"""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_			(		_lowercase):
							snake_case__								= "layoutlmv3"






							def __init__(						self					: str	,		__UpperCamelCase					: Dict=5_0265	,		__UpperCamelCase					: str=768	,		__UpperCamelCase					: List[Any]=12	,		__UpperCamelCase					: Union[str, Any]=12	,		__UpperCamelCase					: List[Any]=3072	,		__UpperCamelCase					: Union[str, Any]="gelu"	,		__UpperCamelCase					: Any=0.1	,		__UpperCamelCase					: Optional[int]=0.1	,		__UpperCamelCase					: str=512	,		__UpperCamelCase					: Dict=2	,		__UpperCamelCase					: Optional[int]=0.0_2	,		__UpperCamelCase					: Any=1E-5	,		__UpperCamelCase					: Any=1	,		__UpperCamelCase					: Optional[int]=0	,		__UpperCamelCase					: Optional[int]=2	,		__UpperCamelCase					: Union[str, Any]=1024	,		__UpperCamelCase					: Dict=128	,		__UpperCamelCase					: List[Any]=128	,		__UpperCamelCase					: Optional[int]=True	,		__UpperCamelCase					: Any=32	,		__UpperCamelCase					: int=128	,		__UpperCamelCase					: List[str]=64	,		__UpperCamelCase					: Union[str, Any]=256	,		__UpperCamelCase					: int=True	,		__UpperCamelCase					: List[str]=True	,		__UpperCamelCase					: Union[str, Any]=True	,		__UpperCamelCase					: Dict=224	,		__UpperCamelCase					: Tuple=3	,		__UpperCamelCase					: Union[str, Any]=16	,		__UpperCamelCase					: Optional[int]=None	,		**__UpperCamelCase					: int	,		)			->			int:
												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_	,		)
												_UpperCamelCase 		=      max_ad_position_embeddings
												_UpperCamelCase 		=      coordinate_size
												_UpperCamelCase 		=      shape_size
												_UpperCamelCase 		=      has_relative_attention_bias
												_UpperCamelCase 		=      rel_pos_bins
												_UpperCamelCase 		=      max_rel_pos
												_UpperCamelCase 		=      has_spatial_attention_bias
												_UpperCamelCase 		=      rel_ad_pos_bins
												_UpperCamelCase 		=      max_rel_ad_pos
												_UpperCamelCase 		=      text_embed
												_UpperCamelCase 		=      visual_embed
												_UpperCamelCase 		=      input_size
												_UpperCamelCase 		=      num_channels
												_UpperCamelCase 		=      patch_size
												_UpperCamelCase 		=      classifier_dropout







class 	UpperCAmelCase_			(		_lowercase):
							snake_case__								= version.parse('''1.12''')






							@property
							def  _UpperCamelCase     (						self					: Union[str, Any]    )			->			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					: List[Any]    )			->			float:
												return 1E-5






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






							def  _UpperCamelCase     (						self					: Any	,		__UpperCamelCase					: Any	,		__UpperCamelCase					: Any = -1	,		__UpperCamelCase					: Tuple = -1	,		__UpperCamelCase					: Optional[Any] = False	,		__UpperCamelCase					: str = None	,		__UpperCamelCase					: str = 3	,		__UpperCamelCase					: str = 40	,		__UpperCamelCase					: Optional[Any] = 40	,		)			->			Mapping[str, Any]:
												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
												_UpperCamelCase 		=      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
												_UpperCamelCase 		=      processor.tokenizer.num_special_tokens_to_add(UpperCAmelCase_    )
												_UpperCamelCase 		=      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
												_UpperCamelCase 		=      [[''' '''.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												_UpperCamelCase 		=      [[[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)
												_UpperCamelCase 		=      self._generate_dummy_images(UpperCAmelCase_	,		UpperCAmelCase_	,		UpperCAmelCase_	,		UpperCAmelCase_    )

												_UpperCamelCase 		=      dict(
												    processor(
												        UpperCAmelCase_	,		text=UpperCAmelCase_	,		boxes=UpperCAmelCase_	,		return_tensors=UpperCAmelCase_	,		)    )

												return inputs





'''simple docstring'''





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:str     =       MgpstrTokenizer
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:Any     =       {}
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self   )	->				Union[str, Any]:
           super().setUp()

           # fmt: off
           lowercase__									=    ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
           # fmt: on
           lowercase__									=    dict(zip(UpperCAmelCase_  ,range(len(UpperCAmelCase_   )   )   )   )

           lowercase__									=    os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["vocab_file"]   )
           with open(self.vocab_file  ,"w"  ,encoding="utf-8"   ) as fp:
               fp.write(json.dumps(UpperCAmelCase_   ) + "\n"   )



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Any:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    "tester"
           lowercase__									=    "tester"
           return input_text, output_text



       @unittest.skip("MGP-STR always lower cases letters."   )
       def 				_a   (					self   )	->				Dict:
           pass



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.get_tokenizers(do_lower_case=UpperCAmelCase_   )
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__									=    "[SPECIAL_TOKEN]"

                   tokenizer.add_special_tokens({"cls_token": special_token}   )
                   lowercase__									=    tokenizer.encode([special_token]  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertEqual(len(UpperCAmelCase_   )  ,1   )

                   lowercase__									=    tokenizer.decode(UpperCAmelCase_  ,skip_special_tokens=UpperCAmelCase_   )
                   self.assertTrue(special_token not in decoded   )



       def 				_a   (					self   )	->				str:
           lowercase__									=    self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__							,							lowercase__									=    self.get_input_output_texts(UpperCAmelCase_   )

                   lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

                   lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
                   self.assertNotEqual(len(UpperCAmelCase_   )  ,0   )
                   lowercase__									=    tokenizer.decode(UpperCAmelCase_   )
                   self.assertIsInstance(UpperCAmelCase_  ,UpperCAmelCase_   )

                   self.assertEqual(text_a.replace(" "  ,""   )  ,UpperCAmelCase_   )



       @unittest.skip("MGP-STR tokenizer only handles one sequence."   )
       def 				_a   (					self   )	->				Optional[int]:
           pass







       @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"   )
       def 				_a   (					self   )	->				Any:
           pass




