
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()
__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger()


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





		def       _A							(							self    :	List[Any]						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	Tensor						,  lowerCamelCase__    :	Tensor						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					List[str]  	=     len(list(m.modules()				)				) == 1 or isinstance(lowerCamelCase__						,  nn.Convad				) or isinstance(lowerCamelCase__						,  nn.BatchNormad				)
							if has_not_submodules:
												self.traced.append(lowerCamelCase__				)
												lowerCAmelCase		:					Optional[int]  	=     m





		def __call__(							self    :	List[Any]						,  lowerCamelCase__    :	Tensor				):
							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       _A							(							self    :	Optional[int]				):
							# 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 		__magic_name__   (						nn.Module    ):





		def __init__(							self    :	Union[str, Any]						,  lowerCamelCase__    :	nn.Module				):
							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		:					List[str]  	=     len(lowerCamelCase__				) + 1
												feature_blocks.append((f'''res{block_index}''', v)				)

							lowerCAmelCase		:					List[str]  	=     nn.ModuleDict(lowerCamelCase__				)





		def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	Tensor				):
							return get_trunk_forward_outputs(
							    lowerCamelCase__						,  out_feat_keys=lowerCamelCase__						,  feature_blocks=self._feature_blocks						,  )



class 		__magic_name__   (						snake_case    ):





		def       _A							(							self    :	Dict				):
							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__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     RegNetConfig(depths=[2, 7, 17, 1]							,  hidden_sizes=[8, 8, 8, 8]							,  groups_width=8  )
					if "in1k" in model_name:
										lowerCAmelCase		:					Dict  	=     RegNetForImageClassification(__magic_name__  )
					else:
										lowerCAmelCase		:					str  	=     RegNetModel(__magic_name__  )
					# create from model (with small weights)
					lowerCAmelCase		:					int  	=     FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27							,  group_width=10_10							,  w_a=17_44							,  w_a=620.83							,  w_m=2.52  )  )  )

					with torch.no_grad():
										lowerCAmelCase		:					Optional[int]  	=     from_model.eval()
										lowerCAmelCase		:					str  	=     our_model.eval()

										lowerCAmelCase		:					Tuple  	=     torch.randn((1, 3, 32, 32)  )
										# trace both
										lowerCAmelCase		:					Union[str, Any]  	=     Tracker(__magic_name__  )
										lowerCAmelCase		:					Optional[Any]  	=     dest_tracker(__magic_name__  ).parametrized

										pprint(dest_tracker.nameamodule  )
										lowerCAmelCase		:					List[Any]  	=     Tracker(__magic_name__  )
										lowerCAmelCase		:					Optional[int]  	=     src_tracker(__magic_name__  ).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(__magic_name__						:     int  ):
										lowerCAmelCase		:					Tuple  	=     OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				lowerCAmelCase		:					Tuple  	=     param
										return params_dict

					lowerCAmelCase		:					Dict  	=     {}

					lowerCAmelCase		:					int  	=     to_params_dict(__magic_name__  )
					lowerCAmelCase		:					str  	=     to_params_dict(__magic_name__  )

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,  dst_state_dict.items()  ):
										lowerCAmelCase		:					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:
										lowerCAmelCase		:					Tuple  	=     '''classifier.1.weight'''
										lowerCAmelCase		:					Tuple  	=     '''classifier.1.bias'''

					return from_to_ours_keys
def     UpperCAmelCase__							(       __magic_name__						:     Path							,  __magic_name__						:     str = None							,  __magic_name__						:     bool = True  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     '''imagenet-1k-id2label.json'''
					lowerCAmelCase		:					Optional[Any]  	=     10_00

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

					lowerCAmelCase		:					Any  	=     idalabel
					lowerCAmelCase		:					str  	=     {v: k for k, v in idalabel.items()}

					lowerCAmelCase		:					Union[str, Any]  	=     partial(__magic_name__							,  num_labels=__magic_name__							,  idalabel=__magic_name__							,  labelaid=__magic_name__  )

					lowerCAmelCase		:					str  	=     {
					    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]							,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,  groups_width=10_10  ),
					    # finetuned on imagenet
					    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]							,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,  groups_width=10_10  ),
					}

					# add seer weights logic
					def load_using_classy_vision(__magic_name__						:     str  ) -> Tuple[Dict, Dict]:
										lowerCAmelCase		:					Optional[Any]  	=     torch.hub.load_state_dict_from_url(__magic_name__							,  model_dir=str(__magic_name__  )							,  map_location='''cpu'''  )
										# check if we have a head, if yes add it
										lowerCAmelCase		:					List[str]  	=     files['''classy_state_dict''']['''base_model''']['''model''']
										return model_state_dict["trunk"], model_state_dict["heads"]

					lowerCAmelCase		:					Optional[Any]  	=     {
					    '''regnet-y-10b-seer''': partial(
					        __magic_name__							,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''							,  ),
					    '''regnet-y-10b-seer-in1k''': partial(
					        __magic_name__							,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''							,  ),
					}

					lowerCAmelCase		:					Any  	=     get_from_to_our_keys(__magic_name__  )

