





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 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Tuple											=	[]
	for d in reversed(__lowerCAmelCase   ):
		idx.append(flat_idx % d   )
		snake_case_		:			Union[str, Any]											=	flat_idx // d

	return tuple(reversed(__lowerCAmelCase   )   )


@torch.jit.ignore
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a = None   , __a = None   , ):


	def reduce_edge_list(__a   ) -> None:
		snake_case_		:			int											=	True
		for i in range(len(__lowerCAmelCase   )   ):
			snake_case_		:			Dict											=	-1 * (i + 1)
			l[reversed_idx] &= tally
			snake_case_		:			Optional[Any]											=	l[reversed_idx]

	if start_edges is None:
		snake_case_		:			Dict											=	[s == 0 for s in start]
		reduce_edge_list(__lowerCAmelCase   )
	if end_edges is None:
		snake_case_		:			Any											=	[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   ),)]

	snake_case_		:			List[Any]											=	[]
	snake_case_		:			int											=	[]

	# 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

	snake_case_		:			Union[str, Any]											=	tuple(__lowerCAmelCase   )
	snake_case_		:			Dict											=	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

		snake_case_		:			Tuple											=	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

		snake_case_		:			str											=	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()   )
		snake_case_		:			Optional[Any]											=	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Any											=	t.shape[:no_batch_dims]
	snake_case_		:			List[Any]											=	list(_flat_idx_to_idx(__lowerCAmelCase   , __lowerCAmelCase   )   )
	# _get_minimal_slice_set is inclusive
	snake_case_		:			Any											=	list(_flat_idx_to_idx(flat_end - 1   , __lowerCAmelCase   )   )

	# Get an ordered list of slices to perform
	snake_case_		:			Union[str, Any]											=	_get_minimal_slice_set(
	    __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , )

	snake_case_		:			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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a = False   , __a = None   , __a = False   , ):


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

	snake_case_		:			Dict											=	[shape[:no_batch_dims] for shape in _fetch_dims(__lowerCAmelCase   )]
	snake_case_		:			str											=	tuple([max(__lowerCAmelCase   ) for s in zip(*__lowerCAmelCase   )]   )

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

	snake_case_		:			Dict											=	tensor_tree_map(_prep_inputs   , __lowerCAmelCase   )
	snake_case_		:			str											=	None
	if _out is not None:
		snake_case_		:			int											=	tensor_tree_map(lambda __a   : t.view([-1] + list(t.shape[no_batch_dims:]   )   )   , _out   )

	snake_case_		:			List[str]											=	1
	for d in orig_batch_dims:
		flat_batch_dim *= d

	snake_case_		:			List[Any]											=	flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

	snake_case_		:			int											=	0
	snake_case_		:			Optional[int]											=	prepped_outputs
	for _ in range(__lowerCAmelCase   ):
		# Chunk the input
		if not low_mem:
			snake_case_		:			Optional[Any]											=	_select_chunk
		else:
			snake_case_		:			Any											=	partial(
			    _chunk_slice   , flat_start=__lowerCAmelCase   , flat_end=min(__lowerCAmelCase   , i + chunk_size   )   , no_batch_dims=len(__lowerCAmelCase   )   , )

		snake_case_		:			Tuple											=	tensor_tree_map(__lowerCAmelCase   , __lowerCAmelCase   )

		# Run the layer on the chunk
		snake_case_		:			str											=	layer(**__lowerCAmelCase   )

		# Allocate space for the output
		if out is None:
			snake_case_		:			List[Any]											=	tensor_tree_map(lambda __a   : t.new_zeros((flat_batch_dim,) + t.shape[1:]   )   , __lowerCAmelCase   )

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

			def assign(__a   , __a   ) -> 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:
							snake_case_		:			Optional[int]											=	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:
					snake_case_		:			List[str]											=	xa
		elif isinstance(__lowerCAmelCase   , torch.Tensor   ):
			if _add_into_out:
				out[i : i + chunk_size] += output_chunk
			else:
				snake_case_		:			Union[str, Any]											=	output_chunk
		else:
			raise ValueError('Not supported'   )

		i += chunk_size

	snake_case_		:			List[Any]											=	tensor_tree_map(lambda __a   : t.view(orig_batch_dims + t.shape[1:]   )   , __lowerCAmelCase   )

	return out


class SCREAMING_SNAKE_CASE_						:






						def __init__(      self      :      Tuple		,   _A      :      int = 512		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	max_chunk_size
							snake_case_		:			Tuple											=	None
							snake_case_		:			Optional[Any]											=	None






