'''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()
__UpperCAmelCase												=logging.get_logger(__name__)






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   str:
    __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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   Union[str, Any]:
    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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  dct.pop(UpperCamelCase__    )
    __lowerCamelCase          =  val






def __lowerCAmelCase			(							)     ->   Dict:
    __lowerCamelCase          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowerCamelCase          =  Image.open(requests.get(UpperCamelCase__    ,  stream=UpperCamelCase__    ).raw    )
    return im






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Optional[Any]:
    __lowerCamelCase          =  DeiTConfig()
    # all deit models have fine-tuned heads
    __lowerCamelCase          =  False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    __lowerCamelCase          =  10_00
    __lowerCamelCase          =  '''huggingface/label-files'''
    __lowerCamelCase          =  '''imagenet-1k-id2label.json'''
    __lowerCamelCase          =  json.load(open(hf_hub_download(UpperCamelCase__    ,  UpperCamelCase__    ,  repo_type='''dataset'''    )    ,  '''r'''    )    )
    __lowerCamelCase          =  {int(UpperCamelCase__    ): 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          =  1_92
        __lowerCamelCase          =  7_68
        __lowerCamelCase          =  12
        __lowerCamelCase          =  3
    elif deit_name[9:].startswith('''small'''    ):
        __lowerCamelCase          =  3_84
        __lowerCamelCase          =  15_36
        __lowerCamelCase          =  12
        __lowerCamelCase          =  6
    if deit_name[9:].startswith('''base'''    ):
        pass
    elif deit_name[4:].startswith('''large'''    ):
        __lowerCamelCase          =  10_24
        __lowerCamelCase          =  40_96
        __lowerCamelCase          =  24
        __lowerCamelCase          =  16

    # load original model from timm
    __lowerCamelCase          =  timm.create_model(UpperCamelCase__    ,  pretrained=UpperCamelCase__    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __lowerCamelCase          =  timm_model.state_dict()
    __lowerCamelCase          =  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
    __lowerCamelCase          =  DeiTForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
    model.load_state_dict(UpperCamelCase__    )

    # Check outputs on an image, prepared by DeiTImageProcessor
    __lowerCamelCase          =  int(
        (2_56 / 2_24) * 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=UpperCamelCase__    ,  crop_size=config.image_size    )
    __lowerCamelCase          =  image_processor(images=prepare_img()    ,  return_tensors='''pt'''    )
    __lowerCamelCase          =  encoding['''pixel_values''']
    __lowerCamelCase          =  model(UpperCamelCase__    )

    __lowerCamelCase          =  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__":
  __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)

'''simple docstring'''




from typing import TYPE_CHECKING

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


__UpperCAmelCase												={
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=["WhisperTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WhisperForConditionalGeneration",
      "WhisperModel",
      "WhisperPreTrainedModel",
      "WhisperForAudioClassification",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFWhisperForConditionalGeneration",
      "TFWhisperModel",
      "TFWhisperPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "FlaxWhisperForConditionalGeneration",
      "FlaxWhisperModel",
      "FlaxWhisperPreTrainedModel",
      "FlaxWhisperForAudioClassification",
  ]


if TYPE_CHECKING:
  from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
  from .feature_extraction_whisper import WhisperFeatureExtractor
  from .processing_whisper import WhisperProcessor
  from .tokenization_whisper import WhisperTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_whisper_fast import WhisperTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_whisper import (
        WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
        WhisperForAudioClassification,
        WhisperForConditionalGeneration,
        WhisperModel,
        WhisperPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_whisper import (
        TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFWhisperForConditionalGeneration,
        TFWhisperModel,
        TFWhisperPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_whisper import (
        FlaxWhisperForAudioClassification,
        FlaxWhisperForConditionalGeneration,
        FlaxWhisperModel,
        FlaxWhisperPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase												=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
