




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		Any,     _lowercase:		int,     _lowercase:		Optional[int]=False):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  (
									    filename_skip_take["""filename"""],
									    filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
									    filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
									)
									open(os.path.join(_lowercase),     """wb""").close()
									__lowerCAmelCase					=  pa.Table.from_pydict({"""filename""": [Path(_lowercase).name] * 100})
									if take == -1:
													__lowerCAmelCase					=  len(_lowercase) - skip
									if skip is not None and take is not None:
													__lowerCAmelCase					=  pa_table.slice(_lowercase,     _lowercase)
									return pa_table





class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  """my_name"""
									__lowerCAmelCase					=  SplitInfo(name="""train""",     num_examples=100)
									__lowerCAmelCase					=  SplitInfo(name="""test""",     num_examples=100)
									__lowerCAmelCase					=  [train_info, test_info]
									__lowerCAmelCase					=  SplitDict()
									split_dict.add(_lowercase)
									split_dict.add(_lowercase)
									__lowerCAmelCase					=  DatasetInfo(splits=_lowercase)

									with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase					=  ReaderTest(_lowercase,     _lowercase)

													__lowerCAmelCase					=  """test[:33%]"""
													__lowerCAmelCase					=  Dataset(**reader.read(_lowercase,     _lowercase,     _lowercase))
													self.assertEqual(dset["""filename"""][0],     f'''{name}-test''')
													self.assertEqual(dset.num_rows,     33)
													self.assertEqual(dset.num_columns,     1)

													__lowerCAmelCase					=  ["""train""", """test[:33%]"""]
													__lowerCAmelCase					=  [Split.TRAIN, ReadInstruction.from_spec("""test[:33%]""")]
													for instructions in [instructionsa, instructionsa]:
																	__lowerCAmelCase					=  [reader.read(_lowercase,     _lowercase,     _lowercase) for instr in instructions]
																	__lowerCAmelCase	,				__lowerCAmelCase					=  (Dataset(**_lowercase) for dataset_kwargs in datasets_kwargs)
																	self.assertEqual(train_dset["""filename"""][0],     f'''{name}-train''')
																	self.assertEqual(train_dset.num_rows,     100)
																	self.assertEqual(train_dset.num_columns,     1)
																	self.assertIsInstance(train_dset.split,     _lowercase)
																	self.assertEqual(str(train_dset.split),     """train""")
																	self.assertEqual(test_dset["""filename"""][0],     f'''{name}-test''')
																	self.assertEqual(test_dset.num_rows,     33)
																	self.assertEqual(test_dset.num_columns,     1)
																	self.assertIsInstance(test_dset.split,     _lowercase)
																	self.assertEqual(str(test_dset.split),     """test[:33%]""")
																	del train_dset, test_dset




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  """my_name"""
									__lowerCAmelCase					=  SplitInfo(name="""train""",     num_examples=1000,     shard_lengths=[100] * 10)
									__lowerCAmelCase					=  [train_info]
									__lowerCAmelCase					=  SplitDict()
									split_dict.add(_lowercase)
									__lowerCAmelCase					=  DatasetInfo(splits=_lowercase)

									with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase					=  ReaderTest(_lowercase,     _lowercase)

													__lowerCAmelCase					=  """train[:33%]"""
													__lowerCAmelCase					=  Dataset(**reader.read(_lowercase,     _lowercase,     _lowercase))
													self.assertEqual(dset["""filename"""][0],     f'''{name}-train-00000-of-00010''')
													self.assertEqual(dset["""filename"""][-1],     f'''{name}-train-00003-of-00010''')
													self.assertEqual(dset.num_rows,     330)
													self.assertEqual(dset.num_columns,     1)







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  SplitInfo(name="""train""",     num_examples=100)
									__lowerCAmelCase					=  SplitInfo(name="""test""",     num_examples=100)
									__lowerCAmelCase					=  SplitDict()
									split_dict.add(_lowercase)
									split_dict.add(_lowercase)
									__lowerCAmelCase					=  DatasetInfo(splits=_lowercase)

									with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase					=  ReaderTest(_lowercase,     _lowercase)

