



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


SCREAMING_SNAKE_CASE_:List[Any] 			=		""""""


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






     class SCREAMING_SNAKE_CASE__	(						tr.AbstractTransform       ):




         '''simple docstring'''






         def __init__(			self,						lowerCamelCase__ = " "						):
             A					:			List[str]		=  sentence_delimiter






         def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
             return list(__UpperCAmelCase						)






         def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
             A					:			Union[str, Any]		=  []
             for sent_idx, sentence in enumerate(__UpperCAmelCase						):
                 chars.extend(self.process_string(__UpperCAmelCase						)						)
                 if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__UpperCAmelCase						) - 1:
                     chars.append(self.sentence_delimiter						)
             return chars


     SCREAMING_SNAKE_CASE_:int 			=		tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     SCREAMING_SNAKE_CASE_:Optional[int] 			=		tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


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

SCREAMING_SNAKE_CASE_:Optional[Any] 			=		"""\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'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
performance of the ASR system with a CER of 0 being a perfect score.
"""

SCREAMING_SNAKE_CASE_:List[str] 			=		"""
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""






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




    '''simple docstring'''






    def      _lowerCAmelCase   (			self						):
        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      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__=False						):



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

        A					:			Tuple		=  0
        A					:			List[Any]		=  0
        for prediction, reference in zip(__UpperCAmelCase,						__UpperCAmelCase						):
            A					:			Dict		=  jiwer.compute_measures(
                __UpperCAmelCase,						__UpperCAmelCase,						truth_transform=__UpperCAmelCase,						hypothesis_transform=__UpperCAmelCase,						)
            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
            total += measures["substitutions"] + measures["deletions"] + measures["hits"]

        return incorrect / total










from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

UpperCAmelCase__      			=						logging.get_logger(__name__)




@add_end_docstrings(__UpperCamelCase      )
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					int ,					**__UpperCAmelCase:					Optional[Any]   )    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											requires_backends(self ,					"vision"   )
											requires_backends(self ,					"torch"   )

											if self.framework != "pt":
																raise ValueError(f'The {self.__class__} is only available in PyTorch.'   )

											self.check_model_type(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					**__UpperCAmelCase:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					{}
											__a					:						Union[str, Any]      =					{}
											__a					:						List[str]      =					{}
											# preprocess args
											if "points_per_batch" in kwargs:
																__a					:						Dict      =					kwargs["points_per_batch"]
											if "points_per_crop" in kwargs:
																__a					:						Tuple      =					kwargs["points_per_crop"]
											if "crops_n_layers" in kwargs:
																__a					:						Dict      =					kwargs["crops_n_layers"]
											if "crop_overlap_ratio" in kwargs:
																__a					:						int      =					kwargs["crop_overlap_ratio"]
											if "crop_n_points_downscale_factor" in kwargs:
																__a					:						Optional[int]      =					kwargs["crop_n_points_downscale_factor"]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																__a					:						int      =					kwargs["pred_iou_thresh"]
											if "stability_score_offset" in kwargs:
																__a					:						Any      =					kwargs["stability_score_offset"]
											if "mask_threshold" in kwargs:
																__a					:						int      =					kwargs["mask_threshold"]
											if "stability_score_thresh" in kwargs:
																__a					:						Any      =					kwargs["stability_score_thresh"]
											if "crops_nms_thresh" in kwargs:
																__a					:						int      =					kwargs["crops_nms_thresh"]
											if "output_rle_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_rle_mask"]
											if "output_bboxes_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_bboxes_mask"]
											return preprocess_kwargs, forward_params, postprocess_kwargs



						def __call__(self:					Union[str, Any] ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					Union[str, Any]=None ,					**__UpperCAmelCase:					Optional[int]   )    ->     Tuple:




