"""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     __UpperCAmelCase					(			lowercase   ,lowercase=False		):







		"""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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase=False		):







		"""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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







		"""simple docstring"""
		_UpperCAmelCase						 =      dct.pop(lowerCamelCase_		)
		_UpperCAmelCase						 =      val






def     __UpperCAmelCase					(			):







		"""simple docstring"""
		_UpperCAmelCase						 =      'http://images.cocodataset.org/val2017/000000039769.jpg'
		_UpperCAmelCase						 =      Image.open(requests.get(lowerCamelCase_   ,stream=lowerCamelCase_		).raw		)
		return im






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase		):







		"""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						 =      10_00
		_UpperCAmelCase						 =      'huggingface/label-files'
		_UpperCAmelCase						 =      'imagenet-1k-id2label.json'
		_UpperCAmelCase						 =      json.load(open(hf_hub_download(lowerCamelCase_   ,lowerCamelCase_   ,repo_type="""dataset"""		)   ,"""r"""		)		)
		_UpperCAmelCase						 =      {int(lowerCamelCase_		): 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						 =      1_92
				_UpperCAmelCase						 =      7_68
				_UpperCAmelCase						 =      12
				_UpperCAmelCase						 =      3
		elif deit_name[9:].startswith("""small"""		):
				_UpperCAmelCase						 =      3_84
				_UpperCAmelCase						 =      15_36
				_UpperCAmelCase						 =      12
				_UpperCAmelCase						 =      6
		if deit_name[9:].startswith("""base"""		):
				pass
		elif deit_name[4:].startswith("""large"""		):
				_UpperCAmelCase						 =      10_24
				_UpperCAmelCase						 =      40_96
				_UpperCAmelCase						 =      24
				_UpperCAmelCase						 =      16

		# load original model from timm
		_UpperCAmelCase						 =      timm.create_model(lowerCamelCase_   ,pretrained=lowerCamelCase_		)
		timm_model.eval()

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

		# load HuggingFace model
		_UpperCAmelCase						 =      DeiTForImageClassificationWithTeacher(lowerCamelCase_		).eval()
		model.load_state_dict(lowerCamelCase_		)

		# Check outputs on an image, prepared by DeiTImageProcessor
		_UpperCAmelCase						 =      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
		_UpperCAmelCase						 =      DeiTImageProcessor(size=lowerCamelCase_   ,crop_size=config.image_size		)
		_UpperCAmelCase						 =      image_processor(images=prepare_img()   ,return_tensors="""pt"""		)
		_UpperCAmelCase						 =      encoding['pixel_values']
		_UpperCAmelCase						 =      model(lowerCamelCase_		)

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

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


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_torch_available,
)


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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