													__lowerCAmelCase					=  [
													    {"""filename""": os.path.join(_lowercase,     """train""")},
													    {"""filename""": os.path.join(_lowercase,     """test"""), """skip""": 10, """take""": 10},
													]
													__lowerCAmelCase					=  Dataset(**reader.read_files(_lowercase,     original_instructions="""train+test[10:20]"""))
													self.assertEqual(dset.num_rows,     110)
													self.assertEqual(dset.num_columns,     1)
													del dset





@pytest.mark.parametrize("""in_memory"""						,			[False, True]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  arrow_file
				with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  ArrowReader.read_table(UpperCamelCase__						,			in_memory=UpperCamelCase__   )
				assert table.shape == dataset.data.shape
				assert set(table.column_names   ) == set(dataset.data.column_names   )
				assert dict(table.to_pydict()   ) == dict(dataset.data.to_pydict()   )  # to_pydict returns OrderedDict





@pytest.mark.parametrize("""in_memory"""						,			[False, True]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  arrow_file
				__lowerCAmelCase					=  ArrowReader(""""""						,			UpperCamelCase__   )
				with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  reader.read_files([{"""filename""": filename}]						,			in_memory=UpperCamelCase__   )
				assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
				__lowerCAmelCase					=  dataset_kwargs["""arrow_table"""]
				assert table.shape == dataset.data.shape
				assert set(table.column_names   ) == set(dataset.data.column_names   )
				assert dict(table.to_pydict()   ) == dict(dataset.data.to_pydict()   )  # to_pydict returns OrderedDict





def      UpperCAmelCase  (						)     ->     List[Any]:







				'''simple docstring'''


				assert ReadInstruction("""train"""						,			to=10						,			unit="""abs"""   ).to_spec() == "train[:10]"
				assert ReadInstruction("""train"""						,			from_=-80						,			to=10						,			unit="""%"""   ).to_spec() == "train[-80%:10%]"

				__lowerCAmelCase					=  """train+test"""
				assert ReadInstruction.from_spec(UpperCamelCase__   ).to_spec() == spec_train_test

				__lowerCAmelCase					=  """train[2:10]"""
				assert ReadInstruction.from_spec(UpperCamelCase__   ).to_spec() == spec_train_abs

				__lowerCAmelCase					=  """train[15%:-20%]"""
				assert ReadInstruction.from_spec(UpperCamelCase__   ).to_spec() == spec_train_pct

				__lowerCAmelCase					=  """train[:10%](closest)"""
				assert ReadInstruction.from_spec(UpperCamelCase__   ).to_spec() == "train[:10%]"

				__lowerCAmelCase					=  """train[:10%](pct1_dropremainder)"""
				assert ReadInstruction.from_spec(UpperCamelCase__   ).to_spec() == spec_train_pct_rounding

				__lowerCAmelCase					=  """train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
				assert ReadInstruction.from_spec(UpperCamelCase__   ).to_spec() == spec_train_test_pct_rounding





def      UpperCAmelCase  (						)     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  """dummy"""
				__lowerCAmelCase					=  [SplitInfo(name="""train"""						,			num_examples=1_00   )]
				__lowerCAmelCase					=  """train[:33%]"""
				__lowerCAmelCase					=  """arrow"""
				__lowerCAmelCase					=  """prefix"""

				__lowerCAmelCase					=  make_file_instructions(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				assert isinstance(UpperCamelCase__						,			UpperCamelCase__   )
				assert file_instructions.num_examples == 33
				assert file_instructions.file_instructions == [
				    {"filename": os.path.join(UpperCamelCase__						,			F'''{name}-train.arrow'''   ), "skip": 0, "take": 33}
				]

