



"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowerCAmelCase__							=				datasets.utils.logging.get_logger(__name__)





@dataclass
class  snake_case     (							datasets.BuilderConfig   ):
							UpperCAmelCase__						     =      10_000
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							datasets.ArrowBasedBuilder   ):
							UpperCAmelCase__						     =      ParquetConfig

							def _lowercase					(self    ):

									"""simple docstring"""







									return datasets.DatasetInfo(features=self.config.features    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if not self.config.data_files:
											raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'    )
									SCREAMING_SNAKE_CASE_						 = dl_manager.download_and_extract(self.config.data_files    )
									if isinstance(SCREAMING_SNAKE_CASE_       ,       (str, list, tuple)    ):
											SCREAMING_SNAKE_CASE_						 = data_files
											if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = [files]
											# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
											SCREAMING_SNAKE_CASE_						 = [dl_manager.iter_files(SCREAMING_SNAKE_CASE_    ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,       gen_kwargs={'''files''': files}    )]
									SCREAMING_SNAKE_CASE_						 = []
									for split_name, files in data_files.items():
											if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = [files]
											# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
											SCREAMING_SNAKE_CASE_						 = [dl_manager.iter_files(SCREAMING_SNAKE_CASE_    ) for file in files]
											# Infer features is they are stoed in the arrow schema
											if self.info.features is None:
													for file in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_    ):
															with open(SCREAMING_SNAKE_CASE_       ,       '''rb'''    ) as f:
																	SCREAMING_SNAKE_CASE_						 = datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE_    )    )
															break
											splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_       ,       gen_kwargs={'''files''': files}    )    )
									return splits

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if self.info.features is not None:
											# more expensive cast to support nested features with keys in a different order
											# allows str <-> int/float or str to Audio for example
											SCREAMING_SNAKE_CASE_						 = table_cast(SCREAMING_SNAKE_CASE_       ,       self.info.features.arrow_schema    )
									return pa_table







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.info.features.arrow_schema if self.info.features is not None else None
									if self.info.features is not None and self.config.columns is not None:
											if sorted(field.name for field in schema    ) != sorted(self.config.columns    ):
													raise ValueError(
													    f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''    )
									for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_    )    ):
											with open(SCREAMING_SNAKE_CASE_       ,       '''rb'''    ) as f:
													SCREAMING_SNAKE_CASE_						 = pq.ParquetFile(SCREAMING_SNAKE_CASE_    )
													try:
															for batch_idx, record_batch in enumerate(
															    parquet_file.iter_batches(batch_size=self.config.batch_size       ,       columns=self.config.columns    )    ):
																	SCREAMING_SNAKE_CASE_						 = pa.Table.from_batches([record_batch]    )
																	# Uncomment for debugging (will print the Arrow table size and elements)
																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																	yield f'{file_idx}_{batch_idx}', self._cast_table(SCREAMING_SNAKE_CASE_    )
													except ValueError as e:
															logger.error(f'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_    )}: {e}'    )
															raise

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index