




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

from ..utils.generic import ModelOutput


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      Optional[Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Optional[int])		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       dataset
           A__ 					=       process
           A__ 					=       params


    def __len__(     self :      List[Any])		->Any:





           '''simple docstring'''

           return len(self.dataset)





    def __getitem__(     self :      List[str]   ,					UpperCAmelCase__ :      int)		->List[str]:





           '''simple docstring'''

           A__ 					=       self.dataset[i]
           A__ 					=       self.process(UpperCAmelCase__   ,					**self.params)
           return processed


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      Optional[Any]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Dict=None)		->List[str]:





           '''simple docstring'''

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

           # Internal bookkeeping
           A__ 					=       None
           A__ 					=       None


    def __len__(     self :      int)		->Tuple:





           '''simple docstring'''

           return len(self.loader)


    def __iter__(     self :      int)		->List[str]:





           '''simple docstring'''

           A__ 					=       iter(self.loader)
           return self


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->int:





           '''simple docstring'''

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

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





    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Optional[int]:





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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      List[str]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Optional[int]=None)		->Optional[Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)


    def __iter__(     self :      Any)		->int:





           '''simple docstring'''

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





    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Dict:





           '''simple docstring'''

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __iter__(     self :      List[str])		->List[str]:





           '''simple docstring'''

           A__ 					=       iter(self.loader)
           return self





    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       False
           A__ 					=       []
           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:
                         A__ 					=       self.loader_batch_item()
                         A__ 					=       item.pop('''is_last''')
                         accumulator.append(UpperCAmelCase__)
                         if is_last:
                                return accumulator

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					UpperCAmelCase__ :      Dataset   ,					UpperCAmelCase__ :      str)		->str:





           '''simple docstring'''

           A__ 					=       dataset
           A__ 					=       key


    def __len__(     self :      Dict)		->Optional[int]:





           '''simple docstring'''

           return len(self.dataset)





    def __getitem__(     self :      int   ,					UpperCAmelCase__ :      int)		->Any:





           '''simple docstring'''

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      Optional[Any]   ,					UpperCAmelCase__ :      Dataset   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      str)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       dataset
           A__ 					=       keya
           A__ 					=       keya


    def __len__(     self :      List[Any])		->Optional[Any]:





           '''simple docstring'''

           return len(self.dataset)





    def __getitem__(     self :      str   ,					UpperCAmelCase__ :      int)		->List[str]:





           '''simple docstring'''

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






"""simple docstring"""


from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =""""""
							__UpperCAmelCase		:       Optional[int]							 ="""hf-legacy"""  # "hf://"" is reserved for hffs

							def __init__(	self       ,  __a = None       ,  __a = None       ,  **__a       ,  ):
										super().__init__(self       ,  **__a      )
										__lowerCAmelCase							       =     repo_info
										__lowerCAmelCase							       =     token
										__lowerCAmelCase							       =     None

							def      snake_case							(	self      ):
										if self.dir_cache is None:
													__lowerCAmelCase							       =     {}
													for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																__lowerCAmelCase							       =     {
																    "name": hf_file.rfilename,
																    "size": None,
																    "type": "file",
																}
																self.dir_cache.update(
																    {
																        str(__a      ): {"name": str(__a      ), "size": None, "type": "directory"}
																        for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
																    }      )

							def      snake_case							(	self       ,  __a       ,  __a = "rb"       ,  **__a       ,  ):
										if not isinstance(self.repo_info       ,  __a      ):
													raise NotImplementedError(f"Open is only implemented for dataset repositories, but got {self.repo_info}"      )
										__lowerCAmelCase							       =     hf_hub_url(self.repo_info.id       ,  __a       ,  revision=self.repo_info.sha      )
										return fsspec.open(
										    __a       ,  mode=__a       ,  headers=get_authentication_headers_for_url(__a       ,  use_auth_token=self.token      )       ,  client_kwargs={"trust_env": True}       ,  ).open()

							def      snake_case							(	self       ,  __a       ,  **__a      ):
										self._get_dirs()
										__lowerCAmelCase							       =     self._strip_protocol(__a      )
										if path in self.dir_cache:
													return self.dir_cache[path]
										else:
													raise FileNotFoundError(__a      )



							def      snake_case							(	self       ,  __a       ,  __a=False       ,  **__a      ):
										self._get_dirs()
										__lowerCAmelCase							       =     PurePosixPath(path.strip("/"      )      )
										__lowerCAmelCase							       =     {}
										for p, f in self.dir_cache.items():
													__lowerCAmelCase							       =     PurePosixPath(p.strip("/"      )      )
													__lowerCAmelCase							       =     p.parent
													if root == path:
																__lowerCAmelCase							       =     f
										__lowerCAmelCase							       =     list(paths.values()      )
										if detail:
													return out
										else:
													return sorted(f["name"] for f in out      )




