




import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


UpperCamelCase__              =						logging.get_logger(__name__)

if is_vision_available():
							import PIL.Image
							from PIL import Image
							from PIL.Image import Image as ImageType
else:
							UpperCamelCase__              =						object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf


class 				__SCREAMING_SNAKE_CASE							:

					def __init__(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							value

					def __str__(				self					):







						'''simple docstring'''




						return self.to_string()

					def 	lowercase_							(				self					):







						'''simple docstring'''




						logger.error(
						    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'					)
						return self._value





					def 	lowercase_							(				self					):







						'''simple docstring'''




						logger.error(
						    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'					)
						return str(self._value					)


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase  ):

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self._value





					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self._value


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase  ):

					def __init__(				self ,       __lowercase					):







						'''simple docstring'''




						super().__init__(__lowercase					)

						if not is_vision_available():
							raise ImportError('PIL must be installed in order to handle images.'					)

						lowerCamelCase_							:		List[Any]							   =							None
						lowerCamelCase_							:		Optional[int]							   =							None
						lowerCamelCase_							:		Tuple							   =							None

						if isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Any							   =							value
						elif isinstance(__lowercase ,       (str, pathlib.Path)					):
							lowerCamelCase_							:		int							   =							value
						elif isinstance(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		Dict							   =							value
						else:
							raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(__lowercase					)}"					)

					def 	lowercase_							(				self ,       __lowercase=None ,       __lowercase=None					):







						'''simple docstring'''




						from IPython.display import Image, display

						display(Image(self.to_string()					)					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						if self._raw is not None:
							return self._raw

						if self._path is not None:
							lowerCamelCase_							:		Tuple							   =							Image.open(self._path					)
							return self._raw





					def 	lowercase_							(				self					):







						'''simple docstring'''




						if self._path is not None:
							return self._path

						if self._raw is not None:
							lowerCamelCase_							:		Union[str, Any]							   =							tempfile.mkdtemp()
							lowerCamelCase_							:		List[Any]							   =							os.path.join(__lowercase ,       str(uuid.uuida()					) + '.png'					)
							self._raw.save(self._path					)

							return self._path

						if self._tensor is not None:
							lowerCamelCase_							:		int							   =							self._tensor.cpu().detach().numpy()

							# There is likely simpler than load into image into save
							lowerCamelCase_							:		Any							   =							Image.fromarray((array * 255).astype(np.uinta					)					)

							lowerCamelCase_							:		str							   =							tempfile.mkdtemp()
							lowerCamelCase_							:		Tuple							   =							os.path.join(__lowercase ,       str(uuid.uuida()					) + '.png'					)

							img.save(self._path					)

							return self._path


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=16_000					):







						'''simple docstring'''




						super().__init__(__lowercase					)

						if not is_soundfile_availble():
							raise ImportError('soundfile must be installed in order to handle audio.'					)

						lowerCamelCase_							:		Union[str, Any]							   =							None
						lowerCamelCase_							:		Tuple							   =							None

						lowerCamelCase_							:		Tuple							   =							samplerate

						if isinstance(__lowercase ,       (str, pathlib.Path)					):
							lowerCamelCase_							:		List[Any]							   =							value
						elif isinstance(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		Union[str, Any]							   =							value
						else:
							raise ValueError(f"Unsupported audio type: {type(__lowercase					)}"					)

					def 	lowercase_							(				self ,       __lowercase=None ,       __lowercase=None					):







						'''simple docstring'''




						from IPython.display import Audio, display

						display(Audio(self.to_string() ,       rate=self.samplerate					)					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						if self._tensor is not None:
							return self._tensor

						if self._path is not None:
							lowerCamelCase_   ,			lowerCamelCase_							:		Optional[Any]							   =							sf.read(self._path					)
							lowerCamelCase_							:		Optional[Any]							   =							torch.tensor(__lowercase					)
							return self._tensor





					def 	lowercase_							(				self					):







						'''simple docstring'''




						if self._path is not None:
							return self._path

						if self._tensor is not None:
							lowerCamelCase_							:		Any							   =							tempfile.mkdtemp()
							lowerCamelCase_							:		List[str]							   =							os.path.join(__lowercase ,       str(uuid.uuida()					) + '.wav'					)
							sf.write(self._path ,       self._tensor ,       samplerate=self.samplerate					)
							return self._path
UpperCamelCase__              =						{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
UpperCamelCase__              =						{str: AgentText}

if is_vision_available():
							UpperCamelCase__              =						AgentImage






def 					snake_case					(							*A						,   **A			)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							[(arg.to_raw() if isinstance(A						,   A			) else arg) for arg in args]
	lowerCamelCase_							:		int							   =							{k: (v.to_raw() if isinstance(A						,   A			) else v) for k, v in kwargs.items()}
	return args, kwargs






def 					snake_case					(							A						,   A=None			)							->							str:




