




"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_nllb import NllbTokenizer
else:
						__A   :  Union[str, Any]					=      None


__A   :  Tuple					=      logging.get_logger(__name__)


__A   :  List[Any]					=      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A   :  Optional[Any]					=      {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

__A   :  List[str]					=      {
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
__A   :  Tuple					=      ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Union[str, Any]       =      VOCAB_FILES_NAMES
				__UpperCAmelCase :     Any       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCAmelCase :     Tuple       =      PRETRAINED_VOCAB_FILES_MAP
				__UpperCAmelCase :     List[str]       =      ["input_ids", "attention_mask"]
				__UpperCAmelCase :     Tuple       =      NllbTokenizer

				__UpperCAmelCase :     List[int]       =      []
				__UpperCAmelCase :     List[int]       =      []



				def __init__(     self					:	Tuple    ,   lowercase__					:	Dict=None    ,   lowercase__					:	Dict=None    ,   lowercase__					:	str="<s>"    ,   lowercase__					:	Optional[int]="</s>"    ,   lowercase__					:	Optional[int]="</s>"    ,   lowercase__					:	Dict="<s>"    ,   lowercase__					:	Tuple="<unk>"    ,   lowercase__					:	List[Any]="<pad>"    ,   lowercase__					:	List[Any]="<mask>"    ,   lowercase__					:	Tuple=None    ,   lowercase__					:	List[str]=None    ,   lowercase__					:	List[Any]=None    ,   lowercase__					:	Optional[int]=False    ,   **lowercase__					:	Tuple    ,   ):
						# Mask token behave like a normal word, i.e. include the space before it
						__lowercase				:		Dict												=				AddedToken(lowercase__    ,   lstrip=lowercase__    ,   rstrip=lowercase__						) if isinstance(lowercase__    ,   lowercase__						) else mask_token
						__lowercase				:		int												=				legacy_behaviour
						super().__init__(
						    vocab_file=lowercase__    ,   tokenizer_file=lowercase__    ,   bos_token=lowercase__    ,   eos_token=lowercase__    ,   sep_token=lowercase__    ,   cls_token=lowercase__    ,   unk_token=lowercase__    ,   pad_token=lowercase__    ,   mask_token=lowercase__    ,   src_lang=lowercase__    ,   tgt_lang=lowercase__    ,   additional_special_tokens=lowercase__    ,   legacy_behaviour=lowercase__    ,   **lowercase__    ,   )

						__lowercase				:		List[str]												=				vocab_file
						__lowercase				:		Tuple												=				False if not self.vocab_file else True

						__lowercase				:		Dict												=				FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
								# Only add those special tokens if they are not already there.
								_additional_special_tokens.extend(
								    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

						self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
						__lowercase				:		Union[str, Any]												=				{
						    lang_code: self.convert_tokens_to_ids(lowercase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						__lowercase				:		int												=				src_lang if src_lang is not None else "eng_Latn"
						__lowercase				:		Dict												=				self.convert_tokens_to_ids(self._src_lang						)
						__lowercase				:		Any												=				tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)



				@property
				def       snake_case    (     self					:	List[str]						):
						return self._src_lang



				@src_lang.setter
				def       snake_case    (     self					:	int    ,   lowercase__					:	str						):
						__lowercase				:		Any												=				new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	List[int]    ,   lowercase__					:	Optional[List[int]] = None						):
						if token_ids_a is None:
								return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	List[int]    ,   lowercase__					:	Optional[List[int]] = None						):
						__lowercase				:		Dict												=				[self.sep_token_id]
						__lowercase				:		Tuple												=				[self.cls_token_id]

						if token_ids_a is None:
								return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str    ,   lowercase__					:	Optional[str]    ,   lowercase__					:	Optional[str]    ,   **lowercase__					:	Union[str, Any]						):
						if src_lang is None or tgt_lang is None:
								raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
						__lowercase				:		Optional[Any]												=				src_lang
						__lowercase				:		Dict												=				self(lowercase__    ,   add_special_tokens=lowercase__    ,   return_tensors=lowercase__    ,   **lowercase__						)
						__lowercase				:		Optional[Any]												=				self.convert_tokens_to_ids(lowercase__						)
						__lowercase				:		Tuple												=				tgt_lang_id
						return inputs



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str = "eng_Latn"    ,   lowercase__					:	Optional[List[str]] = None    ,   lowercase__					:	str = "fra_Latn"    ,   **lowercase__					:	List[str]    ,   ):
						__lowercase				:		List[str]												=				src_lang
						__lowercase				:		List[Any]												=				tgt_lang
						return super().prepare_seqaseq_batch(lowercase__    ,   lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Any						):
						return self.set_src_lang_special_tokens(self.src_lang						)



				def       snake_case    (     self					:	Dict						):
						return self.set_tgt_lang_special_tokens(self.tgt_lang						)



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	Any						):
						__lowercase				:		int												=				self.convert_tokens_to_ids(lowercase__						)

						if self.legacy_behaviour:
								__lowercase				:		Union[str, Any]												=				[]
								__lowercase				:		Any												=				[self.eos_token_id, self.cur_lang_code]
						else:
								__lowercase				:		Optional[Any]												=				[self.cur_lang_code]
								__lowercase				:		List[Any]												=				[self.eos_token_id]

						__lowercase				:		Dict												=				self.convert_ids_to_tokens(self.prefix_tokens						)
						__lowercase				:		Dict												=				self.convert_ids_to_tokens(self.suffix_tokens						)

						__lowercase				:		int												=				processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,   pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,   self.prefix_tokens + self.suffix_tokens						)						)    ,   )



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	str						):
						__lowercase				:		int												=				self.convert_tokens_to_ids(lowercase__						)
						if self.legacy_behaviour:
								__lowercase				:		Optional[int]												=				[]
								__lowercase				:		Dict												=				[self.eos_token_id, self.cur_lang_code]
						else:
								__lowercase				:		Tuple												=				[self.cur_lang_code]
								__lowercase				:		List[Any]												=				[self.eos_token_id]

						__lowercase				:		List[Any]												=				self.convert_ids_to_tokens(self.prefix_tokens						)
						__lowercase				:		List[Any]												=				self.convert_ids_to_tokens(self.suffix_tokens						)

						__lowercase				:		Any												=				processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,   pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,   self.prefix_tokens + self.suffix_tokens						)						)    ,   )



				def       snake_case    (     self					:	Any    ,   lowercase__					:	str    ,   lowercase__					:	Optional[str] = None						):
						if not self.can_save_slow_tokenizer:
								raise ValueError(
								    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
								    "tokenizer."						)

						if not os.path.isdir(lowercase__						):
								logger.error(f'Vocabulary path ({save_directory}) should be a directory.'						)
								return
						__lowercase				:		Optional[int]												=				os.path.join(
						    lowercase__    ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						):
								copyfile(self.vocab_file    ,   lowercase__						)

						return (out_vocab_file,)






"""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
