





'''simple docstring'''






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

from ..utils.generic import ModelOutput




class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     dataset
					__snake_case					:      str							      =     process
					__snake_case					:      Union[str, Any]							      =     params

		def __len__(   self					:   int			)					->    Any:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Any  ,      __magic_name__					:   List[str]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.dataset[i]
					__snake_case					:      Dict							      =     self.process(__magic_name__  ,      **self.params			)
					return processed





class  _A (					__lowercase		):

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


					"""simple docstring"""

					__snake_case					:      Tuple							      =     loader
					__snake_case					:      List[Any]							      =     infer
					__snake_case					:      Tuple							      =     params
					if loader_batch_size == 1:
								# Let's spare some time by deactivating altogether
								__snake_case					:      Any							      =     None
					__snake_case					:      Tuple							      =     loader_batch_size

					# Internal bookkeeping
					__snake_case					:      Union[str, Any]							      =     None
					__snake_case					:      Optional[Any]							      =     None

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


					"""simple docstring"""

					return len(self.loader			)

		def __iter__(   self					:   Optional[int]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     iter(self.loader			)
					return self

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


					"""simple docstring"""

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

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

		def 	lowercase__     (   self					:   Optional[int]			)					->    Optional[Any]:


					"""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
					__snake_case					:      List[Any]							      =     next(self.iterator			)
					__snake_case					:      Union[str, Any]							      =     self.infer(__magic_name__  ,      **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(__magic_name__  ,      torch.Tensor			):
											__snake_case					:      List[Any]							      =     processed
								else:
											__snake_case					:      Optional[Any]							      =     list(processed.keys()			)[0]
											__snake_case					:      Dict							      =     processed[key]
								if isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      List[str]							      =     len(__magic_name__			)
								else:
											__snake_case					:      int							      =     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.
											__snake_case					:      Optional[int]							      =     observed_batch_size
								# Setting internal index to unwrap the batch
								__snake_case					:      Union[str, Any]							      =     processed
								__snake_case					:      Any							      =     0
								return self.loader_batch_item()
					else:
								# We're not unrolling batches
								return processed





class  _A (					__lowercase		):

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


					"""simple docstring"""

					super().__init__(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

		def __iter__(   self					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     iter(self.loader			)
					__snake_case					:      List[str]							      =     None
					return self

		def 	lowercase__     (   self					:   Tuple			)					->    Tuple:


					"""simple docstring"""

					if self.subiterator is None:
								__snake_case					:      Any							      =     self.infer(next(self.iterator			)  ,      **self.params			)
					try:
								# Try to return next item
								__snake_case					:      List[Any]							      =     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
								__snake_case					:      List[Any]							      =     self.infer(next(self.iterator			)  ,      **self.params			)
								__snake_case					:      List[str]							      =     next(self.subiterator			)
					return processed





class  _A (					__lowercase		):

		def __iter__(   self					:   Optional[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     iter(self.loader			)
					return self

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Dict							      =     False
					__snake_case					:      Union[str, Any]							      =     []
					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:
											__snake_case					:      Union[str, Any]							      =     self.loader_batch_item()
											__snake_case					:      List[str]							      =     item.pop("""is_last"""			)
											accumulator.append(__magic_name__			)
											if is_last:
														return accumulator

					while not is_last:
								__snake_case					:      Union[str, Any]							      =     self.infer(next(self.iterator			)  ,      **self.params			)
								if self.loader_batch_size is not None:
											if isinstance(__magic_name__  ,      torch.Tensor			):
														__snake_case					:      str							      =     processed
											else:
														__snake_case					:      Optional[Any]							      =     list(processed.keys()			)[0]
														__snake_case					:      List[str]							      =     processed[key]
											if isinstance(__magic_name__  ,      __magic_name__			):
														__snake_case					:      Tuple							      =     len(__magic_name__			)
											else:
														__snake_case					:      Dict							      =     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.
														__snake_case					:      List[str]							      =     observed_batch_size
											__snake_case					:      List[Any]							      =     processed
											__snake_case					:      str							      =     0
											while self._loader_batch_index < self.loader_batch_size:
														__snake_case					:      List[Any]							      =     self.loader_batch_item()
														__snake_case					:      List[Any]							      =     item.pop("""is_last"""			)
														accumulator.append(__magic_name__			)
														if is_last:
																	return accumulator
								else:
											__snake_case					:      Tuple							      =     processed
											__snake_case					:      List[Any]							      =     item.pop("""is_last"""			)
											accumulator.append(__magic_name__			)
					return accumulator





