"""simple docstring"""







import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE    	=logging.getLogger(__name__)







@dataclass(frozen=__a  )
class        UpperCamelCase		:
   lowercase							=  4_2
   lowercase							=  4_2
   lowercase							=  None
   lowercase							=  None
   lowercase							=  None







@dataclass(frozen=__a  )
class        UpperCamelCase		:
   lowercase							=  4_2
   lowercase							=  None
   lowercase							=  None
   lowercase							=  None
   lowercase							=  None


if is_torch_available():
       import torch
       from torch.utils.data import Dataset







       class        UpperCamelCase		(			__a  ):
          lowercase							=  4_2
          def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase=False     ,__UpperCamelCase = False     ,)	->      Tuple:


               '''simple docstring'''


               lowercase_	:				Optional[Any]													=	hans_processors[task]()

               lowercase_	:				Dict													=	os.path.join(
                   _UpperCamelCase     ,'cached_{}_{}_{}_{}'.format(
                       'dev' if evaluate else 'train'     ,tokenizer.__class__.__name__     ,str(_UpperCamelCase			)     ,_UpperCamelCase     ,)     ,)
               lowercase_	:				Optional[int]													=	processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                    # HACK(label indices are swapped in RoBERTa pretrained model)
                    lowercase_							,  lowercase_	:				Dict													=	label_list[2], label_list[1]
               lowercase_	:				Union[str, Any]													=	label_list

               # Make sure only the first process in distributed training processes the dataset,
               # and the others will use the cache.
               lowercase_	:				str													=	cached_features_file + '.lock'
               with FileLock(_UpperCamelCase			):
                    if os.path.exists(_UpperCamelCase			) and not overwrite_cache:
                         logger.info(f'''Loading features from cached file {cached_features_file}'''			)
                         lowercase_	:				int													=	torch.load(_UpperCamelCase			)
                    else:
                         logger.info(f'''Creating features from dataset file at {data_dir}'''			)

                         lowercase_	:				Union[str, Any]													=	(
                             processor.get_dev_examples(_UpperCamelCase			) if evaluate else processor.get_train_examples(_UpperCamelCase			)
                         )

                         logger.info('Training examples: %s'     ,len(_UpperCamelCase			)			)
                         lowercase_	:				Optional[Any]													=	hans_convert_examples_to_features(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase			)
                         logger.info('Saving features into cached file %s'     ,_UpperCamelCase			)
                         torch.save(self.features     ,_UpperCamelCase			)
          def __len__(			self			)	->      int:


               '''simple docstring'''


               return len(self.features			)
          def __getitem__(			self     ,__UpperCamelCase			)	->      InputFeatures:


               '''simple docstring'''


               return self.features[i]







          def 			_UpperCAmelCase   (			self			)	->      Tuple:


               '''simple docstring'''


               return self.label_list


if is_tf_available():
       import tensorflow as tf







       class        UpperCamelCase		:
          lowercase							=  4_2
          def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = 128     ,__UpperCamelCase=False     ,__UpperCamelCase = False     ,)	->      Optional[int]:


               '''simple docstring'''


               lowercase_	:				List[Any]													=	hans_processors[task]()
               lowercase_	:				Dict													=	processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                    # HACK(label indices are swapped in RoBERTa pretrained model)
                    lowercase_							,  lowercase_	:				Tuple													=	label_list[2], label_list[1]
               lowercase_	:				str													=	label_list

               lowercase_	:				List[Any]													=	processor.get_dev_examples(_UpperCamelCase			) if evaluate else processor.get_train_examples(_UpperCamelCase			)
               lowercase_	:				List[str]													=	hans_convert_examples_to_features(_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase     ,_UpperCamelCase			)

               def gen():
                    for ex_index, ex in tqdm.tqdm(enumerate(self.features			)     ,desc='convert examples to features'			):
                         if ex_index % 1_0000 == 0:
                              logger.info('Writing example %d of %d' % (ex_index, len(_UpperCamelCase			))			)

                         yield (
                             {
                                 "example_id": 0,
                                 "input_ids": ex.input_ids,
                                 "attention_mask": ex.attention_mask,
                                 "token_type_ids": ex.token_type_ids,
                             },
                             ex.label,
                         )

               lowercase_	:				Tuple													=	tf.data.Dataset.from_generator(
                   _UpperCamelCase     ,(
                       {
                           'example_id': tf.intaa,
                           'input_ids': tf.intaa,
                           'attention_mask': tf.intaa,
                           'token_type_ids': tf.intaa,
                       },
                       tf.intaa,
                   )     ,(
                       {
                           'example_id': tf.TensorShape([]			),
                           'input_ids': tf.TensorShape([None, None]			),
                           'attention_mask': tf.TensorShape([None, None]			),
                           'token_type_ids': tf.TensorShape([None, None]			),
                       },
                       tf.TensorShape([]			),
                   )     ,)
          def 			_UpperCAmelCase   (			self			)	->      str:


               '''simple docstring'''


               return self.dataset
          def __len__(			self			)	->      Optional[int]:


               '''simple docstring'''


               return len(self.features			)
          def __getitem__(			self     ,__UpperCamelCase			)	->      InputFeatures:


               '''simple docstring'''


               return self.features[i]







          def 			_UpperCAmelCase   (			self			)	->      List[Any]:


