


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		(				SCREAMING_SNAKE_CASE_					:			Any   ):






							"""simple docstring"""



							a_							: Any								=						[]
							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     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			Optional[int]						,						SCREAMING_SNAKE_CASE_					:			str   ):






							"""simple docstring"""



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

							return tuple(reversed(__a   )   )





@torch.jit.ignore
def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			int						,						SCREAMING_SNAKE_CASE_					:			str						,						SCREAMING_SNAKE_CASE_					:			str						,						SCREAMING_SNAKE_CASE_					:			Optional[Any] = None						,						SCREAMING_SNAKE_CASE_					:			List[Any] = None						,						):






							"""simple docstring"""



							def reduce_edge_list(SCREAMING_SNAKE_CASE_					:			Union[str, Any]   ) -> None:
														a_							: Union[str, Any]								=						True
														for i in range(len(__a   )   ):
																					a_							: List[str]								=						-1 * (i + 1)
																					l[reversed_idx] &= tally
																					a_							: Union[str, Any]								=						l[reversed_idx]

							if start_edges is None:
														a_							: Optional[Any]								=						[s == 0 for s in start]
														reduce_edge_list(__a   )
							if end_edges is None:
														a_							: Tuple								=						[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   ),)]

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

							# 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

							a_							: Tuple[slice, ...]								=						tuple(__a   )
							a_							: List[Any]								=						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

														a_							: List[Any]								=						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

														a_							: int								=						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()   )
														a_							: Optional[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     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			Tuple						,						SCREAMING_SNAKE_CASE_					:			Optional[int]						,						SCREAMING_SNAKE_CASE_					:			Dict						,						SCREAMING_SNAKE_CASE_					:			Optional[int]   ):






							"""simple docstring"""



							a_							: Any								=						t.shape[:no_batch_dims]
							a_							: Tuple								=						list(_flat_idx_to_idx(__a						,						__a   )   )
							# _get_minimal_slice_set is inclusive
							a_							: List[Any]								=						list(_flat_idx_to_idx(flat_end - 1						,						__a   )   )

							# Get an ordered list of slices to perform
							a_							: int								=						_get_minimal_slice_set(
							    __a						,						__a						,						__a						,						)

							a_							: str								=						[t[s] for s in slices]

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





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			Optional[int]						,						SCREAMING_SNAKE_CASE_					:			Union[str, Any]						,						SCREAMING_SNAKE_CASE_					:			Dict						,						SCREAMING_SNAKE_CASE_					:			str						,						SCREAMING_SNAKE_CASE_					:			Tuple = False						,						SCREAMING_SNAKE_CASE_					:			List[Any] = None						,						SCREAMING_SNAKE_CASE_					:			Optional[int] = False						,						):






							"""simple docstring"""



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

							a_							: Any								=						[shape[:no_batch_dims] for shape in _fetch_dims(__a   )]
							a_							: Tuple								=						tuple([max(__a   ) for s in zip(*__a   )]   )

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

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

							a_							: str								=						1
							for d in orig_batch_dims:
														flat_batch_dim *= d

							a_							: Any								=						flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

														a_							: Dict[str, Any]								=						tensor_tree_map(__a						,						__a   )

														# Run the layer on the chunk
														a_							: int								=						layer(**__a   )

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

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

																					def assign(SCREAMING_SNAKE_CASE_					:			List[Any]						,						SCREAMING_SNAKE_CASE_					:			Optional[int]   ) -> 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:
																																																	a_							: List[Any]								=						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:
																																			a_							: Optional[int]								=						xa
														elif isinstance(__a						,						torch.Tensor   ):
																					if _add_into_out:
																												out[i : i + chunk_size] += output_chunk
																					else:
																												a_							: Tuple								=						output_chunk
														else:
																					raise ValueError("""Not supported"""   )

														i += chunk_size

							a_							: List[str]								=						tensor_tree_map(lambda SCREAMING_SNAKE_CASE_   : t.view(orig_batch_dims + t.shape[1:]   )						,						__a   )

							return out





class        snake_case__	:





	def __init__(	self					,     UpperCamelCase_ = 512					,     )		->      Tuple:
								"""simple docstring"""






								a_							: int								=						max_chunk_size
								a_							: Optional[int]								=						None
								a_							: Optional[tuple]								=						None





	def      A					(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_				)		->      str:
								"""simple docstring"""






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

								if min_chunk_size >= self.max_chunk_size:
															return min_chunk_size

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

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

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

								return candidates[min_viable_chunk_size_index]





	def      A					(	self					,     UpperCamelCase_					,     UpperCamelCase_				)		->      Any:
								"""simple docstring"""






								a_							: 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_				):
																						a_							: Dict								=						[v for _, v in sorted(aa.items()					,     key=lambda UpperCamelCase_				: x[0]				)]
																						a_							: int								=						[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      A					(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     )		->      Any:
								"""simple docstring"""






								a_							: int								=						True
								a_							: 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_				)
															a_							: Dict								=						self._compare_arg_caches(self.cached_arg_data					,     UpperCamelCase_				)
								else:
															# Otherwise, we can reuse the precomputed value
															a_							: Any								=						False

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

								assert self.cached_chunk_size is not None

								return self.cached_chunk_size






'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					inspect.getfile(accelerate.test_utils				)
							UpperCamelCase__  :Any				   =					os.path.sep.join(
							    mod_file.split(os.path.sep				)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']				)

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							UpperCamelCase__  :List[str]				   =					test_metrics




		@require_cpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							debug_launcher(self.test_metrics.main       ,  num_processes=1				)




		@require_cpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							debug_launcher(self.test_metrics.main				)




		@require_single_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.test_metrics.main()




		@require_multi_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							print(F'''Found {torch.cuda.device_count()} devices.'''				)
							UpperCamelCase__  :Optional[Any]				   =					['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
							with patch_environment(omp_num_threads=1				):
												execute_subprocess_async(UpperCamelCase_       ,  env=os.environ.copy()				)