class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     dataset
					__snake_case					:      Optional[int]							      =     key

		def __len__(   self					:   str			)					->    Tuple:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Optional[Any]  ,      __magic_name__					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

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





class  _A (					__lowercase		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str  ,      __magic_name__					:   str			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     dataset
					__snake_case					:      Any							      =     keya
					__snake_case					:      int							      =     keya

		def __len__(   self					:   Union[str, Any]			)					->    Tuple:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Union[str, Any]  ,      __magic_name__					:   Tuple			)					->    Dict:


					"""simple docstring"""

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









'''simple docstring'''






import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					    =	logging.getLogger()




@unittest.skip('''Temporarily disable the doc tests.'''		)
@require_torch
@require_tf
@slow
class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Path  ,      __magic_name__					:   Union[str, None] = None  ,      __magic_name__					:   Union[List[str], None] = None  ,      __magic_name__					:   Union[str, List[str], None] = None  ,      __magic_name__					:   bool = True  ,      )					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [file for file in os.listdir(__magic_name__			) if os.path.isfile(os.path.join(__magic_name__  ,      __magic_name__			)			)]

					if identifier is not None:
								__snake_case					:      List[Any]							      =     [file for file in files if identifier in file]

					if n_identifier is not None:
								if isinstance(__magic_name__  ,      __magic_name__			):
											for n_ in n_identifier:
														__snake_case					:      Optional[int]							      =     [file for file in files if n_ not in file]
								else:
											__snake_case					:      Tuple							      =     [file for file in files if n_identifier not in file]

					__snake_case					:      Dict							      =     ignore_files or []
					ignore_files.append("""__init__.py"""			)
					__snake_case					:      List[str]							      =     [file for file in files if file not in ignore_files]

					for file in files:
								# Open all files
								print("""Testing"""  ,      __magic_name__			)

								if only_modules:
											__snake_case					:      List[Any]							      =     file.split("""."""			)[0]
											try:
														__snake_case					:      List[Any]							      =     getattr(__magic_name__  ,      __magic_name__			)
														__snake_case					:      Union[str, Any]							      =     doctest.DocTestSuite(__magic_name__			)
														__snake_case					:      Dict							      =     unittest.TextTestRunner().run(__magic_name__			)
														self.assertIs(len(result.failures			)  ,      0			)
											except AttributeError:
														logger.info(f'''{module_identifier} is not a module.'''			)
								else:
											__snake_case					:      Tuple							      =     doctest.testfile(str("""..""" / directory / file			)  ,      optionflags=doctest.ELLIPSIS			)
											self.assertIs(result.failed  ,      0			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[Any]							      =     """modeling"""
					__snake_case					:      Union[str, Any]							      =     [
					    """modeling_ctrl.py""",
					    """modeling_tf_ctrl.py""",
					]
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__  ,      ignore_files=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      Any							      =     """tokenization"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[str]							      =     """configuration"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Path("""src/transformers"""			)
					__snake_case					:      int							      =     ["""configuration""", """modeling""", """tokenization"""]
					self.analyze_directory(__magic_name__  ,      n_identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     Path("""docs/source"""			)
					__snake_case					:      Optional[int]							      =     ["""favicon.ico"""]
					self.analyze_directory(__magic_name__  ,      ignore_files=__magic_name__  ,      only_modules=__magic_name__			)


