


import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       dataset
						UpperCamelCase__					=       process
						UpperCamelCase__					=       params




			def __len__(							self     ):

						'''simple docstring'''


						return len(self.dataset     )







			def __getitem__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.dataset[i]
						UpperCamelCase__					=       self.process(snake_case      ,						**self.params     )
						return processed







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


						UpperCamelCase__					=       loader
						UpperCamelCase__					=       infer
						UpperCamelCase__					=       params
						if loader_batch_size == 1:
									# Let's spare some time by deactivating altogether
									UpperCamelCase__					=       None
						UpperCamelCase__					=       loader_batch_size

						# Internal bookkeeping
						UpperCamelCase__					=       None
						UpperCamelCase__					=       None




			def __len__(							self     ):

						'''simple docstring'''


						return len(self.loader     )




			def __iter__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       iter(self.loader     )
						return self




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if isinstance(self._loader_batch_data      ,						torch.Tensor     ):
									# Batch data is simple tensor, just fetch the slice
									UpperCamelCase__					=       self._loader_batch_data[self._loader_batch_index]
						else:
									# Batch data is assumed to be BaseModelOutput (or dict)
									UpperCamelCase__					=       {}
									for k, element in self._loader_batch_data.items():
												if isinstance(snake_case      ,						snake_case     ):
															# Convert ModelOutput to tuple first
															UpperCamelCase__					=       element.to_tuple()
															if isinstance(element[0]      ,						torch.Tensor     ):
																		UpperCamelCase__					=       tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
															elif isinstance(element[0]      ,						np.ndarray     ):
																		UpperCamelCase__					=       tuple(np.expand_dims(el[self._loader_batch_index]      ,						0     ) for el in element     )
															continue
												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case      ,						snake_case     ):
															# Those are stored as lists of tensors so need specific unbatching.
															if isinstance(element[0]      ,						torch.Tensor     ):
																		UpperCamelCase__					=       tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
															elif isinstance(element[0]      ,						np.ndarray     ):
																		UpperCamelCase__					=       tuple(np.expand_dims(el[self._loader_batch_index]      ,						0     ) for el in element     )
															continue
												if element is None:
															# This can happen for optional data that get passed around
															UpperCamelCase__					=       None
												elif isinstance(element[self._loader_batch_index]      ,						torch.Tensor     ):
															# Take correct batch data, but make it looked like batch_size=1
															# For compatibility with other methods within transformers

															UpperCamelCase__					=       element[self._loader_batch_index].unsqueeze(0     )
												elif isinstance(element[self._loader_batch_index]      ,						np.ndarray     ):
															# Take correct batch data, but make it looked like batch_size=1
															# For compatibility with other methods within transformers
															UpperCamelCase__					=       np.expand_dims(element[self._loader_batch_index]      ,						0     )
												else:
															# This is typically a list, so no need to `unsqueeze`.
															UpperCamelCase__					=       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
									UpperCamelCase__					=       self._loader_batch_data.__class__(snake_case     )
						self._loader_batch_index += 1
						return result







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
									# We are currently unrolling a batch so we just need to return
									# the current item within a batch
									return self.loader_batch_item()

						# We're out of items within a batch
						UpperCamelCase__					=       next(self.iterator     )
						UpperCamelCase__					=       self.infer(snake_case      ,						**self.params     )
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
									# Try to infer the size of the batch
									if isinstance(snake_case      ,						torch.Tensor     ):
												UpperCamelCase__					=       processed
									else:
												UpperCamelCase__					=       list(processed.keys()     )[0]
												UpperCamelCase__					=       processed[key]
									if isinstance(snake_case      ,						snake_case     ):
												UpperCamelCase__					=       len(snake_case     )
									else:
												UpperCamelCase__					=       first_tensor.shape[0]
									if 0 < observed_batch_size < self.loader_batch_size:
												# could be last batch so we can't unroll as many
												# elements.
												UpperCamelCase__					=       observed_batch_size
									# Setting internal index to unwrap the batch
									UpperCamelCase__					=       processed
									UpperCamelCase__					=       0
									return self.loader_batch_item()
						else:
									# We're not unrolling batches
									return processed







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


						super().__init__(snake_case      ,						snake_case      ,						snake_case     )




			def __iter__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       iter(self.loader     )
						UpperCamelCase__					=       None
						return self







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self.subiterator is None:
									UpperCamelCase__					=       self.infer(next(self.iterator     )      ,						**self.params     )
						try:
									# Try to return next item
									UpperCamelCase__					=       next(self.subiterator     )
						except StopIteration:
									# When a preprocess iterator ends, we can start lookig at the next item
									# ChunkIterator will keep feeding until ALL elements of iterator
									# all have created their subiterator and have been iterating against.
									#
									# Another way to look at it, is we're basically flattening lists of lists
									# into a single list, but with generators
									UpperCamelCase__					=       self.infer(next(self.iterator     )      ,						**self.params     )
									UpperCamelCase__					=       next(self.subiterator     )
						return processed







