
"""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      :	str       		=	logging.get_logger()

@dataclass
class 		a__						:
			__lowerCAmelCase						=    42
			__lowerCAmelCase						=    field(default_factory=a_						)
			__lowerCAmelCase						=    field(default_factory=a_						)
			__lowerCAmelCase						=    field(default_factory=a_						)
			def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a    ):
										lowercase   :    int			=							len(list(m.modules()    )    ) == 1 or isinstance(_a     ,   nn.Convad    ) or isinstance(_a     ,   nn.BatchNormad    )
										if has_not_submodules:
																	self.traced.append(_a    )
																	lowercase   :    List[Any]			=							m
			def __call__(      self     ,   _a    ):
										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       __magic_name__      (      self    ):
										# 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 		a__						(				nn.Module						):
			def __init__(      self     ,   _a    ):
										super().__init__()

										lowercase   :    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}"""
																	lowercase   :    List[Any]			=							len(_a    ) + 1
																	feature_blocks.append((f"""res{block_index}""", v)    )

										lowercase   :    Tuple			=							nn.ModuleDict(_a    )

			def       __magic_name__      (      self     ,   _a    ):
										return get_trunk_forward_outputs(
										    _a     ,   out_feat_keys=_a     ,   feature_blocks=self._feature_blocks     ,   )


class 		a__						(				a_						):
			def       __magic_name__      (      self    ):
										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   __magic_name__     (				__snake_case    :	str  )			->    Dict[str, str]:
							lowercase   :    Any			=							RegNetConfig(depths=[2, 7, 17, 1]					,			hidden_sizes=[8, 8, 8, 8]					,			groups_width=8  )
							if "in1k" in model_name:
														lowercase   :    Tuple			=							RegNetForImageClassification(__snake_case  )
							else:
														lowercase   :    Optional[Any]			=							RegNetModel(__snake_case  )
							# create from model (with small weights)
							lowercase   :    List[str]			=							FakeRegNetVisslWrapper(
							    RegNet(FakeRegNetParams(depth=27					,			group_width=1010					,			w_a=1744					,			w_a=620.83					,			w_m=2.52  )  )  )

							with torch.no_grad():
														lowercase   :    Union[str, Any]			=							from_model.eval()
														lowercase   :    Union[str, Any]			=							our_model.eval()

														lowercase   :    int			=							torch.randn((1, 3, 32, 32)  )
														# trace both
														lowercase   :    Any			=							Tracker(__snake_case  )
														lowercase   :    Tuple			=							dest_tracker(__snake_case  ).parametrized

														pprint(dest_tracker.nameamodule  )
														lowercase   :    str			=							Tracker(__snake_case  )
														lowercase   :    Any			=							src_tracker(__snake_case  ).parametrized

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

							lowercase   :    List[Any]			=							{}

							lowercase   :    Dict			=							to_params_dict(__snake_case  )
							lowercase   :    Optional[Any]			=							to_params_dict(__snake_case  )

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()					,			dst_state_dict.items()  ):
														lowercase   :    Tuple			=							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:
														lowercase   :    List[str]			=							"classifier.1.weight"
														lowercase   :    Optional[int]			=							"classifier.1.bias"

							return from_to_ours_keys


def   __magic_name__     (				__snake_case    :	Path					,			__snake_case    :	str = None					,			__snake_case    :	bool = True  )			->    Dict:
							lowercase   :    Union[str, Any]			=							"imagenet-1k-id2label.json"
							lowercase   :    List[Any]			=							1000

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

							lowercase   :    List[str]			=							idalabel
							lowercase   :    int			=							{v: k for k, v in idalabel.items()}

							lowercase   :    List[str]			=							partial(__snake_case					,			num_labels=__snake_case					,			idalabel=__snake_case					,			labelaid=__snake_case  )

							lowercase   :    int			=							{
							    "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(__snake_case    :	str  ) -> Tuple[Dict, Dict]:
														lowercase   :    Optional[Any]			=							torch.hub.load_state_dict_from_url(__snake_case					,			model_dir=str(__snake_case  )					,			map_location="cpu"  )
														# check if we have a head, if yes add it
														lowercase   :    List[str]			=							files["classy_state_dict"]["base_model"]["model"]
														return model_state_dict["trunk"], model_state_dict["heads"]

