"""simple docstring"""


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__A       			=      get_logger(__name__)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]						):
    return TestCommand(
        args.dataset				, args.name				, args.cache_dir				, args.data_dir				, args.all_configs				, args.save_info or args.save_infos				, args.ignore_verifications				, args.force_redownload				, args.clear_cache				, )






class 					a					(				A_							):
  A_ :				Optional[int] 			=							False  # to tell pytest it's not a test class



  @staticmethod
  def        lowerCAmelCase_				(		lowerCamelCase_   :    ArgumentParser     )      -> Dict:
      __a			      =						parser.add_parser("""test"""						,  help="""Test dataset implementation."""     )
      test_parser.add_argument("""--name"""						,  type=lowerCamelCase_						,  default=lowerCamelCase_						,  help="""Dataset processing name"""     )
      test_parser.add_argument(
          """--cache_dir"""						,  type=lowerCamelCase_						,  default=lowerCamelCase_						,  help="""Cache directory where the datasets are stored."""						,  )
      test_parser.add_argument(
          """--data_dir"""						,  type=lowerCamelCase_						,  default=lowerCamelCase_						,  help="""Can be used to specify a manual directory to get the files from."""						,  )
      test_parser.add_argument("""--all_configs"""						,  action="""store_true"""						,  help="""Test all dataset configurations"""     )
      test_parser.add_argument(
          """--save_info"""						,  action="""store_true"""						,  help="""Save the dataset infos in the dataset card (README.md)"""     )
      test_parser.add_argument(
          """--ignore_verifications"""						,  action="""store_true"""						,  help="""Run the test without checksums and splits checks."""						,  )
      test_parser.add_argument("""--force_redownload"""						,  action="""store_true"""						,  help="""Force dataset redownload"""     )
      test_parser.add_argument(
          """--clear_cache"""						,  action="""store_true"""						,  help="""Remove downloaded files and cached datasets after each config test"""						,  )
      # aliases
      test_parser.add_argument("""--save_infos"""						,  action="""store_true"""						,  help="""alias to save_info"""     )
      test_parser.add_argument("""dataset"""						,  type=lowerCamelCase_						,  help="""Name of the dataset to download"""     )
      test_parser.set_defaults(func=_test_command_factory     )



  def __init__(		self   :    Union[str, Any]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    bool						,  lowerCamelCase_   :    bool						,  lowerCamelCase_   :    bool						,  lowerCamelCase_   :    bool						,  lowerCamelCase_   :    bool						,  )      -> Dict:
      __a			      =						dataset
      __a			      =						name
      __a			      =						cache_dir
      __a			      =						data_dir
      __a			      =						all_configs
      __a			      =						save_infos
      __a			      =						ignore_verifications
      __a			      =						force_redownload
      __a			      =						clear_cache
      if clear_cache and not cache_dir:
          print(
              """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
              """The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
              """Please provide a --cache_dir that will be used to test the dataset script."""     )
          exit(1     )
      if save_infos:
          __a			      =						True



  def        lowerCAmelCase_				(		self   :    str     )      -> Tuple:
      fl_logger().setLevel(lowerCamelCase_     )
      if self._name is not None and self._all_configs:
          print("""Both parameters `config` and `all_configs` can't be used at once."""     )
          exit(1     )
      __a      ,  __a			      =						self._dataset, self._name
      __a			      =						dataset_module_factory(lowerCamelCase_     )
      __a			      =						import_main_class(module.module_path     )
      __a			      =						len(builder_cls.BUILDER_CONFIGS     ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

      def get_builders() -> Generator[DatasetBuilder, None, None]:
          if self._all_configs and builder_cls.BUILDER_CONFIGS:
              for i, config in enumerate(builder_cls.BUILDER_CONFIGS     ):
                  if "config_name" in module.builder_kwargs:
                      yield builder_cls(
                          cache_dir=self._cache_dir						,  data_dir=self._data_dir						,  **module.builder_kwargs						,  )
                  else:
                      yield builder_cls(
                          config_name=config.name						,  cache_dir=self._cache_dir						,  data_dir=self._data_dir						,  **module.builder_kwargs						,  )
          else:
              if "config_name" in module.builder_kwargs:
                  yield builder_cls(cache_dir=self._cache_dir						,  data_dir=self._data_dir						,  **module.builder_kwargs     )
              else:
                  yield builder_cls(
                      config_name=lowerCamelCase_						,  cache_dir=self._cache_dir						,  data_dir=self._data_dir						,  **module.builder_kwargs						,  )

      for j, builder in enumerate(get_builders()     ):
          print(F"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""     )
          __a			      =						os.path.exists(
              os.path.join(builder.get_imported_module_dir()						,  datasets.config.DATASETDICT_INFOS_FILENAME     )     )  # record checksums only if we need to update a (deprecated) dataset_infos.json
          builder.download_and_prepare(
              download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
              if not self._force_redownload
              else DownloadMode.FORCE_REDOWNLOAD						,  verification_mode=VerificationMode.NO_CHECKS
              if self._ignore_verifications
              else VerificationMode.ALL_CHECKS						,  try_from_hf_gcs=lowerCamelCase_						,  )
          builder.as_dataset()
          if self._save_infos:
              builder._save_infos()

          # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
          # The dataset_infos are saved in the YAML part of the README.md

          # Let's move it to the original directory of the dataset script, to allow the user to
          # upload them on S3 at the same time afterwards.
          if self._save_infos:
              __a			      =						os.path.join(builder_cls.get_imported_module_dir()						,  """README.md"""     )
              __a			      =						Path(lowerCamelCase_     ).name + """.py"""
              __a			      =						os.path.join(lowerCamelCase_						,  lowerCamelCase_     )
              if os.path.isfile(lowerCamelCase_     ):
                  __a			      =						os.path.dirname(lowerCamelCase_     )
              elif os.path.isfile(lowerCamelCase_     ):
                  __a			      =						path
              elif os.path.isdir(lowerCamelCase_     ):  # for local directories containing only data files
                  __a			      =						path
              else:  # in case of a remote dataset
                  __a			      =						None
                  print(F"""Dataset card saved at {dataset_readme_path}"""     )

              # Move dataset_info back to the user
              if dataset_dir is not None:
                  __a			      =						os.path.join(lowerCamelCase_						,  """README.md"""     )
                  copyfile(lowerCamelCase_						,  lowerCamelCase_     )
                  print(F"""Dataset card saved at {user_dataset_readme_path}"""     )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
          if self._clear_cache:
              if os.path.isdir(builder._cache_dir     ):
                  logger.warning(F"""Clearing cache at {builder._cache_dir}"""     )
                  rmtree(builder._cache_dir     )
              __a			      =						os.path.join(self._cache_dir						,  datasets.config.DOWNLOADED_DATASETS_DIR     )
              if os.path.isdir(lowerCamelCase_     ):
                  logger.warning(F"""Clearing cache at {download_dir}"""     )
                  rmtree(lowerCamelCase_     )

      print("""Test successful."""     )

"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Union[str, Any]=False				, _lowerCAmelCase   :							Any="data2vec."						):
    __a			      =						"""backbone.""" if is_semantic else """"""

    __a			      =						[]
    for i in range(config.num_hidden_layers						):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")						)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
            (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
            (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
        ]						)

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]						)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]						)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
                ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]						)

    return rename_keys

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[Any]=False				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Tuple="data2vec_vision."						):
    for i in range(config.num_hidden_layers						):
        __a			      =						"""backbone.""" if is_semantic else """"""
        # queries, keys and values
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""						)

        __a			      =						in_proj_weight[
            : config.hidden_size, :
        ]
        __a			      =						q_bias
        __a			      =						in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __a			      =						in_proj_weight[
            -config.hidden_size :, :
        ]
        __a			      =						v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""						)
        __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""						)

        __a			      =						gamma_a
        __a			      =						gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""						)

            __a			      =						table
            __a			      =						index

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser(
        """Convert Data2VecVision to HF for image classification and pretraining"""				, add_help=_lowerCAmelCase						)
    parser.add_argument("""--hf_checkpoint_name"""				, type=_lowerCAmelCase						)
    parser.add_argument("""--input_size"""				, default=224				, type=_lowerCAmelCase				, help="""images input size"""						)
    parser.add_argument("""--beit_checkpoint"""				, default=""""""				, help="""beit checkpoint"""						)

    return parser.parse_args()

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Union[str, Any]						):
    def load_state_dict(_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							Union[str, Any]=""				, _lowerCAmelCase   :							Union[str, Any]="relative_position_index"						):
        __a			      =						[]
        __a			      =						[]
        __a			      =						[]
        # copy state_dict so _load_from_state_dict can modify it
        __a			      =						getattr(_lowerCAmelCase				, """_metadata"""				, _lowerCAmelCase						)
        __a			      =						state_dict.copy()
        if metadata is not None:
            __a			      =						metadata

        def load(_lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							List[str]=""						):
            __a			      =						{} if metadata is None else metadata.get(prefix[:-1]				, {}						)
            module._load_from_state_dict(
                _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
            for name, child in module._modules.items():
                if child is not None:
                    load(_lowerCAmelCase				, prefix + name + """."""						)

        load(_lowerCAmelCase				, prefix=_lowerCAmelCase						)

