




"""simple docstring"""







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy

@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). "  ,			__snake_case  ,			)
class       lowerCAmelCase__       (			__snake_case    ):


				"""simple docstring"""





				__UpperCAmelCase :     Union[str, Any]       =      RobertaConfig
				__UpperCAmelCase :     Optional[int]       =      'roberta'



				def __init__(     self					:	int    ,   lowercase__					:	Optional[Any]						):
						super().__init__(A_						)

						__lowercase				:		int												=				RobertaEmbeddings(A_						)
						self.init_weights()





@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. "  ,			__snake_case  ,			)
class       lowerCAmelCase__       (			__snake_case    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[Any]       =      RobertaConfig
				__UpperCAmelCase :     List[str]       =      'roberta'



				def __init__(     self					:	List[Any]    ,   lowercase__					:	Dict						):
						super().__init__(A_						)
						__lowercase				:		int												=				config.num_labels
						__lowercase				:		str												=				config.num_hidden_layers

						__lowercase				:		Optional[Any]												=				DeeRobertaModel(A_						)
						__lowercase				:		Tuple												=				nn.Dropout(config.hidden_dropout_prob						)
						__lowercase				:		List[Any]												=				nn.Linear(config.hidden_size    ,   self.config.num_labels						)



				@add_start_docstrings_to_model_forward(A_						)
				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Union[str, Any]=None    ,   lowercase__					:	str=None    ,   lowercase__					:	Any=None    ,   lowercase__					:	Tuple=None    ,   lowercase__					:	List[str]=None    ,   lowercase__					:	List[Any]=None    ,   lowercase__					:	Any=None    ,   lowercase__					:	str=-1    ,   lowercase__					:	List[Any]=False    ,   ):
						__lowercase				:		Optional[int]												=				self.num_layers
						try:
								__lowercase				:		Union[str, Any]												=				self.roberta(
								    A_    ,   attention_mask=A_    ,   token_type_ids=A_    ,   position_ids=A_    ,   head_mask=A_    ,   inputs_embeds=A_    ,   )

								__lowercase				:		Any												=				outputs[1]

								__lowercase				:		List[str]												=				self.dropout(A_						)
								__lowercase				:		str												=				self.classifier(A_						)
								__lowercase				:		Optional[int]												=				(logits,) + outputs[2:]  # add hidden states and attention if they are here
						except HighwayException as e:
								__lowercase				:		List[str]												=				e.message
								__lowercase				:		Optional[Any]												=				e.exit_layer
								__lowercase				:		List[Any]												=				outputs[0]

						if not self.training:
								__lowercase				:		List[Any]												=				entropy(A_						)
								__lowercase				:		Optional[Any]												=				[]
								__lowercase				:		List[str]												=				[]
						if labels is not None:
								if self.num_labels == 1:
										#  We are doing regression
										__lowercase				:		Any												=				MSELoss()
										__lowercase				:		Any												=				loss_fct(logits.view(-1						)    ,   labels.view(-1						)						)
								else:
										__lowercase				:		Any												=				CrossEntropyLoss()
										__lowercase				:		str												=				loss_fct(logits.view(-1    ,   self.num_labels						)    ,   labels.view(-1						)						)

								# work with highway exits
								__lowercase				:		Any												=				[]
								for highway_exit in outputs[-1]:
										__lowercase				:		List[str]												=				highway_exit[0]
										if not self.training:
												highway_logits_all.append(A_						)
												highway_entropy.append(highway_exit[2]						)
										if self.num_labels == 1:
												#  We are doing regression
												__lowercase				:		Optional[int]												=				MSELoss()
												__lowercase				:		Tuple												=				loss_fct(highway_logits.view(-1						)    ,   labels.view(-1						)						)
										else:
												__lowercase				:		Tuple												=				CrossEntropyLoss()
												__lowercase				:		List[str]												=				loss_fct(highway_logits.view(-1    ,   self.num_labels						)    ,   labels.view(-1						)						)
										highway_losses.append(A_						)