											'''simple docstring'''



											return super().__call__(__UpperCAmelCase ,					*__UpperCAmelCase ,					num_workers=__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=64 ,					__UpperCAmelCase:					int = 0 ,					__UpperCAmelCase:					float = 512 / 1500 ,					__UpperCAmelCase:					Optional[int] = 32 ,					__UpperCAmelCase:					Optional[int] = 1 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					load_image(__UpperCAmelCase   )
											__a					:						List[str]      =					self.image_processor.size["longest_edge"]
											__a      ,			__a      ,			__a      ,			__a					:						int      =					self.image_processor.generate_crop_boxes(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.image_processor(images=__UpperCAmelCase ,					return_tensors="pt"   )

											with self.device_placement():
																if self.framework == "pt":
																					__a					:						str      =					self.get_inference_context()
																					with inference_context():
																										__a					:						int      =					self._ensure_tensor_on_device(__UpperCAmelCase ,					device=self.device   )
																										__a					:						Union[str, Any]      =					self.model.get_image_embeddings(model_inputs.pop("pixel_values"   )   )
																										__a					:						Optional[Any]      =					image_embeddings

											__a					:						Dict      =					grid_points.shape[1]
											__a					:						Any      =					points_per_batch if points_per_batch is not None else n_points

											if points_per_batch <= 0:
																raise ValueError(
																    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
																    "To return all points at once, set points_per_batch to None"   )

											for i in range(0 ,					__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[str]      =					grid_points[:, i : i + points_per_batch, :, :]
																__a					:						str      =					input_labels[:, i : i + points_per_batch]
																__a					:						str      =					i == n_points - points_per_batch
																yield {
																    "input_points": batched_points,
																    "input_labels": labels,
																    "input_boxes": crop_boxes,
																    "is_last": is_last,
																    **model_inputs,
																}



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Any=0.88 ,					__UpperCAmelCase:					Optional[int]=0.95 ,					__UpperCAmelCase:					Optional[int]=0 ,					__UpperCAmelCase:					int=1 ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						List[Any]      =					model_inputs.pop("input_boxes"   )
											__a					:						Optional[Any]      =					model_inputs.pop("is_last"   )
											__a					:						Dict      =					model_inputs.pop("original_sizes"   ).tolist()
											__a					:						int      =					model_inputs.pop("reshaped_input_sizes"   ).tolist()

											__a					:						Optional[Any]      =					self.model(**__UpperCAmelCase   )

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											__a					:						Union[str, Any]      =					model_outputs["pred_masks"]
											__a					:						Optional[Any]      =					self.image_processor.post_process_masks(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					binarize=__UpperCAmelCase   )
											__a					:						Optional[Any]      =					model_outputs["iou_scores"]
											__a      ,			__a      ,			__a					:						Dict      =					self.image_processor.filter_masks(
											    masks[0] ,					iou_scores[0] ,					original_sizes[0] ,					input_boxes[0] ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					)
											return {
											    "masks": masks,
											    "is_last": is_last,
											    "boxes": boxes,
											    "iou_scores": iou_scores,
											}



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=False ,					__UpperCAmelCase:					int=False ,					__UpperCAmelCase:					List[str]=0.7 ,					)    ->     Dict:




											'''simple docstring'''



											__a					:						Dict      =					[]
											__a					:						Optional[Any]      =					[]
											__a					:						Optional[Any]      =					[]
											for model_output in model_outputs:
																all_scores.append(model_output.pop("iou_scores"   )   )
																all_masks.extend(model_output.pop("masks"   )   )
																all_boxes.append(model_output.pop("boxes"   )   )

											__a					:						Any      =					torch.cat(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					torch.cat(__UpperCAmelCase   )
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					self.image_processor.post_process_for_mask_generation(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[int]      =					defaultdict(__UpperCAmelCase   )
											for output in model_outputs:
																for k, v in output.items():
																					extra[k].append(__UpperCAmelCase   )

											__a					:						int      =					{}
											if output_rle_mask:
																__a					:						Optional[int]      =					rle_mask

											if output_bboxes_mask:
																__a					:						List[str]      =					bounding_boxes

											return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





