

'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	'<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '     )
    UpperCAmelCase													=	dict(zip(a_   , range(len(a_     )     )     )     )

    UpperCAmelCase													=	{
        'pad_token': '<pad>',
        'unk_token': '<unk>',
        'bos_token': '<s>',
        'eos_token': '</s>',
    }
    UpperCAmelCase													=	{
        'feature_size': 1,
        'padding_value': 0.0,
        'sampling_rate': 1_6_0_0_0,
        'return_attention_mask': False,
        'do_normalize': True,
    }

    UpperCAmelCase													=	tempfile.mkdtemp()
    UpperCAmelCase													=	os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES['vocab_file']     )
    UpperCAmelCase													=	os.path.join(self.tmpdirname   , a_     )
    with open(self.vocab_file   , 'w'   , encoding='utf-8'     ) as fp:
      fp.write(json.dumps(a_     ) + '\n'     )

    with open(self.feature_extraction_file   , 'w'   , encoding='utf-8'     ) as fp:
      fp.write(json.dumps(a_     ) + '\n'     )
  def 						snake_case_			(     self   , **a_     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	self.add_kwargs_tokens_map.copy()
    kwargs.update(a_     )
    return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   , **a_     )
  def 						snake_case_			(     self   , **a_     )							->				Optional[int]:


    """simple docstring"""
    return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   , **a_     )
  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.get_tokenizer()
    UpperCAmelCase													=	self.get_feature_extractor()

    UpperCAmelCase													=	WavaVecaProcessor(tokenizer=a_   , feature_extractor=a_     )

    processor.save_pretrained(self.tmpdirname     )
    UpperCAmelCase													=	WavaVecaProcessor.from_pretrained(self.tmpdirname     )

    self.assertEqual(processor.tokenizer.get_vocab()   , tokenizer.get_vocab()     )
    self.assertIsInstance(processor.tokenizer   , a_     )

    self.assertEqual(processor.feature_extractor.to_json_string()   , feature_extractor.to_json_string()     )
    self.assertIsInstance(processor.feature_extractor   , a_     )
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	WavaVecaProcessor(tokenizer=self.get_tokenizer()   , feature_extractor=self.get_feature_extractor()     )
    processor.save_pretrained(self.tmpdirname     )

    UpperCAmelCase													=	self.get_tokenizer(bos_token='(BOS)'   , eos_token='(EOS)'     )
    UpperCAmelCase													=	self.get_feature_extractor(do_normalize=a_   , padding_value=1.0     )

    UpperCAmelCase													=	WavaVecaProcessor.from_pretrained(
        self.tmpdirname   , bos_token='(BOS)'   , eos_token='(EOS)'   , do_normalize=a_   , padding_value=1.0     )

    self.assertEqual(processor.tokenizer.get_vocab()   , tokenizer_add_kwargs.get_vocab()     )
    self.assertIsInstance(processor.tokenizer   , a_     )

    self.assertEqual(processor.feature_extractor.to_json_string()   , feature_extractor_add_kwargs.to_json_string()     )
    self.assertIsInstance(processor.feature_extractor   , a_     )
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.get_feature_extractor()
    UpperCAmelCase													=	self.get_tokenizer()

    UpperCAmelCase													=	WavaVecaProcessor(tokenizer=a_   , feature_extractor=a_     )

    UpperCAmelCase													=	floats_list((3, 1_0_0_0)     )

    UpperCAmelCase													=	feature_extractor(a_   , return_tensors='np'     )
    UpperCAmelCase													=	processor(a_   , return_tensors='np'     )

    for key in input_feat_extract.keys():
      self.assertAlmostEqual(input_feat_extract[key].sum()   , input_processor[key].sum()   , delta=1E-2     )
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.get_feature_extractor()
    UpperCAmelCase													=	self.get_tokenizer()

    UpperCAmelCase													=	WavaVecaProcessor(tokenizer=a_   , feature_extractor=a_     )

    UpperCAmelCase													=	'This is a test string'

    UpperCAmelCase													=	processor(text=a_     )

    UpperCAmelCase													=	tokenizer(a_     )

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]   , encoded_processor[key]     )
  def 						snake_case_			(     self     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	self.get_feature_extractor()
    UpperCAmelCase													=	self.get_tokenizer()

