






"""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				:    Optional[Any]     =						logging.get_logger()






@dataclass
class 						snake_case  :
    __magic_name__         =     42
    __magic_name__         =     field(default_factory=UpperCAmelCase       )
    __magic_name__         =     field(default_factory=UpperCAmelCase       )
    __magic_name__         =     field(default_factory=UpperCAmelCase       )





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Dict				,		A	:					Tensor				,		A	:					Tensor				,		A	:					str			):


      '''simple docstring'''




      a     :			List[str]				=   len(list(m.modules()			)			) == 1 or isinstance(A				,		nn.Convad			) or isinstance(A				,		nn.BatchNormad			)
      if has_not_submodules:
        self.traced.append(A			)
        a     :			List[str]				=   m





    def __call__(							self	:					Optional[Any]				,		A	:					Tensor			):


      '''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 						snake_case  (  nn.Module       ):





    def __init__(							self	:					Optional[Any]				,		A	:					nn.Module			):


      '''simple docstring'''




      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     :			Optional[int]				=   len(A			) + 1
        feature_blocks.append((F'''res{block_index}''', v)			)

      a     :			List[str]				=   nn.ModuleDict(A			)





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					Tensor			):


      '''simple docstring'''




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






class 						snake_case  (  UpperCAmelCase       ):





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      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 			snake_case  (A_      :str      ):







  '''simple docstring'''

  a     :			Any				=   RegNetConfig(depths=[2, 7, 1_7, 1]	,			hidden_sizes=[8, 8, 8, 8]	,			groups_width=8      )
  if "in1k" in model_name:
    a     :			Dict				=   RegNetForImageClassification(A_      )
  else:
    a     :			List[Any]				=   RegNetModel(A_      )
  # create from model (with small weights)
  a     :			List[str]				=   FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=2_7	,			group_width=1_0_1_0	,			w_a=1_7_4_4	,			w_a=620.83	,			w_m=2.52      )      )      )

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

    a     :			int				=   torch.randn((1, 3, 3_2, 3_2)      )
    # trace both
    a     :			Union[str, Any]				=   Tracker(A_      )
    a     :			Tuple				=   dest_tracker(A_      ).parametrized

    pprint(dest_tracker.nameamodule      )
    a     :			List[str]				=   Tracker(A_      )
    a     :			Union[str, Any]				=   src_tracker(A_      ).parametrized

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

  a     :			List[str]				=   {}

  a     :			Optional[int]				=   to_params_dict(A_      )
  a     :			int				=   to_params_dict(A_      )

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

  return from_to_ours_keys




def 			snake_case  (A_      :Path	,			A_      :str = None	,			A_      :bool = True      ):







  '''simple docstring'''

  a     :			str				=   'imagenet-1k-id2label.json'
  a     :			Optional[Any]				=   1_0_0_0

  a     :			List[Any]				=   'huggingface/label-files'
  a     :			str				=   num_labels
  a     :			Dict				=   json.load(open(cached_download(hf_hub_url(A_	,			A_	,			repo_type='dataset'      )      )	,			'r'      )      )
  a     :			Optional[int]				=   {int(A_      ): v for k, v in idalabel.items()}

  a     :			str				=   idalabel
  a     :			Union[str, Any]				=   {v: k for k, v in idalabel.items()}

  a     :			Tuple				=   partial(A_	,			num_labels=A_	,			idalabel=A_	,			labelaid=A_      )

  a     :			str				=   {
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]	,			hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]	,			groups_width=1_0_1_0      ),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]	,			hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]	,			groups_width=1_0_1_0      ),
  }

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

  a     :			Optional[int]				=   {
      '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'	,			),
  }

  a     :			List[str]				=   get_from_to_our_keys(A_      )

  if not (save_directory / f'''{model_name}.pth''').exists():
    logger.info('Loading original state_dict.'      )
    a,     a     :			int				=   names_to_from_model[model_name]()
    a     :			List[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     :			Any				=   {}

    a     :			Tuple				=   list(from_state_dict.keys()      )
    a     :			Union[str, 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     :			List[Any]				=   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
      a     :			str				=   from_to_ours_keys[src_key]
      # store the parameter with our key
      a     :			List[Any]				=   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
    a     :			Optional[Any]				=   names_to_config[model_name]
    a     :			str				=   RegNetModel
    if "in1k" in model_name:
      a     :			Optional[int]				=   RegNetForImageClassification
    a     :			int				=   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
    a     :			Dict				=   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	,			)
    a     :			Optional[int]				=   3_8_4
    # we can use the convnext one
    a     :			Optional[Any]				=   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__":
   _UpperCamelCase				:    List[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.',
   )

   _UpperCamelCase				:    Tuple     =						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 random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

      a     :			Dict				=   {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_save_load_optional_components()





    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,		reason='XFormers attention is only available with CUDA and `xformers` installed'				,		)
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_floataa_inference(expected_max_diff=1E-1			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2				,		)




