
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


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		Optional[int]							      =  {"vocab_file": "sentencepiece.bpe.model"}

snake_case :		List[str]							      =  {
    "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"
        ),
    },
}

snake_case :		Optional[int]							      =  {
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

snake_case :		Optional[Any]							      =  "▁"





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__          =    ['input_ids', 'attention_mask']







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

				__magic_name__		:		List[Any]	   =     {} 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						,   sp_model_kwargs=self.sp_model_kwargs						,   **_a						,   )

				__magic_name__		:		Union[str, Any]	   =     vocab_file
				__magic_name__		:		Optional[int]	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(_a		)		)

				__magic_name__		:		List[Any]	   =     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				__magic_name__		:		str	   =     len(self.sp_model		) - 1
				__magic_name__		:		Optional[int]	   =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__		:		Any	   =     [self.cls_token_id]
				__magic_name__		:		Any	   =     [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = False		):
				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_a						,   token_ids_a=_a						,   already_has_special_tokens=_a		)

				if token_ids_a is None:
					return [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1, 1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		str	   =     [self.sep_token_id]
				__magic_name__		:		Optional[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]







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.sp_model		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     {self.convert_ids_to_tokens(_a		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.encode(_a						,   out_type=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
				__magic_name__		:		List[str]	   =     self.sp_model.PieceToId(_a		)

				return spm_id if spm_id else self.unk_token_id







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Any	   =     []
				__magic_name__		:		Tuple	   =     ""
				__magic_name__		:		Any	   =     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(_a		) + token
						__magic_name__		:		List[str]	   =     True
						__magic_name__		:		Union[str, Any]	   =     []
					else:
						current_sub_tokens.append(_a		)
						__magic_name__		:		Optional[int]	   =     False
				out_string += self.sp_model.decode(_a		)
				return out_string.strip()







			def __getstate__(				self		):
				__magic_name__		:		str	   =     self.__dict__.copy()
				__magic_name__		:		Tuple	   =     None
				return state







			def __setstate__(				self						,   _a		):
				__magic_name__		:		List[Any]	   =     d

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

				__magic_name__		:		Dict	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.vocab_file		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		Dict	   =     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:
						__magic_name__		:		Dict	   =     self.sp_model.serialized_model_proto()
						fi.write(_a		)

				return (out_vocab_file,)


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()
snake_case :		Optional[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     []

	# 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"
		__magic_name__		:		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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__		:		List[Any]	   =     timm_model.state_dict()
	if base_model:
		remove_classification_head_(_snake_case   )
	__magic_name__		:		Tuple	   =     create_rename_keys(_snake_case      ,   _snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )
	read_in_q_k_v(_snake_case      ,   _snake_case      ,   _snake_case   )

	__magic_name__		:		List[str]	   =     "huggingface/label-files"
	__magic_name__		:		int	   =     "imagenet-1k-id2label.json"
	__magic_name__		:		Optional[int]	   =     json.load(open(hf_hub_download(_snake_case      ,   _snake_case      ,   repo_type="dataset"   )      ,   "r"   )   )
	__magic_name__		:		int	   =     {int(_snake_case   ): v for k, v in idalabel.items()}
	__magic_name__		:		List[str]	   =     idalabel
	__magic_name__		:		List[str]	   =     {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

	# create image processor
	__magic_name__		:		List[Any]	   =     create_transform(**resolve_data_config({}      ,   model=_snake_case   )   )
	__magic_name__		:		int	   =     transform.transforms

	__magic_name__		:		List[str]	   =     {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	__magic_name__		:		int	   =     ViTHybridImageProcessor(
	    do_resize=_snake_case      ,   size={"shortest_edge": timm_transforms[0].size}      ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]      ,   do_center_crop=_snake_case      ,   crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}      ,   do_normalize=_snake_case      ,   image_mean=timm_transforms[-1].mean.tolist()      ,   image_std=timm_transforms[-1].std.tolist()      ,   )

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

	print("Predicted class:"      ,   logits.argmax(-1   ).item()   )
	if base_model:
		__magic_name__		:		List[str]	   =     timm_model.forward_features(_snake_case   )
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_snake_case      ,   outputs.pooler_output      ,   atol=1E-3   )
	else:
		__magic_name__		:		Any	   =     timm_model(_snake_case   )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_snake_case      ,   outputs.logits      ,   atol=1E-3   )
	print("Looks ok!"   )

	if pytorch_dump_folder_path is not None:
		Path(_snake_case   ).mkdir(exist_ok=_snake_case   )
		print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''   )
		model.save_pretrained(_snake_case   )
		print(F'''Saving processor to {pytorch_dump_folder_path}'''   )
		processor.save_pretrained(_snake_case   )

	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__":
				snake_case :		Any							      =  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."
				)

				snake_case :		List[Any]							      =  parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
