


"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A	:  Optional[Any]            =     logging.get_logger(__name__)


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     MobileViTConfig()

			# size of the architecture
			if "mobilevit_s" in mobilevit_name:
						__lowerCAmelCase							       =     [144, 192, 240]
						__lowerCAmelCase							       =     [16, 32, 64, 96, 128, 160, 640]
			elif "mobilevit_xs" in mobilevit_name:
						__lowerCAmelCase							       =     [96, 120, 144]
						__lowerCAmelCase							       =     [16, 32, 48, 64, 80, 96, 384]
			elif "mobilevit_xxs" in mobilevit_name:
						__lowerCAmelCase							       =     [64, 80, 96]
						__lowerCAmelCase							       =     [16, 16, 24, 48, 64, 80, 320]
						__lowerCAmelCase							       =     0.05
						__lowerCAmelCase							       =     2.0

			if mobilevit_name.startswith("deeplabv3_" ):
						__lowerCAmelCase							       =     512
						__lowerCAmelCase							       =     16
						__lowerCAmelCase							       =     21
						__lowerCAmelCase							       =     "pascal-voc-id2label.json"
			else:
						__lowerCAmelCase							       =     1000
						__lowerCAmelCase							       =     "imagenet-1k-id2label.json"

			__lowerCAmelCase							       =     "huggingface/label-files"
			__lowerCAmelCase							       =     json.load(open(hf_hub_download(_UpperCamelCase       ,					_UpperCamelCase       ,					repo_type="dataset" )       ,					"r" ) )
			__lowerCAmelCase							       =     {int(_UpperCamelCase ): v for k, v in idalabel.items()}
			__lowerCAmelCase							       =     idalabel
			__lowerCAmelCase							       =     {v: k for k, v in idalabel.items()}

			return config


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=False ):

			'''simple docstring'''




			for i in range(1       ,					6 ):
						if f"layer_{i}." in name:
									__lowerCAmelCase							       =     name.replace(f"layer_{i}."       ,					f"encoder.layer.{i - 1}." )

			if "conv_1." in name:
						__lowerCAmelCase							       =     name.replace("conv_1."       ,					"conv_stem." )
			if ".block." in name:
						__lowerCAmelCase							       =     name.replace(".block."       ,					"." )

			if "exp_1x1" in name:
						__lowerCAmelCase							       =     name.replace("exp_1x1"       ,					"expand_1x1" )
			if "red_1x1" in name:
						__lowerCAmelCase							       =     name.replace("red_1x1"       ,					"reduce_1x1" )
			if ".local_rep.conv_3x3." in name:
						__lowerCAmelCase							       =     name.replace(".local_rep.conv_3x3."       ,					".conv_kxk." )
			if ".local_rep.conv_1x1." in name:
						__lowerCAmelCase							       =     name.replace(".local_rep.conv_1x1."       ,					".conv_1x1." )
			if ".norm." in name:
						__lowerCAmelCase							       =     name.replace(".norm."       ,					".normalization." )
			if ".conv." in name:
						__lowerCAmelCase							       =     name.replace(".conv."       ,					".convolution." )
			if ".conv_proj." in name:
						__lowerCAmelCase							       =     name.replace(".conv_proj."       ,					".conv_projection." )

			for i in range(0       ,					2 ):
						for j in range(0       ,					4 ):
									if f".{i}.{j}." in name:
												__lowerCAmelCase							       =     name.replace(f".{i}.{j}."       ,					f".{i}.layer.{j}." )

			for i in range(2       ,					6 ):
						for j in range(0       ,					4 ):
									if f".{i}.{j}." in name:
												__lowerCAmelCase							       =     name.replace(f".{i}.{j}."       ,					f".{i}." )
												if "expand_1x1" in name:
															__lowerCAmelCase							       =     name.replace("expand_1x1"       ,					"downsampling_layer.expand_1x1" )
												if "conv_3x3" in name:
															__lowerCAmelCase							       =     name.replace("conv_3x3"       ,					"downsampling_layer.conv_3x3" )
												if "reduce_1x1" in name:
															__lowerCAmelCase							       =     name.replace("reduce_1x1"       ,					"downsampling_layer.reduce_1x1" )

