


"""simple docstring"""







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 _A						(						lowercase		):







    """simple docstring"""
    a 		=np.inf

    def set_batch_size(lowercase		) -> None:
        nonlocal batch_size
        if isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		) and feature.dtype == "binary":
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

    _visit(lowercase       ,    lowercase		)

    return None if batch_size is np.inf else batch_size






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = False ,    __A = False ,    __A = None ,    **__A ,    )					->     Optional[Any]:
     super().__init__(
         __A ,    split=__A ,    features=__A ,    cache_dir=__A ,    keep_in_memory=__A ,    streaming=__A ,    num_proc=__A ,    **__A ,    )
     a 		=path_or_paths if isinstance(__A ,    __A						) else {self.split: path_or_paths}
     a 		=_PACKAGED_DATASETS_MODULES['''parquet'''][1]
     a 		=Parquet(
         cache_dir=__A ,    data_files=__A ,    features=__A ,    hash=__A ,    **__A ,    )





 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     # Build iterable dataset
     if self.streaming:
         a 		=self.builder.as_streaming_dataset(split=self.split						)
     # Build regular (map-style) dataset
     else:
         a 		=None
         a 		=None
         a 		=None
         a 		=None

         self.builder.download_and_prepare(
             download_config=__A ,    download_mode=__A ,    verification_mode=__A ,    base_path=__A ,    num_proc=self.num_proc ,    )
         a 		=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 ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     Optional[int]:
     a 		=dataset
     a 		=path_or_buf
     a 		=batch_size or get_writer_batch_size(dataset.features						)
     a 		=parquet_writer_kwargs


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		=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:
             a 		=self._write(file_obj=__A ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     else:
         a 		=self._write(file_obj=self.path_or_buf ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     return written





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    **__A						)					->     int:
     a 		=0
     a 		=parquet_writer_kwargs.pop('''path_or_buf''' ,    __A						)
     a 		=self.dataset.features.arrow_schema

     a 		=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''' ,    ):
         a 		=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





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ])
        self.assertEqual(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