               '''simple docstring'''


               return self.label_list







class        UpperCamelCase		(			__a  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        return self._create_examples(self._read_tsv(os.path.join(_UpperCamelCase     ,'heuristics_train_set.txt'			)			)     ,'train'			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        return self._create_examples(self._read_tsv(os.path.join(_UpperCamelCase     ,'heuristics_evaluation_set.txt'			)			)     ,'dev'			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        return ["contradiction", "entailment", "neutral"]







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	[]
        for i, line in enumerate(_UpperCamelCase			):
             if i == 0:
                  continue
             lowercase_	:				str													=	'%s-%s' % (set_type, line[0])
             lowercase_	:				int													=	line[5]
             lowercase_	:				Dict													=	line[6]
             lowercase_	:				str													=	line[7][2:] if line[7].startswith('ex'			) else line[7]
             lowercase_	:				Tuple													=	line[0]
             examples.append(InputExample(guid=_UpperCamelCase     ,text_a=_UpperCamelCase     ,text_b=_UpperCamelCase     ,label=_UpperCamelCase     ,pairID=_UpperCamelCase			)			)
        return examples






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[InputExample]     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     PreTrainedTokenizer     ,			):
     lowercase_	:				Optional[int]													=	{label: i for i, label in enumerate(__lowerCAmelCase      )}

     lowercase_	:				Union[str, Any]													=	[]
     for ex_index, example in tqdm.tqdm(enumerate(__lowerCAmelCase      )     ,			desc='convert examples to features'      ):
          if ex_index % 1_00_00 == 0:
               logger.info('Writing example %d' % (ex_index)      )

          lowercase_	:				List[Any]													=	tokenizer(
              example.text_a     ,			example.text_b     ,			add_special_tokens=__lowerCAmelCase     ,			max_length=__lowerCAmelCase     ,			padding='max_length'     ,			truncation=__lowerCAmelCase     ,			return_overflowing_tokens=__lowerCAmelCase     ,			)

          lowercase_	:				Dict													=	label_map[example.label] if example.label in label_map else 0

          lowercase_	:				Dict													=	int(example.pairID      )

          features.append(InputFeatures(**__lowerCAmelCase     ,			label=__lowerCAmelCase     ,			pairID=__lowerCAmelCase      )      )

     for i, example in enumerate(examples[:5]      ):
          logger.info('*** Example ***'      )
          logger.info(F'''guid: {example}'''      )
          logger.info(F'''features: {features[i]}'''      )

     return features


__SCREAMING_SNAKE_CASE    	={
    "hans": 3,
}







__SCREAMING_SNAKE_CASE    	={
    "hans": HansProcessor,
}



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()
_A   =     logging.get_logger(__name__)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  str=False				):




			"""simple docstring"""


			lowerCAmelCase_						=	[]
			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"
						lowerCAmelCase_						=	[(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    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  str=False				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Dict				):




			"""simple docstring"""


			lowerCAmelCase_						=	dct.pop(__lowerCAmelCase				)
			lowerCAmelCase_						=	val






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_						=	Image.open(requests.get(__lowerCAmelCase    ,							stream=__lowerCAmelCase				).raw				)
			return im






@torch.no_grad()
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	DeiTConfig()
			# all deit models have fine-tuned heads
			lowerCAmelCase_						=	False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			lowerCAmelCase_						=	1000
			lowerCAmelCase_						=	"huggingface/label-files"
			lowerCAmelCase_						=	"imagenet-1k-id2label.json"
			lowerCAmelCase_						=	json.load(open(hf_hub_download(__lowerCAmelCase    ,							__lowerCAmelCase    ,							repo_type="dataset"				)    ,							"r"				)				)
			lowerCAmelCase_						=	{int(__lowerCAmelCase				): v for k, v in idalabel.items()}
			lowerCAmelCase_						=	idalabel
			lowerCAmelCase_						=	{v: k for k, v in idalabel.items()}
			lowerCAmelCase_						=	int(deit_name[-6:-4]				)
			lowerCAmelCase_						=	int(deit_name[-3:]				)
			# size of the architecture
			if deit_name[9:].startswith("tiny"				):
						lowerCAmelCase_						=	192
						lowerCAmelCase_						=	768
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	3
			elif deit_name[9:].startswith("small"				):
						lowerCAmelCase_						=	384
						lowerCAmelCase_						=	1536
						lowerCAmelCase_						=	12
						lowerCAmelCase_						=	6
			if deit_name[9:].startswith("base"				):
						pass
			elif deit_name[4:].startswith("large"				):
						lowerCAmelCase_						=	1024
						lowerCAmelCase_						=	4096
						lowerCAmelCase_						=	24
						lowerCAmelCase_						=	16

			# load original model from timm
			lowerCAmelCase_						=	timm.create_model(__lowerCAmelCase    ,							pretrained=__lowerCAmelCase				)
			timm_model.eval()

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

			# load HuggingFace model
			lowerCAmelCase_						=	DeiTForImageClassificationWithTeacher(__lowerCAmelCase				).eval()
			model.load_state_dict(__lowerCAmelCase				)

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowerCAmelCase_						=	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
			lowerCAmelCase_						=	DeiTImageProcessor(size=__lowerCAmelCase    ,							crop_size=config.image_size				)
			lowerCAmelCase_						=	image_processor(images=prepare_img()    ,							return_tensors="pt"				)
			lowerCAmelCase_						=	encoding["pixel_values"]
			lowerCAmelCase_						=	model(__lowerCAmelCase				)

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

			Path(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
			print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""				)
			model.save_pretrained(__lowerCAmelCase				)
			print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
			image_processor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
			_A   =     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."
			)

			_A   =     parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



