





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

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
     from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
     UpperCAmelCase	:	int		     =       True
     from torch.cuda.amp import autocast

UpperCAmelCase	:	List[Any]		     =       logging.getLogger(__name__)
def      _A       (		SCREAMING_SNAKE_CASE     :   Any=None       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  return field(default_factory=lambda: default       ,       metadata=_SCREAMING_SNAKE_CASE						)







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					str						=			field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
      _lowercase				:					Optional[str]						=			field(
          default=lowerCAmelCase_						,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,				)
      _lowercase				:					Optional[bool]						=			field(
          default=lowerCAmelCase_						,				metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""})
      _lowercase				:					Optional[float]						=			field(
          default=0.1						,				metadata={"""help""": """The dropout ratio for the attention probabilities."""})
      _lowercase				:					Optional[float]						=			field(
          default=0.1						,				metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""})
      _lowercase				:					Optional[float]						=			field(
          default=0.1						,				metadata={
              """help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
          }						,				)
      _lowercase				:					Optional[float]						=			field(
          default=0.1						,				metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""}						,				)
      _lowercase				:					Optional[float]						=			field(
          default=0.0_5						,				metadata={
              """help""": (
                  """Propability of each feature vector along the time axis to be chosen as the start of the vector"""
                  """span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
                  """vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
              )
          }						,				)
      _lowercase				:					Optional[float]						=			field(default=0.0						,				metadata={"""help""": """The LayerDrop probability."""})







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					Optional[str]						=			field(
          default=lowerCAmelCase_						,				metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
      _lowercase				:					Optional[str]						=			field(
          default="""train+validation"""						,				metadata={
              """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
          }						,				)
      _lowercase				:					bool						=			field(
          default=lowerCAmelCase_						,				metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
      _lowercase				:					Optional[int]						=			field(
          default=lowerCAmelCase_						,				metadata={"""help""": """The number of processes to use for the preprocessing."""}						,				)
      _lowercase				:					Optional[int]						=			field(
          default=lowerCAmelCase_						,				metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of training examples to this """
                  """value if set."""
              )
          }						,				)
      _lowercase				:					Optional[int]						=			field(
          default=lowerCAmelCase_						,				metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of validation examples to this """
                  """value if set."""
              )
          }						,				)
      _lowercase				:					List[str]						=			list_field(
          default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""]						,				metadata={"""help""": """A list of characters to remove from the transcripts."""}						,				)







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					WavaVecaProcessor
      _lowercase				:					Union[bool, str]						=			True
      _lowercase				:					Optional[int]						=			None
      _lowercase				:					Optional[int]						=			None
      _lowercase				:					Optional[int]						=			None
      _lowercase				:					Optional[int]						=			None







      def __call__(    self		,      lowerCAmelCase__							)   ->						Dict[str, torch.Tensor]:



        '''simple docstring'''




        a__	:					Optional[Any]										=[{"input_values": feature["input_values"]} for feature in features]
        a__	:					Any										=[{"input_ids": feature["labels"]} for feature in features]

        a__	:					Union[str, Any]										=self.processor.pad(
            lowerCamelCase__		,      padding=self.padding		,      max_length=self.max_length		,      pad_to_multiple_of=self.pad_to_multiple_of		,      return_tensors="pt"		,      )
        a__	:					str										=self.processor.pad(
            labels=lowerCamelCase__		,      padding=self.padding		,      max_length=self.max_length_labels		,      pad_to_multiple_of=self.pad_to_multiple_of_labels		,      return_tensors="pt"		,      )

        # replace padding with -100 to ignore loss correctly
        a__	:					str										=labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1							)		,      -1_0_0							)

        a__	:					Optional[int]										=labels

        return batch







class 				__lowerCAmelCase       (    lowerCAmelCase_):







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						torch.Tensor:



        '''simple docstring'''




        model.train()
        a__	:					Any										=self._prepare_inputs(lowerCamelCase__							)

        if self.use_amp:
          with autocast():
            a__	:					Any										=self.compute_loss(lowerCamelCase__		,      lowerCamelCase__							)
        else:
          a__	:					List[str]										=self.compute_loss(lowerCamelCase__		,      lowerCamelCase__							)

        if self.args.n_gpu > 1:
          if model.module.config.ctc_loss_reduction == "mean":
            a__	:					List[str]										=loss.mean()
          elif model.module.config.ctc_loss_reduction == "sum":
            a__	:					Union[str, Any]										=loss.sum() / (inputs["labels"] >= 0).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:
          a__	:					Optional[Any]										=loss / self.args.gradient_accumulation_steps

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

        return loss.detach()
def      _A       (		):





