






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_		(lowercase__						:       Union[dict, list, tuple, torch.Tensor]       )		->    List[Tuple[int, ...]]:


				'''simple docstring'''




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

				return shapes





@torch.jit.ignore
def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Tuple[int, ...]       )		->    Tuple[int, ...]:


				'''simple docstring'''




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

				return tuple(reversed(lowercase__       )       )





@torch.jit.ignore
def 	lowerCAmelCase_		(lowercase__						:       Sequence[int]     ,					lowercase__						:       Sequence[int]     ,					lowercase__						:       Sequence[int]     ,					lowercase__						:       Optional[Sequence[bool]] = None     ,					lowercase__						:       Optional[Sequence[bool]] = None     ,					)		->    List[Tuple[slice, ...]]:


				'''simple docstring'''




				def reduce_edge_list(lowercase__						:       List[bool]       ) -> None:
								lowerCAmelCase__      			=						True
								for i in range(len(lowercase__       )       ):
												lowerCAmelCase__      			=						-1 * (i + 1)
												l[reversed_idx] &= tally
												lowerCAmelCase__      			=						l[reversed_idx]

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

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

				lowerCAmelCase__      			=						[]
				lowerCAmelCase__      			=						[]

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

				lowerCAmelCase__      			=						tuple(lowercase__       )
				lowerCAmelCase__      			=						len(lowercase__       )

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

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

								lowerCAmelCase__      			=						start[divergence_idx]
								return tuple(
								    path + (slice(lowercase__     ,					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

								lowerCAmelCase__      			=						end[divergence_idx]
								return tuple(
								    path + (slice(lowercase__     ,					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()       )
								lowerCAmelCase__      			=						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_		(lowercase__						:       torch.Tensor     ,					lowercase__						:       int     ,					lowercase__						:       int     ,					lowercase__						:       int       )		->    torch.Tensor:


				'''simple docstring'''




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

				# Get an ordered list of slices to perform
				lowerCAmelCase__      			=						_get_minimal_slice_set(
				    lowercase__     ,					lowercase__     ,					lowercase__     ,					)

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

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





def 	lowerCAmelCase_		(lowercase__						:       Callable     ,					lowercase__						:       Dict[str, Any]     ,					lowercase__						:       int     ,					lowercase__						:       int     ,					lowercase__						:       bool = False     ,					lowercase__						:       Any = None     ,					lowercase__						:       bool = False     ,					)		->    Any:


				'''simple docstring'''




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

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

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

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

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

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

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

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

								lowerCAmelCase__      			=						tensor_tree_map(lowercase__     ,					lowercase__       )

								# Run the layer on the chunk
								lowerCAmelCase__      			=						layer(**lowercase__       )

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

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

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

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

								i += chunk_size

				lowerCAmelCase__      			=						tensor_tree_map(lambda lowercase__       : t.view(orig_batch_dims + t.shape[1:]       )     ,					lowercase__       )

				return out




class  lowerCAmelCase_       :




							def __init__(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   int = 512 ,							):
											lowerCAmelCase__      			=						max_chunk_size
											lowerCAmelCase__      			=						None
											lowerCAmelCase__      			=						None




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Callable ,							SCREAMING_SNAKE_CASE_     :   tuple ,							SCREAMING_SNAKE_CASE_     :   int	):
											logging.info('''Tuning chunk size...'''	)

											if min_chunk_size >= self.max_chunk_size:
															return min_chunk_size

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

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

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

											return candidates[min_viable_chunk_size_index]




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Iterable ,							SCREAMING_SNAKE_CASE_     :   Iterable	):
											lowerCAmelCase__      			=						True
											for aa, aa in zip(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
															assert type(SCREAMING_SNAKE_CASE_	) == type(SCREAMING_SNAKE_CASE_	)
															if isinstance(SCREAMING_SNAKE_CASE_ ,							(list, tuple)	):
																			consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)
															elif isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			lowerCAmelCase__      			=						[v for _, v in sorted(aa.items() ,							key=lambda SCREAMING_SNAKE_CASE_	: x[0]	)]
																			lowerCAmelCase__      			=						[v for _, v in sorted(aa.items() ,							key=lambda SCREAMING_SNAKE_CASE_	: x[0]	)]
																			consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)
															else:
																			consistent &= aa == aa

											return consistent




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   Callable ,							SCREAMING_SNAKE_CASE_     :   tuple ,							SCREAMING_SNAKE_CASE_     :   int ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						tree_map(lambda SCREAMING_SNAKE_CASE_	: a.shape if isinstance(SCREAMING_SNAKE_CASE_ ,							torch.Tensor	) else a ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_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(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						self._compare_arg_caches(self.cached_arg_data ,							SCREAMING_SNAKE_CASE_	)
											else:
															# Otherwise, we can reuse the precomputed value
															lowerCAmelCase__      			=						False

											if not consistent:
															lowerCAmelCase__      			=						self._determine_favorable_chunk_size(
															    SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						arg_data

											assert self.cached_chunk_size is not None

											return self.cached_chunk_size













import pytest

import datasets


# Import fixture modules as plugins
_UpperCAmelCase			:		Tuple   				=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       int       )		->    Any:


				'''simple docstring'''




				for item in items:
								if any(marker in item.keywords for marker in ['''integration''', '''unit''']       ):
												continue
								item.add_marker(pytest.mark.unit       )





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    List[str]:


				'''simple docstring'''




				config.addinivalue_line('''markers'''     ,					'''torchaudio_latest: mark test to run with torchaudio>=0.12'''       )





@pytest.fixture(autouse=lowercase__       )
def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Dict       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						tmp_path_factory.getbasetemp() / '''cache'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''datasets'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''metrics'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''modules'''
				monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''     ,					str(lowercase__       )       )
				monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''     ,					str(lowercase__       )       )
				monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''     ,					str(lowercase__       )       )
				lowerCAmelCase__      			=						test_hf_datasets_cache / '''downloads'''
				monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''     ,					str(lowercase__       )       )
				lowerCAmelCase__      			=						test_hf_datasets_cache / '''downloads''' / '''extracted'''
				monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''     ,					str(lowercase__       )       )





@pytest.fixture(autouse=lowercase__     ,					scope='''session'''       )
def 	lowerCAmelCase_		()		->    int:


				'''simple docstring'''




				datasets.disable_progress_bar()





@pytest.fixture(autouse=lowercase__       )
def 	lowerCAmelCase_		(lowercase__						:       Dict       )		->    List[str]:


				'''simple docstring'''




				monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''     ,					lowercase__       )





@pytest.fixture
def 	lowerCAmelCase_		(lowercase__						:       List[str]       )		->    List[Any]:


				'''simple docstring'''




				monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''     ,					lowercase__       )





