


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from ..image_utils import load_image


if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase_	   =     logging.get_logger(__name__)


lowercase_	   =     Dict[str, Any]
lowercase_	   =     List[Prediction]


@add_end_docstrings(__UpperCamelCase   )
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	str						,				*a:	Optional[Any]						,				**a:	Tuple    ):
       super().__init__(*a						,				**a    )

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

       requires_backends(self						,				'vision'    )
       self.check_model_type(
           dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )




      def 					_snake_case     (    self:	Union[str, Any]						,				**a:	Dict    ):
       __lowerCamelCase		:							str		 =						{}
       if "threshold" in kwargs:
        __lowerCamelCase		:							str		 =						kwargs['threshold']
       return {}, {}, postprocess_kwargs




      def __call__(    self:	Optional[Any]						,				*a:	Dict						,				**a:	str    ):
       return super().__call__(*a						,				**a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	List[str]    ):
       __lowerCamelCase		:							List[str]		 =						load_image(a    )
       __lowerCamelCase		:							List[str]		 =						torch.IntTensor([[image.height, image.width]]    )
       __lowerCamelCase		:							List[Any]		 =						self.image_processor(images=[image]						,				return_tensors='pt'    )
       if self.tokenizer is not None:
        __lowerCamelCase		:							Optional[int]		 =						self.tokenizer(text=inputs['words']						,				boxes=inputs['boxes']						,				return_tensors='pt'    )
       __lowerCamelCase		:							Optional[Any]		 =						target_size
       return inputs




      def 					_snake_case     (    self:	List[str]						,				a:	Optional[Any]    ):
       __lowerCamelCase		:							Tuple		 =						model_inputs.pop('target_size'    )
       __lowerCamelCase		:							Union[str, Any]		 =						self.model(**a    )
       __lowerCamelCase		:							Any		 =						outputs.__class__({'target_size': target_size, **outputs}    )
       if self.tokenizer is not None:
        __lowerCamelCase		:							Dict		 =						model_inputs['bbox']
       return model_outputs




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	Optional[Any]						,				a:	str=0.9    ):
       __lowerCamelCase		:							Union[str, Any]		 =						model_outputs['target_size']
       if self.tokenizer is not None:
        # This is a LayoutLMForTokenClassification variant.
        # The OCR got the boxes and the model classified the words.
        __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						target_size[0].tolist()

        def unnormalize(a:	Any    ):
         return self._get_bounding_box(
             torch.Tensor(
                 [
                     (width * bbox[0] / 1000),
                     (height * bbox[1] / 1000),
                     (width * bbox[2] / 1000),
                     (height * bbox[3] / 1000),
                 ]    )    )

        __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						model_outputs['logits'].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
        __lowerCamelCase		:							Union[str, Any]		 =						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
        __lowerCamelCase		:							Optional[Any]		 =						[unnormalize(a    ) for bbox in model_outputs['bbox'].squeeze(0    )]
        __lowerCamelCase		:							Optional[Any]		 =						['score', 'label', 'box']
        __lowerCamelCase		:							Optional[int]		 =						[dict(zip(a						,				a    )    ) for vals in zip(scores.tolist()						,				a						,				a    ) if vals[0] > threshold]
       else:
        # This is a regular ForObjectDetectionModel
        __lowerCamelCase		:							Dict		 =						self.image_processor.post_process_object_detection(a						,				a						,				a    )
        __lowerCamelCase		:							Dict		 =						raw_annotations[0]
        __lowerCamelCase		:							Any		 =						raw_annotation['scores']
        __lowerCamelCase		:							int		 =						raw_annotation['labels']
        __lowerCamelCase		:							Optional[Any]		 =						raw_annotation['boxes']

        __lowerCamelCase		:							Optional[int]		 =						scores.tolist()
        __lowerCamelCase		:							int		 =						[self.model.config.idalabel[label.item()] for label in labels]
        __lowerCamelCase		:							List[str]		 =						[self._get_bounding_box(a    ) for box in boxes]