			for i in range(2       ,					5 ):
						if f".global_rep.{i}.weight" in name:
									__lowerCAmelCase							       =     name.replace(f".global_rep.{i}.weight"       ,					".layernorm.weight" )
						if f".global_rep.{i}.bias" in name:
									__lowerCAmelCase							       =     name.replace(f".global_rep.{i}.bias"       ,					".layernorm.bias" )

			if ".global_rep." in name:
						__lowerCAmelCase							       =     name.replace(".global_rep."       ,					".transformer." )
			if ".pre_norm_mha.0." in name:
						__lowerCAmelCase							       =     name.replace(".pre_norm_mha.0."       ,					".layernorm_before." )
			if ".pre_norm_mha.1.out_proj." in name:
						__lowerCAmelCase							       =     name.replace(".pre_norm_mha.1.out_proj."       ,					".attention.output.dense." )
			if ".pre_norm_ffn.0." in name:
						__lowerCAmelCase							       =     name.replace(".pre_norm_ffn.0."       ,					".layernorm_after." )
			if ".pre_norm_ffn.1." in name:
						__lowerCAmelCase							       =     name.replace(".pre_norm_ffn.1."       ,					".intermediate.dense." )
			if ".pre_norm_ffn.4." in name:
						__lowerCAmelCase							       =     name.replace(".pre_norm_ffn.4."       ,					".output.dense." )
			if ".transformer." in name:
						__lowerCAmelCase							       =     name.replace(".transformer."       ,					".transformer.layer." )

			if ".aspp_layer." in name:
						__lowerCAmelCase							       =     name.replace(".aspp_layer."       ,					"." )
			if ".aspp_pool." in name:
						__lowerCAmelCase							       =     name.replace(".aspp_pool."       ,					"." )
			if "seg_head." in name:
						__lowerCAmelCase							       =     name.replace("seg_head."       ,					"segmentation_head." )
			if "segmentation_head.classifier.classifier." in name:
						__lowerCAmelCase							       =     name.replace("segmentation_head.classifier.classifier."       ,					"segmentation_head.classifier." )

			if "classifier.fc." in name:
						__lowerCAmelCase							       =     name.replace("classifier.fc."       ,					"classifier." )
			elif (not base_model) and ("segmentation_head." not in name):
						__lowerCAmelCase							       =     "mobilevit." + name

			return name


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase=False ):

			'''simple docstring'''




			if base_model:
						__lowerCAmelCase							       =     ""
			else:
						__lowerCAmelCase							       =     "mobilevit."

			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase							       =     orig_state_dict.pop(_UpperCamelCase )

						if key[:8] == "encoder.":
									__lowerCAmelCase							       =     key[8:]

						if "qkv" in key:
									__lowerCAmelCase							       =     key.split("." )
									__lowerCAmelCase							       =     int(key_split[0][6:] ) - 1
									__lowerCAmelCase							       =     int(key_split[3] )
									__lowerCAmelCase							       =     model.get_submodule(f"{model_prefix}encoder.layer.{layer_num}" )
									__lowerCAmelCase							       =     layer.transformer.layer[transformer_num].attention.attention.all_head_size
									__lowerCAmelCase							       =     (
									    f"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
									)
									if "weight" in key:
												__lowerCAmelCase							       =     val[:dim, :]
												__lowerCAmelCase							       =     val[dim : dim * 2, :]
												__lowerCAmelCase							       =     val[-dim:, :]
									else:
												__lowerCAmelCase							       =     val[:dim]
												__lowerCAmelCase							       =     val[dim : dim * 2]
												__lowerCAmelCase							       =     val[-dim:]
						else:
									__lowerCAmelCase							       =     val

			return orig_state_dict


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     "http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase							       =     Image.open(requests.get(_UpperCamelCase       ,					stream=_UpperCamelCase ).raw )
			return im


@torch.no_grad()
def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase=False ):

			'''simple docstring'''




