
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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


def       A       (					lowercase    ,       lowercase=False )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     []
		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'''deit.encoder.layer.{i}.layernorm_before.weight''') )
				rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''') )
				rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
				rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
				rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''') )
				rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''') )
				rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
				rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
				rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''') )
				rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''') )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('cls_token', 'deit.embeddings.cls_token'),
		        ('dist_token', 'deit.embeddings.distillation_token'),
		        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
		        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
		        ('pos_embed', 'deit.embeddings.position_embeddings'),
		    ] )

		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 "deit" from all keys that start with "deit"
				UpperCamelCase		=     [(pair[0], pair[1][4:]) if pair[1].startswith('deit' ) else pair for pair in rename_keys]
		else:
				# layernorm + classification heads
				rename_keys.extend(
				    [
				        ('norm.weight', 'deit.layernorm.weight'),
				        ('norm.bias', 'deit.layernorm.bias'),
				        ('head.weight', 'cls_classifier.weight'),
				        ('head.bias', 'cls_classifier.bias'),
				        ('head_dist.weight', 'distillation_classifier.weight'),
				        ('head_dist.bias', 'distillation_classifier.bias'),
				    ] )

		return rename_keys


def       A       (					lowercase    ,       lowercase    ,       lowercase=False )   ->  Dict:
		'''simple docstring'''

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


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     dct.pop(lowercase )
		UpperCamelCase		=     val


def       A       (					)   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase		=     Image.open(requests.get(lowercase    ,       stream=lowercase ).raw )
		return im


@torch.no_grad()
def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     DeiTConfig()
		# all deit models have fine-tuned heads
		UpperCamelCase		=     False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		UpperCamelCase		=     1_000
		UpperCamelCase		=     'huggingface/label-files'
		UpperCamelCase		=     'imagenet-1k-id2label.json'
		UpperCamelCase		=     json.load(open(hf_hub_download(lowercase    ,       lowercase    ,       repo_type='dataset' )    ,       'r' ) )
		UpperCamelCase		=     {int(lowercase ): v for k, v in idalabel.items()}
		UpperCamelCase		=     idalabel
		UpperCamelCase		=     {v: k for k, v in idalabel.items()}
		UpperCamelCase		=     int(deit_name[-6:-4] )
		UpperCamelCase		=     int(deit_name[-3:] )
		# size of the architecture
		if deit_name[9:].startswith('tiny' ):
				UpperCamelCase		=     192
				UpperCamelCase		=     768
				UpperCamelCase		=     12
				UpperCamelCase		=     3
		elif deit_name[9:].startswith('small' ):
				UpperCamelCase		=     384
				UpperCamelCase		=     1_536
				UpperCamelCase		=     12
				UpperCamelCase		=     6
		if deit_name[9:].startswith('base' ):
				pass
		elif deit_name[4:].startswith('large' ):
				UpperCamelCase		=     1_024
				UpperCamelCase		=     4_096
				UpperCamelCase		=     24
				UpperCamelCase		=     16

		# load original model from timm
		UpperCamelCase		=     timm.create_model(lowercase    ,       pretrained=lowercase )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		UpperCamelCase		=     timm_model.state_dict()
		UpperCamelCase		=     create_rename_keys(lowercase    ,       lowercase )
		for src, dest in rename_keys:
				rename_key(lowercase    ,       lowercase    ,       lowercase )
		read_in_q_k_v(lowercase    ,       lowercase    ,       lowercase )

		# load HuggingFace model
		UpperCamelCase		=     DeiTForImageClassificationWithTeacher(lowercase ).eval()
		model.load_state_dict(lowercase )

		# Check outputs on an image, prepared by DeiTImageProcessor
		UpperCamelCase		=     int(
		    (256 / 224) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		UpperCamelCase		=     DeiTImageProcessor(size=lowercase    ,       crop_size=config.image_size )
		UpperCamelCase		=     image_processor(images=prepare_img()    ,       return_tensors='pt' )
		UpperCamelCase		=     encoding['pixel_values']
		UpperCamelCase		=     model(lowercase )

		UpperCamelCase		=     timm_model(lowercase )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(lowercase    ,       outputs.logits    ,       atol=1e-3 )

		Path(lowercase ).mkdir(exist_ok=lowercase )
		print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
		model.save_pretrained(lowercase )
		print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
		image_processor.save_pretrained(lowercase )


if __name__ == "__main__":
					_UpperCAmelCase					: Union[str, Any]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--deit_name",
					    default="vit_deit_base_distilled_patch16_224",
					    type=str,
					    help="Name of the DeiT 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."
					)

					_UpperCAmelCase					: str      =       parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


import os
import re
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					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: Dict      =       {"vocab_file": "spiece.model"}

_UpperCAmelCase					: Dict      =       {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

_UpperCAmelCase					: int      =       {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       int	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       List[Any]	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       List[Any]	     =				["input_ids", "attention_mask"]
		__lowercase       :       List[int]	     =				[]



		def __init__( self				,      A_				,      A_="<unk>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_="[SEP]"				,      A_="[MASK]"				,      A_="[CLS]"				,      A_ = None				,      **A_				,      )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else bos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else eos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else unk_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else pad_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else cls_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else sep_token

				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else mask_token

				UpperCamelCase		=     {} if sp_model_kwargs is None else sp_model_kwargs

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

				UpperCamelCase		=     vocab_file

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(A_       )



		@property
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return self.sp_model.get_piece_size()



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
				vocab.update(self.added_tokens_encoder       )
				return vocab



		def __getstate__( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.__dict__.copy()
				UpperCamelCase		=     None
				return state



		def __setstate__( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     d

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

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



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				return self.sp_model.encode(A_				,      out_type=A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return self.sp_model.piece_to_id(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.sp_model.IdToPiece(A_       )
				return token



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				UpperCamelCase		=     ''
				UpperCamelCase		=     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
								UpperCamelCase		=     True
								UpperCamelCase		=     []
						else:
								current_sub_tokens.append(A_       )
								UpperCamelCase		=     False
				out_string += self.sp_model.decode(A_       )
				return out_string.strip()



		def __UpperCamelCase ( self				,      A_				,      A_ = False				,      A_ = None				,      A_ = True				,      **A_				,      )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.pop('use_source_tokenizer'				,      A_       )

				UpperCamelCase		=     self.convert_ids_to_tokens(A_				,      skip_special_tokens=A_       )

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				UpperCamelCase		=     []
				UpperCamelCase		=     []
				for token in filtered_tokens:
						if skip_special_tokens and token in self.all_special_ids:
								continue
						if token in self.added_tokens_encoder:
								if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(A_       )       )
										UpperCamelCase		=     []
								sub_texts.append(A_       )
						else:
								current_sub_text.append(A_       )
				if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(A_       )       )

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
						UpperCamelCase		=     re.sub(r' (\[(MASK|SEP)\])'				,      r'\1'				,      ' '.join(A_       )       )
				else:
						UpperCamelCase		=     ''.join(A_       )

				UpperCamelCase		=     (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
						UpperCamelCase		=     self.clean_up_tokenization(A_       )
						return clean_text
				else:
						return text



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     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:
								UpperCamelCase		=     self.sp_model.serialized_model_proto()
								fi.write(A_       )

				return (out_vocab_file,)



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase		=     [self.cls_token_id]
				UpperCamelCase		=     [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					List[int]:
				"""simple docstring"""
				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] + ([0] * len(A_       )) + [1]



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]
				UpperCamelCase		=     [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       ) * [0] + len(token_ids_a + sep       ) * [1]
