


"""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 	UpperCAmelCase     (		A	:						Union[dict, list, tuple, torch.Tensor]       ):

		'''simple docstring'''


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

		return shapes




@torch.jit.ignore
def 	UpperCAmelCase     (		A	:						int     ,				A	:						Tuple[int, ...]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]
		for d in reversed(A       ):
				idx.append(flat_idx % d       )
				_UpperCAmelCase							 =		flat_idx // d

		return tuple(reversed(A       )       )




@torch.jit.ignore
def 	UpperCAmelCase     (		A	:						Sequence[int]     ,				A	:						Sequence[int]     ,				A	:						Sequence[int]     ,				A	:						Optional[Sequence[bool]] = None     ,				A	:						Optional[Sequence[bool]] = None     ,				):

		'''simple docstring'''


		def reduce_edge_list(A	:						List[bool]       ) -> None:
				_UpperCAmelCase							 =		True
				for i in range(len(A       )       ):
						_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(A       )
		if end_edges is None:
				_UpperCAmelCase							 =		[e == (d - 1) for e, d in zip(A     ,				A       )]
				reduce_edge_list(A       )

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

		_UpperCAmelCase							 =		tuple(A       )
		_UpperCAmelCase							 =		len(A       )

		# start == end, and we're done
		if divergence_idx == len(A       ):
				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(A     ,				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(A     ,				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 	UpperCAmelCase     (		A	:						torch.Tensor     ,				A	:						int     ,				A	:						int     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		t.shape[:no_batch_dims]
		_UpperCAmelCase							 =		list(_flat_idx_to_idx(A     ,				A       )       )
		# _get_minimal_slice_set is inclusive
		_UpperCAmelCase							 =		list(_flat_idx_to_idx(flat_end - 1     ,				A       )       )

		# Get an ordered list of slices to perform
		_UpperCAmelCase							 =		_get_minimal_slice_set(
		    A     ,				A     ,				A     ,				)

		_UpperCAmelCase							 =		[t[s] for s in slices]

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




def 	UpperCAmelCase     (		A	:						Callable     ,				A	:						Dict[str, Any]     ,				A	:						int     ,				A	:						int     ,				A	:						bool = False     ,				A	:						Any = None     ,				A	:						bool = False     ,				):

		'''simple docstring'''


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

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

		def _prep_inputs(A	:						torch.Tensor       ) -> 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     ,				A       )
		_UpperCAmelCase							 =		None
		if _out is not None:
				_UpperCAmelCase							 =		tensor_tree_map(lambda A       : 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(A	:						torch.Tensor       ) -> torch.Tensor:
				return t[i : i + chunk_size] if t.shape[0] != 1 else t

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

				_UpperCAmelCase							 =		tensor_tree_map(A     ,				A       )

				# Run the layer on the chunk
				_UpperCAmelCase							 =		layer(**A       )

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

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

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

						assign(A     ,				A       )
				elif isinstance(A     ,				A       ):
						for xa, xa in zip(A     ,				A       ):
								if _add_into_out:
										xa[i : i + chunk_size] += xa
								else:
										_UpperCAmelCase							 =		xa
				elif isinstance(A     ,				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 A       : t.view(orig_batch_dims + t.shape[1:]       )     ,				A       )

		return out






class 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case = 512	,       )			->							str:
									_UpperCAmelCase							 =		max_chunk_size
									_UpperCAmelCase							 =		None
									_UpperCAmelCase							 =		None






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

									_UpperCAmelCase							 =		0
									_UpperCAmelCase							 =		len(snake_case						) - 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(snake_case						) - 1) // 2

									return candidates[min_viable_chunk_size_index]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							bool:
									_UpperCAmelCase							 =		True
									for aa, aa in zip(snake_case	,       snake_case						):
											assert type(snake_case						) == type(snake_case						)
											if isinstance(snake_case	,       (list, tuple)						):
													consistent &= self._compare_arg_caches(snake_case	,       snake_case						)
											elif isinstance(snake_case	,       snake_case						):
													_UpperCAmelCase							 =		[v for _, v in sorted(aa.items()	,       key=lambda snake_case						: x[0]						)]
													_UpperCAmelCase							 =		[v for _, v in sorted(aa.items()	,       key=lambda snake_case						: x[0]						)]
													consistent &= self._compare_arg_caches(snake_case	,       snake_case						)
											else:
													consistent &= aa == aa

									return consistent






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       )			->							int:
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		tree_map(lambda snake_case						: a.shape if isinstance(snake_case	,       torch.Tensor						) else a	,       snake_case	,       snake_case						)
									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(snake_case						)
											_UpperCAmelCase							 =		self._compare_arg_caches(self.cached_arg_data	,       snake_case						)
									else:
											# Otherwise, we can reuse the precomputed value
											_UpperCAmelCase							 =		False

									if not consistent:
											_UpperCAmelCase							 =		self._determine_favorable_chunk_size(
											    snake_case	,       snake_case	,       snake_case	,       )
											_UpperCAmelCase							 =		arg_data

									assert self.cached_chunk_size is not None

									return self.cached_chunk_size







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  							=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''MvpTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MvpForCausalLM''',
					    '''MvpForConditionalGeneration''',
					    '''MvpForQuestionAnswering''',
					    '''MvpForSequenceClassification''',
					    '''MvpModel''',
					    '''MvpPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


