





'''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										=  logging.get_logger()




@dataclass
class    _A   :
		lowercase__:      Union[str, Any]							    =      42
		lowercase__:      Optional[int]							    =      field(default_factory=UpperCamelCase_		)
		lowercase__:      Optional[Any]							    =      field(default_factory=UpperCamelCase_		)
		lowercase__:      Any							    =      field(default_factory=UpperCamelCase_		)

		def   lowercase__    (      self				:      int     ,			__magic_name__				:      List[Any]     ,			__magic_name__				:      int     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      Optional[Any] )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    len(list(m.modules() ) ) == 1 or isinstance(__UpperCamelCase     ,			nn.Convad ) or isinstance(__UpperCamelCase     ,			nn.BatchNormad )
					if has_not_submodules:
								self.traced.append(__UpperCamelCase )
								__snake_case				:			Union[str, Any]       			=    m

		def __call__(      self				:      Union[str, Any]     ,			__magic_name__				:      List[Any] )				->						Any:


					"""simple docstring"""

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

		@property
		def   lowercase__    (      self				:      Dict )				->						str:


					"""simple docstring"""

					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				:      Union[str, Any]     ,			__magic_name__				:      Optional[int] )				->						Optional[Any]:


					"""simple docstring"""

					super().__init__()

					__snake_case				:			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}'''
								__snake_case				:			Any       			=    len(__UpperCamelCase ) + 1
								feature_blocks.append((f'''res{block_index}''', v) )

					__snake_case				:			Tuple       			=    nn.ModuleDict(__UpperCamelCase )

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      int )				->						List[str]:


					"""simple docstring"""

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





class    _A   (				UpperCamelCase_		):

		def   lowercase__    (      self				:      int )				->						int:


					"""simple docstring"""

					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 				_a	(					_lowerCamelCase   )   ->							Dict[str, str]:



			"""simple docstring"""


			__snake_case				:			Any       			=    RegNetConfig(depths=[2, 7, 17, 1]						,  hidden_sizes=[8, 8, 8, 8]						,  groups_width=8   )
			if "in1k" in model_name:
						__snake_case				:			Any       			=    RegNetForImageClassification(snake_case_   )
			else:
						__snake_case				:			Tuple       			=    RegNetModel(snake_case_   )
			# create from model (with small weights)
			__snake_case				:			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():
						__snake_case				:			str       			=    from_model.eval()
						__snake_case				:			int       			=    our_model.eval()

						__snake_case				:			Optional[int]       			=    torch.randn((1, 3, 32, 32)   )
						# trace both
						__snake_case				:			int       			=    Tracker(snake_case_   )
						__snake_case				:			Optional[int]       			=    dest_tracker(snake_case_   ).parametrized

						pprint(dest_tracker.nameamodule   )
						__snake_case				:			Optional[int]       			=    Tracker(snake_case_   )
						__snake_case				:			Any       			=    src_tracker(snake_case_   ).parametrized

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

			__snake_case				:			Optional[int]       			=    {}

			__snake_case				:			int       			=    to_params_dict(snake_case_   )
			__snake_case				:			int       			=    to_params_dict(snake_case_   )

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

			return from_to_ours_keys







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase = None						,  _lowerCamelCase = True   )   ->							int:



			"""simple docstring"""


			__snake_case				:			Tuple       			=    '''imagenet-1k-id2label.json'''
			__snake_case				:			Any       			=    1000

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

			__snake_case				:			int       			=    idalabel
			__snake_case				:			Tuple       			=    {v: k for k, v in idalabel.items()}

			__snake_case				:			str       			=    partial(snake_case_						,  num_labels=snake_case_						,  idalabel=snake_case_						,  labelaid=snake_case_   )

			__snake_case				:			Tuple       			=    {
			    '''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   ) -> Tuple[Dict, Dict]:
						__snake_case				:			Optional[int]       			=    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
						__snake_case				:			Dict       			=    files['''classy_state_dict''']['''base_model''']['''model''']
						return model_state_dict["trunk"], model_state_dict["heads"]

			__snake_case				:			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"""						,  ),
			}

			__snake_case				:			Tuple       			=    get_from_to_our_keys(snake_case_   )

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

						__snake_case				:			List[str]       			=    {}

						__snake_case				:			int       			=    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				:			Tuple       			=    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
									__snake_case				:			Any       			=    from_to_ours_keys[src_key]
									# store the parameter with our key
									__snake_case				:			str       			=    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
						__snake_case				:			Optional[int]       			=    names_to_config[model_name]
						__snake_case				:			Any       			=    RegNetModel
						if "in1k" in model_name:
									__snake_case				:			Optional[int]       			=    RegNetForImageClassification
						__snake_case				:			Optional[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
						__snake_case				:			Any       			=    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						,  )
						__snake_case				:			List[Any]       			=    384
						# we can use the convnext one
						__snake_case				:			List[Any]       			=    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__":
				__UpperCamelCase										=  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										=  parser.parse_args()

				__UpperCamelCase										=  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 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:
	__UpperCamelCase										=  None

__UpperCamelCase										=  logging.get_logger(__name__)
__UpperCamelCase										=  {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase						,		__UpperCamelCase										=  "[INST]", "[/INST]"
__UpperCamelCase						,		__UpperCamelCase										=  "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
__UpperCamelCase										=  "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    _A   (				__lowercase		):
		lowercase__:      Union[str, Any]							    =      VOCAB_FILES_NAMES
		lowercase__:      Tuple							    =      LlamaTokenizer
		lowercase__:      List[Any]							    =      '''left'''
		lowercase__:      int							    =      ['''input_ids''', '''attention_mask''']

		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      str=None     ,			__magic_name__				:      Any=None     ,			__magic_name__				:      int=False     ,			__magic_name__				:      Optional[Any]="<unk>"     ,			__magic_name__				:      Dict="<s>"     ,			__magic_name__				:      Dict="</s>"     ,			__magic_name__				:      int=True     ,			__magic_name__				:      List[str]=False     ,			**__magic_name__				:      Optional[Any]     ,			)				->						Optional[int]:


					"""simple docstring"""

					super().__init__(
					    vocab_file=__magic_name__     ,			tokenizer_file=__magic_name__     ,			clean_up_tokenization_spaces=__magic_name__     ,			unk_token=__magic_name__     ,			bos_token=__magic_name__     ,			eos_token=__magic_name__     ,			**__magic_name__     ,			)
					__snake_case				:			List[Any]       			=    add_bos_token
					__snake_case				:			int       			=    add_eos_token
					self.update_post_processor()

					__snake_case				:			Dict       			=    vocab_file
					__snake_case				:			int       			=    False if not self.vocab_file else True

		def   lowercase__    (      self				:      Any )				->						Dict:


					"""simple docstring"""

					__snake_case				:			Any       			=    self.bos_token
					__snake_case				:			Dict       			=    self.bos_token_id

					__snake_case				:			List[str]       			=    self.eos_token
					__snake_case				:			str       			=    self.eos_token_id

					__snake_case				:			List[Any]       			=    f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
					__snake_case				:			str       			=    f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

					__snake_case				:			List[str]       			=    []
					if self.add_bos_token:
								special_tokens.append((bos, bos_token_id) )
					if self.add_eos_token:
								special_tokens.append((eos, eos_token_id) )
					__snake_case				:			Dict       			=    processors.TemplateProcessing(
					    single=__magic_name__     ,			pair=__magic_name__     ,			special_tokens=__magic_name__ )

		@property
		def   lowercase__    (      self				:      Tuple )				->						List[Any]:


					"""simple docstring"""

					return self._add_eos_token

		@property
		def   lowercase__    (      self				:      Union[str, Any] )				->						str:


					"""simple docstring"""

					return self._add_bos_token

		@add_eos_token.setter
		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Optional[Any] )				->						int:


					"""simple docstring"""

					__snake_case				:			Any       			=    value
					self.update_post_processor()

		@add_bos_token.setter
		def   lowercase__    (      self				:      Union[str, Any]     ,			__magic_name__				:      Optional[Any] )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    value
					self.update_post_processor()

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      str     ,			__magic_name__				:      Optional[str] = None )				->						Tuple[str]:


					"""simple docstring"""

					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(__magic_name__ ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
								return
					__snake_case				:			Dict       			=    os.path.join(
					    __magic_name__     ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

					return (out_vocab_file,)

		def   lowercase__    (      self				:      int     ,			__magic_name__				:      "Conversation" )				->						int:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    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...)""" )

					__snake_case				:			str       			=    []
					if len(conversation.past_user_inputs ) > 0:
								if not conversation.past_user_inputs[0].startswith(__magic_name__ ) or E_SYS not in conversation.past_user_inputs[0]:
											__snake_case				:			Tuple       			=    (
											    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
											)
					elif not dialogue[0][1].startswith(__magic_name__ ) or E_SYS not in dialogue[0][1]:
								__snake_case				:			str       			=    (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=__magic_name__ )
					        + [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=__magic_name__ )
					return dialog_tokens