	'''simple docstring'''


	if isinstance(A						,   A			):
		lowerCamelCase_							:		int							   =							{}
		for i, (k, v) in enumerate(outputs.items()			):
			if output_types is not None:
				# If the class has defined outputs, we can map directly according to the class definition
				if output_types[i] in AGENT_TYPE_MAPPING:
					lowerCamelCase_							:		Optional[Any]							   =							AGENT_TYPE_MAPPING[output_types[i]](A			)
				else:
					lowerCamelCase_							:		List[str]							   =							AgentType(A			)

			else:
				# If the class does not have defined output, then we map according to the type
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(A						,   _k			):
						lowerCamelCase_							:		List[str]							   =							_v(A			)
				if k not in decoded_outputs:
					lowerCamelCase_							:		Tuple							   =							AgentType[v]

	elif isinstance(A						,   (list, tuple)			):
		lowerCamelCase_							:		List[Any]							   =							type(A			)()
		for i, v in enumerate(A			):
			if output_types is not None:
				# If the class has defined outputs, we can map directly according to the class definition
				if output_types[i] in AGENT_TYPE_MAPPING:
					decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](A			)			)
				else:
					decoded_outputs.append(AgentType(A			)			)
			else:
				# If the class does not have defined output, then we map according to the type
				lowerCamelCase_							:		List[Any]							   =							False
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(A						,   _k			):
						decoded_outputs.append(_v(A			)			)
						lowerCamelCase_							:		Tuple							   =							True

				if not found:
					decoded_outputs.append(AgentType(A			)			)

	else:
		if output_types[0] in AGENT_TYPE_MAPPING:
			# If the class has defined outputs, we can map directly according to the class definition
			lowerCamelCase_							:		Dict							   =							AGENT_TYPE_MAPPING[output_types[0]](A			)

		else:
			# If the class does not have defined output, then we map according to the type
			for _k, _v in INSTANCE_TYPE_MAPPING.items():
				if isinstance(A						,   _k			):
					return _v(A			)
			return AgentType(A			)

	return decoded_outputs

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()
lowerCamelCase__   = logging.get_logger()




@dataclass
class  _UpperCAmelCase       :



 '''simple docstring'''




 __A           = 42
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Union[str, Any]				,      lowercase_							:					Tensor				,      lowercase_							:					Tensor				,      lowercase_							:					str)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  len(list(m.modules())) == 1 or isinstance(lowercase_				,      nn.Convad) or isinstance(lowercase_				,      nn.BatchNormad)
   if has_not_submodules:
     self.traced.append(lowercase_)
     _UpperCamelCase				=  m







 def __call__(						self							:					Optional[Any]				,      lowercase_							:					Tensor)		->     List[str]:


   """simple docstring"""







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







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







   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							:					Tuple				,      lowercase_							:					nn.Module)		->     List[str]:


   """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(lowercase_) + 1
     feature_blocks.append((f'res{block_index}', v))

   _UpperCamelCase				=  nn.ModuleDict(lowercase_)







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					Tensor)		->     int:


   """simple docstring"""







   return get_trunk_forward_outputs(
       lowercase_				,      out_feat_keys=lowercase_				,      feature_blocks=self._feature_blocks				,      )




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[Any]:


   """simple docstring"""







   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 							lowerCAmelCase__    (       a__      )	->Dict[str, str]:




  '''simple docstring'''

  _UpperCamelCase				=  RegNetConfig(depths=[2, 7, 17, 1]  ,			hidden_sizes=[8, 8, 8, 8]  ,			groups_width=8      )
  if "in1k" in model_name:
    _UpperCamelCase				=  RegNetForImageClassification(a__      )
  else:
    _UpperCamelCase				=  RegNetModel(a__      )
  # create from model (with small weights)
  _UpperCamelCase				=  FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27  ,			group_width=1_010  ,			w_a=1_744  ,			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(a__      )
    _UpperCamelCase				=  dest_tracker(a__      ).parametrized

    pprint(dest_tracker.nameamodule      )
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  src_tracker(a__      ).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(a__      ):
    _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(a__      )
  _UpperCamelCase				=  to_params_dict(a__      )

  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__    (       a__  ,			a__ = None  ,			a__ = True      )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  "imagenet-1k-id2label.json"
  _UpperCamelCase				=  1_000

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

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

  _UpperCamelCase				=  partial(a__  ,			num_labels=a__  ,			idalabel=a__  ,			labelaid=a__      )

  _UpperCamelCase				=  {
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
      # finetuned on imagenet
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
  }

  # add seer weights logic
  def load_using_classy_vision(a__      ) -> Tuple[Dict, Dict]:
    _UpperCamelCase				=  torch.hub.load_state_dict_from_url(a__  ,			model_dir=str(a__      )  ,			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(
          a__  ,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"  ,			),
      "regnet-y-10b-seer-in1k": partial(
          a__  ,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"  ,			),
  }

  _UpperCamelCase				=  get_from_to_our_keys(a__      )

  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(a__  ,			""  ,			a__      )
      # 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(a__      )
    # check that all keys have been updated
    assert len(a__      ) == 0, f'Some keys where not used {",".join(a__      )}'

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

    # save our state dict to disk
    torch.save(a__  ,			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(a__      )
    # 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(
        a__  ,			a__  ,			[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=a__      )
    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__":
   lowerCamelCase__   = 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.''',
   )

   lowerCamelCase__   = parser.parse_args()

   lowerCamelCase__   = 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)