    UpperCAmelCase													=	WavaVecaProcessor(tokenizer=a_   , feature_extractor=a_     )

    UpperCAmelCase													=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase													=	processor.batch_decode(a_     )
    UpperCAmelCase													=	tokenizer.batch_decode(a_     )

    self.assertListEqual(a_   , a_     )





  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.get_feature_extractor()
    UpperCAmelCase													=	self.get_tokenizer()

    UpperCAmelCase													=	WavaVecaProcessor(tokenizer=a_   , feature_extractor=a_     )

    self.assertListEqual(
        processor.model_input_names   , feature_extractor.model_input_names   , msg='`processor` and `feature_extractor` model input names do not match'   , )





'''simple docstring'''




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_a				: Optional[int] 							=						logging.get_logger()



@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: nn.Module
  __lowerCAmelCase					: List[nn.Module]      =   field(default_factory=a )
  __lowerCAmelCase					: list      =   field(default_factory=a )
  __lowerCAmelCase					: Dict[str, nn.Module]      =   field(default_factory=a )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	len(list(m.modules()     )     ) == 1 or isinstance(a_   , nn.Convad     ) or isinstance(a_   , nn.BatchNormad     )
    if has_not_submodules:
      self.traced.append(a_     )
      UpperCAmelCase													=	m
  def __call__(     self   , a_     )							->				List[Any]:


    """simple docstring"""
    for name, m in self.module.named_modules():
      self.handles.append(m.register_forward_hook(partial(self._forward_hook   , name=a_     )     )     )
    self.module(a_     )
    [x.remove() for x in self.handles]
    return self





  @property
  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    # check the len of the state_dict keys to see if we have learnable params
    return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()     )     ) > 0}




class        lowercase_	(		nn.Module ):

  '''simple docstring'''





  def __init__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    super().__init__()

    UpperCAmelCase													=	[]
    # - get the stem
    feature_blocks.append(('conv1', model.stem)     )
    # - get all the feature blocks
    for k, v in model.trunk_output.named_children():
      assert k.startswith('block'     ), F'''Unexpected layer name {k}'''
      UpperCAmelCase													=	len(a_     ) + 1
      feature_blocks.append((F'''res{block_index}''', v)     )

    UpperCAmelCase													=	nn.ModuleDict(a_     )





  def 						snake_case_			(     self   , a_     )							->				Optional[Any]:


    """simple docstring"""
    return get_trunk_forward_outputs(
        a_   , out_feat_keys=a_   , feature_blocks=self._feature_blocks   , )




class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	RegNetConfig(depths=[2, 7, 17, 1]       ,   hidden_sizes=[8, 8, 8, 8]       ,   groups_width=8						)
  if "in1k" in model_name:
    UpperCAmelCase													=	RegNetForImageClassification(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	RegNetModel(SCREAMING_SNAKE_CASE						)
  # create from model (with small weights)
  UpperCAmelCase													=	FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27       ,   group_width=1010       ,   w_a=1744       ,   w_a=620.83       ,   w_m=2.52						)						)						)

  with torch.no_grad():
    UpperCAmelCase													=	from_model.eval()
    UpperCAmelCase													=	our_model.eval()

    UpperCAmelCase													=	torch.randn((1, 3, 32, 32)						)
    # trace both
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	dest_tracker(SCREAMING_SNAKE_CASE						).parametrized

    pprint(dest_tracker.nameamodule						)
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	src_tracker(SCREAMING_SNAKE_CASE						).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(SCREAMING_SNAKE_CASE      :   Tuple						):
    UpperCAmelCase													=	OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        UpperCAmelCase													=	param
    return params_dict

  UpperCAmelCase													=	{}

  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,   dst_state_dict.items()						):
    UpperCAmelCase													=	dest_key
    logger.info(f'''{src_key} -> {dest_key}'''						)
  # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  if "in1k" in model_name:
    UpperCAmelCase													=	'classifier.1.weight'
    UpperCAmelCase													=	'classifier.1.bias'

  return from_to_ours_keys


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Path       ,   SCREAMING_SNAKE_CASE      :   str = None       ,   SCREAMING_SNAKE_CASE      :   bool = True						):
  UpperCAmelCase													=	'imagenet-1k-id2label.json'
  UpperCAmelCase													=	1000

