






"""simple docstring"""


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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}


class    snake_case						(				_lowercase		):


   '''simple docstring'''

   A_		:      List[str]							=					"layoutlm"


   def __init__(      self					:	Dict,			_lowerCamelCase					:	Optional[Any]=3_05_22,			_lowerCamelCase					:	str=7_68,			_lowerCamelCase					:	Tuple=12,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	str=30_72,			_lowerCamelCase					:	Optional[int]="gelu",			_lowerCamelCase					:	Optional[Any]=0.1,			_lowerCamelCase					:	int=0.1,			_lowerCamelCase					:	List[str]=5_12,			_lowerCamelCase					:	Optional[int]=2,			_lowerCamelCase					:	str=0.02,			_lowerCamelCase					:	Optional[Any]=1e-12,			_lowerCamelCase					:	List[str]=0,			_lowerCamelCase					:	Optional[int]="absolute",			_lowerCamelCase					:	Any=True,			_lowerCamelCase					:	int=10_24,			**_lowerCamelCase					:	str,			):

     '''simple docstring'''
     super().__init__(pad_token_id=__snake_case,			**__snake_case    )
     __A		     =					vocab_size
     __A		     =					hidden_size
     __A		     =					num_hidden_layers
     __A		     =					num_attention_heads
     __A		     =					hidden_act
     __A		     =					intermediate_size
     __A		     =					hidden_dropout_prob
     __A		     =					attention_probs_dropout_prob
     __A		     =					max_position_embeddings
     __A		     =					type_vocab_size
     __A		     =					initializer_range
     __A		     =					layer_norm_eps
     __A		     =					position_embedding_type
     __A		     =					use_cache
     __A		     =					max_ad_position_embeddings





class    snake_case						(				_lowercase		):


   '''simple docstring'''

   def __init__(      self					:	str,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any] = "default",			_lowerCamelCase					:	int = None,			):

     '''simple docstring'''
     super().__init__(__snake_case,			task=__snake_case,			patching_specs=__snake_case    )
     __A		     =					config.max_ad_position_embeddings - 1


   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     return OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
             ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
         ]    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Tuple = -1,			_lowerCamelCase					:	Tuple = -1,			_lowerCamelCase					:	int = False,			_lowerCamelCase					:	Dict = None,			):

     '''simple docstring'''
     __A		     =					super().generate_dummy_inputs(
         __snake_case,			batch_size=__snake_case,			seq_length=__snake_case,			is_pair=__snake_case,			framework=__snake_case    )

     # Generate a dummy bbox
     __A		     =					[48, 84, 73, 1_28]

     if not framework == TensorType.PYTORCH:
       raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''    )

     if not is_torch_available():
       raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''    )
     import torch

     __A		     =					input_dict['input_ids'].shape
     __A		     =					torch.tensor([*[box] * seq_length]    ).tile(__snake_case,			1,			1    )
     return input_dict







import unittest

from cer import CER


lowerCAmelCase__             =		CER()







class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		Any								=    ['White House']
			_UpperCamelCase :		Tuple								=    ['white house']
			# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
			_UpperCamelCase :		Tuple								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['were wolf']
			_UpperCamelCase :		List[Any]								=    ['werewolf']
			# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1) < 1e-6)

			_UpperCamelCase :		int								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['weae     wolf']
			# S = 1, D = 1, I = 0, N = 8, CER = 0.25
			_UpperCamelCase :		str								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.2_5) < 1e-6)

			# consecutive whitespaces case 1
			_UpperCamelCase :		Tuple								=    ['were wolf']
			_UpperCamelCase :		Dict								=    ['were               wolf']
			# S = 0, D = 0, I = 0, N = 9, CER = 0
			_UpperCamelCase :		List[str]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)

			# consecutive whitespaces case 2
			_UpperCamelCase :		Dict								=    ['were   wolf']
			_UpperCamelCase :		Dict								=    ['were               wolf']
			# S = 0, D = 0, I = 0, N = 9, CER = 0
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['weaewolf']
			# S = 1, D = 0, I = 0, N = 8, CER = 0.125
			_UpperCamelCase :		List[str]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		int								=    ['werewolf']
			_UpperCamelCase :		Any								=    ['wereawolf']
			# S = 0, D = 1, I = 0, N = 8, CER = 0.125
			_UpperCamelCase :		Optional[int]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		str								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['wereolf']
			# S = 0, D = 0, I = 1, N = 8, CER = 0.125
			_UpperCamelCase :		List[Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Optional[Any]								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertEqual(__snake_case						,							0.0)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['werewolf', 'I am your father']
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertEqual(__snake_case						,							0.0)

			_UpperCamelCase :		List[Any]								=    ['werewolf', 'I am your father', 'doge']
			_UpperCamelCase :		Optional[int]								=    ['werxwolf', 'I       am your father', 'doge']
			# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
			_UpperCamelCase :		Any								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['My hovercraft', 'is full of eels']
			_UpperCamelCase :		List[Any]								=    ['My hovercraft is full', ' of eels']
			# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
			# whitespace at the front of " of eels" will be strip during preporcessing
			# so need to insert 2 whitespaces
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case						,							concatenate_texts=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['我能吞下玻璃而不伤身体']
			_UpperCamelCase :		Optional[Any]								=    [' 能吞虾玻璃而 不霜身体啦']
			# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
			_UpperCamelCase :		Any								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5) < 1e-6)

			_UpperCamelCase :		Union[str, Any]								=    ['我能吞下玻璃', '而不伤身体']
			_UpperCamelCase :		List[str]								=    ['我    能 吞 下 玻 璃', '而不伤身体']
			# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
			_UpperCamelCase :		Union[str, Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5) < 1e-6)

			_UpperCamelCase :		Optional[int]								=    ['我能吞下玻璃而不伤身体']
			_UpperCamelCase :		int								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertFalse(__snake_case						,							0.0)






		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['']
			_UpperCamelCase :		Dict								=    ['Hypothesis']
			with self.assertRaises(__snake_case):
				cer.compute(predictions=__snake_case						,							references=__snake_case)


if __name__ == "__main__":
							unittest.main()