				__lowerCAmelCase					=  [SplitInfo(name="""train"""						,			num_examples=1_00						,			shard_lengths=[10] * 10   )]
				__lowerCAmelCase					=  make_file_instructions(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				assert isinstance(UpperCamelCase__						,			UpperCamelCase__   )
				assert file_instructions.num_examples == 33
				assert file_instructions.file_instructions == [
				    {"filename": os.path.join(UpperCamelCase__						,			F'''{name}-train-00000-of-00010.arrow'''   ), "skip": 0, "take": -1},
				    {"filename": os.path.join(UpperCamelCase__						,			F'''{name}-train-00001-of-00010.arrow'''   ), "skip": 0, "take": -1},
				    {"filename": os.path.join(UpperCamelCase__						,			F'''{name}-train-00002-of-00010.arrow'''   ), "skip": 0, "take": -1},
				    {"filename": os.path.join(UpperCamelCase__						,			F'''{name}-train-00003-of-00010.arrow'''   ), "skip": 0, "take": 3},
				]





@pytest.mark.parametrize("""name, expected_exception"""						,			[(None, TypeError), ("""""", ValueError)]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  [SplitInfo(name="""train"""						,			num_examples=1_00   )]
				__lowerCAmelCase					=  """train"""
				__lowerCAmelCase					=  """arrow"""
				__lowerCAmelCase					=  """prefix_path"""
				with pytest.raises(UpperCamelCase__   ):
								__lowerCAmelCase					=  make_file_instructions(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class     a_       (			TensorFormatter[Mapping, 'tf.Tensor', Mapping]							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->int:
           super().__init__(features=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           import tensorflow as tf

           if isinstance(_lowerCamelCase				,       _lowerCamelCase   ) and column:
               if all(
                   isinstance(_lowerCamelCase				,       tf.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                   return tf.stack(_lowerCamelCase   )
               elif all(
                   isinstance(_lowerCamelCase				,       (tf.Tensor, tf.RaggedTensor)   ) and x.ndim == 1 and x.dtype == column[0].dtype
                   for x in column   ):
                   # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                   return tf.ragged.stack(_lowerCamelCase   )

           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Union[str, Any]:
           import tensorflow as tf

           if value is None:
               return value

           SCREAMING_SNAKE_CASE				:				int				      = {}

           if isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.integer   ):
               SCREAMING_SNAKE_CASE				:				List[Any]				      = {'''dtype''': tf.intaa}
           elif isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.floating   ):
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''dtype''': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(_lowerCamelCase				,       PIL.Image.Image   ):
                   SCREAMING_SNAKE_CASE				:				Any				      = np.asarray(_lowerCamelCase   )

           return tf.convert_to_tensor(_lowerCamelCase				,       **{**default_dtype, **self.tf_tensor_kwargs}   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch

               if isinstance(_lowerCamelCase				,       torch.Tensor   ):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
           if hasattr(_lowerCamelCase				,       '''__array__'''   ) and not isinstance(_lowerCamelCase				,       tf.Tensor   ):
               SCREAMING_SNAKE_CASE				:				int				      = data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(_lowerCamelCase				,       np.ndarray   ):
               if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(_lowerCamelCase   ) for substruct in data_struct]   )
           elif isinstance(_lowerCamelCase				,       (list, tuple)   ):
               return self._consolidate([self.recursive_tensorize(_lowerCamelCase   ) for substruct in data_struct]   )
           return self._tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           return map_nested(self._recursive_tensorize				,       _lowerCamelCase				,       map_list=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				str				      = self.numpy_arrow_extractor().extract_row(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.python_features_decoder.decode_row(_lowerCamelCase   )
           return self.recursive_tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->"tf.Tensor":
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.numpy_arrow_extractor().extract_column(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = self.python_features_decoder.decode_column(_lowerCamelCase				,       pa_table.column_names[0]   )
           SCREAMING_SNAKE_CASE				:				str				      = self.recursive_tensorize(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self._consolidate(_lowerCamelCase   )
           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.numpy_arrow_extractor().extract_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = self.python_features_decoder.decode_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self.recursive_tensorize(_lowerCamelCase   )
           for column_name in batch:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self._consolidate(batch[column_name]   )
           return batch
