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   :     str       =				logging.get_logger(__name__)


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


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     dct.pop(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     val


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


@torch.no_grad()
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE_      =     False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_      =     1_0_0_0
      SCREAMING_SNAKE_CASE_      =     "huggingface/label-files"
      SCREAMING_SNAKE_CASE_      =     "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_      =     json.load(open(hf_hub_download(__UpperCamelCase   ,					__UpperCamelCase   ,					repo_type="dataset" )   ,					"r" ) )
      SCREAMING_SNAKE_CASE_      =     {int(__UpperCamelCase ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     idalabel
      SCREAMING_SNAKE_CASE_      =     {v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-6:-4] )
      SCREAMING_SNAKE_CASE_      =     int(deit_name[-3:] )
      # size of the architecture
      if deit_name[9:].startswith("tiny" ):
            SCREAMING_SNAKE_CASE_      =     1_9_2
            SCREAMING_SNAKE_CASE_      =     7_6_8
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     3
      elif deit_name[9:].startswith("small" ):
            SCREAMING_SNAKE_CASE_      =     3_8_4
            SCREAMING_SNAKE_CASE_      =     1_5_3_6
            SCREAMING_SNAKE_CASE_      =     1_2
            SCREAMING_SNAKE_CASE_      =     6
      if deit_name[9:].startswith("base" ):
            pass
      elif deit_name[4:].startswith("large" ):
            SCREAMING_SNAKE_CASE_      =     1_0_2_4
            SCREAMING_SNAKE_CASE_      =     4_0_9_6
            SCREAMING_SNAKE_CASE_      =     2_4
            SCREAMING_SNAKE_CASE_      =     1_6

      # load original model from timm
      SCREAMING_SNAKE_CASE_      =     timm.create_model(__UpperCamelCase   ,					pretrained=__UpperCamelCase )
      timm_model.eval()

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

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

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE_      =     int(
          (2_5_6 / 2_2_4) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE_      =     DeiTImageProcessor(size=__UpperCamelCase   ,					crop_size=config.image_size )
      SCREAMING_SNAKE_CASE_      =     image_processor(images=prepare_img()   ,					return_tensors="pt" )
      SCREAMING_SNAKE_CASE_      =     encoding["pixel_values"]
      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )

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

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


if __name__ == "__main__":
      A   :     Tuple       =				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   :     Dict       =				parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A   :     Tuple       =				datasets.utils.logging.get_logger(__name__)


@dataclass
class      lowerCamelCase    (datasets.BuilderConfig    ):
  """simple docstring"""







  lowerCamelCase__   =  None
  lowerCamelCase__   =  "utf-8"
  lowerCamelCase__   =  None
  lowerCamelCase__   =  None
  lowerCamelCase__   =  True  # deprecated
  lowerCamelCase__   =  None  # deprecated
  lowerCamelCase__   =  1_0 << 2_0  # 10MB
  lowerCamelCase__   =  None



class      lowerCamelCase    (datasets.ArrowBasedBuilder    ):
  """simple docstring"""







  lowerCamelCase__   =  JsonConfig







  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        if self.config.block_size is not None:
              logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"      )
              SCREAMING_SNAKE_CASE_      =     self.config.block_size
        if self.config.use_threads is not True:
              logger.warning(
                  "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."      )
        if self.config.newlines_in_values is not None:
              raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"      )
        return datasets.DatasetInfo(features=self.config.features      )







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				str      )     ->		Tuple:
        if not self.config.data_files:
              raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''      )
        SCREAMING_SNAKE_CASE_      =     dl_manager.download_and_extract(self.config.data_files      )
        if isinstance(__magic_name__     ,   (str, list, tuple)      ):
              SCREAMING_SNAKE_CASE_      =     data_files
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={"files": files}      )]
        SCREAMING_SNAKE_CASE_      =     []
        for split_name, files in data_files.items():
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              splits.append(datasets.SplitGenerator(name=__magic_name__     ,   gen_kwargs={"files": files}      )      )
        return splits







  def       __A   (							self       :				str     ,   __magic_name__       :				pa.Table      )     ->		pa.Table:
        if self.config.features is not None:
              # adding missing columns
              for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
                    SCREAMING_SNAKE_CASE_      =     self.config.features.arrow_schema.field(__magic_name__      ).type
                    SCREAMING_SNAKE_CASE_      =     pa_table.append_column(__magic_name__     ,   pa.array([None] * len(__magic_name__      )     ,   type=__magic_name__      )      )
              # more expensive cast to support nested structures with keys in a different order
              # allows str <-> int/float or str to Audio for example
              SCREAMING_SNAKE_CASE_      =     table_cast(__magic_name__     ,   self.config.features.arrow_schema      )
        return pa_table







