
'''simple docstring'''




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available





class  A				(							__snake_case						):
     __magic_name__						=		['''image_processor''', '''tokenizer''']
     __magic_name__						=		'''OwlViTImageProcessor'''
     __magic_name__						=		('''CLIPTokenizer''', '''CLIPTokenizerFast''')





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         A       :   Optional[int]					=						None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''						,			SCREAMING_SNAKE_CASE						,			)
             A       :   Optional[int]					=						kwargs.pop('''feature_extractor'''	)

         A       :   Tuple					=						image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.'''	)
         if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.'''	)

         super().__init__(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE="max_length"						,			SCREAMING_SNAKE_CASE="np"						,			**SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         if text is None and query_images is None and images is None:
             raise ValueError(
                 '''You have to specify at least one text or query image or image. All three cannot be none.'''	)

         if text is not None:
             if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) or (isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) and not isinstance(text[0]						,			SCREAMING_SNAKE_CASE	)):
                 A       :   List[Any]					=						[self.tokenizer(SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)]

             elif isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) and isinstance(text[0]						,			SCREAMING_SNAKE_CASE	):
                 A       :   str					=						[]

                 # Maximum number of queries across batch
                 A       :   List[Any]					=						max([len(SCREAMING_SNAKE_CASE	) for t in text]	)

                 # Pad all batch samples to max number of text queries
                 for t in text:
                     if len(SCREAMING_SNAKE_CASE	) != max_num_queries:
                         A       :   Dict					=						t + [''' '''] * (max_num_queries - len(SCREAMING_SNAKE_CASE	))

                     A       :   int					=						self.tokenizer(SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
                     encodings.append(SCREAMING_SNAKE_CASE	)
             else:
                 raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''	)

             if return_tensors == "np":
                 A       :   str					=						np.concatenate([encoding['''input_ids'''] for encoding in encodings]						,			axis=0	)
                 A       :   int					=						np.concatenate([encoding['''attention_mask'''] for encoding in encodings]						,			axis=0	)

             elif return_tensors == "jax" and is_flax_available():
                 import jax.numpy as jnp

                 A       :   str					=						jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]						,			axis=0	)
                 A       :   Optional[int]					=						jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]						,			axis=0	)

             elif return_tensors == "pt" and is_torch_available():
                 import torch

                 A       :   Any					=						torch.cat([encoding['''input_ids'''] for encoding in encodings]						,			dim=0	)
                 A       :   Optional[Any]					=						torch.cat([encoding['''attention_mask'''] for encoding in encodings]						,			dim=0	)

             elif return_tensors == "tf" and is_tf_available():
                 import tensorflow as tf

                 A       :   Tuple					=						tf.stack([encoding['''input_ids'''] for encoding in encodings]						,			axis=0	)
                 A       :   Optional[Any]					=						tf.stack([encoding['''attention_mask'''] for encoding in encodings]						,			axis=0	)

             else:
                 raise ValueError('''Target return tensor type could not be returned'''	)

             A       :   List[Any]					=						BatchEncoding()
             A       :   Optional[int]					=						input_ids
             A       :   int					=						attention_mask

         if query_images is not None:
             A       :   int					=						BatchEncoding()
             A       :   List[str]					=						self.image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	).pixel_values
             A       :   int					=						query_pixel_values

         if images is not None:
             A       :   Union[str, Any]					=						self.image_processor(SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         if text is not None and images is not None:
             A       :   Dict					=						image_features.pixel_values
             return encoding
         elif query_images is not None and images is not None:
             A       :   Tuple					=						image_features.pixel_values
             return encoding
         elif text is not None or query_images is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE	)						,			tensor_type=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         return self.image_processor.post_process(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         return self.image_processor.post_process_object_detection(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         return self.image_processor.post_process_image_guided_detection(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,			SCREAMING_SNAKE_CASE						,			)
         return self.image_processor_class





     @property
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,			SCREAMING_SNAKE_CASE						,			)
         return self.image_processor






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()
snake_case :		Optional[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Union[str, 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"
		__magic_name__		:		int	   =     [(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_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__		:		List[Any]	   =     timm_model.state_dict()
	if base_model:
		remove_classification_head_(_snake_case   )
	__magic_name__		:		Tuple	   =     create_rename_keys(_snake_case      ,   _snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )
	read_in_q_k_v(_snake_case      ,   _snake_case      ,   _snake_case   )

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

	# create image processor
	__magic_name__		:		List[Any]	   =     create_transform(**resolve_data_config({}      ,   model=_snake_case   )   )
	__magic_name__		:		int	   =     transform.transforms

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

	__magic_name__		:		int	   =     ViTHybridImageProcessor(
	    do_resize=_snake_case      ,   size={"shortest_edge": timm_transforms[0].size}      ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]      ,   do_center_crop=_snake_case      ,   crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}      ,   do_normalize=_snake_case      ,   image_mean=timm_transforms[-1].mean.tolist()      ,   image_std=timm_transforms[-1].std.tolist()      ,   )

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

	print("Predicted class:"      ,   logits.argmax(-1   ).item()   )
	if base_model:
		__magic_name__		:		List[str]	   =     timm_model.forward_features(_snake_case   )
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_snake_case      ,   outputs.pooler_output      ,   atol=1E-3   )
	else:
		__magic_name__		:		Any	   =     timm_model(_snake_case   )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_snake_case      ,   outputs.logits      ,   atol=1E-3   )
	print("Looks ok!"   )

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

	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__":
				snake_case :		Any							      =  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."
				)

				snake_case :		List[Any]							      =  parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
