





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


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


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=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 						__a(SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       int=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 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   dct.pop(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   val


def 						__a():



    '''simple docstring'''





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


@torch.no_grad()
def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''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(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							repo_type="dataset"     )		,							"r"     )     )
    _lowerCAmelCase						      =   {int(SCREAMING_SNAKE_CASE_     ): 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(SCREAMING_SNAKE_CASE_		,							pretrained=SCREAMING_SNAKE_CASE_     )
    timm_model.eval()

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

    # load HuggingFace model
    _lowerCAmelCase						      =   DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_     ).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_     )

    # 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=SCREAMING_SNAKE_CASE_		,							crop_size=config.image_size     )
    _lowerCAmelCase						      =   image_processor(images=prepare_img()		,							return_tensors="pt"     )
    _lowerCAmelCase						      =   encoding["pixel_values"]
    _lowerCAmelCase						      =   model(SCREAMING_SNAKE_CASE_     )

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

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


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

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









'''simple docstring'''


import re
import string

import numpy as np

import datasets


_SCREAMING_SNAKE_CASE									=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

_SCREAMING_SNAKE_CASE									=				"\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION		)
class  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				List[str]:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
             {
                 "predictions": datasets.Value("string"      ,  id="sequence"		),
                 "references": datasets.Value("string"      ,  id="sequence"		),
             }		)      ,  reference_urls=[]      ,  )





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  )     ->				str:
     if regexes_to_ignore is not None:
         for s in regexes_to_ignore:
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in predictions]		)
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in references]		)
     else:
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)

     if ignore_case:
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)

     if ignore_punctuation:
         _lowerCAmelCase						      =   string.punctuation.maketrans(""      ,  ""      ,  string.punctuation		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     if ignore_numbers:
         _lowerCAmelCase						      =   string.digits.maketrans(""      ,  ""      ,  string.digits		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     _lowerCAmelCase						      =   predictions == references

     return {"exact_match": np.mean(_lowerCAmelCase		) * 100}


