'''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 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			__magic_name__   :							nn.Module
			__magic_name__   :							List[nn.Module]             =					field(default_factory=lowerCAmelCase_			)
			__magic_name__   :							list             =					field(default_factory=lowerCAmelCase_			)
			__magic_name__   :							Dict[str, nn.Module]             =					field(default_factory=lowerCAmelCase_			)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					Tuple							=						len(list(m.modules()				)				) == 1 or isinstance(__SCREAMING_SNAKE_CASE							, nn.Convad				) or isinstance(__SCREAMING_SNAKE_CASE							, nn.BatchNormad				)
									if has_not_submodules:
															self.traced.append(__SCREAMING_SNAKE_CASE				)
															snake_case   :					List[Any]							=						m



			def __call__(       self							, snake_case_				):

									for name, m in self.module.named_modules():
															self.handles.append(m.register_forward_hook(partial(self._forward_hook							, name=__SCREAMING_SNAKE_CASE				)				)				)
									self.module(__SCREAMING_SNAKE_CASE				)
									[x.remove() for x in self.handles]
									return self






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







class 		SCREAMING_SNAKE_CASE_				(nn.Module			):



			"""simple docstring"""






			def __init__(       self							, snake_case_				):
									super().__init__()

									snake_case   :					List[Any]							=						[]
									# - 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   :					Union[str, Any]							=						len(__SCREAMING_SNAKE_CASE				) + 1
															feature_blocks.append((F"""res{block_index}""", v)				)

									snake_case   :					List[Any]							=						nn.ModuleDict(__SCREAMING_SNAKE_CASE				)






			def      lowerCamelCase   (       self							, snake_case_				):
									return get_trunk_forward_outputs(
									    __SCREAMING_SNAKE_CASE							, out_feat_keys=__SCREAMING_SNAKE_CASE							, feature_blocks=self._feature_blocks							, )







class 		SCREAMING_SNAKE_CASE_				(lowerCAmelCase_			):



			"""simple docstring"""






			def      lowerCamelCase   (       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 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any])						->		Dict[str, str]:



						"""simple docstring"""

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

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

												snake_case   :					Tuple							=						torch.randn((1, 3, 32, 32))
												# trace both
												snake_case   :					int							=						Tracker(_lowerCAmelCase)
												snake_case   :					Dict							=						dest_tracker(_lowerCAmelCase).parametrized

												pprint(dest_tracker.nameamodule)
												snake_case   :					Any							=						Tracker(_lowerCAmelCase)
												snake_case   :					int							=						src_tracker(_lowerCAmelCase).parametrized

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

						snake_case   :					Dict							=						{}

						snake_case   :					Union[str, Any]							=						to_params_dict(_lowerCAmelCase)
						snake_case   :					Dict							=						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   :					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:
												snake_case   :					int							=						"classifier.1.weight"
												snake_case   :					List[str]							=						"classifier.1.bias"

						return from_to_ours_keys


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[str],			_lowerCAmelCase						: Optional[Any] = None,			_lowerCAmelCase						: List[Any] = True)						->		List[Any]:



						"""simple docstring"""

						snake_case   :					Optional[int]							=						"imagenet-1k-id2label.json"
						snake_case   :					Union[str, Any]							=						1000

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

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

						snake_case   :					Any							=						partial(_lowerCAmelCase,			num_labels=_lowerCAmelCase,			idalabel=_lowerCAmelCase,			labelaid=_lowerCAmelCase)

						snake_case   :					List[str]							=						{
						    "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(_lowerCAmelCase						: Dict) -> Tuple[Dict, Dict]:
												snake_case   :					Tuple							=						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   :					Optional[int]							=						files["classy_state_dict"]["base_model"]["model"]
												return model_state_dict["trunk"], model_state_dict["heads"]

						snake_case   :					int							=						{
						    "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   :					Dict							=						get_from_to_our_keys(_lowerCAmelCase)

