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'):
      lowercase_  : Any								=					True
      from torch.cuda.amp import autocast

lowercase_  : Tuple								=					logging.getLogger(__name__)







def       A__   (						snake_case_			:		Optional[int]=None							,					snake_case_			:		Optional[int]=None   ):
 return field(default_factory=lambda: default							,					metadata=snake_case_   )






@dataclass
class 		_lowerCamelCase		:
    __a						=   field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
    __a						=   field(
        default=UpperCamelCase_     ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,  )
    __a						=   field(
        default=UpperCamelCase_     ,  metadata={"help": "Whether to freeze the feature extractor layers of the model."}     )
    __a						=   field(
        default=0.1     ,  metadata={"help": "The dropout ratio for the attention probabilities."}     )
    __a						=   field(
        default=0.1     ,  metadata={"help": "The dropout ratio for activations inside the fully connected layer."}     )
    __a						=   field(
        default=0.1     ,  metadata={
            "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
        }     ,  )
    __a						=   field(
        default=0.1     ,  metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}     ,  )
    __a						=   field(
        default=0.05     ,  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``."
            )
        }     ,  )
    __a						=   field(default=0.0     ,  metadata={"help": "The LayerDrop probability."}     )






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






@dataclass
class 		_lowerCamelCase		:
    __a						=   42
    __a						=   True
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   None
    def __call__(	self			,			lowerCAmelCase )							->       Dict[str, torch.Tensor]:
     # split inputs and labels since they have to be of different lenghts and need
     # different padding methods
     SCREAMING_SNAKE_CASE__: Optional[int]=   [{'''input_values''': feature['''input_values''']} for feature in features]
     SCREAMING_SNAKE_CASE__: Dict=   [{'''input_ids''': feature['''labels''']} for feature in features]

     SCREAMING_SNAKE_CASE__: Dict=   self.processor.pad(
         lowerCAmelCase			,			padding=self.padding			,			max_length=self.max_length			,			pad_to_multiple_of=self.pad_to_multiple_of			,			return_tensors='''pt'''			,			)
     SCREAMING_SNAKE_CASE__: int=   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
     SCREAMING_SNAKE_CASE__: Optional[int]=   labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 )			,			-100 )

     SCREAMING_SNAKE_CASE__: List[Any]=   labels

     return batch






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       torch.Tensor:
     model.train()
     SCREAMING_SNAKE_CASE__: Any=   self._prepare_inputs(lowerCAmelCase )

     if self.use_amp:
      with autocast():
       SCREAMING_SNAKE_CASE__: Tuple=   self.compute_loss(lowerCAmelCase			,			lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: List[Any]=   self.compute_loss(lowerCAmelCase			,			lowerCAmelCase )

     if self.args.n_gpu > 1:
      if model.module.config.ctc_loss_reduction == "mean":
       SCREAMING_SNAKE_CASE__: Dict=   loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       SCREAMING_SNAKE_CASE__: str=   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:
      SCREAMING_SNAKE_CASE__: Union[str, 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__   (						):
 # 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.

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

 # Detecting last checkpoint.
 SCREAMING_SNAKE_CASE__: int=   None
 if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
  SCREAMING_SNAKE_CASE__: Optional[Any]=   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'''							,					snake_case_   )

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

 # Get the datasets:
 SCREAMING_SNAKE_CASE__: Optional[int]=   datasets.load_dataset(
     '''common_voice'''							,					data_args.dataset_config_name							,					split=data_args.train_split_name   )
 SCREAMING_SNAKE_CASE__: str=   datasets.load_dataset('''common_voice'''							,					data_args.dataset_config_name							,					split='''test'''   )

 # Create and save tokenizer
 SCREAMING_SNAKE_CASE__: Optional[int]=   F'[{"".join(data_args.chars_to_ignore   )}]'

 def remove_special_characters(snake_case_			:		Union[str, Any]   ):
  SCREAMING_SNAKE_CASE__: Any=   re.sub(snake_case_							,					''''''							,					batch['''sentence''']   ).lower() + ''' '''
  return batch