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Callable		,   _A      :      tuple		,   _A      :      int	)							->    Union[str, Any]:







							"""simple docstring"""


							logging.info('Tuning chunk size...'	)

							if min_chunk_size >= self.max_chunk_size:
								return min_chunk_size

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

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

							snake_case_		:			Dict											=	0
							snake_case_		:			Dict											=	len(lowerCAmelCase_	) - 1
							while i > min_viable_chunk_size_index:
								snake_case_		:			Any											=	test_chunk_size(candidates[i]	)
								if not viable:
									snake_case_		:			List[Any]											=	(min_viable_chunk_size_index + i) // 2
								else:
									snake_case_		:			List[str]											=	i
									snake_case_		:			Tuple											=	(i + len(lowerCAmelCase_	) - 1) // 2

							return candidates[min_viable_chunk_size_index]






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Iterable		,   _A      :      Iterable	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	True
							for aa, aa in zip(lowerCAmelCase_		,   lowerCAmelCase_	):
								assert type(lowerCAmelCase_	) == type(lowerCAmelCase_	)
								if isinstance(lowerCAmelCase_		,   (list, tuple)	):
									consistent &= self._compare_arg_caches(lowerCAmelCase_		,   lowerCAmelCase_	)
								elif isinstance(lowerCAmelCase_		,   lowerCAmelCase_	):
									snake_case_		:			List[Any]											=	[v for _, v in sorted(aa.items()		,   key=lambda _A	: x[0]	)]
									snake_case_		:			Tuple											=	[v for _, v in sorted(aa.items()		,   key=lambda _A	: x[0]	)]
									consistent &= self._compare_arg_caches(lowerCAmelCase_		,   lowerCAmelCase_	)
								else:
									consistent &= aa == aa

							return consistent






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Callable		,   _A      :      tuple		,   _A      :      int		,   )							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	True
							snake_case_		:			Optional[int]											=	tree_map(lambda _A	: a.shape if isinstance(lowerCAmelCase_		,   torch.Tensor	) else a		,   lowerCAmelCase_		,   lowerCAmelCase_	)
							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(lowerCAmelCase_	)
								snake_case_		:			Dict											=	self._compare_arg_caches(self.cached_arg_data		,   lowerCAmelCase_	)
							else:
								# Otherwise, we can reuse the precomputed value
								snake_case_		:			Optional[Any]											=	False

							if not consistent:
								snake_case_		:			Any											=	self._determine_favorable_chunk_size(
								    lowerCAmelCase_		,   lowerCAmelCase_		,   lowerCAmelCase_		,   )
								snake_case_		:			Union[str, Any]											=	arg_data

							assert self.cached_chunk_size is not None

							return self.cached_chunk_size










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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "lilt"






						def __init__(      self      :      Any		,   _A      :      Dict=30522		,   _A      :      List[Any]=768		,   _A      :      str=12		,   _A      :      int=12		,   _A      :      Optional[int]=3072		,   _A      :      Optional[Any]="gelu"		,   _A      :      Dict=0.1		,   _A      :      Tuple=0.1		,   _A      :      str=512		,   _A      :      Union[str, Any]=2		,   _A      :      Union[str, Any]=0.0_2		,   _A      :      Optional[Any]=1E-12		,   _A      :      Optional[int]=0		,   _A      :      Dict="absolute"		,   _A      :      Any=None		,   _A      :      Optional[Any]=4		,   _A      :      Any=1024		,   **_A      :      Union[str, Any]		,   )							->    List[str]:







							"""simple docstring"""


							super().__init__(pad_token_id=_A		,   **_A	)

							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			Dict											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			Tuple											=	hidden_act
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			int											=	hidden_dropout_prob
							snake_case_		:			Tuple											=	attention_probs_dropout_prob
							snake_case_		:			Dict											=	max_position_embeddings
							snake_case_		:			Any											=	type_vocab_size
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			Optional[int]											=	layer_norm_eps
							snake_case_		:			List[str]											=	position_embedding_type
							snake_case_		:			Optional[int]											=	classifier_dropout
							snake_case_		:			Dict											=	channel_shrink_ratio
							snake_case_		:			Tuple											=	max_ad_position_embeddings

