



import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCAmelCase_									=							pytest.mark.integration
@pytest.mark.parametrize('''path'''     ,				['''paws''', '''csv''']				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->int:
      inspect_dataset(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  path + '''.py'''
      assert script_name in os.listdir(_SCREAMING_SNAKE_CASE				)
      assert "__pycache__" not in os.listdir(_SCREAMING_SNAKE_CASE				)
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''				)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''				)
@pytest.mark.parametrize('''path'''     ,				['''accuracy''']				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->str:
      inspect_metric(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  path + '''.py'''
      assert script_name in os.listdir(_SCREAMING_SNAKE_CASE				)
      assert "__pycache__" not in os.listdir(_SCREAMING_SNAKE_CASE				)
@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''     ,				[
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]     ,				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	Dict				)->Tuple:
      _lowerCAmelCase 			=  get_dataset_config_info(_SCREAMING_SNAKE_CASE     ,				config_name=_SCREAMING_SNAKE_CASE				)
      assert info.config_name == config_name
      assert list(info.splits.keys()				) == expected_splits
@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''     ,				[
        ('''paws''', None, ValueError),
    ]     ,				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	str				)->Dict:
      with pytest.raises(_SCREAMING_SNAKE_CASE				):
            get_dataset_config_info(_SCREAMING_SNAKE_CASE     ,				config_name=_SCREAMING_SNAKE_CASE				)
@pytest.mark.parametrize(
    '''path, expected'''     ,				[
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]     ,				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->List[Any]:
      _lowerCAmelCase 			=  get_dataset_config_names(_SCREAMING_SNAKE_CASE				)
      assert expected in config_names
@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''     ,				[
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]     ,				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Union[str, Any]:
      _lowerCAmelCase 			=  get_dataset_infos(_SCREAMING_SNAKE_CASE				)
      assert list(infos.keys()				) == expected_configs
      _lowerCAmelCase 			=  expected_configs[0]
      assert expected_config in infos
      _lowerCAmelCase 			=  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()				) == expected_splits_in_first_config
@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''     ,				[
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]     ,				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	Any				)->Optional[int]:
      _lowerCAmelCase 			=  get_dataset_infos(_SCREAMING_SNAKE_CASE				)
      assert expected_config in infos
      _lowerCAmelCase 			=  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()				) == expected_splits


@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''     ,				[
        ('''paws''', None, ValueError),
    ]     ,				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	Dict				)->Optional[Any]:
      with pytest.raises(_SCREAMING_SNAKE_CASE				):
            get_dataset_split_names(_SCREAMING_SNAKE_CASE     ,				config_name=_SCREAMING_SNAKE_CASE				)




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

from ..utils.generic import ModelOutput





class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  process
         _lowerCAmelCase 			=  params





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.dataset[i]
         _lowerCAmelCase 			=  self.process(_lowerCAmelCase       ,     **self.params			)
         return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         _lowerCAmelCase 			=  loader
         _lowerCAmelCase 			=  infer
         _lowerCAmelCase 			=  params
         if loader_batch_size == 1:
               # Let's spare some time by deactivating altogether
               _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  loader_batch_size

         # Internal bookkeeping
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None





   def __len__(	self			):
         return len(self.loader			)





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         return self





   def 	__lowerCAmelCase							(	self			):
         if isinstance(self._loader_batch_data       ,     torch.Tensor			):
               # Batch data is simple tensor, just fetch the slice
               _lowerCAmelCase 			=  self._loader_batch_data[self._loader_batch_index]
         else:
               # Batch data is assumed to be BaseModelOutput (or dict)
               _lowerCAmelCase 			=  {}
               for k, element in self._loader_batch_data.items():
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           # Convert ModelOutput to tuple first
                           _lowerCAmelCase 			=  element.to_tuple()
                           if isinstance(element[0]       ,     torch.Tensor			):
                                 _lowerCAmelCase 			=  tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                           elif isinstance(element[0]       ,     np.ndarray			):
                                 _lowerCAmelCase 			=  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(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           # Those are stored as lists of tensors so need specific unbatching.
                           if isinstance(element[0]       ,     torch.Tensor			):
                                 _lowerCAmelCase 			=  tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                           elif isinstance(element[0]       ,     np.ndarray			):
                                 _lowerCAmelCase 			=  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
                           _lowerCAmelCase 			=  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