  """simple docstring"""



  a__	:					Optional[Any]										=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.
    a__					,							a__					,							a__	:					Any										=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    a__					,							a__					,							a__	:					Optional[Any]										=parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  a__	:					Tuple										=None
  if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
    a__	:					str										=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 overcome."						)
    elif last_checkpoint is not 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."						)

    # 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						)]       ,       )
  logger.setLevel(logging.INFO if is_main_process(training_args.local_rank						) else logging.WARN						)

  # 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}'''						)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank						):
    transformers.utils.logging.set_verbosity_info()
  logger.info("Training/evaluation parameters %s"       ,       _SCREAMING_SNAKE_CASE						)

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

  # Get the datasets:
  a__	:					Dict										=datasets.load_dataset(
      "common_voice"       ,       data_args.dataset_config_name       ,       split=data_args.train_split_name						)
  a__	:					Tuple										=datasets.load_dataset("common_voice"       ,       data_args.dataset_config_name       ,       split="test"						)

  # Create and save tokenizer
  a__	:					Union[str, Any]										=f'''[{"".join(data_args.chars_to_ignore						)}]'''

  def remove_special_characters(SCREAMING_SNAKE_CASE     :   str						):
    a__	:					List[Any]										=re.sub(_SCREAMING_SNAKE_CASE       ,       ""       ,       batch["sentence"]						).lower() + " "
    return batch

  a__	:					Optional[Any]										=train_dataset.map(_SCREAMING_SNAKE_CASE       ,       remove_columns=["sentence"]						)
  a__	:					Optional[Any]										=eval_dataset.map(_SCREAMING_SNAKE_CASE       ,       remove_columns=["sentence"]						)

  def extract_all_chars(SCREAMING_SNAKE_CASE     :   Tuple						):
    a__	:					int										=" ".join(batch["text"]						)
    a__	:					str										=list(set(_SCREAMING_SNAKE_CASE						)						)
    return {"vocab": [vocab], "all_text": [all_text]}

  a__	:					Optional[int]										=train_dataset.map(
      _SCREAMING_SNAKE_CASE       ,       batched=_SCREAMING_SNAKE_CASE       ,       batch_size=-1       ,       keep_in_memory=_SCREAMING_SNAKE_CASE       ,       remove_columns=train_dataset.column_names       ,       )
  a__	:					Optional[Any]										=train_dataset.map(
      _SCREAMING_SNAKE_CASE       ,       batched=_SCREAMING_SNAKE_CASE       ,       batch_size=-1       ,       keep_in_memory=_SCREAMING_SNAKE_CASE       ,       remove_columns=eval_dataset.column_names       ,       )

  a__	:					List[Any]										=list(set(vocab_train["vocab"][0]						) | set(vocab_test["vocab"][0]						)						)
  a__	:					List[Any]										={v: k for k, v in enumerate(_SCREAMING_SNAKE_CASE						)}
  a__	:					int										=vocab_dict[" "]
  del vocab_dict[" "]
  a__	:					Optional[Any]										=len(_SCREAMING_SNAKE_CASE						)
  a__	:					List[str]										=len(_SCREAMING_SNAKE_CASE						)

  with open("vocab.json"       ,       "w"						) as vocab_file:
    json.dump(_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE						)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  a__	:					Any										=WavaVecaCTCTokenizer(
      "vocab.json"       ,       unk_token="[UNK]"       ,       pad_token="[PAD]"       ,       word_delimiter_token="|"       ,       )
  a__	:					int										=WavaVecaFeatureExtractor(
      feature_size=1       ,       sampling_rate=16_000       ,       padding_value=0.0       ,       do_normalize=_SCREAMING_SNAKE_CASE       ,       return_attention_mask=_SCREAMING_SNAKE_CASE						)
  a__	:					Dict										=WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE       ,       tokenizer=_SCREAMING_SNAKE_CASE						)
  a__	:					int										=WavaVecaForCTC.from_pretrained(
      model_args.model_name_or_path       ,       cache_dir=model_args.cache_dir       ,       activation_dropout=model_args.activation_dropout       ,       attention_dropout=model_args.attention_dropout       ,       hidden_dropout=model_args.hidden_dropout       ,       feat_proj_dropout=model_args.feat_proj_dropout       ,       mask_time_prob=model_args.mask_time_prob       ,       gradient_checkpointing=training_args.gradient_checkpointing       ,       layerdrop=model_args.layerdrop       ,       ctc_loss_reduction="mean"       ,       pad_token_id=processor.tokenizer.pad_token_id       ,       vocab_size=len(processor.tokenizer						)       ,       )

  if data_args.max_train_samples is not None:
    a__	:					Optional[int]										=min(len(_SCREAMING_SNAKE_CASE						)       ,       data_args.max_train_samples						)
    a__	:					Any										=train_dataset.select(range(_SCREAMING_SNAKE_CASE						)						)

  if data_args.max_val_samples is not None:
    a__	:					Optional[Any]										=eval_dataset.select(range(data_args.max_val_samples						)						)

  a__	:					Tuple										=torchaudio.transforms.Resample(48_000       ,       16_000						)

