



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_snake_case		=  logging.get_logger(__name__)


_snake_case		=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
_snake_case		=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

_snake_case		=  {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		VOCAB_FILES_NAMES
							lowerCamelCase__   			=		PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__   			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__   			=		['input_ids', 'attention_mask']
							lowerCamelCase__   			=		BartTokenizer







							def __init__(   self,			__a=None,			__a=None,			__a=None,			__a="replace",			__a="<s>",			__a="</s>",			__a="</s>",			__a="<s>",			__a="<unk>",			__a="<pad>",			__a="<mask>",			__a=False,			__a=True,			**__a,			):

														'''simple docstring'''

														super().__init__(
														    __a,			__a,			tokenizer_file=__a,			errors=__a,			bos_token=__a,			eos_token=__a,			sep_token=__a,			cls_token=__a,			unk_token=__a,			pad_token=__a,			mask_token=__a,			add_prefix_space=__a,			trim_offsets=__a,			**__a,			)

														_lowerCAmelCase     :							Tuple    =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
														if pre_tok_state.get("add_prefix_space",			__a) != add_prefix_space:
																					_lowerCAmelCase     :							List[Any]    =    getattr(__a,			pre_tok_state.pop("type"))
																					_lowerCAmelCase     :							Any    =    add_prefix_space
																					_lowerCAmelCase     :							Any    =    pre_tok_class(**__a)

														_lowerCAmelCase     :							Dict    =    add_prefix_space

														# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
														_lowerCAmelCase     :							Tuple    =    "post_processor"
														_lowerCAmelCase     :							Union[str, Any]    =    getattr(self.backend_tokenizer,			__a,			__a)
														if tokenizer_component_instance:
																					_lowerCAmelCase     :							Optional[int]    =    json.loads(tokenizer_component_instance.__getstate__())

																					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																					if "sep" in state:
																												_lowerCAmelCase     :							Tuple    =    tuple(state["sep"])
																					if "cls" in state:
																												_lowerCAmelCase     :							List[Any]    =    tuple(state["cls"])

																					_lowerCAmelCase     :							List[str]    =    False

																					if state.get("add_prefix_space",			__a) != add_prefix_space:
																												_lowerCAmelCase     :							Union[str, Any]    =    add_prefix_space
																												_lowerCAmelCase     :							List[Any]    =    True

																					if state.get("trim_offsets",			__a) != trim_offsets:
																												_lowerCAmelCase     :							Any    =    trim_offsets
																												_lowerCAmelCase     :							Union[str, Any]    =    True

																					if changes_to_apply:
																												_lowerCAmelCase     :							Optional[int]    =    getattr(__a,			state.pop("type"))
																												_lowerCAmelCase     :							Optional[Any]    =    component_class(**__a)
																												setattr(self.backend_tokenizer,			__a,			__a)







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														if self._mask_token is None:
																					if self.verbose:
																												logger.error("Using mask_token, but it is not set yet.")
																					return None
														return str(self._mask_token)







							@mask_token.setter
							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    AddedToken(__a,			lstrip=__a,			rstrip=__a) if isinstance(__a,			__a) else value
														_lowerCAmelCase     :							Optional[int]    =    value







							def    snake_case__      (   self,			*__a,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    kwargs.get("is_split_into_words",			__a)

														if is_split_into_words and not self.add_prefix_space:
																					raise ValueError(
																					    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
																					    "to use it with pretokenized inputs.")

														return super()._batch_encode_plus(*__a,			**__a)







							def    snake_case__      (   self,			*__a,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    kwargs.get("is_split_into_words",			__a)

														if is_split_into_words and not self.add_prefix_space:
																					raise ValueError(
																					    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
																					    "to use it with pretokenized inputs.")

														return super()._encode_plus(*__a,			**__a)







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self._tokenizer.model.save(__a,			name=__a)
														return tuple(__a)







							def    snake_case__      (   self,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return output

														return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    [self.sep_token_id]
														_lowerCAmelCase     :							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]





import argparse
import json
from pathlib import Path

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

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							_lowerCAmelCase     :							Tuple    =    "std_conv" if "bit" in model_name else False

							# note that when using BiT as backbone for ViT-hybrid checkpoints,
							# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
							# config.conv_layer = "std_conv_same"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "stem.conv" in name:
														_lowerCAmelCase     :							List[str]    =    name.replace("stem.conv"				, "bit.embedder.convolution"  )
							if "blocks" in name:
														_lowerCAmelCase     :							Any    =    name.replace("blocks"				, "layers"  )
							if "head.fc" in name:
														_lowerCAmelCase     :							Optional[Any]    =    name.replace("head.fc"				, "classifier.1"  )
							if name.startswith("norm"  ):
														_lowerCAmelCase     :							Any    =    "bit." + name
							if "bit" not in name and "classifier" not in name:
														_lowerCAmelCase     :							Dict    =    "bit.encoder." + name

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

							# load HuggingFace model
							_lowerCAmelCase     :							Optional[Any]    =    BitForImageClassification(_lowerCamelCase  )
							model.eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

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

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

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

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
														print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"  )
														model.save_pretrained(_lowerCamelCase  )
														processor.save_pretrained(_lowerCamelCase  )

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


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="resnetv2_50x1_bitm",
						    type=str,
						    help="Name of the BiT 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 push the model to the hub.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
