

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

from ..utils.generic import ModelOutput

class 				a  (	snake_case__				):


						def __init__(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    dataset
										_UpperCAmelCase       :		Optional[Any] 		=    process
										_UpperCAmelCase       :		str 		=    params


						def __len__(       self	):







										'''simple docstring'''


										return len(self.dataset	)





						def __getitem__(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.dataset[i]
										_UpperCAmelCase       :		List[Any] 		=    self.process(UpperCAmelCase_						,					**self.params	)
										return processed







class 				a  (	snake_case__				):


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







										'''simple docstring'''


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

										# Internal bookkeeping
										_UpperCAmelCase       :		Tuple 		=    None
										_UpperCAmelCase       :		Tuple 		=    None


						def __len__(       self	):







										'''simple docstring'''


										return len(self.loader	)


						def __iter__(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    iter(self.loader	)
										return self


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if isinstance(self._loader_batch_data						,					torch.Tensor	):
														# Batch data is simple tensor, just fetch the slice
														_UpperCAmelCase       :		List[str] 		=    self._loader_batch_data[self._loader_batch_index]
										else:
														# Batch data is assumed to be BaseModelOutput (or dict)
														_UpperCAmelCase       :		Dict 		=    {}
														for k, element in self._loader_batch_data.items():
																		if isinstance(UpperCAmelCase_						,					UpperCAmelCase_	):
																						# Convert ModelOutput to tuple first
																						_UpperCAmelCase       :		List[Any] 		=    element.to_tuple()
																						if isinstance(element[0]						,					torch.Tensor	):
																										_UpperCAmelCase       :		List[str] 		=    tuple(el[self._loader_batch_index].unsqueeze(0	) for el in element	)
																						elif isinstance(element[0]						,					np.ndarray	):
																										_UpperCAmelCase       :		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(UpperCAmelCase_						,					UpperCAmelCase_	):
																						# Those are stored as lists of tensors so need specific unbatching.
																						if isinstance(element[0]						,					torch.Tensor	):
																										_UpperCAmelCase       :		Tuple 		=    tuple(el[self._loader_batch_index].unsqueeze(0	) for el in element	)
																						elif isinstance(element[0]						,					np.ndarray	):
																										_UpperCAmelCase       :		int 		=    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       :		int 		=    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       :		int 		=    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       :		str 		=    np.expand_dims(element[self._loader_batch_index]						,					0	)
																		else:
																						# This is typically a list, so no need to `unsqueeze`.
																						_UpperCAmelCase       :		Optional[Any] 		=    element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
														_UpperCAmelCase       :		Any 		=    self._loader_batch_data.__class__(UpperCAmelCase_	)
										self._loader_batch_index += 1
										return result





						def        _UpperCAmelCase				(       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       :		str 		=    next(self.iterator	)
										_UpperCAmelCase       :		Tuple 		=    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	):
																		_UpperCAmelCase       :		Dict 		=    processed
														else:
																		_UpperCAmelCase       :		Any 		=    list(processed.keys()	)[0]
																		_UpperCAmelCase       :		Optional[Any] 		=    processed[key]
														if isinstance(UpperCAmelCase_						,					UpperCAmelCase_	):
																		_UpperCAmelCase       :		Tuple 		=    len(UpperCAmelCase_	)
														else:
																		_UpperCAmelCase       :		Any 		=    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       :		Tuple 		=    observed_batch_size
														# Setting internal index to unwrap the batch
														_UpperCAmelCase       :		Tuple 		=    processed
														_UpperCAmelCase       :		List[str] 		=    0
														return self.loader_batch_item()
										else:
														# We're not unrolling batches
														return processed







class 				a  (	snake_case__				):


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







										'''simple docstring'''


										super().__init__(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_	)


						def __iter__(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    iter(self.loader	)
										_UpperCAmelCase       :		Any 		=    None
										return self





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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







class 				a  (	snake_case__				):


						def __iter__(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    iter(self.loader	)
										return self





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    False
										_UpperCAmelCase       :		Tuple 		=    []
										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       :		Optional[Any] 		=    self.loader_batch_item()
																		_UpperCAmelCase       :		str 		=    item.pop("is_last"	)
																		accumulator.append(UpperCAmelCase_	)
																		if is_last:
																						return accumulator

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







class 				a  (	snake_case__				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    dataset
										_UpperCAmelCase       :		List[str] 		=    key


						def __len__(       self	):







										'''simple docstring'''


										return len(self.dataset	)





						def __getitem__(       self						,					A_	):







										'''simple docstring'''


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







class 				a  (	snake_case__				):


						def __init__(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    dataset
										_UpperCAmelCase       :		Optional[int] 		=    keya
										_UpperCAmelCase       :		str 		=    keya


						def __len__(       self	):







										'''simple docstring'''


										return len(self.dataset	)





						def __getitem__(       self						,					A_	):







										'''simple docstring'''


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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"openai-gpt"
						_lowercase    						=			{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=40478						,					A_=512						,					A_=768						,					A_=12						,					A_=12						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=0.1						,					A_=1e-5						,					A_=0.02						,					A_="cls_index"						,					A_=True						,					A_=None						,					A_=True						,					A_=0.1						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		str 		=    n_positions
										_UpperCAmelCase       :		List[Any] 		=    n_embd
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Any 		=    n_head
										_UpperCAmelCase       :		int 		=    afn
										_UpperCAmelCase       :		int 		=    resid_pdrop
										_UpperCAmelCase       :		Tuple 		=    embd_pdrop
										_UpperCAmelCase       :		Optional[Any] 		=    attn_pdrop
										_UpperCAmelCase       :		Any 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[str] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_type
										_UpperCAmelCase       :		List[Any] 		=    summary_use_proj
										_UpperCAmelCase       :		Optional[Any] 		=    summary_activation
										_UpperCAmelCase       :		int 		=    summary_first_dropout
										_UpperCAmelCase       :		List[str] 		=    summary_proj_to_labels
										super().__init__(**A_	)