			__lowerCAmelCase							       =     get_mobilevit_config(_UpperCamelCase )

			# load original state_dict
			__lowerCAmelCase							       =     torch.load(_UpperCamelCase       ,					map_location="cpu" )

			# load 🤗 model
			if mobilevit_name.startswith("deeplabv3_" ):
						__lowerCAmelCase							       =     MobileViTForSemanticSegmentation(_UpperCamelCase ).eval()
			else:
						__lowerCAmelCase							       =     MobileViTForImageClassification(_UpperCamelCase ).eval()

			__lowerCAmelCase							       =     convert_state_dict(_UpperCamelCase       ,					_UpperCamelCase )
			model.load_state_dict(_UpperCamelCase )

			# Check outputs on an image, prepared by MobileViTImageProcessor
			__lowerCAmelCase							       =     MobileViTImageProcessor(crop_size=config.image_size       ,					size=config.image_size + 32 )
			__lowerCAmelCase							       =     image_processor(images=prepare_img()       ,					return_tensors="pt" )
			__lowerCAmelCase							       =     model(**_UpperCamelCase )
			__lowerCAmelCase							       =     outputs.logits

			if mobilevit_name.startswith("deeplabv3_" ):
						assert logits.shape == (1, 21, 32, 32)

						if mobilevit_name == "deeplabv3_mobilevit_s":
									__lowerCAmelCase							       =     torch.tensor(
									    [
									        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
									        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
									        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
									    ] )
						elif mobilevit_name == "deeplabv3_mobilevit_xs":
									__lowerCAmelCase							       =     torch.tensor(
									    [
									        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
									        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
									        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
									    ] )
						elif mobilevit_name == "deeplabv3_mobilevit_xxs":
									__lowerCAmelCase							       =     torch.tensor(
									    [
									        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
									        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
									        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
									    ] )
						else:
									raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}" )

						assert torch.allclose(logits[0, :3, :3, :3]       ,					_UpperCamelCase       ,					atol=1e-4 )
			else:
						assert logits.shape == (1, 1000)

						if mobilevit_name == "mobilevit_s":
									__lowerCAmelCase							       =     torch.tensor([-0.98_66, 0.23_92, -1.12_41] )
						elif mobilevit_name == "mobilevit_xs":
									__lowerCAmelCase							       =     torch.tensor([-2.47_61, -0.93_99, -1.95_87] )
						elif mobilevit_name == "mobilevit_xxs":
									__lowerCAmelCase							       =     torch.tensor([-1.93_64, -1.23_27, -0.46_53] )
						else:
									raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}" )

						assert torch.allclose(logits[0, :3]       ,					_UpperCamelCase       ,					atol=1e-4 )

			Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
			print(f"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" )
			model.save_pretrained(_UpperCamelCase )
			print(f"Saving image processor to {pytorch_dump_folder_path}" )
			image_processor.save_pretrained(_UpperCamelCase )

			if push_to_hub:
						__lowerCAmelCase							       =     {
						    "mobilevit_s": "mobilevit-small",
						    "mobilevit_xs": "mobilevit-x-small",
						    "mobilevit_xxs": "mobilevit-xx-small",
						    "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
						    "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
						    "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
						}

						print("Pushing to the hub..." )
						__lowerCAmelCase							       =     model_mapping[mobilevit_name]
						image_processor.push_to_hub(_UpperCamelCase       ,					organization="apple" )
						model.push_to_hub(_UpperCamelCase       ,					organization="apple" )


if __name__ == "__main__":
	A	:  Optional[Any]            =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--mobilevit_name",
	    default="mobilevit_s",
	    type=str,
	    help=(
	        "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
	        " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
	    ),
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	A	:  Optional[Any]            =     parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)








"""simple docstring"""


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
	from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
	A	:  List[Any]            =     True
	from torch.cuda.amp import autocast


A	:  Any            =     logging.getLogger(__name__)
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,)
							__UpperCAmelCase		:       Optional[bool]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
							__UpperCAmelCase		:       Optional[bool]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Whether to log verbose messages or not."""}			,)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=2.0			,metadata={"""help""": """Maximum temperature for gumbel softmax."""}						)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=0.5			,metadata={"""help""": """Minimum temperature for gumbel softmax."""}						)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=0.999_995			,metadata={"""help""": """Decay of gumbel temperature during training."""}						)