 SCREAMING_SNAKE_CASE__: int=   train_dataset.map(snake_case_							,					remove_columns=['''sentence''']   )
 SCREAMING_SNAKE_CASE__: List[str]=   eval_dataset.map(snake_case_							,					remove_columns=['''sentence''']   )

 def extract_all_chars(snake_case_			:		Optional[int]   ):
  SCREAMING_SNAKE_CASE__: int=   ''' '''.join(batch['''text''']   )
  SCREAMING_SNAKE_CASE__: List[Any]=   list(set(snake_case_   )   )
  return {"vocab": [vocab], "all_text": [all_text]}

 SCREAMING_SNAKE_CASE__: Tuple=   train_dataset.map(
     snake_case_							,					batched=snake_case_							,					batch_size=-1							,					keep_in_memory=snake_case_							,					remove_columns=train_dataset.column_names							,					)
 SCREAMING_SNAKE_CASE__: int=   train_dataset.map(
     snake_case_							,					batched=snake_case_							,					batch_size=-1							,					keep_in_memory=snake_case_							,					remove_columns=eval_dataset.column_names							,					)

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

 with open('''vocab.json'''							,					'''w'''   ) as vocab_file:
  json.dump(snake_case_							,					snake_case_   )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 SCREAMING_SNAKE_CASE__: str=   WavaVecaCTCTokenizer(
     '''vocab.json'''							,					unk_token='''[UNK]'''							,					pad_token='''[PAD]'''							,					word_delimiter_token='''|'''							,					)
 SCREAMING_SNAKE_CASE__: Dict=   WavaVecaFeatureExtractor(
     feature_size=1							,					sampling_rate=16_000							,					padding_value=0.0							,					do_normalize=snake_case_							,					return_attention_mask=snake_case_   )
 SCREAMING_SNAKE_CASE__: int=   WavaVecaProcessor(feature_extractor=snake_case_							,					tokenizer=snake_case_   )
 SCREAMING_SNAKE_CASE__: str=   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:
  SCREAMING_SNAKE_CASE__: Any=   min(len(snake_case_   )							,					data_args.max_train_samples   )
  SCREAMING_SNAKE_CASE__: List[Any]=   train_dataset.select(range(snake_case_   )   )

 if data_args.max_val_samples is not None:
  SCREAMING_SNAKE_CASE__: Dict=   eval_dataset.select(range(data_args.max_val_samples   )   )

 SCREAMING_SNAKE_CASE__: 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(snake_case_			:		Optional[Any]   ):
  SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Any=   torchaudio.load(batch['''path''']   )
  SCREAMING_SNAKE_CASE__: Dict=   resampler(snake_case_   ).squeeze().numpy()
  SCREAMING_SNAKE_CASE__: Any=   16_000
  SCREAMING_SNAKE_CASE__: str=   batch['''text''']
  return batch

 SCREAMING_SNAKE_CASE__: Optional[int]=   train_dataset.map(
     snake_case_							,					remove_columns=train_dataset.column_names							,					num_proc=data_args.preprocessing_num_workers							,					)
 SCREAMING_SNAKE_CASE__: Tuple=   eval_dataset.map(
     snake_case_							,					remove_columns=eval_dataset.column_names							,					num_proc=data_args.preprocessing_num_workers							,					)

 def prepare_dataset(snake_case_			:		Dict   ):
  # 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}.'

  SCREAMING_SNAKE_CASE__: Tuple=   processor(
      audio=batch['''speech''']							,					text=batch['''target_text''']							,					sampling_rate=batch['''sampling_rate'''][0]   )
  batch.update(snake_case_   )
  return batch

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   train_dataset.map(
     snake_case_							,					remove_columns=train_dataset.column_names							,					batch_size=training_args.per_device_train_batch_size							,					batched=snake_case_							,					num_proc=data_args.preprocessing_num_workers							,					)
 SCREAMING_SNAKE_CASE__: str=   eval_dataset.map(
     snake_case_							,					remove_columns=eval_dataset.column_names							,					batch_size=training_args.per_device_train_batch_size							,					batched=snake_case_							,					num_proc=data_args.preprocessing_num_workers							,					)

 # Metric
 SCREAMING_SNAKE_CASE__: str=   datasets.load_metric('''wer'''   )

 def compute_metrics(snake_case_			:		Any   ):
  SCREAMING_SNAKE_CASE__: Optional[Any]=   pred.predictions
  SCREAMING_SNAKE_CASE__: Any=   np.argmax(snake_case_							,					axis=-1   )

