

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
'''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    lowerCamelCase       (			UpperCAmelCase__				:  Union[dict, list, tuple, torch.Tensor]		)			->	List[Tuple[int, ...]]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = []
							if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		):
														for v in tree.values():
																					shapes.extend(_fetch_dims(UpperCAmelCase__		)		)
							elif isinstance(UpperCAmelCase__				,						(list, tuple)		):
														for t in tree:
																					shapes.extend(_fetch_dims(UpperCAmelCase__		)		)
							elif isinstance(UpperCAmelCase__				,						torch.Tensor		):
														shapes.append(tree.shape		)
							else:
														raise ValueError('Not supported'		)

							return shapes


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


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Dict						    = []
							for d in reversed(UpperCAmelCase__		):
														idx.append(flat_idx % d		)
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = flat_idx // d

							return tuple(reversed(UpperCAmelCase__		)		)


@torch.jit.ignore
def    lowerCamelCase       (			UpperCAmelCase__				:  Sequence[int]				,						UpperCAmelCase__				:  Sequence[int]				,						UpperCAmelCase__				:  Sequence[int]				,						UpperCAmelCase__				:  Optional[Sequence[bool]] = None				,						UpperCAmelCase__				:  Optional[Sequence[bool]] = None				,						)			->	List[Tuple[slice, ...]]:


							'''simple docstring'''






							def reduce_edge_list(UpperCAmelCase__				:  List[bool]		) -> None:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = True
														for i in range(len(UpperCAmelCase__		)		):
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = -1 * (i + 1)
																					l[reversed_idx] &= tally
																					SCREAMING_SNAKE_CASE__     :Tuple						    = l[reversed_idx]

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

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

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

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

							SCREAMING_SNAKE_CASE__     :Tuple[slice, ...]						    = tuple(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = len(UpperCAmelCase__		)

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

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

														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = start[divergence_idx]
														return tuple(
														    path + (slice(UpperCAmelCase__				,						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

														SCREAMING_SNAKE_CASE__     :Any						    = end[divergence_idx]
														return tuple(
														    path + (slice(UpperCAmelCase__				,						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()		)
														SCREAMING_SNAKE_CASE__     :str						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  torch.Tensor				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int		)			->	torch.Tensor:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = t.shape[:no_batch_dims]
							SCREAMING_SNAKE_CASE__     :int						    = list(_flat_idx_to_idx(UpperCAmelCase__				,						UpperCAmelCase__		)		)
							# _get_minimal_slice_set is inclusive
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = list(_flat_idx_to_idx(flat_end - 1				,						UpperCAmelCase__		)		)

							# Get an ordered list of slices to perform
							SCREAMING_SNAKE_CASE__     :List[Any]						    = _get_minimal_slice_set(
							    UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						)

							SCREAMING_SNAKE_CASE__     :Dict						    = [t[s] for s in slices]

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


def    lowerCamelCase       (			UpperCAmelCase__				:  Callable				,						UpperCAmelCase__				:  Dict[str, Any]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  bool = False				,						UpperCAmelCase__				:  Any = None				,						UpperCAmelCase__				:  bool = False				,						)			->	Any:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [shape[:no_batch_dims] for shape in _fetch_dims(UpperCAmelCase__		)]
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = tuple([max(UpperCAmelCase__		) for s in zip(*UpperCAmelCase__		)]		)

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

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

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 1
							for d in orig_batch_dims:
														flat_batch_dim *= d

							SCREAMING_SNAKE_CASE__     :str						    = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

							SCREAMING_SNAKE_CASE__     :str						    = 0
							SCREAMING_SNAKE_CASE__     :Any						    = prepped_outputs
							for _ in range(UpperCAmelCase__		):
														# Chunk the input
														if not low_mem:
																					SCREAMING_SNAKE_CASE__     :Tuple						    = _select_chunk
														else:
																					SCREAMING_SNAKE_CASE__     :Any						    = partial(
																					    _chunk_slice				,						flat_start=UpperCAmelCase__				,						flat_end=min(UpperCAmelCase__				,						i + chunk_size		)				,						no_batch_dims=len(UpperCAmelCase__		)				,						)

														SCREAMING_SNAKE_CASE__     :Dict[str, Any]						    = tensor_tree_map(UpperCAmelCase__				,						UpperCAmelCase__		)