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

										lowerCAmelCase		:					Union[str, Any]  	=     {}

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

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

										# save our state dict to disk
										torch.save(__magic_name__							,  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		:					int  	=     names_to_config[model_name]
										lowerCAmelCase		:					str  	=     RegNetModel
										if "in1k" in model_name:
															lowerCAmelCase		:					List[str]  	=     RegNetForImageClassification
										lowerCAmelCase		:					str  	=     our_model_func(__magic_name__  )
										# 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		:					Optional[Any]  	=     our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    __magic_name__							,  __magic_name__							,  [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		:					Union[str, Any]  	=     3_84
										# we can use the convnext one
										lowerCAmelCase		:					List[str]  	=     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							,  size=__magic_name__  )
										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__":
		__SCREAMING_SNAKE_CASE			:   Optional[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.',
		)

		__SCREAMING_SNAKE_CASE			:   str         =     parser.parse_args()

		__SCREAMING_SNAKE_CASE			:   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)


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
		from .tokenization_llama import LlamaTokenizer
else:
		__SCREAMING_SNAKE_CASE			:   int         =     None

__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE			:   Optional[int]         =     {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Dict         =     '[INST]', '[/INST]'
__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[Any]         =     '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__SCREAMING_SNAKE_CASE			:   List[Any]         =     'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	VOCAB_FILES_NAMES
		_lowerCAmelCase		=	LlamaTokenizer
		_lowerCAmelCase		=	"left"
		_lowerCAmelCase		=	["input_ids", "attention_mask"]





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	Union[str, Any]=None						,  lowerCamelCase__    :	List[str]=False						,  lowerCamelCase__    :	int="<unk>"						,  lowerCamelCase__    :	int="<s>"						,  lowerCamelCase__    :	str="</s>"						,  lowerCamelCase__    :	Optional[Any]=True						,  lowerCamelCase__    :	int=False						,  **lowerCamelCase__    :	List[str]						,  ):
							super().__init__(
							    vocab_file=lowerCamelCase__						,  tokenizer_file=lowerCamelCase__						,  clean_up_tokenization_spaces=lowerCamelCase__						,  unk_token=lowerCamelCase__						,  bos_token=lowerCamelCase__						,  eos_token=lowerCamelCase__						,  **lowerCamelCase__						,  )
							lowerCAmelCase		:					Optional[int]  	=     add_bos_token
							lowerCAmelCase		:					int  	=     add_eos_token
							self.update_post_processor()

							lowerCAmelCase		:					List[Any]  	=     vocab_file
							lowerCAmelCase		:					List[str]  	=     False if not self.vocab_file else True





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					List[str]  	=     self.bos_token
							lowerCAmelCase		:					Optional[Any]  	=     self.bos_token_id

							lowerCAmelCase		:					Optional[Any]  	=     self.eos_token
							lowerCAmelCase		:					List[str]  	=     self.eos_token_id

							lowerCAmelCase		:					Dict  	=     f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
							lowerCAmelCase		:					Optional[int]  	=     f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

							lowerCAmelCase		:					Any  	=     []
							if self.add_bos_token:
												special_tokens.append((bos, bos_token_id)				)
							if self.add_eos_token:
												special_tokens.append((eos, eos_token_id)				)
							lowerCAmelCase		:					Union[str, Any]  	=     processors.TemplateProcessing(
							    single=lowerCamelCase__						,  pair=lowerCamelCase__						,  special_tokens=lowerCamelCase__				)





		@property
		def       _A							(							self    :	str				):
							return self._add_eos_token





		@property
		def       _A							(							self    :	Dict				):
							return self._add_bos_token





		@add_eos_token.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Any  	=     value
							self.update_post_processor()





		@add_bos_token.setter
		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Optional[Any]				):
							lowerCAmelCase		:					List[Any]  	=     value
							self.update_post_processor()





		def       _A							(							self    :	int						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[str] = None				):
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''				)

							if not os.path.isdir(lowerCamelCase__				):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
												return
							lowerCAmelCase		:					str  	=     os.path.join(
							    lowerCamelCase__						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
												copyfile(self.vocab_file						,  lowerCamelCase__				)

							return (out_vocab_file,)





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	"Conversation"				):
							lowerCAmelCase		:					int  	=     list(conversation.iter_texts()				)
							if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]				):
												raise ValueError(
												    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)

							lowerCAmelCase		:					List[str]  	=     []
							if len(conversation.past_user_inputs				) > 0:
												if not conversation.past_user_inputs[0].startswith(lowerCamelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																	lowerCAmelCase		:					Tuple  	=     (
																	    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																	)
							elif not dialogue[0][1].startswith(lowerCamelCase__				) or E_SYS not in dialogue[0][1]:
												lowerCAmelCase		:					Optional[Any]  	=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''						,  add_special_tokens=lowerCamelCase__				)
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2]						,  dialogue[1::2]				)
							    ]						,  []						,  )
							if not (dialogue[-1][0]):
												raise ValueError(f'''Last message must be from user, got {dialogue[-1]["role"]}'''				)
							dialog_tokens += [self.bos_token_id] + self.encode(
							    f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''						,  add_special_tokens=lowerCamelCase__				)
							return dialog_tokens
