



from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
   import importlib_metadata
else:
   import importlib.metadata as importlib_metadata


UpperCAmelCase_		 =					""""""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):


   class 	lowerCamelCase__	(       tr.AbstractTransform):



    """simple docstring"""

    def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					str = " "      )   ->	Optional[Any]:
       _A      =       sentence_delimiter

    def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					str      )   ->	Tuple:
       return list(snake_case_      )

    def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					List[str]      )   ->	Tuple:
       _A      =       []
       for sent_idx, sentence in enumerate(snake_case_      ):
          chars.extend(self.process_string(snake_case_      )      )
          if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case_      ) - 1:
             chars.append(self.sentence_delimiter      )
       return chars
   UpperCAmelCase_		 =					tr.Compose(
       [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
   )
else:
   UpperCAmelCase_		 =					tr.Compose(
       [
           tr.RemoveMultipleSpaces(),
           tr.Strip(),
           tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
           tr.ReduceToListOfListOfChars(),
       ]
   )


UpperCAmelCase_		 =					"""\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"""

UpperCAmelCase_		 =					"""\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"""

UpperCAmelCase_		 =					"""\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,  _KWARGS_DESCRIPTION)
class 	lowerCamelCase__	(       datasets.Metric):



 """simple docstring"""

 def     snake_case_      (    self					:					Optional[Any]      )   ->	List[Any]:
    return datasets.MetricInfo(
        description=_DESCRIPTION			,						citation=_CITATION			,						inputs_description=_KWARGS_DESCRIPTION			,						features=datasets.Features(
            {
                '''predictions''': datasets.Value('''string'''			,						id='''sequence'''      ),
                '''references''': datasets.Value('''string'''			,						id='''sequence'''      ),
            }      )			,						codebase_urls=['''https://github.com/jitsi/jiwer/''']			,						reference_urls=[
            '''https://en.wikipedia.org/wiki/Word_error_rate''',
            '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
        ]			,						)

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[int]=False      )   ->	int:
    if concatenate_texts:
       return jiwer.compute_measures(
           snake_case_			,						snake_case_			,						truth_transform=snake_case_			,						hypothesis_transform=snake_case_			,						)["wer"]

    _A      =       0
    _A      =       0
    for prediction, reference in zip(snake_case_			,						snake_case_      ):
       _A      =       jiwer.compute_measures(
           snake_case_			,						snake_case_			,						truth_transform=snake_case_			,						hypothesis_transform=snake_case_			,						)
       incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
       total += measures["substitutions"] + measures["deletions"] + measures["hits"]

    return incorrect / total








'''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


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          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_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	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 lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          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
          snake_case__    :       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
          snake_case__    :       List[Any]       		= processor.tokenizer.num_special_tokens_to_add(snake_case_		)
          snake_case__    :       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
          snake_case__    :       int       		= [[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

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

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

          return inputs