								if train_highway:
										__lowercase				:		int												=				(sum(highway_losses[:-1]						),) + outputs
										# exclude the final highway, of course
								else:
										__lowercase				:		str												=				(loss,) + outputs
						if not self.training:
								__lowercase				:		Optional[int]												=				outputs + ((original_entropy, highway_entropy), exit_layer)
								if output_layer >= 0:
										__lowercase				:		Optional[Any]												=				(
										    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
										)  # use the highway of the last layer

						return outputs  # (loss), logits, (hidden_states), (attentions), entropy






"""simple docstring"""







import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def 						snake_case__       (					_lowerCamelCase				)			->List[Tuple[int, ...]]:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				[]
		if isinstance(_lowerCamelCase,  _lowerCamelCase				):
				for v in tree.values():
						shapes.extend(_fetch_dims(_lowerCamelCase				)				)
		elif isinstance(_lowerCamelCase,  (list, tuple)				):
				for t in tree:
						shapes.extend(_fetch_dims(_lowerCamelCase				)				)
		elif isinstance(_lowerCamelCase,  torch.Tensor				):
				shapes.append(tree.shape				)
		else:
				raise ValueError("Not supported"				)

		return shapes

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple[int, ...]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				[]
		for d in reversed(_lowerCamelCase				):
				idx.append(flat_idx % d				)
				__lowercase				:		List[Any]												=				flat_idx // d

		return tuple(reversed(_lowerCamelCase				)				)

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = None,  _lowerCamelCase = None,  )			->List[Tuple[slice, ...]]:






		"""simple docstring"""


		def reduce_edge_list(_lowerCamelCase				) -> None:
				__lowercase				:		Optional[Any]												=				True
				for i in range(len(_lowerCamelCase				)				):
						__lowercase				:		Any												=				-1 * (i + 1)
						l[reversed_idx] &= tally
						__lowercase				:		List[str]												=				l[reversed_idx]

		if start_edges is None:
				__lowercase				:		Optional[Any]												=				[s == 0 for s in start]
				reduce_edge_list(_lowerCamelCase				)
		if end_edges is None:
				__lowercase				:		Dict												=				[e == (d - 1) for e, d in zip(_lowerCamelCase,  _lowerCamelCase				)]
				reduce_edge_list(_lowerCamelCase				)

		# Base cases. Either start/end are empty and we're done, or the final,
		# one-dimensional tensor can be simply sliced
		if len(_lowerCamelCase				) == 0:
				return [()]
		elif len(_lowerCamelCase				) == 1:
				return [(slice(start[0],  end[0] + 1				),)]

		__lowercase				:		List[Tuple[slice, ...]]												=				[]
		__lowercase				:		List[slice]												=				[]

		# Dimensions common to start and end can be selected directly
		for s, e in zip(_lowerCamelCase,  _lowerCamelCase				):
				if s == e:
						path_list.append(slice(_lowerCamelCase,  s + 1				)				)
				else:
						break

		__lowercase				:		Tuple[slice, ...]												=				tuple(_lowerCamelCase				)
		__lowercase				:		Any												=				len(_lowerCamelCase				)

		# start == end, and we're done
		if divergence_idx == len(_lowerCamelCase				):
				return [path]

		def upper() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				__lowercase				:		Any												=				start[divergence_idx]
				return tuple(
				    path + (slice(_lowerCamelCase,  sdi + 1				),) + s
				    for s in _get_minimal_slice_set(
				        start[divergence_idx + 1 :],  [d - 1 for d in dims[divergence_idx + 1 :]],  dims[divergence_idx + 1 :],  start_edges=start_edges[divergence_idx + 1 :],  end_edges=[True for _ in end_edges[divergence_idx + 1 :]],  )				)

		def lower() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				__lowercase				:		int												=				end[divergence_idx]
				return tuple(
				    path + (slice(_lowerCamelCase,  edi + 1				),) + s
				    for s in _get_minimal_slice_set(
				        [0 for _ in start[divergence_idx + 1 :]],  end[divergence_idx + 1 :],  dims[divergence_idx + 1 :],  start_edges=[True for _ in start_edges[divergence_idx + 1 :]],  end_edges=end_edges[divergence_idx + 1 :],  )				)

