




"""simple docstring"""





import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


A				=							logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase = 1_6000				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       int(round(sample_rate * max_length				)				)
			if len(UpperCamelCase				) <= sample_length:
						return wav
			__UpperCAmelCase						:			int												=       randint(0					,							len(UpperCamelCase				) - sample_length - 1				)
			return wav[random_offset : random_offset + sample_length]



@dataclass
class   a__					:
			lowercase_		=						field(default=__magic_name__				,     metadata={"help": "Name of a dataset from the datasets package"} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "A file containing the training audio paths and labels."} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "A file containing the validation audio paths and labels."} )
			lowercase_		=						field(
			    default="train"				,     metadata={
			        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
			    }				,     )
			lowercase_		=						field(
			    default="validation"				,     metadata={
			        "help": (
			            "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
			        )
			    }				,     )
			lowercase_		=						field(
			    default="audio"				,     metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}				,     )
			lowercase_		=						field(
			    default="label"				,     metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }				,     )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
			            "value if set."
			        )
			    }				,     )
			lowercase_		=						field(
			    default=2_0				,     metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}				,     )






@dataclass
class   a__					:
			lowercase_		=						field(
			    default="facebook/wav2vec2-base"				,     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				,     )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Pretrained config name or path if not the same as model_name"} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
			lowercase_		=						field(
			    default="main"				,     metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,     )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Name or path of preprocessor config."} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }				,     )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}				,     )


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						if not self.freeze_feature_extractor and self.freeze_feature_encoder:
									warnings.warn(
									    "The argument `--freeze_feature_extractor` is deprecated and "
									    "will be removed in a future version. Use `--freeze_feature_encoder`"
									    "instead. Setting `freeze_feature_encoder==True`."   ,	UpperCamelCase_   ,	)
						if self.freeze_feature_extractor and not self.freeze_feature_encoder:
									raise ValueError(
									    "The argument `--freeze_feature_extractor` is deprecated and "
									    "should not be used in combination with `--freeze_feature_encoder`."
									    "Only make use of `--freeze_feature_encoder`.")






def        _UpperCamelCase     (      )      ->      Any:


			"""simple docstring"""
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__UpperCAmelCase						:			Tuple												=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			if len(sys.argv				) == 2 and sys.argv[1].endswith(".json"				):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
			else:
						__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			int												=       parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("run_audio_classification"					,							UpperCamelCase					,							UpperCamelCase				)

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,							datefmt="%m/%d/%Y %H:%M:%S"					,							handlers=[logging.StreamHandler(sys.stdout				)]					,							)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			__UpperCAmelCase						:			List[str]												=       training_args.get_process_log_level()
			logger.setLevel(UpperCamelCase				)
			transformers.utils.logging.set_verbosity(UpperCamelCase				)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
			    + f"distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"				)
			logger.info(f"Training/evaluation parameters {training_args}"				)

			# Set seed before initializing model.
			set_seed(training_args.seed				)

			# Detecting last checkpoint.
			__UpperCAmelCase						:			str												=       None
			if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
						__UpperCAmelCase						:			Dict												=       get_last_checkpoint(training_args.output_dir				)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
									raise ValueError(
									    f"Output directory ({training_args.output_dir}) already exists and is not empty. "
									    "Use --overwrite_output_dir to train from scratch."				)
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
									    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."				)

    # Initialize our dataset and prepare it for the audio classification task.
			__UpperCAmelCase						:			Optional[int]												=       DatasetDict()
			__UpperCAmelCase						:			Union[str, Any]												=       load_dataset(
			    data_args.dataset_name					,							data_args.dataset_config_name					,							split=data_args.train_split_name					,							use_auth_token=True if model_args.use_auth_token else None					,							)
			__UpperCAmelCase						:			Tuple												=       load_dataset(
			    data_args.dataset_name					,							data_args.dataset_config_name					,							split=data_args.eval_split_name					,							use_auth_token=True if model_args.use_auth_token else None					,							)

			if data_args.audio_column_name not in raw_datasets["train"].column_names:
						raise ValueError(
						    f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
						    "Make sure to set `--audio_column_name` to the correct audio column - one of "
						    f"{', '.join(raw_datasets['train'].column_names				)}."				)

			if data_args.label_column_name not in raw_datasets["train"].column_names:
						raise ValueError(
						    f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
						    "Make sure to set `--label_column_name` to the correct text column - one of "
						    f"{', '.join(raw_datasets['train'].column_names				)}."				)

			# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
			# transformer outputs in the classifier, but it doesn't always lead to better accuracy
			__UpperCAmelCase						:			Optional[Any]												=       AutoFeatureExtractor.from_pretrained(
			    model_args.feature_extractor_name or model_args.model_name_or_path					,							return_attention_mask=model_args.attention_mask					,							cache_dir=model_args.cache_dir					,							revision=model_args.model_revision					,							use_auth_token=True if model_args.use_auth_token else None					,							)

			# `datasets` takes care of automatically loading and resampling the audio,
			# so we just need to set the correct target sampling rate.
			__UpperCAmelCase						:			List[str]												=       raw_datasets.cast_column(
			    data_args.audio_column_name					,							datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate				)				)

			__UpperCAmelCase						:			Tuple												=       feature_extractor.model_input_names[0]

			def train_transforms(UpperCamelCase				):
						__UpperCAmelCase						:			Optional[int]												=       []
						for audio in batch[data_args.audio_column_name]:
									__UpperCAmelCase						:			int												=       random_subsample(
									    audio["array"]					,							max_length=data_args.max_length_seconds					,							sample_rate=feature_extractor.sampling_rate				)
									subsampled_wavs.append(UpperCamelCase				)
						__UpperCAmelCase						:			Any												=       feature_extractor(UpperCamelCase					,							sampling_rate=feature_extractor.sampling_rate				)
						__UpperCAmelCase						:			Any												=       {model_input_name: inputs.get(UpperCamelCase				)}
						__UpperCAmelCase						:			int												=       list(batch[data_args.label_column_name]				)

						return output_batch

			def val_transforms(UpperCamelCase				):
						__UpperCAmelCase						:			Any												=       [audio["array"] for audio in batch[data_args.audio_column_name]]
						__UpperCAmelCase						:			List[Any]												=       feature_extractor(UpperCamelCase					,							sampling_rate=feature_extractor.sampling_rate				)
						__UpperCAmelCase						:			Union[str, Any]												=       {model_input_name: inputs.get(UpperCamelCase				)}
						__UpperCAmelCase						:			List[Any]												=       list(batch[data_args.label_column_name]				)

						return output_batch

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			__UpperCAmelCase						:			Tuple												=       raw_datasets["train"].features[data_args.label_column_name].names
			__UpperCAmelCase						,  __UpperCAmelCase						:			List[Any]												=       {}, {}
			for i, label in enumerate(UpperCamelCase				):
						__UpperCAmelCase						:			List[str]												=       str(UpperCamelCase				)
						__UpperCAmelCase						:			str												=       label

			# Load the accuracy metric from the datasets package
			__UpperCAmelCase						:			Union[str, Any]												=       evaluate.load("accuracy"				)

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
			# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
			def compute_metrics(UpperCamelCase				):
						__UpperCAmelCase						:			str												=       np.argmax(eval_pred.predictions					,							axis=1				)
						return metric.compute(predictions=UpperCamelCase					,							references=eval_pred.label_ids				)

			__UpperCAmelCase						:			Dict												=       AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path					,							num_labels=len(UpperCamelCase				)					,							labelaid=UpperCamelCase					,							idalabel=UpperCamelCase					,							finetuning_task="audio-classification"					,							cache_dir=model_args.cache_dir					,							revision=model_args.model_revision					,							use_auth_token=True if model_args.use_auth_token else None					,							)
			__UpperCAmelCase						:			Tuple												=       AutoModelForAudioClassification.from_pretrained(
			    model_args.model_name_or_path					,							from_tf=bool(".ckpt" in model_args.model_name_or_path				)					,							config=UpperCamelCase					,							cache_dir=model_args.cache_dir					,							revision=model_args.model_revision					,							use_auth_token=True if model_args.use_auth_token else None					,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes					,							)

			# freeze the convolutional waveform encoder
			if model_args.freeze_feature_encoder:
						model.freeze_feature_encoder()

			if training_args.do_train:
						if data_args.max_train_samples is not None:
									__UpperCAmelCase						:			Any												=       (
									    raw_datasets["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
									)
						# Set the training transforms
						raw_datasets["train"].set_transform(UpperCamelCase					,							output_all_columns=UpperCamelCase				)

			if training_args.do_eval:
						if data_args.max_eval_samples is not None:
									__UpperCAmelCase						:			Union[str, Any]												=       (
									    raw_datasets["eval"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
									)
						# Set the validation transforms
						raw_datasets["eval"].set_transform(UpperCamelCase					,							output_all_columns=UpperCamelCase				)

