-headless""": 5_12,
}


a_							=    {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			VOCAB_FILES_NAMES
			a_       :			List[str]												=			PRETRAINED_VOCAB_FILES_MAP
			a_       :			Optional[Any]												=			PRETRAINED_INIT_CONFIGURATION
			a_       :			Tuple												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_       :			Optional[int]												=			SqueezeBertTokenizer
			def __init__(	self				,   A=None				,   A=None				,   A=True				,   A="[UNK]"				,   A="[SEP]"				,   A="[PAD]"				,   A="[CLS]"				,   A="[MASK]"				,   A=True				,   A=None				,   **A				,   ):
						super().__init__(
						    A				,   tokenizer_file=A				,   do_lower_case=A				,   unk_token=A				,   sep_token=A				,   pad_token=A				,   cls_token=A				,   mask_token=A				,   tokenize_chinese_chars=A				,   strip_accents=A				,   **A				,   )

						_lowerCamelCase					:					Union[str, Any]	       =    json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
						if (
						    normalizer_state.get('lowercase'				,   A							) != do_lower_case
						    or normalizer_state.get('strip_accents'				,   A							) != strip_accents
						    or normalizer_state.get('handle_chinese_chars'				,   A							) != tokenize_chinese_chars
						):
									_lowerCamelCase					:					List[Any]	       =    getattr(A				,   normalizer_state.pop('type'							)							)
									_lowerCamelCase					:					Optional[Any]	       =    do_lower_case
									_lowerCamelCase					:					str	       =    strip_accents
									_lowerCamelCase					:					Any	       =    tokenize_chinese_chars
									_lowerCamelCase					:					int	       =    normalizer_class(**A							)

						_lowerCamelCase					:					Dict	       =    do_lower_case
			def    _lowerCAmelCase		(	self				,   A				,   A=None							):
						_lowerCamelCase					:					Dict	       =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output
			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					Union[str, Any]	       =    [self.sep_token_id]
						_lowerCamelCase					:					Optional[Any]	       =    [self.cls_token_id]
						if token_ids_a is None:
									return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]

			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					str	       =    self._tokenizer.model.save(A				,   name=A							)
						return tuple(A							)