  UpperCAmelCase													=	'huggingface/label-files'
  UpperCAmelCase													=	num_labels
  UpperCAmelCase													=	json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)						)       ,   'r'						)						)
  UpperCAmelCase													=	{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

  UpperCAmelCase													=	idalabel
  UpperCAmelCase													=	{v: k for k, v in idalabel.items()}

  UpperCAmelCase													=	partial(SCREAMING_SNAKE_CASE       ,   num_labels=SCREAMING_SNAKE_CASE       ,   idalabel=SCREAMING_SNAKE_CASE       ,   labelaid=SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	{
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
  }

  # add seer weights logic
  def load_using_classy_vision(SCREAMING_SNAKE_CASE      :   str						) -> Tuple[Dict, Dict]:
    UpperCAmelCase													=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE       ,   model_dir=str(SCREAMING_SNAKE_CASE						)       ,   map_location='cpu'						)
    # check if we have a head, if yes add it
    UpperCAmelCase													=	files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  UpperCAmelCase													=	{
      'regnet-y-10b-seer': partial(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,   ),
      'regnet-y-10b-seer-in1k': partial(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,   ),
  }

  UpperCAmelCase													=	get_from_to_our_keys(SCREAMING_SNAKE_CASE						)

  if not (save_directory / f'''{model_name}.pth''').exists():
    logger.info('Loading original state_dict.'						)
    UpperCAmelCase							,      UpperCAmelCase													=	names_to_from_model[model_name]()
    UpperCAmelCase													=	from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      UpperCAmelCase													=	{**from_state_dict_trunk, **from_state_dict_head}
    logger.info('Done!'						)

    UpperCAmelCase													=	{}

    UpperCAmelCase													=	list(from_state_dict.keys()						)
    UpperCAmelCase													=	R'\.block.-part.'
    # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
    for key in from_state_dict.keys():
      # remove the weird "block[0,1]-part" from the key
      UpperCAmelCase													=	re.sub(SCREAMING_SNAKE_CASE       ,   ''       ,   SCREAMING_SNAKE_CASE						)
      # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
      UpperCAmelCase													=	from_to_ours_keys[src_key]
      # store the parameter with our key
      UpperCAmelCase													=	from_state_dict[key]
      not_used_keys.remove(SCREAMING_SNAKE_CASE						)
    # check that all keys have been updated
    assert len(SCREAMING_SNAKE_CASE						) == 0, f'''Some keys where not used {','.join(SCREAMING_SNAKE_CASE						)}'''

    logger.info(f'''The following keys were not used: {','.join(SCREAMING_SNAKE_CASE						)}'''						)

    # save our state dict to disk
    torch.save(SCREAMING_SNAKE_CASE       ,   save_directory / f'''{model_name}.pth'''						)

    del converted_state_dict
  else:
    logger.info('The state_dict was already stored on disk.'						)
  if push_to_hub:
    logger.info(f'''Token is {os.environ['HF_TOKEN']}'''						)
    logger.info('Loading our model.'						)
    # create our model
    UpperCAmelCase													=	names_to_config[model_name]
    UpperCAmelCase													=	RegNetModel
    if "in1k" in model_name:
      UpperCAmelCase													=	RegNetForImageClassification
    UpperCAmelCase													=	our_model_func(SCREAMING_SNAKE_CASE						)
    # place our model to the meta device (so remove all the weights)
    our_model.to(torch.device('meta'						)						)
    logger.info('Loading state_dict in our model.'						)
    # load state dict
    UpperCAmelCase													=	our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   [save_directory / f'''{model_name}.pth''']						)
    logger.info('Finally, pushing!'						)
    # push it to hub
    our_model.push_to_hub(
        repo_path_or_name=save_directory / model_name       ,   commit_message='Add model'       ,   output_dir=save_directory / model_name       ,   )
    UpperCAmelCase													=	384
    # we can use the convnext one
    UpperCAmelCase													=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,   size=SCREAMING_SNAKE_CASE						)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / model_name       ,   commit_message='Add image processor'       ,   output_dir=save_directory / model_name       ,   )


if __name__ == "__main__":
  _a				: Dict 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help=(
          'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
          ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=Path,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      default=True,
      type=bool,
      required=False,
      help='If True, push model and image processor to the hub.',
  )

  _a				: Union[str, Any] 							=						parser.parse_args()

  _a				: Path 							=						args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