                           _lowerCAmelCase 			=  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
                           _lowerCAmelCase 			=  np.expand_dims(element[self._loader_batch_index]       ,     0			)
                     else:
                           # This is typically a list, so no need to `unsqueeze`.
                           _lowerCAmelCase 			=  element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
               _lowerCAmelCase 			=  self._loader_batch_data.__class__(_lowerCAmelCase			)
         self._loader_batch_index += 1
         return result





   def 	__lowerCAmelCase							(	self			):
         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
         _lowerCAmelCase 			=  next(self.iterator			)
         _lowerCAmelCase 			=  self.infer(_lowerCAmelCase       ,     **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(_lowerCAmelCase       ,     torch.Tensor			):
                     _lowerCAmelCase 			=  processed
               else:
                     _lowerCAmelCase 			=  list(processed.keys()			)[0]
                     _lowerCAmelCase 			=  processed[key]
               if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                     _lowerCAmelCase 			=  len(_lowerCAmelCase			)
               else:
                     _lowerCAmelCase 			=  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.
                     _lowerCAmelCase 			=  observed_batch_size
               # Setting internal index to unwrap the batch
               _lowerCAmelCase 			=  processed
               _lowerCAmelCase 			=  0
               return self.loader_batch_item()
         else:
               # We're not unrolling batches
               return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         _lowerCAmelCase 			=  None
         return self





   def 	__lowerCAmelCase							(	self			):
         if self.subiterator is None:
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
         try:
               # Try to return next item
               _lowerCAmelCase 			=  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
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
               _lowerCAmelCase 			=  next(self.subiterator			)
         return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         return self





   def 	__lowerCAmelCase							(	self			):
         # Extremely similar to PipelineIterator in its unpacking mechanism
         # BUT, we have an extra required item which is the presence of `is_last`
         # That is because everything is flattened by `PipelineChunkIterator` we
         # need to keep track of how to regroup here in the original `process`
         # boundaries so that `process` and `postprocess` see the same data.

         # This iterator accumulates items (possibly while unbatching) until it
         # its a `is_last` and then just passes it on to the caller.
         _lowerCAmelCase 			=  False
         _lowerCAmelCase 			=  []
         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:
                     _lowerCAmelCase 			=  self.loader_batch_item()
                     _lowerCAmelCase 			=  item.pop('''is_last'''			)
                     accumulator.append(_lowerCAmelCase			)
                     if is_last:
                           return accumulator

         while not is_last:
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
               if self.loader_batch_size is not None:
                     if isinstance(_lowerCAmelCase       ,     torch.Tensor			):
                           _lowerCAmelCase 			=  processed
                     else:
                           _lowerCAmelCase 			=  list(processed.keys()			)[0]
                           _lowerCAmelCase 			=  processed[key]
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           _lowerCAmelCase 			=  len(_lowerCAmelCase			)
                     else:
                           _lowerCAmelCase 			=  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.
                           _lowerCAmelCase 			=  observed_batch_size
                     _lowerCAmelCase 			=  processed
                     _lowerCAmelCase 			=  0
                     while self._loader_batch_index < self.loader_batch_size:
                           _lowerCAmelCase 			=  self.loader_batch_item()
                           _lowerCAmelCase 			=  item.pop('''is_last'''			)
                           accumulator.append(_lowerCAmelCase			)
                           if is_last:
                                 return accumulator
               else:
                     _lowerCAmelCase 			=  processed
                     _lowerCAmelCase 			=  item.pop('''is_last'''			)
                     accumulator.append(_lowerCAmelCase			)
         return accumulator






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  key





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return self.dataset[i][self.key]






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  keya
         _lowerCAmelCase 			=  keya





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}