'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Tuple=False     )							->	List[str]:
  __UpperCAmelCase:					Union[str, Any]		= []
  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'''vit.encoder.layer.{i}.layernorm_before.weight''')     )
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')     )
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')     )
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')     )
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')     )
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')     )
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')     )
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')     )
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')     )
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')     )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""cls_token""", """vit.embeddings.cls_token"""),
          ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""pos_embed""", """vit.embeddings.position_embeddings"""),
      ]     )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""norm.weight""", """layernorm.weight"""),
            ("""norm.bias""", """layernorm.bias"""),
        ]     )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    __UpperCAmelCase:					Optional[Any]		= [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""     ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""norm.weight""", """vit.layernorm.weight"""),
            ("""norm.bias""", """vit.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ]     )

  return rename_keys






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				int   ,    _lowercase    :				Dict=False     )							->	int:
  for i in range(config.num_hidden_layers     ):
    if base_model:
      __UpperCAmelCase:					Union[str, Any]		= """"""
    else:
      __UpperCAmelCase:					Union[str, Any]		= """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    __UpperCAmelCase:					Any		= state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''     )
    __UpperCAmelCase:					str		= state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''     )
    # next, add query, keys and values (in that order) to the state dict
    __UpperCAmelCase:					int		= in_proj_weight[
        : config.hidden_size, :
    ]
    __UpperCAmelCase:					Tuple		= in_proj_bias[: config.hidden_size]
    __UpperCAmelCase:					Union[str, Any]		= in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __UpperCAmelCase:					str		= in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    __UpperCAmelCase:					str		= in_proj_weight[
        -config.hidden_size :, :
    ]
    __UpperCAmelCase:					int		= in_proj_bias[-config.hidden_size :]






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Any   ,    _lowercase    :				Union[str, Any]     )							->	Optional[Any]:
  __UpperCAmelCase:					Any		= dct.pop(_lowercase     )
  __UpperCAmelCase:					Dict		= val






def  UpperCamelCase__					(					)							->	Tuple:
  __UpperCAmelCase:					str		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase:					List[Any]		= Image.open(requests.get(_lowercase   ,    stream=_lowercase     ).raw     )
  return im






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				Any   ,    _lowercase    :				Any=True     )							->	List[Any]:
  __UpperCAmelCase:					Dict		= ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    __UpperCAmelCase:					Optional[Any]		= 8
  # set labels if required
  if not base_model:
    __UpperCAmelCase:					Union[str, Any]		= 1_0_0_0
    __UpperCAmelCase:					Tuple		= """huggingface/label-files"""
    __UpperCAmelCase:					List[str]		= """imagenet-1k-id2label.json"""
    __UpperCAmelCase:					Tuple		= json.load(open(hf_hub_download(_lowercase   ,    _lowercase   ,    repo_type="""dataset"""     )   ,    """r"""     )     )
    __UpperCAmelCase:					Optional[Any]		= {int(_lowercase     ): v for k, v in idalabel.items()}
    __UpperCAmelCase:					List[Any]		= idalabel
    __UpperCAmelCase:					List[str]		= {v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    __UpperCAmelCase:					Optional[Any]		= 3_8_4
    __UpperCAmelCase:					Tuple		= 1_5_3_6
    __UpperCAmelCase:					Any		= 1_2
    __UpperCAmelCase:					Union[str, Any]		= 6

  # load original model from torch hub
  __UpperCAmelCase:					Optional[Any]		= torch.hub.load("""facebookresearch/dino:main"""   ,    _lowercase     )
  original_model.eval()

  # load state_dict of original model, remove and rename some keys
  __UpperCAmelCase:					Optional[int]		= original_model.state_dict()
  if base_model:
    remove_classification_head_(_lowercase     )
  __UpperCAmelCase:					Optional[Any]		= create_rename_keys(_lowercase   ,    base_model=_lowercase     )
  for src, dest in rename_keys:
    rename_key(_lowercase   ,    _lowercase   ,    _lowercase     )
  read_in_q_k_v(_lowercase   ,    _lowercase   ,    _lowercase     )

  # load HuggingFace model
  if base_model:
    __UpperCAmelCase:					Optional[Any]		= ViTModel(_lowercase   ,    add_pooling_layer=_lowercase     ).eval()
  else:
    __UpperCAmelCase:					Optional[Any]		= ViTForImageClassification(_lowercase     ).eval()
  model.load_state_dict(_lowercase     )

  # Check outputs on an image, prepared by ViTImageProcessor
  __UpperCAmelCase:					Tuple		= ViTImageProcessor()
  __UpperCAmelCase:					Union[str, Any]		= image_processor(images=prepare_img()   ,    return_tensors="""pt"""     )
  __UpperCAmelCase:					Tuple		= encoding["""pixel_values"""]
  __UpperCAmelCase:					Tuple		= model(_lowercase     )

  if base_model:
    __UpperCAmelCase:					List[Any]		= original_model(_lowercase     )
    assert torch.allclose(_lowercase   ,    outputs.last_hidden_state[:, 0, :]   ,    atol=1E-1     )
  else:
    __UpperCAmelCase:					str		= original_model(_lowercase     )
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(_lowercase   ,    outputs.logits   ,    atol=1E-3     )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='dino_vitb16',
        type=str,
        help='Name of the model trained with DINO you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--base_model',
        action='store_true',
        help='Whether to only convert the base model (no projection head weights).',
    )

    parser.set_defaults(base_model=True)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['ElectraTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ElectraForCausalLM',
        'ElectraForMaskedLM',
        'ElectraForMultipleChoice',
        'ElectraForPreTraining',
        'ElectraForQuestionAnswering',
        'ElectraForSequenceClassification',
        'ElectraForTokenClassification',
        'ElectraModel',
        'ElectraPreTrainedModel',
        'load_tf_weights_in_electra',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFElectraForMaskedLM',
        'TFElectraForMultipleChoice',
        'TFElectraForPreTraining',
        'TFElectraForQuestionAnswering',
        'TFElectraForSequenceClassification',
        'TFElectraForTokenClassification',
        'TFElectraModel',
        'TFElectraPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'FlaxElectraForCausalLM',
        'FlaxElectraForMaskedLM',
        'FlaxElectraForMultipleChoice',
        'FlaxElectraForPreTraining',
        'FlaxElectraForQuestionAnswering',
        'FlaxElectraForSequenceClassification',
        'FlaxElectraForTokenClassification',
        'FlaxElectraModel',
        'FlaxElectraPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)