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __iter__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       iter(self.loader     )
						return self







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       False
						UpperCamelCase__					=       []
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
									while self._loader_batch_index < self.loader_batch_size:
												UpperCamelCase__					=       self.loader_batch_item()
												UpperCamelCase__					=       item.pop("is_last"     )
												accumulator.append(snake_case     )
												if is_last:
															return accumulator

						while not is_last:
									UpperCamelCase__					=       self.infer(next(self.iterator     )      ,						**self.params     )
									if self.loader_batch_size is not None:
												if isinstance(snake_case      ,						torch.Tensor     ):
															UpperCamelCase__					=       processed
												else:
															UpperCamelCase__					=       list(processed.keys()     )[0]
															UpperCamelCase__					=       processed[key]
												if isinstance(snake_case      ,						snake_case     ):
															UpperCamelCase__					=       len(snake_case     )
												else:
															UpperCamelCase__					=       first_tensor.shape[0]
												if 0 < observed_batch_size < self.loader_batch_size:
															# could be last batch so we can't unroll as many
															# elements.
															UpperCamelCase__					=       observed_batch_size
												UpperCamelCase__					=       processed
												UpperCamelCase__					=       0
												while self._loader_batch_index < self.loader_batch_size:
															UpperCamelCase__					=       self.loader_batch_item()
															UpperCamelCase__					=       item.pop("is_last"     )
															accumulator.append(snake_case     )
															if is_last:
																		return accumulator
									else:
												UpperCamelCase__					=       processed
												UpperCamelCase__					=       item.pop("is_last"     )
												accumulator.append(snake_case     )
						return accumulator







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       dataset
						UpperCamelCase__					=       key




			def __len__(							self     ):

						'''simple docstring'''


						return len(self.dataset     )







			def __getitem__(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.dataset[i][self.key]







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       dataset
						UpperCamelCase__					=       keya
						UpperCamelCase__					=       keya




			def __len__(							self     ):

						'''simple docstring'''


						return len(self.dataset     )







			def __getitem__(							self      ,						snake_case     ):

						'''simple docstring'''


						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil
@pytest.fixture
def        UpperCamelCase_(				_A   :Union[str, Any]  )->		int:
			UpperCamelCase__					=       tmp_path / "file.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :int  )->		Union[str, Any]:
			UpperCamelCase__					=       tmp_path / "malformed_file.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20,\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       tmp_path / "csv_with_image.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    F'''\
        image
        {image_file}
        '''  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :List[Any]  )->		Any:
			UpperCamelCase__					=       tmp_path / "csv_with_label.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        label\n        good\n        bad\n        good\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :Any  )->		int:
			UpperCamelCase__					=       tmp_path / "csv_with_int_list.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Optional[Any]				,     _A   :List[str]  )->		Union[str, Any]:
			UpperCamelCase__					=       Csv()
			UpperCamelCase__					=       csv._generate_tables([[csv_file, malformed_csv_file]]  )
			with pytest.raises(_A				,     match="Error tokenizing data"  ):
						for _ in generator:
									pass
			assert any(
			    record.levelname == "ERROR"
			    and "Failed to read file" in record.message
			    and os.path.basename(_A  ) in record.message
			    for record in caplog.records  )
@require_pil
def        UpperCamelCase_(				_A   :Any  )->		int:
			with open(_A				,     encoding="utf-8"  ) as f:
						UpperCamelCase__					=       f.read().splitlines()[1]
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     features=Features({"image": Image()}  )  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_image]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field("image"  ).type == Image()()
			UpperCamelCase__					=       pa_table.to_pydict()["image"]
			assert generated_content == [{"path": image_file, "bytes": None}]
def        UpperCamelCase_(				_A   :Any  )->		Dict:
			with open(_A				,     encoding="utf-8"  ) as f:
						UpperCamelCase__					=       f.read().splitlines()[1:]
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     features=Features({"label": ClassLabel(names=["good", "bad"]  )}  )  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_label]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field("label"  ).type == ClassLabel(names=["good", "bad"]  )()
			UpperCamelCase__					=       pa_table.to_pydict()["label"]
			assert generated_content == [ClassLabel(names=["good", "bad"]  ).straint(_A  ) for label in labels]







def        UpperCamelCase_(				_A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     sep=","				,     converters={"int_list": lambda _A  : [int(_A  ) for i in x.split()]}  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_int_list]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa.types.is_list(pa_table.schema.field("int_list"  ).type  )
			UpperCamelCase__					=       pa_table.to_pydict()["int_list"]
			assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]


