
"""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				=					logging.get_logger()






@dataclass
class 			_lowercase  :
	lowercase_								=      42
	lowercase_								=      field(default_factory=_UpperCAmelCase						)
	lowercase_								=      field(default_factory=_UpperCAmelCase						)
	lowercase_								=      field(default_factory=_UpperCAmelCase						)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				List[str]:
			lowerCamelCase	:     List[str]            =       len(list(m.modules()   )   ) == 1 or isinstance(__UpperCAmelCase						,					nn.Convad   ) or isinstance(__UpperCAmelCase						,					nn.BatchNormad   )
			if has_not_submodules:
					self.traced.append(__UpperCAmelCase   )
					lowerCamelCase	:     Optional[int]            =       m




	def __call__(					self						,					UpperCAmelCase_   )						->				List[str]:
			for name, m in self.module.named_modules():
					self.handles.append(m.register_forward_hook(partial(self._forward_hook						,					name=__UpperCAmelCase   )   )   )
			self.module(__UpperCAmelCase   )
			[x.remove() for x in self.handles]
			return self




	@property
	def      _UpperCamelCase       (					self   )						->				Tuple:
			# 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						):




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

			lowerCamelCase	:     List[Tuple[str, nn.Module]]            =       []
			# - 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	:     Dict            =       len(__UpperCAmelCase   ) + 1
					feature_blocks.append((F"""res{block_index}""", v)   )

			lowerCamelCase	:     List[Any]            =       nn.ModuleDict(__UpperCAmelCase   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Any:
			return get_trunk_forward_outputs(
			    __UpperCAmelCase						,					out_feat_keys=__UpperCAmelCase						,					feature_blocks=self._feature_blocks						,					)






class 			_lowercase  (							_UpperCAmelCase						):




	def      _UpperCamelCase       (					self   )						->				Tuple:
			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   (						a_     ):





		'''simple docstring'''


		lowerCamelCase	:     List[str]            =       RegNetConfig(depths=[2, 7, 17, 1],		hidden_sizes=[8, 8, 8, 8],		groups_width=8     )
		if "in1k" in model_name:
				lowerCamelCase	:     List[Any]            =       RegNetForImageClassification(UpperCAmelCase__     )
		else:
				lowerCamelCase	:     Tuple            =       RegNetModel(UpperCAmelCase__     )
		# create from model (with small weights)
		lowerCamelCase	:     Any            =       FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27,		group_width=1010,		w_a=1744,		w_a=620.83,		w_m=2.5_2     )     )     )

		with torch.no_grad():
				lowerCamelCase	:     Tuple            =       from_model.eval()
				lowerCamelCase	:     int            =       our_model.eval()

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

				pprint(dest_tracker.nameamodule     )
				lowerCamelCase	:     Tuple            =       Tracker(UpperCAmelCase__     )
				lowerCamelCase	:     Any            =       src_tracker(UpperCAmelCase__     ).parametrized

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

		lowerCamelCase	:     Optional[int]            =       {}

		lowerCamelCase	:     str            =       to_params_dict(UpperCAmelCase__     )
		lowerCamelCase	:     Optional[int]            =       to_params_dict(UpperCAmelCase__     )

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

		return from_to_ours_keys
def 						UpperCAmelCase   (						a_,		a_ = None,		a_ = True     ):





		'''simple docstring'''


		lowerCamelCase	:     Dict            =       'imagenet-1k-id2label.json'
		lowerCamelCase	:     Any            =       1000

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

		lowerCamelCase	:     Dict            =       idalabel
		lowerCamelCase	:     int            =       {v: k for k, v in idalabel.items()}

		lowerCamelCase	:     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(a_     ) -> Tuple[Dict, Dict]:
				lowerCamelCase	:     Optional[Any]            =       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	:     Tuple            =       files['classy_state_dict']['base_model']['model']
				return model_state_dict["trunk"], model_state_dict["heads"]

		lowerCamelCase	:     List[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	:     Union[str, Any]            =       get_from_to_our_keys(UpperCAmelCase__     )

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

				lowerCamelCase	:     int            =       {}

				lowerCamelCase	:     Optional[int]            =       list(from_state_dict.keys()     )
				lowerCamelCase	:     List[str]            =       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	:     Tuple            =       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	:     Optional[Any]            =       from_to_ours_keys[src_key]
						# store the parameter with our key
						lowerCamelCase	:     int            =       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	:     Tuple            =       RegNetModel
				if "in1k" in model_name:
						lowerCamelCase	:     Union[str, Any]            =       RegNetForImageClassification
				lowerCamelCase	:     Optional[int]            =       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	:     Dict            =       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	:     Optional[Any]            =       384
				# we can use the convnext one
				lowerCamelCase	:     Optional[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__":
					_A				=					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				=					parser.parse_args()