        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
        __lowerCamelCase		:							List[Any]		 =						['score', 'label', 'box']
        __lowerCamelCase		:							Tuple		 =						[
            dict(zip(a						,				a    )    )
            for vals in zip(raw_annotation['scores']						,				raw_annotation['labels']						,				raw_annotation['boxes']    )
        ]

       return annotation




      def 					_snake_case     (    self:	List[Any]						,				a:	"torch.Tensor"    ):
       if self.framework != "pt":
        raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'    )
       __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							int		 =						box.int().tolist()
       __lowerCamelCase		:							Any		 =						{
           'xmin': xmin,
           'ymin': ymin,
           'xmax': xmax,
           'ymax': ymax,
       }
       return bbox





import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[str]		 =						np.inf

 def set_batch_size(SCREAMING_SNAKE_CASE__						) -> None:
  nonlocal batch_size
  if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							Union[str, Any]		 =						min(SCREAMING_SNAKE_CASE__      ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
  elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							Optional[Any]		 =						min(SCREAMING_SNAKE_CASE__      ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
  elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						) and feature.dtype == "binary":
   __lowerCamelCase		:							List[str]		 =						min(SCREAMING_SNAKE_CASE__      ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS						)

 _visit(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 return None if batch_size is np.inf else batch_size


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	Tuple						,				a:	NestedDataStructureLike[PathLike]						,				a:	Optional[NamedSplit] = None						,				a:	Optional[Features] = None						,				a:	str = None						,				a:	bool = False						,				a:	bool = False						,				a:	Optional[int] = None						,				**a:	Optional[Any]						,				):
       super().__init__(
           a						,				split=a						,				features=a						,				cache_dir=a						,				keep_in_memory=a						,				streaming=a						,				num_proc=a						,				**a						,				)
       __lowerCamelCase		:							List[Any]		 =						path_or_paths if isinstance(a						,				a    ) else {self.split: path_or_paths}
       __lowerCamelCase		:							Optional[Any]		 =						_PACKAGED_DATASETS_MODULES['parquet'][1]
       __lowerCamelCase		:							List[str]		 =						Parquet(
           cache_dir=a						,				data_files=a						,				features=a						,				hash=a						,				**a						,				)




      def 					_snake_case     (    self:	List[str]    ):
       # Build iterable dataset
       if self.streaming:
        __lowerCamelCase		:							str		 =						self.builder.as_streaming_dataset(split=self.split    )
       # Build regular (map-style) dataset
       else:
        __lowerCamelCase		:							str		 =						None
        __lowerCamelCase		:							Optional[Any]		 =						None
        __lowerCamelCase		:							Union[str, Any]		 =						None
        __lowerCamelCase		:							int		 =						None

        self.builder.download_and_prepare(
            download_config=a						,				download_mode=a						,				verification_mode=a						,				base_path=a						,				num_proc=self.num_proc						,				)
        __lowerCamelCase		:							Tuple		 =						self.builder.as_dataset(
            split=self.split						,				verification_mode=a						,				in_memory=self.keep_in_memory    )
       return dataset



class 	A_ :




      '''simple docstring'''


      def __init__(    self:	Optional[int]						,				a:	Dataset						,				a:	Union[PathLike, BinaryIO]						,				a:	Optional[int] = None						,				**a:	List[Any]						,				):
       __lowerCamelCase		:							Optional[int]		 =						dataset
       __lowerCamelCase		:							List[Any]		 =						path_or_buf
       __lowerCamelCase		:							List[str]		 =						batch_size or get_writer_batch_size(dataset.features    )
       __lowerCamelCase		:							List[Any]		 =						parquet_writer_kwargs




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

       if isinstance(self.path_or_buf						,				(str, bytes, os.PathLike)    ):
        with open(self.path_or_buf						,				'wb+'    ) as buffer:
         __lowerCamelCase		:							Optional[int]		 =						self._write(file_obj=a						,				batch_size=a						,				**self.parquet_writer_kwargs    )
       else:
        __lowerCamelCase		:							Any		 =						self._write(file_obj=self.path_or_buf						,				batch_size=a						,				**self.parquet_writer_kwargs    )
       return written




      def 					_snake_case     (    self:	Optional[int]						,				a:	BinaryIO						,				a:	int						,				**a:	str    ):
       __lowerCamelCase		:							Dict		 =						0
       __lowerCamelCase		:							Union[str, Any]		 =						parquet_writer_kwargs.pop('path_or_buf'						,				a    )
       __lowerCamelCase		:							str		 =						self.dataset.features.arrow_schema

       __lowerCamelCase		:							Any		 =						pq.ParquetWriter(a						,				schema=a						,				**a    )

       for offset in logging.tqdm(
           range(0						,				len(self.dataset    )						,				a    )						,				unit='ba'						,				disable=not logging.is_progress_bar_enabled()						,				desc='Creating parquet from Arrow format'						,				):
        __lowerCamelCase		:							Any		 =						query_table(
            table=self.dataset._data						,				key=slice(a						,				offset + batch_size    )						,				indices=self.dataset._indices if self.dataset._indices is not None else None						,				)
        writer.write_table(a    )
        written += batch.nbytes
       writer.close()
       return written