														# Run the layer on the chunk
														SCREAMING_SNAKE_CASE__     :Any						    = layer(**UpperCAmelCase__		)

														# Allocate space for the output
														if out is None:
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tensor_tree_map(lambda UpperCAmelCase__		: t.new_zeros((flat_batch_dim,) + t.shape[1:]		)				,						UpperCAmelCase__		)

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

																					def assign(UpperCAmelCase__				:  dict				,						UpperCAmelCase__				:  dict		) -> None:
																												for k, v in da.items():
																																			if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		):
																																										assign(UpperCAmelCase__				,						da[k]		)
																																			else:
																																										if _add_into_out:
																																																	v[i : i + chunk_size] += da[k]
																																										else:
																																																	SCREAMING_SNAKE_CASE__     :str						    = da[k]

																					assign(UpperCAmelCase__				,						UpperCAmelCase__		)
														elif isinstance(UpperCAmelCase__				,						UpperCAmelCase__		):
																					for xa, xa in zip(UpperCAmelCase__				,						UpperCAmelCase__		):
																												if _add_into_out:
																																			xa[i : i + chunk_size] += xa
																												else:
																																			SCREAMING_SNAKE_CASE__     :int						    = xa
														elif isinstance(UpperCAmelCase__				,						torch.Tensor		):
																					if _add_into_out:
																												out[i : i + chunk_size] += output_chunk
																					else:
																												SCREAMING_SNAKE_CASE__     :List[Any]						    = output_chunk
														else:
																					raise ValueError('Not supported'		)

														i += chunk_size

							SCREAMING_SNAKE_CASE__     :str						    = tensor_tree_map(lambda UpperCAmelCase__		: t.view(orig_batch_dims + t.shape[1:]		)				,						UpperCAmelCase__		)

							return out





class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Any							,							UpperCamelCase_ :       int = 5_12							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :int						    = max_chunk_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = None
													SCREAMING_SNAKE_CASE__     :Optional[tuple]						    = None




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Callable							,							UpperCamelCase_ :       tuple							,							UpperCamelCase_ :       int      )     ->				int:
													logging.info('Tuning chunk size...'      )

													if min_chunk_size >= self.max_chunk_size:
																				return min_chunk_size

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

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

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 0
													SCREAMING_SNAKE_CASE__     :List[str]						    = len(UpperCamelCase_      ) - 1
													while i > min_viable_chunk_size_index:
																				SCREAMING_SNAKE_CASE__     :int						    = test_chunk_size(candidates[i]      )
																				if not viable:
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = (min_viable_chunk_size_index + i) // 2
																				else:
																											SCREAMING_SNAKE_CASE__     :int						    = i
																											SCREAMING_SNAKE_CASE__     :List[str]						    = (i + len(UpperCamelCase_      ) - 1) // 2

													return candidates[min_viable_chunk_size_index]




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       Iterable							,							UpperCamelCase_ :       Iterable      )     ->				bool:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = True
													for aa, aa in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				assert type(UpperCamelCase_      ) == type(UpperCamelCase_      )
																				if isinstance(UpperCamelCase_							,							(list, tuple)      ):
																											consistent &= self._compare_arg_caches(UpperCamelCase_							,							UpperCamelCase_      )
																				elif isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = [v for _, v in sorted(aa.items()							,							key=lambda UpperCamelCase_      : x[0]      )]
																											SCREAMING_SNAKE_CASE__     :Tuple						    = [v for _, v in sorted(aa.items()							,							key=lambda UpperCamelCase_      : x[0]      )]
																											consistent &= self._compare_arg_caches(UpperCamelCase_							,							UpperCamelCase_      )
																				else:
																											consistent &= aa == aa

													return consistent




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Callable							,							UpperCamelCase_ :       tuple							,							UpperCamelCase_ :       int							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :Dict						    = True
													SCREAMING_SNAKE_CASE__     :tuple						    = tree_map(lambda UpperCamelCase_      : a.shape if isinstance(UpperCamelCase_							,							torch.Tensor      ) else a							,							UpperCamelCase_							,							UpperCamelCase_      )
													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(UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self._compare_arg_caches(self.cached_arg_data							,							UpperCamelCase_      )
													else:
																				# Otherwise, we can reuse the precomputed value
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = False

													if not consistent:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._determine_favorable_chunk_size(
																				    UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							)
																				SCREAMING_SNAKE_CASE__     :int						    = arg_data

													assert self.cached_chunk_size is not None

													return self.cached_chunk_size