					_A				=					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 os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class 	A_       (     tf.keras.layers.Layer   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "longest"  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = 5_12  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					**__UpperCAmelCase  ,					)				->					Union[str, Any]:
     super().__init__()
     if use_fast_bert_tokenizer:
        a   :	int						=    FastBertTokenizer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case_nfd_strip_accents=__UpperCAmelCase  ,					**__UpperCAmelCase						)
     else:
        a   :	Optional[Any]						=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=__UpperCAmelCase  ,					key_dtype=tf.string  ,					values=tf.range(tf.size(__UpperCAmelCase  ,					out_type=tf.intaa						)  ,					dtype=tf.intaa						)  ,					value_dtype=tf.intaa  ,					)  ,					num_oov_buckets=1  ,					)
        a   :	str						=    BertTokenizerLayer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case=__UpperCAmelCase  ,					**__UpperCAmelCase						)

     a   :	str						=    vocab_list
     a   :	Dict						=    do_lower_case
     a   :	Dict						=    cls_token_id or vocab_list.index('[CLS]'						)
     a   :	Tuple						=    sep_token_id or vocab_list.index('[SEP]'						)
     a   :	Optional[Any]						=    pad_token_id or vocab_list.index('[PAD]'						)
     a   :	Tuple						=    ShrinkLongestTrimmer(max_length - 3  ,					axis=1						)  # Allow room for special tokens
     a   :	Union[str, Any]						=    max_length
     a   :	Tuple						=    padding
     a   :	List[Any]						=    truncation
     a   :	Union[str, Any]						=    pad_to_multiple_of
     a   :	List[Any]						=    return_token_type_ids
     a   :	Optional[int]						=    return_attention_mask
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:  # noqa: F821
     a   :	List[Any]						=    kwargs.pop('do_lower_case'  ,					__UpperCAmelCase						)
     a   :	str						=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     a   :	List[Any]						=    kwargs.pop('cls_token_id'  ,					__UpperCAmelCase						)
     a   :	int						=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     a   :	Tuple						=    kwargs.pop('sep_token_id'  ,					__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     a   :	List[Any]						=    kwargs.pop('pad_token_id'  ,					__UpperCAmelCase						)
     a   :	Any						=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     a   :	Optional[Any]						=    tokenizer.get_vocab()
     a   :	Optional[Any]						=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]  ,					key=lambda __UpperCAmelCase						: x[1]						)
     a   :	Optional[Any]						=    [entry[0] for entry in vocab]
     return cls(
         vocab_list=__UpperCAmelCase  ,					do_lower_case=__UpperCAmelCase  ,					cls_token_id=__UpperCAmelCase  ,					sep_token_id=__UpperCAmelCase  ,					pad_token_id=__UpperCAmelCase  ,					**__UpperCAmelCase  ,					)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:
     try:
        a   :	Dict						=    BertTokenizer.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        a   :	Tuple						=    BertTokenizerFast.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     return cls.from_tokenizer(__UpperCAmelCase  ,					**__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[Any]:
     if self.do_lower_case:
        a   :	int						=    case_fold_utfa(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    self.tf_tokenizer.tokenize(__UpperCAmelCase						)
     return tokens.merge_dims(1  ,					-1						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					)				->					Optional[int]:
     if padding is None:
        a   :	List[Any]						=    self.padding
     if padding not in ("longest", "max_length"):
        raise ValueError('Padding must be either \'longest\' or \'max_length\'!'						)
     if max_length is not None and text_pair is not None:
        # Because we have to instantiate a Trimmer to do it properly
        raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'						)
     if max_length is None:
        a   :	Any						=    self.max_length
     if truncation is None:
        a   :	Any						=    self.truncation
     if pad_to_multiple_of is None:
        a   :	Optional[int]						=    self.pad_to_multiple_of
     if return_token_type_ids is None:
        a   :	str						=    self.return_token_type_ids
     if return_attention_mask is None:
        a   :	Dict						=    self.return_attention_mask
     if not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	List[str]						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None and not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	Any						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None:
        if text.shape.rank > 1:
           raise ValueError('text argument should not be multidimensional when a text pair is supplied!'						)
        if text_pair.shape.rank > 1:
           raise ValueError('text_pair should not be multidimensional!'						)
     if text.shape.rank == 2:
        a	, a   :	Union[str, Any]						=    text[:, 0], text[:, 1]
     a   :	Union[str, Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
     if text_pair is None:  # Unpaired text
        if truncation:
           a   :	List[Any]						=    text[:, : max_length - 2]  # Allow room for special tokens
        a	, a   :	Dict						=    combine_segments(
            (text,)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     else:  # Paired text
        a   :	Optional[Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
        if truncation:
           a	, a   :	List[str]						=    self.paired_trimmer.trim([text, text_pair]						)
        a	, a   :	List[Any]						=    combine_segments(
            (text, text_pair)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     if padding == "longest":
        a   :	List[Any]						=    input_ids.bounding_shape(axis=1						)
        if pad_to_multiple_of is not None:
           # No ceiling division in tensorflow, so we negate floordiv instead
           a   :	Optional[int]						=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length  ,					__UpperCAmelCase						))
     else:
        a   :	Any						=    max_length

     a	, a   :	Any						=    pad_model_inputs(__UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
     a   :	Optional[Any]						=    {'input_ids': input_ids}
     if return_attention_mask:
        a   :	Tuple						=    attention_mask
     if return_token_type_ids:
        a	, a   :	int						=    pad_model_inputs(
            __UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
        a   :	Union[str, Any]						=    token_type_ids
     return output






  def 							lowercase_     (						self						)				->					List[str]:
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }





