




'''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()
UpperCAmelCase_    :					Tuple					      =						logging.get_logger()





@dataclass
class       lowerCAmelCase				:
       __lowercase					: nn.Module
       __lowercase					: List[nn.Module]	    =     field(default_factory=snake_case__)
       __lowercase					: list	    =     field(default_factory=snake_case__)
       __lowercase					: Dict[str, nn.Module]	    =     field(default_factory=snake_case__)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					List[Any]:



            '''simple docstring'''




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


       def __call__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, 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  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




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





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




            super().__init__()

            __snake_case         =					[]
            # - 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}'''
                 __snake_case         =					len(_A					) + 1
                 feature_blocks.append((F'''res{block_index}''', v)					)

            __snake_case         =					nn.ModuleDict(_A					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




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





class       lowerCAmelCase				(	snake_case__):


       def  lowerCAmelCase (				self					) ->					List[str]:



            '''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      _UpperCamelCase	(_lowerCamelCase							:       List[str]		)->				Dict[str, str]:
     __snake_case         =					RegNetConfig(depths=[2, 7, 17, 1]				,		hidden_sizes=[8, 8, 8, 8]				,		groups_width=8		)
     if "in1k" in model_name:
          __snake_case         =					RegNetForImageClassification(_lowerCamelCase		)
     else:
          __snake_case         =					RegNetModel(_lowerCamelCase		)
     # create from model (with small weights)
     __snake_case         =					FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27				,		group_width=10_10				,		w_a=17_44				,		w_a=620.83				,		w_m=2.52		)		)		)

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

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

          pprint(dest_tracker.nameamodule		)
          __snake_case         =					Tracker(_lowerCamelCase		)
          __snake_case         =					src_tracker(_lowerCamelCase		).parametrized

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

     __snake_case         =					{}

     __snake_case         =					to_params_dict(_lowerCamelCase		)
     __snake_case         =					to_params_dict(_lowerCamelCase		)

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

     return from_to_ours_keys







def      _UpperCamelCase	(_lowerCamelCase							:       str				,		_lowerCamelCase							:       str = None				,		_lowerCamelCase							:       List[Any] = True		)->				Union[str, Any]:
     __snake_case         =					'imagenet-1k-id2label.json'
     __snake_case         =					10_00

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

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

     __snake_case         =					partial(_lowerCamelCase				,		num_labels=_lowerCamelCase				,		idalabel=_lowerCamelCase				,		labelaid=_lowerCamelCase		)

     __snake_case         =					{
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10		),
         # finetuned on imagenet
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10		),
     }

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

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

     __snake_case         =					get_from_to_our_keys(_lowerCamelCase		)

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

          __snake_case         =					{}

          __snake_case         =					list(from_state_dict.keys()		)
          __snake_case         =					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
               __snake_case         =					re.sub(_lowerCamelCase				,		''''''				,		_lowerCamelCase		)
               # 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
               __snake_case         =					from_to_ours_keys[src_key]
               # store the parameter with our key
               __snake_case         =					from_state_dict[key]
               not_used_keys.remove(_lowerCamelCase		)
          # check that all keys have been updated
          assert len(_lowerCamelCase		) == 0, f'''Some keys where not used {",".join(_lowerCamelCase		)}'''

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

          # save our state dict to disk
          torch.save(_lowerCamelCase				,		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
          __snake_case         =					names_to_config[model_name]
          __snake_case         =					RegNetModel
          if "in1k" in model_name:
               __snake_case         =					RegNetForImageClassification
          __snake_case         =					our_model_func(_lowerCamelCase		)
          # 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
          __snake_case         =					our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              _lowerCamelCase				,		_lowerCamelCase				,		[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				,		)
          __snake_case         =					3_84
          # we can use the convnext one
          __snake_case         =					AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				,		size=_lowerCamelCase		)
          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__":
     UpperCAmelCase_    :					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.''',
     )

     UpperCAmelCase_    :					Optional[int]					      =						parser.parse_args()

     UpperCAmelCase_    :					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)












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_encoder_rel_embedding

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __snake_case         =					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(_lowerCamelCase		)
     print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     UpperCAmelCase_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