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				List[str]      )     ->		int:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__      )      ):
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                    with open(__magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                          SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )

                    # We keep only the field we are interested in
                    SCREAMING_SNAKE_CASE_      =     dataset[self.config.field]

                    # We accept two format: a list of dicts or a dict of lists
                    if isinstance(__magic_name__     ,   (list, tuple)      ):
                          SCREAMING_SNAKE_CASE_      =     set().union(*[row.keys() for row in dataset]      )
                          SCREAMING_SNAKE_CASE_      =     {col: [row.get(__magic_name__      ) for row in dataset] for col in keys}
                    else:
                          SCREAMING_SNAKE_CASE_      =     dataset
                    SCREAMING_SNAKE_CASE_      =     pa.Table.from_pydict(__magic_name__      )
                    yield file_idx, self._cast_table(__magic_name__      )

              # If the file has one json object per line
              else:
                    with open(__magic_name__     ,   "rb"      ) as f:
                          SCREAMING_SNAKE_CASE_      =     0
                          # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                          # Set a default minimum value of 16kB if the chunk size is really small
                          SCREAMING_SNAKE_CASE_      =     max(self.config.chunksize // 32     ,   16 << 10      )
                          SCREAMING_SNAKE_CASE_      =     (
                              self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                          )
                          while True:
                                SCREAMING_SNAKE_CASE_      =     f.read(self.config.chunksize      )
                                if not batch:
                                      break
                                # Finish current line
                                try:
                                      batch += f.readline()
                                except (AttributeError, io.UnsupportedOperation):
                                      batch += readline(__magic_name__      )
                                # PyArrow only accepts utf-8 encoded bytes
                                if self.config.encoding != "utf-8":
                                      SCREAMING_SNAKE_CASE_      =     batch.decode(self.config.encoding     ,   errors=__magic_name__      ).encode("utf-8"      )
                                try:
                                      while True:
                                            try:
                                                  SCREAMING_SNAKE_CASE_      =     paj.read_json(
                                                      io.BytesIO(__magic_name__      )     ,   read_options=paj.ReadOptions(block_size=__magic_name__      )      )
                                                  break
                                            except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                  if (
                                                      isinstance(__magic_name__     ,   pa.ArrowInvalid      )
                                                      and "straddling" not in str(__magic_name__      )
                                                      or block_size > len(__magic_name__      )
                                                  ):
                                                        raise
                                                  else:
                                                        # Increase the block size in case it was too small.
                                                        # The block size will be reset for the next file.
                                                        logger.debug(
                                                            F'''Batch of {len(__magic_name__      )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''      )
                                                        block_size *= 2
                                except pa.ArrowInvalid as e:
                                      try:
                                            with open(
                                                __magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                                                  SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )
                                      except json.JSONDecodeError:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                            raise e
                                      # If possible, parse the file as a list of json objects and exit the loop
                                      if isinstance(__magic_name__     ,   __magic_name__      ):  # list is the only sequence type supported in JSON
                                            try:
                                                  SCREAMING_SNAKE_CASE_      =     set().union(*[row.keys() for row in dataset]      )
                                                  SCREAMING_SNAKE_CASE_      =     {col: [row.get(__magic_name__      ) for row in dataset] for col in keys}
                                                  SCREAMING_SNAKE_CASE_      =     pa.Table.from_pydict(__magic_name__      )
                                            except (pa.ArrowInvalid, AttributeError) as e:
                                                  logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                                  raise ValueError(F'''Not able to read records in the JSON file at {file}.'''      ) from None
                                            yield file_idx, self._cast_table(__magic_name__      )
                                            break
                                      else:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__      )}: {e}'''      )
                                            raise ValueError(
                                                F'''Not able to read records in the JSON file at {file}. '''
                                                F'''You should probably indicate the field of the JSON file containing your records. '''
                                                F'''This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. '''
                                                F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                yield (file_idx, batch_idx), self._cast_table(__magic_name__      )
                                batch_idx += 1


