


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__ :    int						=			logging.get_logger()







@dataclass
class 					lowerCAmelCase__	:




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			42
     __SCREAMING_SNAKE_CASE        =			field(default_factory=a)
     __SCREAMING_SNAKE_CASE        =			field(default_factory=a)
     __SCREAMING_SNAKE_CASE        =			field(default_factory=a)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	List[Any]       =			len(list(m.modules())) == 1 or isinstance(__lowerCamelCase			,						nn.Convad) or isinstance(__lowerCamelCase			,						nn.BatchNormad)
            if has_not_submodules:
                   self.traced.append(__lowerCamelCase)
                   _A				:	Dict       =			m


     def __call__(						self			,						__lowerCamelCase)      ->    Any:
            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 			_lowerCamelCase			(						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 					lowerCAmelCase__	(    nn.Module):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase)      ->    Optional[Any]:
            super().__init__()

            _A				:	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}"
                   _A				:	Union[str, Any]       =			len(__lowerCamelCase) + 1
                   feature_blocks.append((F"res{block_index}", v))

            _A				:	int       =			nn.ModuleDict(__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[str]:
            return get_trunk_forward_outputs(
                __lowerCamelCase			,						out_feat_keys=__lowerCamelCase			,						feature_blocks=self._feature_blocks			,						)







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''


     def 			_lowerCamelCase			(						self)      ->    Any:
            return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




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

       with torch.no_grad():
              _A				:	Any       =			from_model.eval()
              _A				:	List[str]       =			our_model.eval()

              _A				:	Tuple       =			torch.randn((1, 3, 32, 32)						)
              # trace both
              _A				:	List[Any]       =			Tracker(UpperCamelCase__						)
              _A				:	Any       =			dest_tracker(UpperCamelCase__						).parametrized

              pprint(dest_tracker.nameamodule						)
              _A				:	str       =			Tracker(UpperCamelCase__						)
              _A				:	List[Any]       =			src_tracker(UpperCamelCase__						).parametrized

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

       _A				:	int       =			{}

       _A				:	List[str]       =			to_params_dict(UpperCamelCase__						)
       _A				:	Optional[Any]       =			to_params_dict(UpperCamelCase__						)

       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()    ,							dst_state_dict.items()						):
              _A				:	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:
              _A				:	Union[str, Any]       =			"classifier.1.weight"
              _A				:	Dict       =			"classifier.1.bias"

       return from_to_ours_keys
def 							_UpperCAmelCase    (UpperCamelCase__					:  Path    ,							UpperCamelCase__					:  str = None    ,							UpperCamelCase__					:  bool = True						):
       _A				:	Dict       =			"imagenet-1k-id2label.json"
       _A				:	Dict       =			1000

       _A				:	int       =			"huggingface/label-files"
       _A				:	Tuple       =			num_labels
       _A				:	List[str]       =			json.load(open(cached_download(hf_hub_url(UpperCamelCase__    ,							UpperCamelCase__    ,							repo_type="dataset"						)						)    ,							"r"						)						)
       _A				:	Union[str, Any]       =			{int(UpperCamelCase__						): v for k, v in idalabel.items()}

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

       _A				:	str       =			partial(UpperCamelCase__    ,							num_labels=UpperCamelCase__    ,							idalabel=UpperCamelCase__    ,							labelaid=UpperCamelCase__						)

       _A				:	str       =			{
           "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(UpperCamelCase__					:  str						) -> Tuple[Dict, Dict]:
              _A				:	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
              _A				:	str       =			files["classy_state_dict"]["base_model"]["model"]
              return model_state_dict["trunk"], model_state_dict["heads"]

       _A				:	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"    ,							),
       }

       _A				:	Optional[Any]       =			get_from_to_our_keys(UpperCamelCase__						)

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

              _A				:	Tuple       =			{}

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

    lowerCAmelCase__ :    Optional[Any]						=			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 unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import BlipTextModel
    from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_2			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=3_2			,						__lowerCamelCase=5			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=0			,						__lowerCamelCase=None			,						)      ->    str:
            _A				:	Optional[int]       =			parent
            _A				:	Optional[int]       =			batch_size
            _A				:	Optional[int]       =			seq_length
            _A				:	Dict       =			is_training
            _A				:	Union[str, Any]       =			use_input_mask
            _A				:	Optional[int]       =			use_labels
            _A				:	Any       =			vocab_size
            _A				:	Dict       =			hidden_size
            _A				:	Optional[Any]       =			projection_dim
            _A				:	Union[str, Any]       =			num_hidden_layers
            _A				:	Tuple       =			num_attention_heads
            _A				:	List[str]       =			intermediate_size
            _A				:	Any       =			dropout
            _A				:	List[str]       =			attention_dropout
            _A				:	str       =			max_position_embeddings
            _A				:	List[Any]       =			initializer_range
            _A				:	int       =			scope
            _A				:	List[str]       =			bos_token_id


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Tuple       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	str       =			None
            if self.use_input_mask:
                   _A				:	Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length])

            if input_mask is not None:
                   _A   ,      _A				:	int       =			input_mask.shape
                   _A				:	Optional[Any]       =			np.random.randint(1			,						seq_length - 1			,						size=(batch_size,))
                   for batch_idx, start_index in enumerate(__lowerCamelCase):
                          _A				:	Tuple       =			1
                          _A				:	List[str]       =			0

            _A				:	str       =			self.get_config()

            return config, input_ids, input_mask


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            return BlipTextConfig(
                vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						projection_dim=self.projection_dim			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						dropout=self.dropout			,						attention_dropout=self.attention_dropout			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						bos_token_id=self.bos_token_id			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            _A				:	int       =			BlipTextModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            with torch.no_grad():
                   _A				:	int       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)
                   _A				:	List[str]       =			model(__lowerCamelCase)
            self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
            self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size))


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Dict       =			self.prepare_config_and_inputs()
            _A   ,      _A   ,      _A				:	str       =			config_and_inputs
            _A				:	List[str]       =			{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict







@require_torch
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(BlipTextModel,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			BlipTextModelTester(self)
            _A				:	List[str]       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     @unittest.skip(reason="Blip does not use inputs_embeds")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A				:	Union[str, Any]       =			BlipTextModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase)
