


'''simple docstring'''

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
@dataclass
class 						__lowerCAmelCase		:





      '''simple docstring'''






      lowerCAmelCase__      :  nn.Module
      lowerCAmelCase__      :  List[nn.Module]						      =							field(default_factory=lowercase_  )
      lowerCAmelCase__      :  list						      =							field(default_factory=lowercase_  )



      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					Tensor     ,		UpperCamelCase					:					Tensor				):

             '''simple docstring'''







             lowercase__       =				len(list(m.modules()				)				) == 1 or isinstance(UpperCamelCase     ,		nn.Convad				) or isinstance(UpperCamelCase     ,		nn.BatchNormad				)
             if has_not_submodules:
                    if not isinstance(UpperCamelCase     ,		UpperCamelCase				):
                           self.traced.append(UpperCamelCase				)



      def __call__(self					:					Optional[int]     ,		UpperCamelCase					:					Tensor				):

             '''simple docstring'''







             for m in self.module.modules():
                    self.handles.append(m.register_forward_hook(self._forward_hook				)				)
             self.module(UpperCamelCase				)
             [x.remove() for x in self.handles]
             return self






      @property
      def    UpperCamelCase__	(self					:					Dict				):

             '''simple docstring'''







             return list(filter(lambda UpperCamelCase				: len(list(x.state_dict().keys()				)				) > 0     ,		self.traced				)				)






@dataclass
class 						__lowerCAmelCase		:





      '''simple docstring'''






      lowerCAmelCase__      :  nn.Module
      lowerCAmelCase__      :  nn.Module
      lowerCAmelCase__      :  int						      =							0
      lowerCAmelCase__      :  List						      =							field(default_factory=lowercase_  )
      lowerCAmelCase__      :  List						      =							field(default_factory=lowercase_  )



      def __call__(self					:					int     ,		UpperCamelCase					:					Tensor				):

             '''simple docstring'''







             lowercase__       =				Tracker(self.dest				)(UpperCamelCase				).parametrized
             lowercase__       =				Tracker(self.src				)(UpperCamelCase				).parametrized

             lowercase__       =				list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.src_skip     ,		UpperCamelCase				)				)
             lowercase__       =				list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.dest_skip     ,		UpperCamelCase				)				)

             if len(UpperCamelCase				) != len(UpperCamelCase				):
                    raise Exception(
                        f"Numbers of operations are different. Source module has {len(UpperCamelCase				)} operations while"
                        f" destination module has {len(UpperCamelCase				)}."				)

             for dest_m, src_m in zip(UpperCamelCase     ,		UpperCamelCase				):
                    dest_m.load_state_dict(src_m.state_dict()				)
                    if self.verbose == 1:
                           print(f"Transfered from={src_m} to={dest_m}"				)






def _SCREAMING_SNAKE_CASE   (A						,				A ) -> nn.Module:






       """simple docstring"""







       lowercase__       =				from_model.state_dict()
       lowercase__       =				our_model.state_dict()
       lowercase__       =				our_model.config
       lowercase__       =				[]
       for stage_idx in range(len(config.hidden_sizes ) ):
              for block_id in range(config.depths[stage_idx] ):
                     lowercase__       =				f"block{stage_idx + 1}.{block_id}.layer_scale_1"
                     lowercase__       =				f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

                     all_keys.append((from_key, to_key) )
                     lowercase__       =				f"block{stage_idx + 1}.{block_id}.layer_scale_2"
                     lowercase__       =				f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

                     all_keys.append((from_key, to_key) )

       for from_key, to_key in all_keys:
              lowercase__       =				from_state_dict.pop(A )

       our_model.load_state_dict(A )
       return our_model

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				A						,				A = True						,				) -> Union[str, Any]:






       """simple docstring"""







       print(f"Downloading weights for {name}..." )
       lowercase__       =				cached_download(A )
       print(f"Converting {name}..." )
       lowercase__       =				torch.load(A )['''state_dict''']
       from_model.load_state_dict(A )
       from_model.eval()
       with torch.no_grad():
              lowercase__       =				VanForImageClassification(A ).eval()
              lowercase__       =				ModuleTransfer(src=A						,				dest=A )
              lowercase__       =				torch.randn((1, 3, 224, 224) )
              module_transfer(A )
              lowercase__       =				copy_parameters(A						,				A )

       if not torch.allclose(from_model(A )						,				our_model(A ).logits ):
              raise ValueError('''The model logits don\'t match the original one.''' )

       lowercase__       =				name
       print(A )

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name						,				commit_message='''Add model'''						,				use_temp_dir=A						,				)

              # we can use the convnext one
              lowercase__       =				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name						,				commit_message='''Add image processor'''						,				use_temp_dir=A						,				)

              print(f"Pushed {checkpoint_name}" )

def _SCREAMING_SNAKE_CASE   (A						,				A = None						,				A = True ) -> Tuple:






       """simple docstring"""







       lowercase__       =				'''imagenet-1k-id2label.json'''
       lowercase__       =				1_000

       lowercase__       =				'''huggingface/label-files'''
       lowercase__       =				num_labels
       lowercase__       =				json.load(open(hf_hub_download(A						,				A						,				repo_type='''dataset''' )						,				'''r''' ) )
       lowercase__       =				{int(A ): v for k, v in idalabel.items()}

       lowercase__       =				idalabel
       lowercase__       =				{v: k for k, v in idalabel.items()}

       lowercase__       =				partial(A						,				num_labels=A						,				idalabel=A						,				labelaid=A )

       lowercase__       =				{
           '''van-tiny''': ImageNetPreTrainedConfig(
               hidden_sizes=[32, 64, 160, 256]						,				depths=[3, 3, 5, 2]						,				mlp_ratios=[8, 8, 4, 4]						,				),
           '''van-small''': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]						,				depths=[2, 2, 4, 2]						,				mlp_ratios=[8, 8, 4, 4]						,				),
           '''van-base''': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]						,				depths=[3, 3, 12, 3]						,				mlp_ratios=[8, 8, 4, 4]						,				),
           '''van-large''': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]						,				depths=[3, 5, 27, 3]						,				mlp_ratios=[8, 8, 4, 4]						,				),
       }

       lowercase__       =				{
           '''van-tiny''': van_tiny,
           '''van-small''': van_small,
           '''van-base''': van_base,
           '''van-large''': van_large,
       }

       lowercase__       =				{
           '''van-tiny''': (
               '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
           ),
           '''van-small''': (
               '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
           ),
           '''van-base''': (
               '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
           ),
           '''van-large''': (
               '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
           ),
       }

       if model_name:
              convert_weight_and_push(
                  A						,				names_to_config[model_name]						,				checkpoint=names_to_original_checkpoints[model_name]						,				from_model=names_to_original_models[model_name]()						,				save_directory=A						,				push_to_hub=A						,				)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         A						,				A						,				checkpoint=names_to_original_checkpoints[model_name]						,				from_model=names_to_original_models[model_name]()						,				save_directory=A						,				push_to_hub=A						,				)


if __name__ == "__main__":
 lowerCamelCase							:     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 resnet* architecture,'
         ' currently: van-tiny/small/base/large. 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(
     '--van_dir',
     required=True,
     type=Path,
     help=(
         'A path to VAN\'s original implementation directory. You can download from here:'
         ' https://github.com/Visual-Attention-Network/VAN-Classification'
     ),
 )
 parser.add_argument(
     '--push_to_hub',
     default=True,
     type=bool,
     required=False,
     help='If True, push model and image processor to the hub.',
 )

 lowerCamelCase							:     Union[str, Any]								=			parser.parse_args()
 lowerCamelCase							:     Path								=			args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowerCamelCase							:     List[str]								=			args.van_dir
 # append the path to the parents to maskformer dir
 sys.path.append(str(van_dir.parent))
 from van.models.van import van_base, van_large, van_small, van_tiny

 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:
 lowerCamelCase							:     Optional[int]								=			None

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

lowerCamelCase		,						lowerCamelCase							:     str								=			'[INST]', '[/INST]'
lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
lowerCamelCase							:     Optional[int]								=			'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 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
      lowerCAmelCase__      :  List[str]						      =							LlamaTokenizer
      lowerCAmelCase__      :  Tuple						      =							"""left"""
      lowerCAmelCase__      :  int						      =							["""input_ids""", """attention_mask"""]



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

             '''simple docstring'''







             super().__init__(
                 vocab_file=UpperCamelCase     ,		tokenizer_file=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
             lowercase__       =				add_bos_token
             lowercase__       =				add_eos_token
             self.update_post_processor()

             lowercase__       =				vocab_file
             lowercase__       =				False if not self.vocab_file else True



      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				self.bos_token
             lowercase__       =				self.bos_token_id

             lowercase__       =				self.eos_token
             lowercase__       =				self.eos_token_id

             lowercase__       =				f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
             lowercase__       =				f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

             lowercase__       =				[]
             if self.add_bos_token:
                    special_tokens.append((bos, bos_token_id)				)
             if self.add_eos_token:
                    special_tokens.append((eos, eos_token_id)				)
             lowercase__       =				processors.TemplateProcessing(
                 single=UpperCamelCase     ,		pair=UpperCamelCase     ,		special_tokens=UpperCamelCase				)



      @property
      def    UpperCamelCase__	(self					:					Union[str, Any]				):

             '''simple docstring'''







             return self._add_eos_token



      @property
      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             return self._add_bos_token



      @add_eos_token.setter
      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int				):

             '''simple docstring'''







             lowercase__       =				value
             self.update_post_processor()



      @add_bos_token.setter
      def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             lowercase__       =				value
             self.update_post_processor()



      def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):

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

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

             return (out_vocab_file,)






      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					"Conversation"				):

             '''simple docstring'''







             lowercase__       =				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...)'''				)

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