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,					datefmt="%m/%d/%Y %H:%M:%S"       ,					handlers=[logging.StreamHandler(sys.stdout )]       ,					)
			__lowerCAmelCase							       =     logging.WARNING
			if model_args.verbose_logging:
						__lowerCAmelCase							       =     logging.DEBUG
			elif trainer_utils.is_main_process(training_args.local_rank ):
						__lowerCAmelCase							       =     logging.INFO
			logger.setLevel(_UpperCamelCase )
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""train"""			,metadata={
							        """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
							    }			,)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""validation"""			,metadata={
							        """help""": (
							            """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
							        )
							    }			,)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""file"""			,metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}			,)
							__UpperCAmelCase		:       bool							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}						)
							__UpperCAmelCase		:       Optional[int]							 =field(
							    default=1			,metadata={
							        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
							    }			,)
							__UpperCAmelCase		:       Optional[int]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The number of processes to use for the preprocessing."""}			,)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=20.0			,metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}						)
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       WavaVecaForPreTraining
							__UpperCAmelCase		:       WavaVecaFeatureExtractor
							__UpperCAmelCase		:       Union[bool, str]							 ="longest"
							__UpperCAmelCase		:       Optional[int]							 =None
							__UpperCAmelCase		:       Optional[int]							 =None

							def __call__(	self       ,  __a      ):
										# reformat list to dict and set to pytorch format
										__lowerCAmelCase							       =     self.feature_extractor.pad(
										    __a       ,  max_length=self.max_length       ,  padding=self.padding       ,  pad_to_multiple_of=self.pad_to_multiple_of       ,  return_tensors="pt"       ,  )
										__lowerCAmelCase							       =     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]      )

										__lowerCAmelCase							       =     batch["input_values"].shape[0]

										# make sure that no loss is computed on padded inputs
										if batch["attention_mask"] is not None:
													# compute real output lengths according to convolution formula
													__lowerCAmelCase							       =     self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1      )      ).to(
													    torch.long      )

													__lowerCAmelCase							       =     torch.zeros(
													    (batch_size, mask_indices_seq_length)       ,  dtype=torch.long       ,  device=batch["input_values"].device      )

													# these two operations makes sure that all values
													# before the output lengths indices are attended to
													__lowerCAmelCase							       =     1
													__lowerCAmelCase							       =     attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

										# sample randomly masked indices
										__lowerCAmelCase							       =     _compute_mask_indices(
										    (batch_size, mask_indices_seq_length)       ,  self.model.config.mask_time_prob       ,  self.model.config.mask_time_length       ,  attention_mask=__a       ,  min_masks=2       ,  )

										return batch
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  __a=1       ,  __a=0       ,  __a=1.0       ,  **__a      ):
										super().__init__(*__a       ,  **__a      )
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     max_gumbel_temp
										__lowerCAmelCase							       =     min_gumbel_temp
										__lowerCAmelCase							       =     gumbel_temp_decay



							def      snake_case							(	self       ,  __a       ,  __a      ):
										model.train()
										__lowerCAmelCase							       =     self._prepare_inputs(__a      )

										if self.use_amp:
													with autocast():
																__lowerCAmelCase							       =     self.compute_loss(__a       ,  __a      )
										else:
													__lowerCAmelCase							       =     self.compute_loss(__a       ,  __a      )

										if self.args.n_gpu > 1 or self.deepspeed:
													if model.module.config.ctc_loss_reduction == "mean":
																__lowerCAmelCase							       =     loss.mean()
													elif model.module.config.ctc_loss_reduction == "sum":
																__lowerCAmelCase							       =     loss.sum() / (inputs["mask_time_indices"]).sum()
													else:
																raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

										if self.args.gradient_accumulation_steps > 1:
													__lowerCAmelCase							       =     loss / self.args.gradient_accumulation_steps

