




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()
SCREAMING_SNAKE_CASE__ :   List[str]						=     logging.get_logger()




@dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



    lowerCamelCase_   :			Optional[Any]			  =   4_2
    lowerCamelCase_   :			str			  =   field(default_factory=lowerCamelCase__  )
    lowerCamelCase_   :			Optional[int]			  =   field(default_factory=lowerCamelCase__  )
    lowerCamelCase_   :			List[str]			  =   field(default_factory=lowerCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   Union[str, Any]    				=      len(list(m.modules()  )  ) == 1 or isinstance(SCREAMING_SNAKE_CASE_  ,						nn.Convad  ) or isinstance(SCREAMING_SNAKE_CASE_  ,						nn.BatchNormad  )
           if has_not_submodules:
                  self.traced.append(SCREAMING_SNAKE_CASE_  )
                  lowerCamelCase     :   Any    				=      m







    def __call__(					self  ,						UpperCamelCase__  )  ->   Dict:
           for name, m in self.module.named_modules():
                  self.handles.append(m.register_forward_hook(partial(self._forward_hook  ,						name=SCREAMING_SNAKE_CASE_  )  )  )
           self.module(SCREAMING_SNAKE_CASE_  )
           [x.remove() for x in self.handles]
           return self







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           # 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 	UpperCamelCase__			(nn.Module  ):

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  )  ->   Tuple:
           super().__init__()

           lowerCamelCase     :   Optional[Any]    				=      []
           # - 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}'''
                  lowerCamelCase     :   int    				=      len(SCREAMING_SNAKE_CASE_  ) + 1
                  feature_blocks.append((F'''res{block_index}''', v)  )

           lowerCamelCase     :   Dict    				=      nn.ModuleDict(SCREAMING_SNAKE_CASE_  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           return get_trunk_forward_outputs(
               SCREAMING_SNAKE_CASE_  ,						out_feat_keys=SCREAMING_SNAKE_CASE_  ,						feature_blocks=self._feature_blocks  ,						)




class 	UpperCamelCase__			(lowerCamelCase__  ):

    '''simple docstring'''



    def 					_lowercase  (					self  )  ->   List[Any]:
           return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def   A   (   _SCREAMING_SNAKE_CASE					)			->				Dict[str, str]:

       '''simple docstring'''





       lowerCamelCase     :   Dict    				=      RegNetConfig(depths=[2, 7, 17, 1]				,hidden_sizes=[8, 8, 8, 8]				,groups_width=8					)
       if "in1k" in model_name:
              lowerCamelCase     :   int    				=      RegNetForImageClassification(_UpperCAmelCase					)
       else:
              lowerCamelCase     :   Tuple    				=      RegNetModel(_UpperCAmelCase					)
       # create from model (with small weights)
       lowerCamelCase     :   Optional[int]    				=      FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27				,group_width=1010				,w_a=1744				,w_a=620.83				,w_m=2.52					)					)					)

       with torch.no_grad():
              lowerCamelCase     :   Optional[int]    				=      from_model.eval()
              lowerCamelCase     :   List[Any]    				=      our_model.eval()

              lowerCamelCase     :   int    				=      torch.randn((1, 3, 32, 32)					)
              # trace both
              lowerCamelCase     :   List[Any]    				=      Tracker(_UpperCAmelCase					)
              lowerCamelCase     :   Optional[int]    				=      dest_tracker(_UpperCAmelCase					).parametrized

              pprint(dest_tracker.nameamodule					)
              lowerCamelCase     :   List[Any]    				=      Tracker(_UpperCAmelCase					)
              lowerCamelCase     :   Optional[Any]    				=      src_tracker(_UpperCAmelCase					).parametrized

       # convert the keys -> module dict to keys -> params
       def to_params_dict(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   Tuple    				=      OrderedDict()
              for name, module in dict_with_modules.items():
                     for param_name, param in module.state_dict().items():
                            lowerCamelCase     :   Optional[int]    				=      param
              return params_dict

       lowerCamelCase     :   List[Any]    				=      {}

       lowerCamelCase     :   Any    				=      to_params_dict(_UpperCAmelCase					)
       lowerCamelCase     :   Any    				=      to_params_dict(_UpperCAmelCase					)

       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()				,dst_state_dict.items()					):
              lowerCamelCase     :   List[str]    				=      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:
              lowerCamelCase     :   Tuple    				=      "classifier.1.weight"
              lowerCamelCase     :   Optional[Any]    				=      "classifier.1.bias"

       return from_to_ours_keys

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE = None				,_SCREAMING_SNAKE_CASE = True					)			->				Union[str, Any]:

       '''simple docstring'''





       lowerCamelCase     :   Optional[Any]    				=      "imagenet-1k-id2label.json"
       lowerCamelCase     :   Any    				=      1000

       lowerCamelCase     :   Any    				=      "huggingface/label-files"
       lowerCamelCase     :   Dict    				=      num_labels
       lowerCamelCase     :   Optional[int]    				=      json.load(open(cached_download(hf_hub_url(_UpperCAmelCase				,_UpperCAmelCase				,repo_type="dataset"					)					)				,"r"					)					)
       lowerCamelCase     :   Optional[int]    				=      {int(_UpperCAmelCase					): v for k, v in idalabel.items()}

       lowerCamelCase     :   Optional[Any]    				=      idalabel
       lowerCamelCase     :   Tuple    				=      {v: k for k, v in idalabel.items()}

       lowerCamelCase     :   Union[str, Any]    				=      partial(_UpperCAmelCase				,num_labels=_UpperCAmelCase				,idalabel=_UpperCAmelCase				,labelaid=_UpperCAmelCase					)

       lowerCamelCase     :   Tuple    				=      {
           "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					) -> Tuple[Dict, Dict]:
              lowerCamelCase     :   Dict    				=      torch.hub.load_state_dict_from_url(_UpperCAmelCase				,model_dir=str(_UpperCAmelCase					)				,map_location="cpu"					)
              # check if we have a head, if yes add it
              lowerCamelCase     :   Optional[Any]    				=      files["classy_state_dict"]["base_model"]["model"]
              return model_state_dict["trunk"], model_state_dict["heads"]

       lowerCamelCase     :   Union[str, Any]    				=      {
           "regnet-y-10b-seer": partial(
               _UpperCAmelCase				,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"				,),
           "regnet-y-10b-seer-in1k": partial(
               _UpperCAmelCase				,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"				,),
       }

       lowerCamelCase     :   Optional[Any]    				=      get_from_to_our_keys(_UpperCAmelCase					)

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

              lowerCamelCase     :   Union[str, Any]    				=      {}

              lowerCamelCase     :   str    				=      list(from_state_dict.keys()					)
              lowerCamelCase     :   Dict    				=      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
                     lowerCamelCase     :   Optional[int]    				=      re.sub(_UpperCAmelCase				,""				,_UpperCAmelCase					)
                     # 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
                     lowerCamelCase     :   List[str]    				=      from_to_ours_keys[src_key]
                     # store the parameter with our key
                     lowerCamelCase     :   Any    				=      from_state_dict[key]
                     not_used_keys.remove(_UpperCAmelCase					)
              # check that all keys have been updated
              assert len(_UpperCAmelCase					) == 0, f'''Some keys where not used {",".join(_UpperCAmelCase					)}'''

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

              # save our state dict to disk
              torch.save(_UpperCAmelCase				,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
              lowerCamelCase     :   List[str]    				=      names_to_config[model_name]
              lowerCamelCase     :   Optional[Any]    				=      RegNetModel
              if "in1k" in model_name:
                     lowerCamelCase     :   List[Any]    				=      RegNetForImageClassification
              lowerCamelCase     :   Optional[Any]    				=      our_model_func(_UpperCAmelCase					)
              # 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
              lowerCamelCase     :   Tuple    				=      our_model.state_dict().keys()
              PreTrainedModel._load_pretrained_model_low_mem(
                  _UpperCAmelCase				,_UpperCAmelCase				,[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				,)
              lowerCamelCase     :   Tuple    				=      384
              # we can use the convnext one
              lowerCamelCase     :   int    				=      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				,size=_UpperCAmelCase					)
              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__":
       SCREAMING_SNAKE_CASE__ :   int						=     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.',
       )

       SCREAMING_SNAKE_CASE__ :   str						=     parser.parse_args()

