


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


_snake_case					:		Optional[Any]      =    ""


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

			class     a		(tr.AbstractTransform				):




										"""simple docstring"""


										def __init__(							self	:  Tuple   ,     lowerCamelCase	:  Optional[int] = " "    )     ->   List[Any]:
																	__snake_case		:      Dict										=       sentence_delimiter

										def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  Union[str, Any]    )     ->   str:
																	return list(lowercase_    )






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


_snake_case					:		str      =    "\\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"

_snake_case					:		str      =    "\\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"

_snake_case					:		Optional[Any]      =    "\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     a		(datasets.Metric				):




							"""simple docstring"""


							def    __snake_case (							self	:  Optional[int]    )     ->   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	:  List[str]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Optional[Any]=False    )     ->   Any:

														if concatenate_texts:
																					return jiwer.compute_measures(
																					    lowercase_   ,     lowercase_   ,     truth_transform=lowercase_   ,     hypothesis_transform=lowercase_   ,     )["wer"]

														__snake_case		:      Optional[int]										=       0
														__snake_case		:      str										=       0
														for prediction, reference in zip(lowercase_   ,     lowercase_    ):
																					__snake_case		:      int										=       jiwer.compute_measures(
																					    lowercase_   ,     lowercase_   ,     truth_transform=lowercase_   ,     hypothesis_transform=lowercase_   ,     )
																					incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																					total += measures["substitutions"] + measures["deletions"] + measures["hits"]

														return incorrect / total





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case					:		int      =    logging.get_logger(__name__)

_snake_case					:		int      =    {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "beit"

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Any=8192   ,     lowerCamelCase	:  Dict=768   ,     lowerCamelCase	:  int=12   ,     lowerCamelCase	:  Optional[Any]=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  Tuple="gelu"   ,     lowerCamelCase	:  Union[str, Any]=0.0   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Dict=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Optional[Any]=224   ,     lowerCamelCase	:  Optional[int]=16   ,     lowerCamelCase	:  Any=3   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  int=False   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Tuple=0.1   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  int=[3, 5, 7, 11]   ,     lowerCamelCase	:  str=[1, 2, 3, 6]   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  List[Any]=0.4   ,     lowerCamelCase	:  int=256   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  List[str]=255   ,     **lowerCamelCase	:  Dict   ,     )     ->   int:
														super().__init__(**lowerCamelCase    )

														__snake_case		:      Any										=       vocab_size
														__snake_case		:      List[str]										=       hidden_size
														__snake_case		:      List[Any]										=       num_hidden_layers
														__snake_case		:      Tuple										=       num_attention_heads
														__snake_case		:      Dict										=       intermediate_size
														__snake_case		:      Union[str, Any]										=       hidden_act
														__snake_case		:      Optional[Any]										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      Union[str, Any]										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps

														__snake_case		:      Optional[Any]										=       image_size
														__snake_case		:      List[str]										=       patch_size
														__snake_case		:      Optional[Any]										=       num_channels
														__snake_case		:      Any										=       use_mask_token
														__snake_case		:      List[str]										=       use_absolute_position_embeddings
														__snake_case		:      List[Any]										=       use_relative_position_bias
														__snake_case		:      str										=       use_shared_relative_position_bias
														__snake_case		:      str										=       layer_scale_init_value
														__snake_case		:      Any										=       drop_path_rate
														__snake_case		:      int										=       use_mean_pooling
														# decode head attributes (semantic segmentation)
														__snake_case		:      Optional[Any]										=       out_indices
														__snake_case		:      List[str]										=       pool_scales
														# auxiliary head attributes (semantic segmentation)
														__snake_case		:      int										=       use_auxiliary_head
														__snake_case		:      int										=       auxiliary_loss_weight
														__snake_case		:      Optional[int]										=       auxiliary_channels
														__snake_case		:      int										=       auxiliary_num_convs
														__snake_case		:      str										=       auxiliary_concat_input
														__snake_case		:      List[str]										=       semantic_loss_ignore_index

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  version.parse("1.11"				)

							@property
							def    __snake_case (							self	:  Dict    )     ->   Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ]    )






							@property
							def    __snake_case (							self	:  str    )     ->   float:
														return 1E-4

