"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_a					:					List[str]=			logging.get_logger(__name__)

_a					:					Dict=			"▁"

_a					:					List[str]=			{"vocab_file": "sentencepiece.bpe.model"}

_a					:					Optional[int]=			{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_a					:					Any=			{
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
_a					:					int=			["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]

class 		UpperCamelCase      (   lowercase						):






				"""simple docstring"""




				UpperCAmelCase    :				Tuple   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				Union[str, Any]   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				Optional[int]   =		PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				Tuple   =		["""input_ids""", """attention_mask"""]

				UpperCAmelCase    :				List[int]   =		[]
				UpperCAmelCase    :				List[int]   =		[]



				def __init__(self    :				str     ,  _A    :				Optional[int]     ,  _A    :				Tuple="<s>"     ,  _A    :				Tuple="</s>"     ,  _A    :				Any="</s>"     ,  _A    :				str="<s>"     ,  _A    :				Dict="<unk>"     ,  _A    :				int="<pad>"     ,  _A    :				Any="<mask>"     ,  _A    :				Any=None     ,  _A    :				Tuple=None     ,  _A    :				Optional[Any]=None     ,  _A    :				Optional[Dict[str, Any]] = None     ,  _A    :				Optional[Any]=None     ,  **_A    :				str     ,  )   ->     Dict:
					# Mask token behave like a normal word, i.e. include the space before it
					__snake_case      :							Dict						=			AddedToken(_A     ,  lstrip=_A     ,  rstrip=_A) if isinstance(_A     ,  _A) else mask_token

					__snake_case      :							int						=			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=_A     ,  eos_token=_A     ,  unk_token=_A     ,  sep_token=_A     ,  cls_token=_A     ,  pad_token=_A     ,  mask_token=_A     ,  tokenizer_file=_A     ,  src_lang=_A     ,  tgt_lang=_A     ,  additional_special_tokens=_A     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **_A     ,  )

					__snake_case      :							Optional[Any]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(str(_A))
					__snake_case      :							str						=			vocab_file

					# Original fairseq vocab and spm vocab must be "aligned":
					# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
					# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
					# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
					# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

					# Mimic fairseq token-to-id alignment for the first 4 token
					__snake_case      :							Optional[Any]						=			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

					# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
					__snake_case      :							Dict						=			1

					__snake_case      :							Tuple						=			len(self.sp_model)
					__snake_case      :							int						=			{
					    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_A)
					}
					__snake_case      :							Union[str, Any]						=			{v: k for k, v in self.lang_code_to_id.items()}
					__snake_case      :							List[Any]						=			len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

					self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
					__snake_case      :							int						=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
					__snake_case      :							List[Any]						=			list(self.lang_code_to_id.keys())

					if additional_special_tokens is not None:
						# Only add those special tokens if they are not already there.
						self._additional_special_tokens.extend(
						    [t for t in additional_special_tokens if t not in self._additional_special_tokens])

					__snake_case      :							List[Any]						=			src_lang if src_lang is not None else 'en_XX'
					__snake_case      :							str						=			self.lang_code_to_id[self._src_lang]
					__snake_case      :							Tuple						=			tgt_lang
					self.set_src_lang_special_tokens(self._src_lang)



				def __getstate__(self    :				List[Any])   ->     List[str]:
					__snake_case      :							int						=			self.__dict__.copy()
					__snake_case      :							List[str]						=			None
					__snake_case      :							Optional[Any]						=			self.sp_model.serialized_model_proto()
					return state



				def __setstate__(self    :				List[str]     ,  _A    :				Tuple)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			d

					# for backward compatibility
					if not hasattr(self     ,  'sp_model_kwargs'):
						__snake_case      :							int						=			{}

					__snake_case      :							Union[str, Any]						=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



				@property
				def  _lowercase					(self    :				int)   ->     List[str]:
					return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token



				@property
				def  _lowercase					(self    :				Dict)   ->     str:
					return self._src_lang



				@src_lang.setter
				def  _lowercase					(self    :				Tuple     ,  _A    :				str)   ->     None:
					__snake_case      :							Optional[Any]						=			new_src_lang
					self.set_src_lang_special_tokens(self._src_lang)



				def  _lowercase					(self    :				Any     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None     ,  _A    :				bool = False)   ->     List[int]:

					if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=_A     ,  token_ids_a=_A     ,  already_has_special_tokens=_A)

					__snake_case      :							Optional[Any]						=			[1] * len(self.prefix_tokens)
					__snake_case      :							Optional[int]						=			[1] * len(self.suffix_tokens)
					if token_ids_a is None:
						return prefix_ones + ([0] * len(_A)) + suffix_ones
					return prefix_ones + ([0] * len(_A)) + ([0] * len(_A)) + suffix_ones



				def  _lowercase					(self    :				List[str]     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					if token_ids_a is None:
						return self.prefix_tokens + token_ids_a + self.suffix_tokens
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



				def  _lowercase					(self    :				Any     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					__snake_case      :							List[Any]						=			[self.sep_token_id]
					__snake_case      :							Union[str, Any]						=			[self.cls_token_id]

					if token_ids_a is None:
						return len(cls + token_ids_a + sep) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Optional[Any]     ,  _A    :				str     ,  _A    :				Optional[str]     ,  _A    :				Optional[str]     ,  **_A    :				int)   ->     Optional[int]:
					if src_lang is None or tgt_lang is None:
						raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
					__snake_case      :							Dict						=			src_lang
					__snake_case      :							Union[str, Any]						=			self(_A     ,  add_special_tokens=_A     ,  return_tensors=_A     ,  **_A)
					__snake_case      :							Tuple						=			self.convert_tokens_to_ids(_A)
					__snake_case      :							List[str]						=			tgt_lang_id
					return inputs



				def  _lowercase					(self    :				Dict)   ->     List[str]:
					__snake_case      :							int						=			{self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



				def  _lowercase					(self    :				int     ,  _A    :				str)   ->     List[str]:
					return self.sp_model.encode(_A     ,  out_type=_A)



				def  _lowercase					(self    :				Tuple     ,  _A    :				Union[str, Any])   ->     List[Any]:
					if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
					__snake_case      :							str						=			self.sp_model.PieceToId(_A)

					# Need to return unknown token if the SP model returned 0
					return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				Optional[int])   ->     Dict:
					if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset)



				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				str)   ->     Union[str, Any]:
					__snake_case      :							int						=			''.join(_A).replace(_A     ,  ' ').strip()
					return out_string



				def  _lowercase					(self    :				str     ,  _A    :				str     ,  _A    :				Optional[str] = None)   ->     Tuple[str]:
					if not os.path.isdir(_A):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case      :							List[Any]						=			os.path.join(
					    _A     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

					if os.path.abspath(self.vocab_file) != os.path.abspath(_A) and os.path.isfile(self.vocab_file):
						copyfile(self.vocab_file     ,  _A)
					elif not os.path.isfile(self.vocab_file):
						with open(_A     ,  'wb') as fi:
							__snake_case      :							Optional[int]						=			self.sp_model.serialized_model_proto()
							fi.write(_A)

					return (out_vocab_file,)



				def  _lowercase					(self    :				Dict     ,  _A    :				List[str]     ,  _A    :				str = "en_XX"     ,  _A    :				Optional[List[str]] = None     ,  _A    :				str = "ro_RO"     ,  **_A    :				Optional[Any]     ,  )   ->     BatchEncoding:
					__snake_case      :							List[str]						=			src_lang
					__snake_case      :							List[Any]						=			tgt_lang
					return super().prepare_seqaseq_batch(_A     ,  _A     ,  **_A)



				def  _lowercase					(self    :				str)   ->     Tuple:
					return self.set_src_lang_special_tokens(self.src_lang)



				def  _lowercase					(self    :				Any)   ->     Optional[Any]:
					return self.set_tgt_lang_special_tokens(self.tgt_lang)



				def  _lowercase					(self    :				List[Any]     ,  _A    :				List[str])   ->     None:
					__snake_case      :							Optional[int]						=			self.lang_code_to_id[src_lang]
					__snake_case      :							List[str]						=			[]
					__snake_case      :							str						=			[self.eos_token_id, self.cur_lang_code]






				def  _lowercase					(self    :				Dict     ,  _A    :				str)   ->     None:
					__snake_case      :							Any						=			self.lang_code_to_id[lang]
					__snake_case      :							Union[str, Any]						=			[]
					__snake_case      :							Optional[Any]						=			[self.eos_token_id, self.cur_lang_code]






"""simple docstring"""







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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_a					:					Optional[int]=			logging.get_logger()

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				List[nn.Module]   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				list   =		field(default_factory=lowercase						)



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			len(list(m.modules())) == 1 or isinstance(_A     ,  nn.Convad) or isinstance(_A     ,  nn.BatchNormad)
					if has_not_submodules:
						self.traced.append(_A)



				def __call__(self    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
					self.module(_A)
					[x.remove() for x in self.handles]
					return self






				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda _A: len(list(x.state_dict().keys())) > 0     ,  self.traced))







@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



				def __call__(self    :				List[str]     ,  _A    :				Tensor)   ->     List[Any]:
					__snake_case      :							Any						=			Tracker(self.dest)(_A).parametrized
					__snake_case      :							int						=			Tracker(self.src)(_A).parametrized

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			list(filter(lambda _A: type(_A) not in self.dest_skip     ,  _A))

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

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







def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

	assert torch.allclose(from_model(UpperCAmelCase_  )      ,							our_model(UpperCAmelCase_  ).logits  ), "The model logits don't match the original one."

	__snake_case      :							str						=			F"resnet{'-'.join(name.split('resnet'  )  )}"
	print(UpperCAmelCase_  )

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

		# we can use the convnext one
		__snake_case      :							int						=			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=UpperCAmelCase_      ,							)

		print(F"Pushed {checkpoint_name}"  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							Optional[Any]						=			10_00
	__snake_case      :							Any						=			(1, num_labels)

	__snake_case      :							List[Any]						=			'huggingface/label-files'
	__snake_case      :							Dict						=			num_labels
	__snake_case      :							Any						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
	__snake_case      :							Any						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	}

	if model_name:
		convert_weight_and_push(UpperCAmelCase_      ,							names_to_config[model_name]      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	return config, expected_shape


if __name__ == "__main__":
	_a					:					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 resnet* architecture,"
	        " currently: resnet18,26,34,50,101,152. 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					:					Union[str, 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)





