
"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase__           =	{
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__           =	[
							    """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlenderbotSmallForCausalLM""",
							    """BlenderbotSmallForConditionalGeneration""",
							    """BlenderbotSmallModel""",
							    """BlenderbotSmallPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__           =	[
							    """TFBlenderbotSmallForConditionalGeneration""",
							    """TFBlenderbotSmallModel""",
							    """TFBlenderbotSmallPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__           =	[
							    """FlaxBlenderbotSmallForConditionalGeneration""",
							    """FlaxBlenderbotSmallModel""",
							    """FlaxBlenderbotSmallPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_blenderbot_small import (
							    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotSmallConfig,
							    BlenderbotSmallOnnxConfig,
							)
							from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot_small import (
														    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotSmallForCausalLM,
														    BlenderbotSmallForConditionalGeneration,
														    BlenderbotSmallModel,
														    BlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot_small import (
														    TFBlenderbotSmallForConditionalGeneration,
														    TFBlenderbotSmallModel,
														    TFBlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot_small import (
														    FlaxBlenderbotSmallForConditionalGeneration,
														    FlaxBlenderbotSmallModel,
														    FlaxBlenderbotSmallPreTrainedModel,
														)

else:
							import sys

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

"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[Any]		    =   []

	# fmt: off
	# stem:
	rename_keys.append(('cls_token', 'vit.embeddings.cls_token')	)
	rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')	)

	rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')	)
	rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')	)

	# backbone
	rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')	)
	rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')	)
	rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')	)

	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")	)

		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")	)
		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")	)
		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")	)

	# transformer encoder
	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")	)

	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 "vit" from all keys that start with "vit"
		__lowerCAmelCase    :		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'),
		    ]	)
	# fmt: on

	return rename_keys
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	for i in range(config.num_hidden_layers	):
		if base_model:
			__lowerCAmelCase    :		Tuple		    =   ''
		else:
			__lowerCAmelCase    :		int		    =   'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"blocks.{i}.attn.qkv.weight"	)
		__lowerCAmelCase    :		Dict		    =   state_dict.pop(F"blocks.{i}.attn.qkv.bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[
		    : config.hidden_size, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[: config.hidden_size]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__lowerCAmelCase    :		str		    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[-config.hidden_size :]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_UpperCamelCase ,						_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   val
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Optional[int]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Optional[Any]		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)
	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[str]		    =   BitConfig(
	    global_padding='same' ,						layer_type='bottleneck' ,						depths=(3, 4, 9) ,						out_features=['stage3'] ,						embedding_dynamic_padding=_UpperCamelCase ,						)
	__lowerCAmelCase    :		Tuple		    =   ViTHybridConfig(backbone_config=_UpperCamelCase ,						image_size=384 ,						num_labels=1000	)
	__lowerCAmelCase    :		Optional[Any]		    =   False

	# load original model from timm
	__lowerCAmelCase    :		Tuple		    =   timm.create_model(_UpperCamelCase ,						pretrained=_UpperCamelCase	)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__lowerCAmelCase    :		str		    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   create_rename_keys(_UpperCamelCase ,						_UpperCamelCase	)
	for src, dest in rename_keys:
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	read_in_q_k_v(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		int		    =   'huggingface/label-files'
	__lowerCAmelCase    :		str		    =   'imagenet-1k-id2label.json'
	__lowerCAmelCase    :		Any		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
	__lowerCAmelCase    :		int		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
	__lowerCAmelCase    :		List[str]		    =   idalabel
	__lowerCAmelCase    :		Optional[Any]		    =   {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__lowerCAmelCase    :		str		    =   ViTHybridModel(_UpperCamelCase	).eval()
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   ViTHybridForImageClassification(_UpperCamelCase	).eval()
	model.load_state_dict(_UpperCamelCase	)

	# create image processor
	__lowerCAmelCase    :		Union[str, Any]		    =   create_transform(**resolve_data_config({} ,						model=_UpperCamelCase	)	)
	__lowerCAmelCase    :		Optional[int]		    =   transform.transforms

	__lowerCAmelCase    :		List[str]		    =   {
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	__lowerCAmelCase    :		List[str]		    =   ViTHybridImageProcessor(
	    do_resize=_UpperCamelCase ,						size={'shortest_edge': timm_transforms[0].size} ,						resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,						do_center_crop=_UpperCamelCase ,						crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} ,						do_normalize=_UpperCamelCase ,						image_mean=timm_transforms[-1].mean.tolist() ,						image_std=timm_transforms[-1].std.tolist() ,						)

	__lowerCAmelCase    :		Optional[Any]		    =   prepare_img()
	__lowerCAmelCase    :		Union[str, Any]		    =   transform(_UpperCamelCase	).unsqueeze(0	)
	__lowerCAmelCase    :		Union[str, Any]		    =   processor(_UpperCamelCase ,						return_tensors='pt'	).pixel_values

	# verify pixel values
	assert torch.allclose(_UpperCamelCase ,						_UpperCamelCase	)

	# verify logits
	with torch.no_grad():
		__lowerCAmelCase    :		Any		    =   model(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   outputs.logits

	print('Predicted class:' ,						logits.argmax(-1	).item()	)
	if base_model:
		__lowerCAmelCase    :		Optional[int]		    =   timm_model.forward_features(_UpperCamelCase	)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_UpperCamelCase ,						outputs.pooler_output ,						atol=1e-3	)
	else:
		__lowerCAmelCase    :		List[str]		    =   timm_model(_UpperCamelCase	)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_UpperCamelCase ,						outputs.logits ,						atol=1e-3	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
		print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"	)
		model.save_pretrained(_UpperCamelCase	)
		print(F"Saving processor to {pytorch_dump_folder_path}"	)
		processor.save_pretrained(_UpperCamelCase	)

	if push_to_hub:
		print(F"Pushing model and processor to the hub {vit_name}"	)
		model.push_to_hub(F"ybelkada/{vit_name}"	)
		processor.push_to_hub(F"ybelkada/{vit_name}"	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--vit_name""",
							    default="""vit_base_r50_s16_384""",
							    type=str,
							    help="""Name of the hybrid ViT 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."""
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
							)

							lowerCamelCase__           =	parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)