  SCREAMING_SNAKE_CASE__: Any=   processor.tokenizer.pad_token_id

  SCREAMING_SNAKE_CASE__: Optional[Any]=   processor.batch_decode(snake_case_   )
  # we do not want to group tokens when computing the metrics
  SCREAMING_SNAKE_CASE__: Dict=   processor.batch_decode(pred.label_ids							,					group_tokens=snake_case_   )

  SCREAMING_SNAKE_CASE__: List[Any]=   wer_metric.compute(predictions=snake_case_							,					references=snake_case_   )

  return {"wer": wer}

 if model_args.freeze_feature_extractor:
  model.freeze_feature_extractor()

 # Data collator
 SCREAMING_SNAKE_CASE__: List[Any]=   DataCollatorCTCWithPadding(processor=snake_case_							,					padding=snake_case_   )

 # Initialize our Trainer
 SCREAMING_SNAKE_CASE__: Optional[Any]=   CTCTrainer(
     model=snake_case_							,					data_collator=snake_case_							,					args=snake_case_							,					compute_metrics=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:
   SCREAMING_SNAKE_CASE__: Dict=   last_checkpoint
  elif os.path.isdir(model_args.model_name_or_path   ):
   SCREAMING_SNAKE_CASE__: Any=   model_args.model_name_or_path
  else:
   SCREAMING_SNAKE_CASE__: 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   )

  SCREAMING_SNAKE_CASE__: Union[str, Any]=   trainer.train(resume_from_checkpoint=snake_case_   )
  trainer.save_model()

  SCREAMING_SNAKE_CASE__: List[str]=   train_result.metrics
  SCREAMING_SNAKE_CASE__: Optional[int]=   (
      data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_   )
  )
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   min(snake_case_							,					len(snake_case_   )   )

  trainer.log_metrics('''train'''							,					snake_case_   )
  trainer.save_metrics('''train'''							,					snake_case_   )
  trainer.save_state()

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

  trainer.log_metrics('''eval'''							,					snake_case_   )
  trainer.save_metrics('''eval'''							,					snake_case_   )

 return results


if __name__ == "__main__":
      main()







import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	old_name

	if "patch_embed" in old_name:
		snake_case_       , snake_case_       , snake_case_						       =	old_name.split("."				)

		if layer == "0":
			snake_case_						       =	old_name.replace("0"	,      "convolution1"				)
		elif layer == "1":
			snake_case_						       =	old_name.replace("1"	,      "batchnorm_before"				)
		elif layer == "3":
			snake_case_						       =	old_name.replace("3"	,      "convolution2"				)
		else:
			snake_case_						       =	old_name.replace("4"	,      "batchnorm_after"				)

	if "network" in old_name and re.search(R"\d\.\d"	,      _A				):
		snake_case_						       =	R"\b\d{2}\b"
		if bool(re.search(_A	,      _A				)				):
			snake_case_						       =	re.search(R"\d\.\d\d."	,      _A				).group()
		else:
			snake_case_						       =	re.search(R"\d\.\d."	,      _A				).group()
		if int(match[0]				) < 6:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			snake_case_						       =	trimmed_name.replace("network"	,      match[0] + ".meta4D_layers.blocks." + match[2:-1]				)
			snake_case_						       =	"intermediate_stages." + trimmed_name
		else:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			if int(match[2]				) < num_meta4D_last_stage:
				snake_case_						       =	trimmed_name.replace("network"	,      "meta4D_layers.blocks." + match[2]				)
			else:
				snake_case_						       =	str(int(match[2]				) - num_meta4D_last_stage				)
				snake_case_						       =	trimmed_name.replace("network"	,      "meta3D_layers.blocks." + layer_index				)
				if "norm1" in old_name:
					snake_case_						       =	trimmed_name.replace("norm1"	,      "layernorm1"				)
				elif "norm2" in old_name:
					snake_case_						       =	trimmed_name.replace("norm2"	,      "layernorm2"				)
				elif "fc1" in old_name:
					snake_case_						       =	trimmed_name.replace("fc1"	,      "linear_in"				)
				elif "fc2" in old_name:
					snake_case_						       =	trimmed_name.replace("fc2"	,      "linear_out"				)

