




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

import sentencepiece as spm

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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase_									=	{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

UpperCAmelCase_									=	{
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

UpperCAmelCase_									=	'▁'





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Optional[Any]    		=   VOCAB_FILES_NAMES
      UpperCAmelCase__      :	Dict    		=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__      :	int    		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__      :	Optional[Any]    		=   ['input_ids', 'attention_mask']
      def __init__(				self: List[Any]    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: Dict="<s>"    ,       UpperCamelCase_: str="</s>"    ,       UpperCamelCase_: Tuple="</s>"    ,       UpperCamelCase_: Dict="<s>"    ,       UpperCamelCase_: int="<unk>"    ,       UpperCamelCase_: List[Any]="<pad>"    ,       UpperCamelCase_: Union[str, Any]="<mask>"    ,       UpperCamelCase_: Optional[Dict[str, Any]] = None    ,       **UpperCamelCase_: List[Any]    ,       ):
             # Mask token behave like a normal word, i.e. include the space before it
             __lowerCamelCase   =						AddedToken(UpperCamelCase_    ,       lstrip=UpperCamelCase_    ,       rstrip=UpperCamelCase_   ) if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ) else mask_token

             __lowerCamelCase   =						{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=UpperCamelCase_    ,       eos_token=UpperCamelCase_    ,       unk_token=UpperCamelCase_    ,       sep_token=UpperCamelCase_    ,       cls_token=UpperCamelCase_    ,       pad_token=UpperCamelCase_    ,       mask_token=UpperCamelCase_    ,       sp_model_kwargs=self.sp_model_kwargs    ,       **UpperCamelCase_    ,       )

             __lowerCamelCase   =						vocab_file
             __lowerCamelCase   =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(str(UpperCamelCase_   )   )

             __lowerCamelCase   =						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

             __lowerCamelCase   =						len(self.sp_model   ) - 1
             __lowerCamelCase   =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None   ):

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __lowerCamelCase   =						[self.cls_token_id]
             __lowerCamelCase   =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None    ,       UpperCamelCase_: bool = False   ):
             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=UpperCamelCase_    ,       token_ids_a=UpperCamelCase_    ,       already_has_special_tokens=UpperCamelCase_   )

             if token_ids_a is None:
                    return [1] + ([0] * len(UpperCamelCase_   )) + [1]
             return [1] + ([0] * len(UpperCamelCase_   )) + [1, 1] + ([0] * len(UpperCamelCase_   )) + [1]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None   ):
             __lowerCamelCase   =						[self.sep_token_id]
             __lowerCamelCase   =						[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]
      @property
      def 			lowerCAmelCase__						(				self: str   ):
             return len(self.sp_model   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						{self.convert_ids_to_tokens(UpperCamelCase_   ): i for i in range(self.vocab_size   )}
             vocab.update(self.added_tokens_encoder   )
             return vocab
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: str   ):
             return self.sp_model.encode(UpperCamelCase_    ,       out_type=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Optional[int]   ):
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             __lowerCamelCase   =						self.sp_model.PieceToId(UpperCamelCase_   )

             return spm_id if spm_id else self.unk_token_id
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: int   ):
             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: int   ):
             __lowerCamelCase   =						[]
             __lowerCamelCase   =						""""""
             __lowerCamelCase   =						False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(UpperCamelCase_   ) + token
                           __lowerCamelCase   =						True
                           __lowerCamelCase   =						[]
                    else:
                           current_sub_tokens.append(UpperCamelCase_   )
                           __lowerCamelCase   =						False
             out_string += self.sp_model.decode(UpperCamelCase_   )
             return out_string.strip()
      def __getstate__(				self: str   ):
             __lowerCamelCase   =						self.__dict__.copy()
             __lowerCamelCase   =						None
             return state
      def __setstate__(				self: Optional[int]    ,       UpperCamelCase_: List[Any]   ):
             __lowerCamelCase   =						d

             # for backward compatibility
             if not hasattr(self    ,       """sp_model_kwargs"""   ):
                    __lowerCamelCase   =						{}

             __lowerCamelCase   =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(self.vocab_file   )




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Optional[str] = None   ):
             if not os.path.isdir(UpperCamelCase_   ):
                    logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                    return
             __lowerCamelCase   =						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_   ) and os.path.isfile(self.vocab_file   ):
                    copyfile(self.vocab_file    ,       UpperCamelCase_   )
             elif not os.path.isfile(self.vocab_file   ):
                    with open(UpperCamelCase_    ,       """wb"""   ) as fi:
                           __lowerCamelCase   =						self.sp_model.serialized_model_proto()
                           fi.write(UpperCamelCase_   )

             return (out_vocab_file,)






"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__    :  Dict							    =	logging.get_logger(__name__)




def __lowercase			(							_a		,			_a=False    ):
    snake_case_ :     List[str]				=	[]

    # fmt: off
    # stem:
    rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')    )
    rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')    )

    rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')    )
    rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')    )

    # backbone
    rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')    )

    for stage_idx in range(len(config.backbone_config.depths    )    ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")    )
            rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")    )

        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")    )
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")    )
        rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")    )

    # transformer encoder
    for i in range(config.num_hidden_layers    ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")    )
        rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")    )
        rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")    )
        rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")    )
        rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")    )
        rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")    )
        rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")    )
        rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")    )
        rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")    )
        rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")    )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
                ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
            ]    )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        snake_case_ :     Optional[int]				=	[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''    ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]    )
    # fmt: on

    return rename_keys




def __lowercase			(							_a		,			_a		,			_a=False    ):
    for i in range(config.num_hidden_layers    ):
        if base_model:
            snake_case_ :     List[str]				=	''''''
        else:
            snake_case_ :     Dict				=	'''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        snake_case_ :     List[str]				=	state_dict.pop(f"blocks.{i}.attn.qkv.weight"    )
        snake_case_ :     Optional[int]				=	state_dict.pop(f"blocks.{i}.attn.qkv.bias"    )
        # next, add query, keys and values (in that order) to the state dict
        snake_case_ :     Any				=	in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case_ :     Dict				=	in_proj_bias[: config.hidden_size]
        snake_case_ :     str				=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case_ :     Optional[int]				=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case_ :     Dict				=	in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case_ :     str				=	in_proj_bias[-config.hidden_size :]




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	dct.pop(_a    )
    snake_case_ :     Union[str, Any]				=	val




def __lowercase			(							):
    snake_case_ :     Any				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     Tuple				=	Image.open(requests.get(_a		,			stream=_a    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a=False    ):
    snake_case_ :     str				=	BitConfig(
        global_padding='''same'''		,			layer_type='''bottleneck'''		,			depths=(3, 4, 9)		,			out_features=['''stage3''']		,			embedding_dynamic_padding=_a		,			)
    snake_case_ :     Tuple				=	ViTHybridConfig(backbone_config=_a		,			image_size=384		,			num_labels=1_000    )
    snake_case_ :     int				=	False

    # load original model from timm
    snake_case_ :     str				=	timm.create_model(_a		,			pretrained=_a    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    snake_case_ :     Any				=	timm_model.state_dict()
    if base_model:
        remove_classification_head_(_a    )
    snake_case_ :     int				=	create_rename_keys(_a		,			_a    )
    for src, dest in rename_keys:
        rename_key(_a		,			_a		,			_a    )
    read_in_q_k_v(_a		,			_a		,			_a    )

    snake_case_ :     Optional[Any]				=	'''huggingface/label-files'''
    snake_case_ :     Any				=	'''imagenet-1k-id2label.json'''
    snake_case_ :     Dict				=	json.load(open(hf_hub_download(_a		,			_a		,			repo_type='''dataset'''    )		,			'''r'''    )    )
    snake_case_ :     Dict				=	{int(_a    ): v for k, v in idalabel.items()}
    snake_case_ :     Optional[int]				=	idalabel
    snake_case_ :     Optional[Any]				=	{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        snake_case_ :     Optional[Any]				=	ViTHybridModel(_a    ).eval()
    else:
        snake_case_ :     Any				=	ViTHybridForImageClassification(_a    ).eval()
    model.load_state_dict(_a    )

    # create image processor
    snake_case_ :     Optional[Any]				=	create_transform(**resolve_data_config({}		,			model=_a    )    )
    snake_case_ :     List[Any]				=	transform.transforms

    snake_case_ :     Optional[Any]				=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    snake_case_ :     List[Any]				=	ViTHybridImageProcessor(
        do_resize=_a		,			size={'''shortest_edge''': timm_transforms[0].size}		,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,			do_center_crop=_a		,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}		,			do_normalize=_a		,			image_mean=timm_transforms[-1].mean.tolist()		,			image_std=timm_transforms[-1].std.tolist()		,			)

    snake_case_ :     Optional[int]				=	prepare_img()
    snake_case_ :     Optional[int]				=	transform(_a    ).unsqueeze(0    )
    snake_case_ :     int				=	processor(_a		,			return_tensors='''pt'''    ).pixel_values

    # verify pixel values
    assert torch.allclose(_a		,			_a    )

    # verify logits
    with torch.no_grad():
        snake_case_ :     List[str]				=	model(_a    )
        snake_case_ :     Any				=	outputs.logits

    print('''Predicted class:'''		,			logits.argmax(-1    ).item()    )
    if base_model:
        snake_case_ :     Optional[Any]				=	timm_model.forward_features(_a    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(_a		,			outputs.pooler_output		,			atol=1E-3    )
    else:
        snake_case_ :     int				=	timm_model(_a    )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_a		,			outputs.logits		,			atol=1E-3    )
    print('''Looks ok!'''    )

    if pytorch_dump_folder_path is not None:
        Path(_a    ).mkdir(exist_ok=_a    )
        print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"    )
        model.save_pretrained(_a    )
        print(f"Saving processor to {pytorch_dump_folder_path}"    )
        processor.save_pretrained(_a    )

    if push_to_hub:
        print(f"Pushing model and processor to the hub {vit_name}"    )
        model.push_to_hub(f"ybelkada/{vit_name}"    )
        processor.push_to_hub(f"ybelkada/{vit_name}"    )


if __name__ == "__main__":
     lowercase__    :  int							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--vit_name''',
         default='''vit_base_r50_s16_384''',
         type=str,
         help='''Name of the hybrid ViT timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
     )

     lowercase__    :  Any							    =	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