			# Initialize our trainer
			__UpperCAmelCase						:			Optional[int]												=       Trainer(
			    model=UpperCamelCase					,							args=UpperCamelCase					,							train_dataset=raw_datasets["train"] if training_args.do_train else None					,							eval_dataset=raw_datasets["eval"] if training_args.do_eval else None					,							compute_metrics=UpperCamelCase					,							tokenizer=UpperCamelCase					,							)

			# Training
			if training_args.do_train:
						__UpperCAmelCase						:			List[Any]												=       None
						if training_args.resume_from_checkpoint is not None:
									__UpperCAmelCase						:			Any												=       training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									__UpperCAmelCase						:			Tuple												=       last_checkpoint
						__UpperCAmelCase						:			Optional[Any]												=       trainer.train(resume_from_checkpoint=UpperCamelCase				)
						trainer.save_model()
						trainer.log_metrics("train"					,							train_result.metrics				)
						trainer.save_metrics("train"					,							train_result.metrics				)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						__UpperCAmelCase						:			Union[str, Any]												=       trainer.evaluate()
						trainer.log_metrics("eval"					,							UpperCamelCase				)
						trainer.save_metrics("eval"					,							UpperCamelCase				)

			# Write model card and (optionally) push to hub
			__UpperCAmelCase						:			Any												=       {
			    "finetuned_from": model_args.model_name_or_path,
			    "tasks": "audio-classification",
			    "dataset": data_args.dataset_name,
			    "tags": ["audio-classification"],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**UpperCamelCase				)
			else:
						trainer.create_model_card(**UpperCamelCase				)


if __name__ == "__main__":
					main()






"""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       a_ (	lowercase__ :Union[dict, list, tuple, torch.Tensor]     ):
			__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       a_ (	lowercase__ :int,			lowercase__ :Tuple[int, ...]     ):
			__lowerCamelCase          =      []
			for d in reversed(lowercase__     ):
						idx.append(flat_idx % d     )
						__lowerCamelCase          =      flat_idx // d

			return tuple(reversed(lowercase__     )     )





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

			# start_edges and end_edges both indicate whether, starting from any given
			# dimension, the start/end index is at the top/bottom edge of the
			# corresponding tensor, modeled as a tree
			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       a_ (	lowercase__ :torch.Tensor,			lowercase__ :int,			lowercase__ :int,			lowercase__ :int     ):
			__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       a_ (	lowercase__ :Callable,			lowercase__ :Dict[str, Any],			lowercase__ :int,			lowercase__ :int,			lowercase__ :bool = False,			lowercase__ :Any = None,			lowercase__ :bool = False,			):
			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 			__snake_case   :




							def __init__(     self:    Union[str, Any]     ,							A_:    int = 5_12     ,							):
										__lowerCamelCase          =      max_chunk_size
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None




							def   __a   (     self:    Tuple     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    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(A_:    int   ) -> bool:
													try:
																with torch.no_grad():
																			fn(*A_     ,							chunk_size=A_   )
																return True
													except RuntimeError:
																return False

										__lowerCamelCase          =      0
										__lowerCamelCase          =      len(A_   ) - 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(A_   ) - 1) // 2

										return candidates[min_viable_chunk_size_index]




							def   __a   (     self:    Tuple     ,							A_:    Iterable     ,							A_:    Iterable   ):
										__lowerCamelCase          =      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_   ):
																__lowerCamelCase          =      [v for _, v in sorted(aa.items()     ,							key=lambda A_   : x[0]   )]
																__lowerCamelCase          =      [v for _, v in sorted(aa.items()     ,							key=lambda A_   : x[0]   )]
																consistent &= self._compare_arg_caches(A_     ,							A_   )
													else:
																consistent &= aa == aa

										return consistent




							def   __a   (     self:    str     ,							A_:    Callable     ,							A_:    tuple     ,							A_:    int     ,							):
										__lowerCamelCase          =      True
										__lowerCamelCase          =      tree_map(lambda A_   : 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_   )
													__lowerCamelCase          =      self._compare_arg_caches(self.cached_arg_data     ,							A_   )
										else:
													# Otherwise, we can reuse the precomputed value
													__lowerCamelCase          =      False

										if not consistent:
													__lowerCamelCase          =      self._determine_favorable_chunk_size(
													    A_     ,							A_     ,							A_     ,							)
													__lowerCamelCase          =      arg_data

										assert self.cached_chunk_size is not None

										return self.cached_chunk_size




