import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    "compression_format, is_archive",  [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ],  )
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  )					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          "7z": (seven_zip_file, SevenZipExtractor),
          "bz2": (bza_file, BzipaExtractor),
          "gzip": (gz_file, GzipExtractor),
          "lz4": (lza_file, LzaExtractor),
          "tar": (tar_file, TarExtractor),
          "xz": (xz_file, XzExtractor),
          "zip": (zip_file, ZipExtractor),
          "zstd": (zstd_file, ZstdExtractor),
      }
      UpperCAmelCase__		,				UpperCAmelCase__								=  input_paths_and_base_extractors[compression_format]
      if input_path is None:
            UpperCAmelCase__								=  f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__A							)
      assert base_extractor.is_extractable(__A							)
      UpperCAmelCase__								=  tmp_path / ("extracted" if is_archive else "extracted.txt")
      base_extractor.extract(__A,  __A							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  UpperCAmelCase__								=  file_path.read_text(encoding="utf-8"							)
      else:
            UpperCAmelCase__								=  output_path.read_text(encoding="utf-8"							)
      UpperCAmelCase__								=  text_file.read_text(encoding="utf-8"							)
      assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    "compression_format, is_archive",  [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ],  )
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          "7z": seven_zip_file,
          "bz2": bza_file,
          "gzip": gz_file,
          "lz4": lza_file,
          "tar": tar_file,
          "xz": xz_file,
          "zip": zip_file,
          "zstd": zstd_file,
      }
      UpperCAmelCase__								=  input_paths[compression_format]
      if input_path is None:
            UpperCAmelCase__								=  f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__A							)
      UpperCAmelCase__								=  Extractor.infer_extractor_format(__A							)
      assert extractor_format is not None
      UpperCAmelCase__								=  tmp_path / ("extracted" if is_archive else "extracted.txt")
      Extractor.extract(__A,  __A,  __A							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  UpperCAmelCase__								=  file_path.read_text(encoding="utf-8"							)
      else:
            UpperCAmelCase__								=  output_path.read_text(encoding="utf-8"							)
      UpperCAmelCase__								=  text_file.read_text(encoding="utf-8"							)
      assert extracted_file_content == expected_file_content




@pytest.fixture
def        lowerCAmelCase_			(					__A,  __A							)					->    List[str]:







      '''simple docstring'''


      import tarfile

      UpperCAmelCase__								=  tmp_path / "data_dot_dot"
      directory.mkdir()
      UpperCAmelCase__								=  directory / "tar_file_with_dot_dot.tar"
      with tarfile.TarFile(__A,  "w"							) as f:
            f.add(__A,  arcname=os.path.join("..",  text_file.name							)							)
      return path




@pytest.fixture
def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      import tarfile

      UpperCAmelCase__								=  tmp_path / "data_sym_link"
      directory.mkdir()
      UpperCAmelCase__								=  directory / "tar_file_with_sym_link.tar"
      os.symlink("..",  directory / "subdir",  target_is_directory=__A							)
      with tarfile.TarFile(__A,  "w"							) as f:
            f.add(str(directory / "subdir"							),  arcname="subdir"							)  # str required by os.readlink on Windows and Python < 3.8
      return path




@pytest.mark.parametrize(
    "insecure_tar_file, error_log",  [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")],  )
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  {
          "tar_file_with_dot_dot": tar_file_with_dot_dot,
          "tar_file_with_sym_link": tar_file_with_sym_link,
      }
      UpperCAmelCase__								=  insecure_tar_files[insecure_tar_file]
      UpperCAmelCase__								=  tmp_path / "extracted"
      TarExtractor.extract(__A,  __A							)
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg







def        lowerCAmelCase_			(					__A							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  tmpdir / "not_a_zip_file"
      # From: https://github.com/python/cpython/pull/5053
      UpperCAmelCase__								=  (
          B"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
          B"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
          B"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
          B"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
      )
      with not_a_zip_file.open("wb"							) as f:
            f.write(__A							)
      assert zipfile.is_zipfile(str(__A							)							)  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(__A							)  # but we're right




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__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A,  __A=False							)					->    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        lowerCAmelCase_			(					__A,  __A,  __A=False							)					->    Tuple:







      '''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        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  dct.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					)					->    Dict:







      '''simple docstring'''


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




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[Any]:







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

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

      # load HuggingFace model
      UpperCAmelCase__								=  DeiTForImageClassificationWithTeacher(__A							).eval()
      model.load_state_dict(__A							)

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

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

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


if __name__ == "__main__":
      UpperCamelCase__    							= 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__    							= parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