		# If both start and end are at the edges of the subtree rooted at
		# divergence_idx, we can just select the whole subtree at once
		if start_edges[divergence_idx] and end_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx],  end[divergence_idx] + 1				),)				)
		# If just start is at the edge, we can grab almost all of the subtree,
		# treating only the ragged bottom edge as an edge case
		elif start_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx],  end[divergence_idx]				),)				)
				slices.extend(lower()				)
		# Analogous to the previous case, but the top is ragged this time
		elif end_edges[divergence_idx]:
				slices.extend(upper()				)
				slices.append(path + (slice(start[divergence_idx] + 1,  end[divergence_idx] + 1				),)				)
		# If both sides of the range are ragged, we need to handle both sides
		# separately. If there's contiguous meat in between them, we can index it
		# in one big chunk
		else:
				slices.extend(upper()				)
				__lowercase				:		int												=				end[divergence_idx] - start[divergence_idx]
				if middle_ground > 1:
						slices.append(path + (slice(start[divergence_idx] + 1,  end[divergence_idx]				),)				)
				slices.extend(lower()				)

		return slices

@torch.jit.ignore
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->torch.Tensor:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				t.shape[:no_batch_dims]
		__lowercase				:		Dict												=				list(_flat_idx_to_idx(_lowerCamelCase,  _lowerCamelCase				)				)
		# _get_minimal_slice_set is inclusive
		__lowercase				:		List[str]												=				list(_flat_idx_to_idx(flat_end - 1,  _lowerCamelCase				)				)

		# Get an ordered list of slices to perform
		__lowercase				:		Dict												=				_get_minimal_slice_set(
		    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )

		__lowercase				:		Optional[Any]												=				[t[s] for s in slices]

		return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = False,  _lowerCamelCase = None,  _lowerCamelCase = False,  )			->Any:






		"""simple docstring"""


		if not (len(_lowerCamelCase				) > 0):
				raise ValueError("Must provide at least one input"				)

		__lowercase				:		Dict												=				[shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase				)]
		__lowercase				:		List[Any]												=				tuple([max(_lowerCamelCase				) for s in zip(*_lowerCamelCase				)]				)

		def _prep_inputs(_lowerCamelCase				) -> torch.Tensor:
				if not low_mem:
						if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
								__lowercase				:		Optional[int]												=				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
						__lowercase				:		Optional[Any]												=				t.reshape(-1,  *t.shape[no_batch_dims:]				)
				else:
						__lowercase				:		Union[str, Any]												=				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
				return t

		__lowercase				:		Dict[str, Any]												=				tensor_tree_map(_prep_inputs,  _lowerCamelCase				)
		__lowercase				:		str												=				None
		if _out is not None:
				__lowercase				:		Union[str, Any]												=				tensor_tree_map(lambda _lowerCamelCase				: t.view([-1] + list(t.shape[no_batch_dims:]				)				),  _out				)

		__lowercase				:		int												=				1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		__lowercase				:		Dict												=				flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

		def _select_chunk(_lowerCamelCase				) -> torch.Tensor:
				return t[i : i + chunk_size] if t.shape[0] != 1 else t

		__lowercase				:		Dict												=				0
		__lowercase				:		List[str]												=				prepped_outputs
		for _ in range(_lowerCamelCase				):
				# Chunk the input
				if not low_mem:
						__lowercase				:		Dict												=				_select_chunk
				else:
						__lowercase				:		List[Any]												=				partial(
						    _chunk_slice,  flat_start=_lowerCamelCase,  flat_end=min(_lowerCamelCase,  i + chunk_size				),  no_batch_dims=len(_lowerCamelCase				),  )

				__lowercase				:		Dict[str, Any]												=				tensor_tree_map(_lowerCamelCase,  _lowerCamelCase				)

				# Run the layer on the chunk
				__lowercase				:		int												=				layer(**_lowerCamelCase				)

				# Allocate space for the output
				if out is None:
						__lowercase				:		int												=				tensor_tree_map(lambda _lowerCamelCase				: t.new_zeros((flat_batch_dim,) + t.shape[1:]				),  _lowerCamelCase				)