										if self.use_amp:
													self.scaler.scale(__a      ).backward()
										elif self.use_apex:
													with amp.scale_loss(__a       ,  self.optimizer      ) as scaled_loss:
																scaled_loss.backward()
										elif self.deepspeed:
													self.deepspeed.backward(__a      )
										else:
													loss.backward()

										self.num_update_step += 1
										# make sure gumbel softmax temperature is decayed
										if self.args.n_gpu > 1 or self.deepspeed:
													model.module.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step       ,  self.min_gumbel_temp      )      )
										else:
													model.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step       ,  self.min_gumbel_temp      )      )

										return loss.detach()


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

			__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							       =     parser.parse_args_into_dataclasses()
			configure_logger(_UpperCamelCase       ,					_UpperCamelCase )

			# Downloading and loading a dataset from the hub.
			__lowerCAmelCase							       =     load_dataset(data_args.dataset_name       ,					data_args.dataset_config_name       ,					cache_dir=model_args.cache_dir )

			if "validation" not in datasets.keys():
						# make sure only "validation" and "train" keys remain"
						__lowerCAmelCase							       =     DatasetDict()
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"       ,					cache_dir=model_args.cache_dir       ,					)
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"       ,					cache_dir=model_args.cache_dir       ,					)
			else:
						# make sure only "validation" and "train" keys remain"
						__lowerCAmelCase							       =     DatasetDict()
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split="validation"       ,					cache_dir=model_args.cache_dir       ,					)
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}"       ,					cache_dir=model_args.cache_dir       ,					)

			# only normalized-inputs-training is supported
			__lowerCAmelCase							       =     WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					do_normalize=_UpperCamelCase )

			def prepare_dataset(_UpperCamelCase ):
						# check that all files have the correct sampling rate
						__lowerCAmelCase       ,      __lowerCAmelCase							       =     librosa.load(batch[data_args.speech_file_column]       ,					sr=feature_extractor.sampling_rate )
						return batch

			# load audio files into numpy arrays
			__lowerCAmelCase							       =     datasets.map(
			    _UpperCamelCase       ,					num_proc=data_args.preprocessing_num_workers       ,					remove_columns=datasets["train"].column_names )

			# filter audio files that are too long
			__lowerCAmelCase							       =     vectorized_datasets.filter(
			    lambda _UpperCamelCase : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )

			def normalize(_UpperCamelCase ):
						return feature_extractor(batch["speech"]       ,					sampling_rate=feature_extractor.sampling_rate )

			# normalize and transform to `BatchFeatures`
			__lowerCAmelCase							       =     vectorized_datasets.map(
			    _UpperCamelCase       ,					batched=_UpperCamelCase       ,					num_proc=data_args.preprocessing_num_workers       ,					load_from_cache_file=not data_args.overwrite_cache       ,					remove_columns=vectorized_datasets["train"].column_names       ,					)

			# pretraining is only supported for "newer" stable layer norm architecture
			# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
			__lowerCAmelCase							       =     WavaVecaConfig.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					gradient_checkpointing=training_args.gradient_checkpointing       ,					)

			if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
						raise ValueError(
						    "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
						    " ``config.feat_extract_norm='layer'" )

			__lowerCAmelCase							       =     WavaVecaForPreTraining(_UpperCamelCase )

			__lowerCAmelCase							       =     DataCollatorForWavaVecaPretraining(model=_UpperCamelCase       ,					feature_extractor=_UpperCamelCase )

			__lowerCAmelCase							       =     WavaVecaPreTrainer(
			    model=_UpperCamelCase       ,					data_collator=_UpperCamelCase       ,					args=_UpperCamelCase       ,					train_dataset=vectorized_datasets["train"]       ,					eval_dataset=vectorized_datasets["validation"]       ,					tokenizer=_UpperCamelCase       ,					max_gumbel_temp=model_args.max_gumbel_temperature       ,					min_gumbel_temp=model_args.min_gumbel_temperature       ,					gumbel_temp_decay=model_args.gumbel_temperature_decay       ,					)
			trainer.train()


if __name__ == "__main__":
	main()




