





'''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'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