        __a			      =						[]
        __a			      =						[]
        for key in missing_keys:
            __a			      =						True
            for ignore_key in ignore_missing.split("""|"""						):
                if ignore_key in key:
                    __a			      =						False
                    break
            if keep_flag:
                warn_missing_keys.append(_lowerCAmelCase						)
            else:
                ignore_missing_keys.append(_lowerCAmelCase						)

        __a			      =						warn_missing_keys

        if len(_lowerCAmelCase						) > 0:
            print(
                """Weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print(
                """Ignored weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""\n""".join(_lowerCAmelCase						)						)

    __a			      =						{
        """pretrained""": False,
        """use_shared_rel_pos_bias""": True,
        """use_abs_pos_emb""": False,
        """init_values""": 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                """num_classes""": 1000,
                """use_mean_pooling""": True,
                """init_scale""": 0.001,
                """use_rel_pos_bias""": True,
            }						)

    __a			      =						create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				, **_lowerCAmelCase				, )
    __a			      =						model.patch_embed.patch_size
    __a			      =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
    __a			      =						torch.load(args.beit_checkpoint				, map_location="""cpu"""						)

    print(f"""Load ckpt from {args.beit_checkpoint}"""						)
    __a			      =						None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            __a			      =						checkpoint[model_key]
            print(f"""Load state_dict by model_key = {model_key}"""						)
            break

    __a			      =						list(checkpoint_model.keys()						)
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(_lowerCAmelCase						)

        if "relative_position_bias_table" in key:
            __a			      =						checkpoint_model[key]
            __a      ,  __a			      =						rel_pos_bias.size()
            __a      ,  __a			      =						model.state_dict()[key].size()
            __a			      =						model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(_lowerCAmelCase				, _lowerCAmelCase				, prefix=""""""						)

    return model

def 						UpperCamelCase				(	):
    __a			      =						get_args()

    __a			      =						"""ft1k""" in args.hf_checkpoint_name
    __a			      =						"""large""" in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    __a			      =						DataaVecVisionConfig()
    if is_finetuned:
        __a			      =						True
        __a			      =						False
        __a			      =						True
        __a			      =						1000

        __a			      =						"""huggingface/label-files"""
        __a			      =						"""imagenet-1k-id2label.json"""
        __a			      =						json.load(open(hf_hub_download(_lowerCAmelCase				, _lowerCAmelCase				, repo_type="""dataset"""						)				, """r"""						)						)
        __a			      =						{int(_lowerCAmelCase						): v for k, v in idalabel.items()}
        __a			      =						idalabel
        __a			      =						{v: k for k, v in idalabel.items()}
    else:
        __a			      =						False
        __a			      =						True
        __a			      =						False

    if is_large:
        __a			      =						1024
        __a			      =						4096
        __a			      =						24
        __a			      =						16

    # 2. Load Beit model
    __a			      =						load_beit_model(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
    orig_model.eval()

    # 3. Forward Beit model
    __a			      =						BeitImageProcessor(size=config.image_size				, do_center_crop=_lowerCAmelCase						)
    __a			      =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    __a			      =						image_processor(images=_lowerCAmelCase				, return_tensors="""pt"""						)
    __a			      =						encoding["""pixel_values"""]

    __a			      =						(pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        __a			      =						orig_model(*_lowerCAmelCase						)

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        __a			      =						DataaVecVisionForImageClassification(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						False
        __a			      =						"""data2vec_vision."""
    else:
        __a			      =						DataaVecVisionModel(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						True
        __a			      =						""""""

    __a			      =						create_rename_keys(_lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a			      =						orig_model.state_dict()
    for src, dest in rename_keys:
        __a			      =						state_dict.pop(_lowerCAmelCase						)
        __a			      =						val

    read_in_q_k_v(_lowerCAmelCase				, _lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a      ,  __a			      =						hf_model.load_state_dict(_lowerCAmelCase				, strict=_lowerCAmelCase						)
    print("""HF missing"""				, _lowerCAmelCase						)
    print("""HF unexpected_keys"""				, _lowerCAmelCase						)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        __a			      =						hf_model(_lowerCAmelCase						)

    __a			      =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    __a			      =						torch.max(torch.abs(hf_output - orig_model_output						)						).item()

    print(f"""max_absolute_diff = {max_absolute_diff}"""						)
    __a			      =						torch.allclose(_lowerCAmelCase				, _lowerCAmelCase				, atol=1E-3						)
    print("""Do both models output the same tensors?"""				, """🔥""" if success else """💩"""						)
    if not success:
        raise Exception("""Something went wRoNg"""						)

    # 7. Save
    print(f"""Saving to {args.hf_checkpoint_name}"""						)
    hf_model.save_pretrained(args.hf_checkpoint_name						)
    image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
 main()
 # Run the following to convert checkpoints
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