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

												snake_case   :					Optional[int]							=						{}

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

from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


A_        =   {
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "Constraint",
						    "ConstraintListState",
						    "DisjunctiveConstraint",
						    "PhrasalConstraint",
						]
						A_        =   [
						    "BeamHypotheses",
						    "BeamScorer",
						    "BeamSearchScorer",
						    "ConstrainedBeamSearchScorer",
						]
						A_        =   [
						    "EpsilonLogitsWarper",
						    "EtaLogitsWarper",
						    "ForcedBOSTokenLogitsProcessor",
						    "ForcedEOSTokenLogitsProcessor",
						    "HammingDiversityLogitsProcessor",
						    "InfNanRemoveLogitsProcessor",
						    "LogitsProcessor",
						    "LogitsProcessorList",
						    "LogitsWarper",
						    "MinLengthLogitsProcessor",
						    "MinNewTokensLengthLogitsProcessor",
						    "NoBadWordsLogitsProcessor",
						    "NoRepeatNGramLogitsProcessor",
						    "PrefixConstrainedLogitsProcessor",
						    "RepetitionPenaltyLogitsProcessor",
						    "SequenceBiasLogitsProcessor",
						    "EncoderRepetitionPenaltyLogitsProcessor",
						    "TemperatureLogitsWarper",
						    "TopKLogitsWarper",
						    "TopPLogitsWarper",
						    "TypicalLogitsWarper",
						    "EncoderNoRepeatNGramLogitsProcessor",
						    "ExponentialDecayLengthPenalty",
						    "LogitNormalization",
						]
						A_        =   [
						    "MaxNewTokensCriteria",
						    "MaxLengthCriteria",
						    "MaxTimeCriteria",
						    "StoppingCriteria",
						    "StoppingCriteriaList",
						    "validate_stopping_criteria",
						]
						A_        =   [
						    "GenerationMixin",
						    "top_k_top_p_filtering",
						    "GreedySearchEncoderDecoderOutput",
						    "GreedySearchDecoderOnlyOutput",
						    "SampleEncoderDecoderOutput",
						    "SampleDecoderOnlyOutput",
						    "BeamSearchEncoderDecoderOutput",
						    "BeamSearchDecoderOnlyOutput",
						    "BeamSampleEncoderDecoderOutput",
						    "BeamSampleDecoderOnlyOutput",
						    "ContrastiveSearchEncoderDecoderOutput",
						    "ContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "TFForcedBOSTokenLogitsProcessor",
						    "TFForcedEOSTokenLogitsProcessor",
						    "TFLogitsProcessor",
						    "TFLogitsProcessorList",
						    "TFLogitsWarper",
						    "TFMinLengthLogitsProcessor",
						    "TFNoBadWordsLogitsProcessor",
						    "TFNoRepeatNGramLogitsProcessor",
						    "TFRepetitionPenaltyLogitsProcessor",
						    "TFTemperatureLogitsWarper",
						    "TFTopKLogitsWarper",
						    "TFTopPLogitsWarper",
						    "TFForceTokensLogitsProcessor",
						    "TFSuppressTokensAtBeginLogitsProcessor",
						    "TFSuppressTokensLogitsProcessor",
						]
						A_        =   [
						    "TFGenerationMixin",
						    "tf_top_k_top_p_filtering",
						    "TFGreedySearchDecoderOnlyOutput",
						    "TFGreedySearchEncoderDecoderOutput",
						    "TFSampleEncoderDecoderOutput",
						    "TFSampleDecoderOnlyOutput",
						    "TFBeamSearchEncoderDecoderOutput",
						    "TFBeamSearchDecoderOnlyOutput",
						    "TFBeamSampleEncoderDecoderOutput",
						    "TFBeamSampleDecoderOnlyOutput",
						    "TFContrastiveSearchEncoderDecoderOutput",
						    "TFContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "FlaxForcedBOSTokenLogitsProcessor",
						    "FlaxForcedEOSTokenLogitsProcessor",
						    "FlaxLogitsProcessor",
						    "FlaxLogitsProcessorList",
						    "FlaxLogitsWarper",
						    "FlaxMinLengthLogitsProcessor",
						    "FlaxTemperatureLogitsWarper",
						    "FlaxTopKLogitsWarper",
						    "FlaxTopPLogitsWarper",
						]
						A_        =   [
						    "FlaxGenerationMixin",
						    "FlaxGreedySearchOutput",
						    "FlaxSampleOutput",
						    "FlaxBeamSearchOutput",
						]