			snake_case_						       =	"last_stage." + trimmed_name

	elif "network" in old_name and re.search(R".\d."	,      _A				):
		snake_case_						       =	old_name.replace("network"	,      "intermediate_stages"				)

	if "fc" in new_name:
		snake_case_						       =	new_name.replace("fc"	,      "convolution"				)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		snake_case_						       =	new_name.replace("norm1"	,      "batchnorm_before"				)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		snake_case_						       =	new_name.replace("norm2"	,      "batchnorm_after"				)
	if "proj" in new_name:
		snake_case_						       =	new_name.replace("proj"	,      "projection"				)
	if "dist_head" in new_name:
		snake_case_						       =	new_name.replace("dist_head"	,      "distillation_classifier"				)
	elif "head" in new_name:
		snake_case_						       =	new_name.replace("head"	,      "classifier"				)
	elif "patch_embed" in new_name:
		snake_case_						       =	"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		snake_case_						       =	new_name.replace("norm"	,      "layernorm"				)
		snake_case_						       =	"efficientformer." + new_name
	else:
		snake_case_						       =	"efficientformer.encoder." + new_name

	return new_name


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	for key in checkpoint.copy().keys():
		snake_case_						       =	checkpoint.pop(_A				)
		snake_case_						       =	val

	return checkpoint


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_						       =	Image.open(requests.get(_A	,      stream=_A				).raw				)

	return image


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	torch.load(_A	,      map_location="cpu"				)["model"]
	snake_case_						       =	EfficientFormerConfig.from_json_file(_A				)
	snake_case_						       =	EfficientFormerForImageClassificationWithTeacher(_A				)
	snake_case_						       =	"_".join(checkpoint_path.split("/"				)[-1].split("."				)[0].split("_"				)[:-1]				)

	snake_case_						       =	config.depths[-1] - config.num_metaad_blocks + 1
	snake_case_						       =	convert_torch_checkpoint(_A	,      _A				)

	model.load_state_dict(_A				)
	model.eval()

	snake_case_						       =	{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	snake_case_						       =	prepare_img()
	snake_case_						       =	256
	snake_case_						       =	224
	snake_case_						       =	EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}	,      crop_size={"height": crop_size, "width": crop_size}	,      resample=pillow_resamplings["bicubic"]	,      )
	snake_case_						       =	processor(images=_A	,      return_tensors="pt"				).pixel_values

	# original processing pipeline
	snake_case_						       =	Compose(
	    [
	        Resize(_A	,      interpolation=pillow_resamplings["bicubic"]				),
	        CenterCrop(_A				),
	        ToTensor(),
	        Normalize(_A	,      _A				),
	    ]				)
	snake_case_						       =	image_transforms(_A				).unsqueeze(0				)

	assert torch.allclose(_A	,      _A				)

	snake_case_						       =	model(_A				)
	snake_case_						       =	outputs.logits

	snake_case_						       =	(1, 1000)

	if "l1" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]				)
		assert torch.allclose(logits[0, :10]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]				)
		assert torch.allclose(logits[0, :10]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		snake_case_						       =	torch.Tensor(
		    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]				)
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"				)

	# Save Checkpoints
	Path(_A				).mkdir(exist_ok=_A				)
	model.save_pretrained(_A				)
	print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"				)
	processor.save_pretrained(_A				)
	print(f"Processor successfuly saved at {pytorch_dump_path}"				)

	if push_to_hub:
		print("Pushing model to the hub..."				)

		model.push_to_hub(
		    repo_id=f"Bearnardd/{pytorch_dump_path}"	,      commit_message="Add model"	,      use_temp_dir=_A	,      )
		processor.push_to_hub(
		    repo_id=f"Bearnardd/{pytorch_dump_path}"	,      commit_message="Add image processor"	,      use_temp_dir=_A	,      )


if __name__ == "__main__":
		lowercase__							:	Tuple											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to EfficientFormer pytorch checkpoint.",
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for EfficientFormer model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)

		parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
		parser.add_argument(
		    "--no-push_to_hub",
		    dest="push_to_hub",
		    action="store_false",
		    help="Do not push model and image processor to the hub",
		)
		parser.set_defaults(push_to_hub=True)

		lowercase__							:	Optional[Any]											=		parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)