  # Preprocessing the datasets.
  # We need to read the aduio files as arrays and tokenize the targets.
  def speech_file_to_array_fn(SCREAMING_SNAKE_CASE     :   Any						):
    a__					,							a__	:					Optional[Any]										=torchaudio.load(batch["path"]						)
    a__	:					Optional[Any]										=resampler(_SCREAMING_SNAKE_CASE						).squeeze().numpy()
    a__	:					int										=16_000
    a__	:					int										=batch["text"]
    return batch

  a__	:					int										=train_dataset.map(
      _SCREAMING_SNAKE_CASE       ,       remove_columns=train_dataset.column_names       ,       num_proc=data_args.preprocessing_num_workers       ,       )
  a__	:					List[Any]										=eval_dataset.map(
      _SCREAMING_SNAKE_CASE       ,       remove_columns=eval_dataset.column_names       ,       num_proc=data_args.preprocessing_num_workers       ,       )

  def prepare_dataset(SCREAMING_SNAKE_CASE     :   List[Any]						):
    # check that all files have the correct sampling rate
    assert (
        len(set(batch["sampling_rate"]						)						) == 1
    ), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

    a__	:					Any										=processor(
        audio=batch["speech"]       ,       text=batch["target_text"]       ,       sampling_rate=batch["sampling_rate"][0]						)
    batch.update(_SCREAMING_SNAKE_CASE						)
    return batch

  a__	:					Any										=train_dataset.map(
      _SCREAMING_SNAKE_CASE       ,       remove_columns=train_dataset.column_names       ,       batch_size=training_args.per_device_train_batch_size       ,       batched=_SCREAMING_SNAKE_CASE       ,       num_proc=data_args.preprocessing_num_workers       ,       )
  a__	:					List[Any]										=eval_dataset.map(
      _SCREAMING_SNAKE_CASE       ,       remove_columns=eval_dataset.column_names       ,       batch_size=training_args.per_device_train_batch_size       ,       batched=_SCREAMING_SNAKE_CASE       ,       num_proc=data_args.preprocessing_num_workers       ,       )

  # Metric
  a__	:					Any										=datasets.load_metric("wer"						)

  def compute_metrics(SCREAMING_SNAKE_CASE     :   Optional[int]						):
    a__	:					Union[str, Any]										=pred.predictions
    a__	:					int										=np.argmax(_SCREAMING_SNAKE_CASE       ,       axis=-1						)

    a__	:					Dict										=processor.tokenizer.pad_token_id

    a__	:					List[str]										=processor.batch_decode(_SCREAMING_SNAKE_CASE						)
    # we do not want to group tokens when computing the metrics
    a__	:					List[Any]										=processor.batch_decode(pred.label_ids       ,       group_tokens=_SCREAMING_SNAKE_CASE						)

    a__	:					List[Any]										=wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE       ,       references=_SCREAMING_SNAKE_CASE						)

    return {"wer": wer}

  if model_args.freeze_feature_extractor:
    model.freeze_feature_extractor()

  # Data collator
  a__	:					List[str]										=DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE       ,       padding=_SCREAMING_SNAKE_CASE						)

  # Initialize our Trainer
  a__	:					Dict										=CTCTrainer(
      model=_SCREAMING_SNAKE_CASE       ,       data_collator=_SCREAMING_SNAKE_CASE       ,       args=_SCREAMING_SNAKE_CASE       ,       compute_metrics=_SCREAMING_SNAKE_CASE       ,       train_dataset=train_dataset if training_args.do_train else None       ,       eval_dataset=eval_dataset if training_args.do_eval else None       ,       tokenizer=processor.feature_extractor       ,       )

  # Training
  if training_args.do_train:
    if last_checkpoint is not None:
      a__	:					int										=last_checkpoint
    elif os.path.isdir(model_args.model_name_or_path						):
      a__	:					Optional[int]										=model_args.model_name_or_path
    else:
      a__	:					Optional[Any]										=None

