'''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()
__lowercase				   =  logging.get_logger()






@dataclass
class     a__:


			'''simple docstring'''






			UpperCAmelCase_      :					nn.Module
			UpperCAmelCase_      :					List[nn.Module]								=	field(default_factory=lowerCAmelCase__     )
			UpperCAmelCase_      :					list								=	field(default_factory=lowerCAmelCase__     )
			UpperCAmelCase_      :					Dict[str, nn.Module]								=	field(default_factory=lowerCAmelCase__     )



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


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



			def __call__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


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







			@property
			def 							a_	(       self):




					"""simple docstring"""


					return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}







class     a__( nn.Module     ):


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					super().__init__()

					lowerCAmelCase        =  []
					# - 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        =  len(__lowerCAmelCase) + 1
							feature_blocks.append((f"res{block_index}", v))

					lowerCAmelCase        =  nn.ModuleDict(__lowerCAmelCase)







			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


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







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			def 							a_	(       self):




					"""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     snake_case__							(      _A: str			)   ->     Dict[str, str]:

		'''simple docstring'''


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

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

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

				pprint(dest_tracker.nameamodule			)
				lowerCAmelCase        =  Tracker(_A			)
				lowerCAmelCase        =  src_tracker(_A			).parametrized

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

		lowerCAmelCase        =  {}

		lowerCAmelCase        =  to_params_dict(_A			)
		lowerCAmelCase        =  to_params_dict(_A			)

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()				,       dst_state_dict.items()			):
				lowerCAmelCase        =  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        =  """classifier.1.weight"""
				lowerCAmelCase        =  """classifier.1.bias"""

		return from_to_ours_keys






def     snake_case__							(      _A: Path				,       _A: str = None				,       _A: bool = True			)   ->     Optional[int]:

		'''simple docstring'''


		lowerCAmelCase        =  """imagenet-1k-id2label.json"""
		lowerCAmelCase        =  1000

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

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

		lowerCAmelCase        =  partial(_A				,       num_labels=_A				,       idalabel=_A				,       labelaid=_A			)

		lowerCAmelCase        =  {
		    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 11110, 28280]				,       groups_width=1010			),
		    # finetuned on imagenet
		    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 11110, 28280]				,       groups_width=1010			),
		}

		# add seer weights logic
		def load_using_classy_vision(_A: str			) -> Tuple[Dict, Dict]:
				lowerCAmelCase        =  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
				lowerCAmelCase        =  files["""classy_state_dict"""]["""base_model"""]["""model"""]
				return model_state_dict["trunk"], model_state_dict["heads"]

		lowerCAmelCase        =  {
		    """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"""				,       ),
		}

		lowerCAmelCase        =  get_from_to_our_keys(_A			)

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

				lowerCAmelCase        =  {}

				lowerCAmelCase        =  list(from_state_dict.keys()			)
				lowerCAmelCase        =  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        =  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
						lowerCAmelCase        =  from_to_ours_keys[src_key]
						# store the parameter with our key
						lowerCAmelCase        =  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
				lowerCAmelCase        =  names_to_config[model_name]
				lowerCAmelCase        =  RegNetModel
				if "in1k" in model_name:
						lowerCAmelCase        =  RegNetForImageClassification
				lowerCAmelCase        =  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
				lowerCAmelCase        =  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				,       )
				lowerCAmelCase        =  384
				# we can use the convnext one
				lowerCAmelCase        =  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__":
						__lowercase				   =  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.''',
						)

						__lowercase				   =  parser.parse_args()

						__lowercase				   =  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)





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					int								=	'''flaubert'''
			UpperCAmelCase_      :					Optional[Any]								=	{
			    '''hidden_size''': '''emb_dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			    '''n_words''': '''vocab_size''',  # For backward compatibility
			}



			def __init__(       self	,   __lowerCAmelCase=False	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=30145	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=12	,   __lowerCAmelCase=16	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=512	,   __lowerCAmelCase=2048**-0.5	,   __lowerCAmelCase=1E-1_2	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=0	,   __lowerCAmelCase=1	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=5	,   __lowerCAmelCase=True	,   __lowerCAmelCase="first"	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   __lowerCAmelCase=True	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=5	,   __lowerCAmelCase=5	,   __lowerCAmelCase=0	,   __lowerCAmelCase=0	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  pre_norm
					lowerCAmelCase        =  layerdrop
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  emb_dim
					lowerCAmelCase        =  n_layers
					lowerCAmelCase        =  n_heads
					lowerCAmelCase        =  dropout
					lowerCAmelCase        =  attention_dropout
					lowerCAmelCase        =  gelu_activation
					lowerCAmelCase        =  sinusoidal_embeddings
					lowerCAmelCase        =  causal
					lowerCAmelCase        =  asm
					lowerCAmelCase        =  n_langs
					lowerCAmelCase        =  use_lang_emb
					lowerCAmelCase        =  layer_norm_eps
					lowerCAmelCase        =  bos_index
					lowerCAmelCase        =  eos_index
					lowerCAmelCase        =  pad_index
					lowerCAmelCase        =  unk_index
					lowerCAmelCase        =  mask_index
					lowerCAmelCase        =  is_encoder
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  embed_init_std
					lowerCAmelCase        =  init_std
					lowerCAmelCase        =  summary_type
					lowerCAmelCase        =  summary_use_proj
					lowerCAmelCase        =  summary_activation
					lowerCAmelCase        =  summary_proj_to_labels
					lowerCAmelCase        =  summary_first_dropout
					lowerCAmelCase        =  start_n_top
					lowerCAmelCase        =  end_n_top
					lowerCAmelCase        =  mask_token_id
					lowerCAmelCase        =  lang_id

					if "n_words" in kwargs:
							lowerCAmelCase        =  kwargs["""n_words"""]

					super().__init__(pad_token_id=__lowerCAmelCase	,   bos_token_id=__lowerCAmelCase	,   **__lowerCAmelCase)







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@property
			def 							a_	(       self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase        =  {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase        =  {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ])




