



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__  (			__SCREAMING_SNAKE_CASE )		->							List[Tuple[int, ...]]:
		UpperCAmelCase_						=				[]
		if isinstance(__snake_case			,     __snake_case ):
				for v in tree.values():
						shapes.extend(_fetch_dims(__snake_case ) )
		elif isinstance(__snake_case			,     (list, tuple) ):
				for t in tree:
						shapes.extend(_fetch_dims(__snake_case ) )
		elif isinstance(__snake_case			,     torch.Tensor ):
				shapes.append(tree.shape )
		else:
				raise ValueError("Not supported" )

		return shapes







@torch.jit.ignore
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple[int, ...]:
		UpperCAmelCase_						=				[]
		for d in reversed(__snake_case ):
				idx.append(flat_idx % d )
				UpperCAmelCase_						=				flat_idx // d

		return tuple(reversed(__snake_case ) )







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





		def reduce_edge_list(__SCREAMING_SNAKE_CASE ) -> None:
				UpperCAmelCase_						=				True
				for i in range(len(__snake_case ) ):
						UpperCAmelCase_						=				-1 * (i + 1)
						l[reversed_idx] &= tally
						UpperCAmelCase_						=				l[reversed_idx]

		if start_edges is None:
				UpperCAmelCase_						=				[s == 0 for s in start]
				reduce_edge_list(__snake_case )
		if end_edges is None:
				UpperCAmelCase_						=				[e == (d - 1) for e, d in zip(__snake_case			,     __snake_case )]
				reduce_edge_list(__snake_case )

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

		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				[]

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

		UpperCAmelCase_						=				tuple(__snake_case )
		UpperCAmelCase_						=				len(__snake_case )

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

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

				UpperCAmelCase_						=				start[divergence_idx]
				return tuple(
				    path + (slice(__snake_case			,     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

				UpperCAmelCase_						=				end[divergence_idx]
				return tuple(
				    path + (slice(__snake_case			,     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() )
				UpperCAmelCase_						=				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__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							torch.Tensor:
		UpperCAmelCase_						=				t.shape[:no_batch_dims]
		UpperCAmelCase_						=				list(_flat_idx_to_idx(__snake_case			,     __snake_case ) )
		# _get_minimal_slice_set is inclusive
		UpperCAmelCase_						=				list(_flat_idx_to_idx(flat_end - 1			,     __snake_case ) )

		# Get an ordered list of slices to perform
		UpperCAmelCase_						=				_get_minimal_slice_set(
		    __snake_case			,     __snake_case			,     __snake_case			,     )

		UpperCAmelCase_						=				[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__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = False			,     )		->							Any:





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

		UpperCAmelCase_						=				[shape[:no_batch_dims] for shape in _fetch_dims(__snake_case )]
		UpperCAmelCase_						=				tuple([max(__snake_case ) for s in zip(*__snake_case )] )

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

		UpperCAmelCase_						=				tensor_tree_map(_prep_inputs			,     __snake_case )
		UpperCAmelCase_						=				None
		if _out is not None:
				UpperCAmelCase_						=				tensor_tree_map(lambda __SCREAMING_SNAKE_CASE : t.view([-1] + list(t.shape[no_batch_dims:] ) )			,     _out )

		UpperCAmelCase_						=				1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		UpperCAmelCase_						=				flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

				UpperCAmelCase_						=				tensor_tree_map(__snake_case			,     __snake_case )

				# Run the layer on the chunk
				UpperCAmelCase_						=				layer(**__snake_case )

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

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

						def assign(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) -> None:
								for k, v in da.items():
										if isinstance(__snake_case			,     __snake_case ):
												assign(__snake_case			,     da[k] )
										else:
												if _add_into_out:
														v[i : i + chunk_size] += da[k]
												else:
														UpperCAmelCase_						=				da[k]

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

				i += chunk_size

		UpperCAmelCase_						=				tensor_tree_map(lambda __SCREAMING_SNAKE_CASE : t.view(orig_batch_dims + t.shape[1:] )			,     __snake_case )

		return out



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase = 512    ,	):
							UpperCAmelCase_						=				max_chunk_size
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				None



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							logging.info("Tuning chunk size..."     )

							if min_chunk_size >= self.max_chunk_size:
									return min_chunk_size

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

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

							UpperCAmelCase_						=				0
							UpperCAmelCase_						=				len(__a     ) - 1
							while i > min_viable_chunk_size_index:
									UpperCAmelCase_						=				test_chunk_size(candidates[i]     )
									if not viable:
											UpperCAmelCase_						=				(min_viable_chunk_size_index + i) // 2
									else:
											UpperCAmelCase_						=				i
											UpperCAmelCase_						=				(i + len(__a     ) - 1) // 2

							return candidates[min_viable_chunk_size_index]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				True
							for aa, aa in zip(__a    ,	__a     ):
									assert type(__a     ) == type(__a     )
									if isinstance(__a    ,	(list, tuple)     ):
											consistent &= self._compare_arg_caches(__a    ,	__a     )
									elif isinstance(__a    ,	__a     ):
											UpperCAmelCase_						=				[v for _, v in sorted(aa.items()    ,	key=lambda lowerCAmelCase     : x[0]     )]
											UpperCAmelCase_						=				[v for _, v in sorted(aa.items()    ,	key=lambda lowerCAmelCase     : x[0]     )]
											consistent &= self._compare_arg_caches(__a    ,	__a     )
									else:
											consistent &= aa == aa

							return consistent



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	):
							UpperCAmelCase_						=				True
							UpperCAmelCase_						=				tree_map(lambda lowerCAmelCase     : a.shape if isinstance(__a    ,	torch.Tensor     ) else a    ,	__a    ,	__a     )
							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(__a     )
									UpperCAmelCase_						=				self._compare_arg_caches(self.cached_arg_data    ,	__a     )
							else:
									# Otherwise, we can reuse the precomputed value
									UpperCAmelCase_						=				False

							if not consistent:
									UpperCAmelCase_						=				self._determine_favorable_chunk_size(
									    __a    ,	__a    ,	__a    ,	)
									UpperCAmelCase_						=				arg_data

							assert self.cached_chunk_size is not None

							return self.cached_chunk_size






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