				# Put the chunk in its pre-allocated space
				if isinstance(_lowerCamelCase,  _lowerCamelCase				):

						def assign(_lowerCamelCase,  _lowerCamelCase				) -> None:
								for k, v in da.items():
										if isinstance(_lowerCamelCase,  _lowerCamelCase				):
												assign(_lowerCamelCase,  da[k]				)
										else:
												if _add_into_out:
														v[i : i + chunk_size] += da[k]
												else:
														__lowercase				:		Optional[Any]												=				da[k]

						assign(_lowerCamelCase,  _lowerCamelCase				)
				elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
						for xa, xa in zip(_lowerCamelCase,  _lowerCamelCase				):
								if _add_into_out:
										xa[i : i + chunk_size] += xa
								else:
										__lowercase				:		Optional[int]												=				xa
				elif isinstance(_lowerCamelCase,  torch.Tensor				):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								__lowercase				:		int												=				output_chunk
				else:
						raise ValueError("Not supported"				)

				i += chunk_size

		__lowercase				:		int												=				tensor_tree_map(lambda _lowerCamelCase				: t.view(orig_batch_dims + t.shape[1:]				),  _lowerCamelCase				)

		return out





class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	int = 5_1_2    ,   ):
						__lowercase				:		str												=				max_chunk_size
						__lowercase				:		Optional[int]												=				None
						__lowercase				:		Optional[tuple]												=				None



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int						):
						logging.info("Tuning chunk size..."						)

						if min_chunk_size >= self.max_chunk_size:
								return min_chunk_size

						__lowercase				:		List[int]												=				[2**l for l in range(int(math.log(self.max_chunk_size    ,   2						)						) + 1						)]
						__lowercase				:		Optional[int]												=				[c for c in candidates if c > min_chunk_size]
						__lowercase				:		str												=				[min_chunk_size] + candidates
						candidates[-1] += 4

						def test_chunk_size(lowercase__					:	int						) -> bool:
								try:
										with torch.no_grad():
												fn(*lowercase__    ,   chunk_size=lowercase__						)
										return True
								except RuntimeError:
										return False

						__lowercase				:		Union[str, Any]												=				0
						__lowercase				:		Optional[Any]												=				len(lowercase__						) - 1
						while i > min_viable_chunk_size_index:
								__lowercase				:		List[Any]												=				test_chunk_size(candidates[i]						)
								if not viable:
										__lowercase				:		Tuple												=				(min_viable_chunk_size_index + i) // 2
								else:
										__lowercase				:		Optional[Any]												=				i
										__lowercase				:		List[Any]												=				(i + len(lowercase__						) - 1) // 2

						return candidates[min_viable_chunk_size_index]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Iterable    ,   lowercase__					:	Iterable						):
						__lowercase				:		List[Any]												=				True
						for aa, aa in zip(lowercase__    ,   lowercase__						):
								assert type(lowercase__						) == type(lowercase__						)
								if isinstance(lowercase__    ,   (list, tuple)						):
										consistent &= self._compare_arg_caches(lowercase__    ,   lowercase__						)
								elif isinstance(lowercase__    ,   lowercase__						):
										__lowercase				:		Dict												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										__lowercase				:		Optional[int]												=				[v for _, v in sorted(aa.items()    ,   key=lambda lowercase__						: x[0]						)]
										consistent &= self._compare_arg_caches(lowercase__    ,   lowercase__						)
								else:
										consistent &= aa == aa

						return consistent



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Callable    ,   lowercase__					:	tuple    ,   lowercase__					:	int    ,   ):
						__lowercase				:		Tuple												=				True
						__lowercase				:		tuple												=				tree_map(lambda lowercase__						: a.shape if isinstance(lowercase__    ,   torch.Tensor						) else a    ,   lowercase__    ,   lowercase__						)
						if self.cached_arg_data is not None:
								# If args have changed shape/value, we need to re-tune
								assert len(self.cached_arg_data						) == len(lowercase__						)
								__lowercase				:		Optional[int]												=				self._compare_arg_caches(self.cached_arg_data    ,   lowercase__						)
						else:
								# Otherwise, we can reuse the precomputed value
								__lowercase				:		int												=				False

						if not consistent:
								__lowercase				:		List[Any]												=				self._determine_favorable_chunk_size(
								    lowercase__    ,   lowercase__    ,   lowercase__    ,   )
								__lowercase				:		Union[str, Any]												=				arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size