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

							lowercase   :    Optional[Any]			=							get_from_to_our_keys(__snake_case  )

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

														lowercase   :    str			=							{}

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

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

														# save our state dict to disk
														torch.save(__snake_case					,			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
														lowercase   :    Union[str, Any]			=							names_to_config[model_name]
														lowercase   :    Optional[Any]			=							RegNetModel
														if "in1k" in model_name:
																					lowercase   :    Optional[Any]			=							RegNetForImageClassification
														lowercase   :    int			=							our_model_func(__snake_case  )
														# 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
														lowercase   :    Tuple			=							our_model.state_dict().keys()
														PreTrainedModel._load_pretrained_model_low_mem(
														    __snake_case					,			__snake_case					,			[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					,			)
														lowercase   :    List[str]			=							384
														# we can use the convnext one
														lowercase   :    Optional[int]			=							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"					,			size=__snake_case  )
														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      :	List[Any]       		=	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      :	Optional[Any]       		=	parser.parse_args()

			_A      :	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"""




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
   from PIL import Image

   from transformers import ConvNextImageProcessor

class 		a__						:
   def __init__(      self     ,   _a     ,   _a=13     ,   _a=32     ,   _a=3     ,   _a=4     ,   _a=[10, 20, 30, 40]     ,   _a=[2, 2, 3, 2]     ,   _a=True     ,   _a=True     ,   _a=37     ,   _a="gelu"     ,   _a=10     ,   _a=0.0_2     ,   _a=3     ,   _a=None     ,   ):
          lowercase   :    Any			=							parent
          lowercase   :    Optional[int]			=							batch_size
          lowercase   :    Optional[Any]			=							image_size
          lowercase   :    Any			=							num_channels
          lowercase   :    str			=							num_stages
          lowercase   :    Union[str, Any]			=							hidden_sizes
          lowercase   :    Optional[Any]			=							depths
          lowercase   :    str			=							is_training
          lowercase   :    int			=							use_labels
          lowercase   :    str			=							intermediate_size
          lowercase   :    Dict			=							hidden_act
          lowercase   :    int			=							type_sequence_label_size
          lowercase   :    Optional[int]			=							initializer_range
          lowercase   :    Dict			=							scope
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

          lowercase   :    Any			=							None
          if self.use_labels:
                 lowercase   :    List[str]			=							ids_tensor([self.batch_size]     ,   self.type_sequence_label_size    )

          lowercase   :    str			=							self.get_config()

          return config, pixel_values, labels
   def       __magic_name__      (      self    ):
          return ConvNextConfig(
              num_channels=self.num_channels     ,   hidden_sizes=self.hidden_sizes     ,   depths=self.depths     ,   num_stages=self.num_stages     ,   hidden_act=self.hidden_act     ,   is_decoder=_a     ,   initializer_range=self.initializer_range     ,   )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    Any			=							TFConvNextModel(config=_a    )
          lowercase   :    int			=							model(_a     ,   training=_a    )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,   )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    List[Any]			=							self.type_sequence_label_size
          lowercase   :    List[str]			=							TFConvNextForImageClassification(_a    )
          lowercase   :    int			=							model(_a     ,   labels=_a     ,   training=_a    )
          self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)    )

   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, Any]			=							self.prepare_config_and_inputs()
          lowercase		,   lowercase		,   lowercase   :    str			=							config_and_inputs
          lowercase   :    List[Any]			=							{"pixel_values": pixel_values}
          return config, inputs_dict


@require_tf
class 		a__						(				a_,      a_,      unittest.TestCase						):
   __lowerCAmelCase						=    (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
   __lowerCAmelCase						=    (
       {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							TFConvNextModelTester(self    )
          lowercase   :    int			=							ConfigTester(
              self     ,   config_class=_a     ,   has_text_modality=_a     ,   hidden_size=37     ,   )
   @unittest.skip(reason="ConvNext does not use inputs_embeds"    )
   def       __magic_name__      (      self    ):
          pass
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) == 0     ,   reason="TF does not support backprop for grouped convolutions on CPU."     ,   )
   @slow
   def       __magic_name__      (      self    ):
          super().test_keras_fit()
   @unittest.skip(reason="ConvNext does not support input and output embeddings"    )
   def       __magic_name__      (      self    ):
          pass
   def       __magic_name__      (      self    ):
          lowercase		,   lowercase   :    List[str]			=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase   :    Dict			=							model_class(_a    )
                 lowercase   :    str			=							inspect.signature(model.call    )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowercase   :    Optional[int]			=							[*signature.parameters.keys()]

                 lowercase   :    Optional[Any]			=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, Any]			=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a    )
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) == 0     ,   reason="TF does not support backprop for grouped convolutions on CPU."     ,   )
   def       __magic_name__      (      self    ):
          super().test_dataset_conversion()
   def       __magic_name__      (      self    ):
          def check_hidden_states_output(_a     ,   _a     ,   _a    ):
                 lowercase   :    str			=							model_class(_a    )

                 lowercase   :    Any			=							model(**self._prepare_for_class(_a     ,   _a    )    )
                 lowercase   :    Optional[Any]			=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowercase   :    str			=							self.model_tester.num_stages
                 self.assertEqual(len(_a    )     ,   expected_num_stages + 1    )

                 # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]    )     ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,   )

          lowercase		,   lowercase   :    Any			=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase   :    Union[str, Any]			=							True
                 check_hidden_states_output(_a     ,   _a     ,   _a    )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase   :    Dict			=							True

                 check_hidden_states_output(_a     ,   _a     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase		,   lowercase   :    List[str]			=							self.model_tester.prepare_config_and_inputs_for_common()

          def check_equivalence(_a     ,   _a     ,   _a     ,   _a={}    ):
                 lowercase   :    Union[str, Any]			=							model(_a     ,   return_dict=_a     ,   **_a    )
                 lowercase   :    Dict			=							model(_a     ,   return_dict=_a     ,   **_a    ).to_tuple()

                 def recursive_check(_a     ,   _a    ):
                        if isinstance(_a     ,   (List, Tuple)    ):
                               for tuple_iterable_value, dict_iterable_value in zip(_a     ,   _a    ):
                                      recursive_check(_a     ,   _a    )
                        elif tuple_object is None:
                               return
                        else:
                               self.assertTrue(
                                   all(tf.equal(_a     ,   _a    )    )     ,   msg=(
                                       "Tuple and dict output are not equal. Difference:"
                                       f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object    )    )}"""
                                   )     ,   )

                        recursive_check(_a     ,   _a    )

          for model_class in self.all_model_classes:
                 lowercase   :    str			=							model_class(_a    )

                 lowercase   :    int			=							self._prepare_for_class(_a     ,   _a    )
                 lowercase   :    Dict			=							self._prepare_for_class(_a     ,   _a    )
                 check_equivalence(_a     ,   _a     ,   _a    )

                 lowercase   :    Tuple			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 lowercase   :    Union[str, Any]			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 check_equivalence(_a     ,   _a     ,   _a    )

                 lowercase   :    Any			=							self._prepare_for_class(_a     ,   _a    )
                 lowercase   :    List[str]			=							self._prepare_for_class(_a     ,   _a    )
                 check_equivalence(_a     ,   _a     ,   _a     ,   {"output_hidden_states": True}    )

                 lowercase   :    Union[str, Any]			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 lowercase   :    Optional[Any]			=							self._prepare_for_class(_a     ,   _a     ,   return_labels=_a    )
                 check_equivalence(_a     ,   _a     ,   _a     ,   {"output_hidden_states": True}    )
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a    )

   @slow
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"    )
          self.assertIsNotNone(_a    )


def   __magic_name__     (				)			->    Any:
       lowercase   :    List[str]			=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_tf
@require_vision
class 		a__						(				unittest.TestCase						):
   @cached_property
   def       __magic_name__      (      self    ):
          return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"    ) if is_vision_available() else None

   @slow
   def       __magic_name__      (      self    ):
          lowercase   :    Dict			=							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"    )

          lowercase   :    List[str]			=							self.default_image_processor
          lowercase   :    List[str]			=							prepare_img()
          lowercase   :    Union[str, Any]			=							image_processor(images=_a     ,   return_tensors="tf"    )

          # forward pass
          lowercase   :    str			=							model(**_a    )

          # verify the logits
          lowercase   :    List[str]			=							tf.TensorShape((1, 1_000)    )
          self.assertEqual(outputs.logits.shape     ,   _a    )

          lowercase   :    int			=							tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]    )

          tf.debugging.assert_near(outputs.logits[0, :3]     ,   _a     ,   atol=1E-4    )