if TYPE_CHECKING:
						from .configuration_utils import GenerationConfig
						from .streamers import TextIteratorStreamer, TextStreamer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
												from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
												from .logits_process import (
												    EncoderNoRepeatNGramLogitsProcessor,
												    EncoderRepetitionPenaltyLogitsProcessor,
												    EpsilonLogitsWarper,
												    EtaLogitsWarper,
												    ExponentialDecayLengthPenalty,
												    ForcedBOSTokenLogitsProcessor,
												    ForcedEOSTokenLogitsProcessor,
												    HammingDiversityLogitsProcessor,
												    InfNanRemoveLogitsProcessor,
												    LogitNormalization,
												    LogitsProcessor,
												    LogitsProcessorList,
												    LogitsWarper,
												    MinLengthLogitsProcessor,
												    MinNewTokensLengthLogitsProcessor,
												    NoBadWordsLogitsProcessor,
												    NoRepeatNGramLogitsProcessor,
												    PrefixConstrainedLogitsProcessor,
												    RepetitionPenaltyLogitsProcessor,
												    SequenceBiasLogitsProcessor,
												    TemperatureLogitsWarper,
												    TopKLogitsWarper,
												    TopPLogitsWarper,
												    TypicalLogitsWarper,
												)
												from .stopping_criteria import (
												    MaxLengthCriteria,
												    MaxNewTokensCriteria,
												    MaxTimeCriteria,
												    StoppingCriteria,
												    StoppingCriteriaList,
												    validate_stopping_criteria,
												)
												from .utils import (
												    BeamSampleDecoderOnlyOutput,
												    BeamSampleEncoderDecoderOutput,
												    BeamSearchDecoderOnlyOutput,
												    BeamSearchEncoderDecoderOutput,
												    ContrastiveSearchDecoderOnlyOutput,
												    ContrastiveSearchEncoderDecoderOutput,
												    GenerationMixin,
												    GreedySearchDecoderOnlyOutput,
												    GreedySearchEncoderDecoderOutput,
												    SampleDecoderOnlyOutput,
												    SampleEncoderDecoderOutput,
												    top_k_top_p_filtering,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tf_logits_process import (
												    TFForcedBOSTokenLogitsProcessor,
												    TFForcedEOSTokenLogitsProcessor,
												    TFForceTokensLogitsProcessor,
												    TFLogitsProcessor,
												    TFLogitsProcessorList,
												    TFLogitsWarper,
												    TFMinLengthLogitsProcessor,
												    TFNoBadWordsLogitsProcessor,
												    TFNoRepeatNGramLogitsProcessor,
												    TFRepetitionPenaltyLogitsProcessor,
												    TFSuppressTokensAtBeginLogitsProcessor,
												    TFSuppressTokensLogitsProcessor,
												    TFTemperatureLogitsWarper,
												    TFTopKLogitsWarper,
												    TFTopPLogitsWarper,
												)
												from .tf_utils import (
												    TFBeamSampleDecoderOnlyOutput,
												    TFBeamSampleEncoderDecoderOutput,
												    TFBeamSearchDecoderOnlyOutput,
												    TFBeamSearchEncoderDecoderOutput,
												    TFContrastiveSearchDecoderOnlyOutput,
												    TFContrastiveSearchEncoderDecoderOutput,
												    TFGenerationMixin,
												    TFGreedySearchDecoderOnlyOutput,
												    TFGreedySearchEncoderDecoderOutput,
												    TFSampleDecoderOnlyOutput,
												    TFSampleEncoderDecoderOutput,
												    tf_top_k_top_p_filtering,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .flax_logits_process import (
												    FlaxForcedBOSTokenLogitsProcessor,
												    FlaxForcedEOSTokenLogitsProcessor,
												    FlaxLogitsProcessor,
												    FlaxLogitsProcessorList,
												    FlaxLogitsWarper,
												    FlaxMinLengthLogitsProcessor,
												    FlaxTemperatureLogitsWarper,
												    FlaxTopKLogitsWarper,
												    FlaxTopPLogitsWarper,
												)
												from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
						import sys

						A_        =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