       SCREAMING_SNAKE_CASE__ :   int						=     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)












import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     logging.get_logger(__name__)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				np.array:
       lowerCamelCase     :   Optional[Any]    				=      f'''{sampling_rate}'''
       lowerCamelCase     :   Optional[Any]    				=      "1"
       lowerCamelCase     :   Tuple    				=      "f32le"
       lowerCamelCase     :   Any    				=      [
           "ffmpeg",
           "-i",
           "pipe:0",
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]

       try:
              lowerCamelCase     :   Optional[int]    				=      subprocess.Popen(_SCREAMING_SNAKE_CASE				,stdin=subprocess.PIPE				,stdout=subprocess.PIPE					)
       except FileNotFoundError:
              raise ValueError("ffmpeg was not found but is required to load audio files from filename"					)
       lowerCamelCase     :   Optional[int]    				=      ffmpeg_process.communicate(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Union[str, Any]    				=      output_stream[0]

       lowerCamelCase     :   Dict    				=      np.frombuffer(_SCREAMING_SNAKE_CASE				,np.floataa					)
       if audio.shape[0] == 0:
              raise ValueError("Malformed soundfile"					)
       return audio




@add_end_docstrings(lowerCAmelCase__  )
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __init__(					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Dict:
           # Default, might be overriden by the model.config.
           lowerCamelCase     :   Union[str, Any]    				=      5
           super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__  )

           if self.framework != "pt":
                  raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''  )

           self.check_model_type(UpperCamelCase__  )







    def __call__(					self  ,						UpperCamelCase__  ,						**UpperCamelCase__  ,						)  ->   Optional[int]:
           return super().__call__(UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   str:
           # No parameters on this pipeline right now
           lowerCamelCase     :   int    				=      {}
           if top_k is not None:
                  if top_k > self.model.config.num_labels:
                         lowerCamelCase     :   Any    				=      self.model.config.num_labels
                  lowerCamelCase     :   Any    				=      top_k
           return {}, {}, postprocess_params







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Dict:
           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  if inputs.startswith("http://"  ) or inputs.startswith("https://"  ):
                         # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                         # like http_huggingface_co.png
                         lowerCamelCase     :   str    				=      requests.get(UpperCamelCase__  ).content
                  else:
                         with open(UpperCamelCase__  ,						"rb"  ) as f:
                                lowerCamelCase     :   int    				=      f.read()

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  lowerCamelCase     :   str    				=      ffmpeg_read(UpperCamelCase__  ,						self.feature_extractor.sampling_rate  )

           if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ):
                  # Accepting `"array"` which is the key defined in `datasets` for
                  # better integration
                  if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                         raise ValueError(
                             "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
                             "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
                             "containing the sampling_rate associated with that array"  )

                  lowerCamelCase     :   Optional[int]    				=      inputs.pop("raw"  ,						UpperCamelCase__  )
                  if _inputs is None:
                         # Remove path which will not be used from `datasets`.
                         inputs.pop("path"  ,						UpperCamelCase__  )
                         lowerCamelCase     :   List[str]    				=      inputs.pop("array"  ,						UpperCamelCase__  )
                  lowerCamelCase     :   str    				=      inputs.pop("sampling_rate"  )
                  lowerCamelCase     :   Union[str, Any]    				=      _inputs
                  if in_sampling_rate != self.feature_extractor.sampling_rate:
                         import torch

                         if is_torchaudio_available():
                                from torchaudio import functional as F
                         else:
                                raise ImportError(
                                    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
                                    "The torchaudio package can be installed through: `pip install torchaudio`."  )

                         lowerCamelCase     :   Tuple    				=      F.resample(
                             torch.from_numpy(UpperCamelCase__  )  ,						UpperCamelCase__  ,						self.feature_extractor.sampling_rate  ).numpy()

           if not isinstance(UpperCamelCase__  ,						np.ndarray  ):
                  raise ValueError("We expect a numpy ndarray as input"  )
           if len(inputs.shape  ) != 1:
                  raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"  )

           lowerCamelCase     :   Optional[int]    				=      self.feature_extractor(
               UpperCamelCase__  ,						sampling_rate=self.feature_extractor.sampling_rate  ,						return_tensors="pt"  )
           return processed







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   str:
           lowerCamelCase     :   Optional[Any]    				=      self.model(**UpperCamelCase__  )
           return model_outputs







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=5  )  ->   Tuple:
           lowerCamelCase     :   Tuple    				=      model_outputs.logits[0].softmax(-1  )
           lowerCamelCase				,   lowerCamelCase     :   str    				=      probs.topk(UpperCamelCase__  )

           lowerCamelCase     :   str    				=      scores.tolist()
           lowerCamelCase     :   Optional[int]    				=      ids.tolist()

           lowerCamelCase     :   Optional[int]    				=      [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__  ,						UpperCamelCase__  )]

           return labels