    # Save the feature_extractor and the tokenizer
    if is_main_process(training_args.local_rank						):
      processor.save_pretrained(training_args.output_dir						)

    a__	:					List[Any]										=trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE						)
    trainer.save_model()

    a__	:					str										=train_result.metrics
    a__	:					Union[str, Any]										=(
        data_args.max_train_samples if data_args.max_train_samples is not None else len(_SCREAMING_SNAKE_CASE						)
    )
    a__	:					str										=min(_SCREAMING_SNAKE_CASE       ,       len(_SCREAMING_SNAKE_CASE						)						)

    trainer.log_metrics("train"       ,       _SCREAMING_SNAKE_CASE						)
    trainer.save_metrics("train"       ,       _SCREAMING_SNAKE_CASE						)
    trainer.save_state()

  # Evaluation
  a__	:					Optional[int]										={}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"						)
    a__	:					Dict										=trainer.evaluate()
    a__	:					int										=data_args.max_val_samples if data_args.max_val_samples is not None else len(_SCREAMING_SNAKE_CASE						)
    a__	:					Union[str, Any]										=min(_SCREAMING_SNAKE_CASE       ,       len(_SCREAMING_SNAKE_CASE						)						)

    trainer.log_metrics("eval"       ,       _SCREAMING_SNAKE_CASE						)
    trainer.save_metrics("eval"       ,       _SCREAMING_SNAKE_CASE						)

  return results


if __name__ == "__main__":
     main()













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()
SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            SCREAMING_SNAKE_CASE		=						[1_44, 1_92, 2_40]
            SCREAMING_SNAKE_CASE		=						[16, 32, 64, 96, 1_28, 1_60, 6_40]
      elif "mobilevit_xs" in mobilevit_name:
            SCREAMING_SNAKE_CASE		=						[96, 1_20, 1_44]
            SCREAMING_SNAKE_CASE		=						[16, 32, 48, 64, 80, 96, 3_84]
      elif "mobilevit_xxs" in mobilevit_name:
            SCREAMING_SNAKE_CASE		=						[64, 80, 96]
            SCREAMING_SNAKE_CASE		=						[16, 16, 24, 48, 64, 80, 3_20]
            SCREAMING_SNAKE_CASE		=						0.05
            SCREAMING_SNAKE_CASE		=						2.0

      if mobilevit_name.startswith("""deeplabv3_"""	):
            SCREAMING_SNAKE_CASE		=						5_12
            SCREAMING_SNAKE_CASE		=						16
            SCREAMING_SNAKE_CASE		=						21
            SCREAMING_SNAKE_CASE		=						"""pascal-voc-id2label.json"""
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""

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

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   str:


      '''simple docstring'''


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

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

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

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

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

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

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

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

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   Dict:


      '''simple docstring'''


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

      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

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

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

      return orig_state_dict



def    __lowercase   (      ) ->   str:


      '''simple docstring'''


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



@torch.no_grad()
def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						get_mobilevit_config(_SCREAMING_SNAKE_CASE	)

      # load original state_dict
      SCREAMING_SNAKE_CASE		=						torch.load(_SCREAMING_SNAKE_CASE ,						map_location="""cpu"""	)

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

      SCREAMING_SNAKE_CASE		=						convert_state_dict(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

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

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  SCREAMING_SNAKE_CASE		=						torch.tensor(
                      [
                          [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                          [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                          [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                      ]	)
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  SCREAMING_SNAKE_CASE		=						torch.tensor(
                      [
                          [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                          [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                          [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                      ]	)
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  SCREAMING_SNAKE_CASE		=						torch.tensor(
                      [
                          [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                          [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                          [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                      ]	)
            else:
                  raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""	)

            assert torch.allclose(logits[0, :3, :3, :3] ,						_SCREAMING_SNAKE_CASE ,						atol=1E-4	)
      else:
            assert logits.shape == (1, 10_00)

            if mobilevit_name == "mobilevit_s":
                  SCREAMING_SNAKE_CASE		=						torch.tensor([-0.9_866, 0.2_392, -1.1_241]	)
            elif mobilevit_name == "mobilevit_xs":
                  SCREAMING_SNAKE_CASE		=						torch.tensor([-2.4_761, -0.9_399, -1.9_587]	)
            elif mobilevit_name == "mobilevit_xxs":
                  SCREAMING_SNAKE_CASE		=						torch.tensor([-1.9_364, -1.2_327, -0.4_653]	)
            else:
                  raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""	)

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

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

      if push_to_hub:
            SCREAMING_SNAKE_CASE		=						{
                """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..."""	)
            SCREAMING_SNAKE_CASE		=						model_mapping[mobilevit_name]
            image_processor.push_to_hub(_SCREAMING_SNAKE_CASE ,						organization="""apple"""	)
            model.push_to_hub(_SCREAMING_SNAKE_CASE ,						organization="""apple"""	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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."""
      )

      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )


