




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()
UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]=False     ):
					__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"""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"
										__lowerCAmelCase				   =       [(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 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]=False     ):
					for i in range(config.num_hidden_layers     ):
										if base_model:
															__lowerCAmelCase				   =       ""
										else:
															__lowerCAmelCase				   =       "vit."
										# 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 	_a      (						SCREAMING_SNAKE_CASE_							:				str     ):
					__lowerCAmelCase				   =       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       dct.pop(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       val







def 	_a      (						):
					__lowerCAmelCase				   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
					__lowerCAmelCase				   =       Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,      stream=SCREAMING_SNAKE_CASE_     ).raw     )
					return im







@torch.no_grad()
def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				int=True     ):
					__lowerCAmelCase				   =       ViTConfig()
					# patch_size
					if model_name[-1] == "8":
										__lowerCAmelCase				   =       8
					# set labels if required
					if not base_model:
										__lowerCAmelCase				   =       10_00
										__lowerCAmelCase				   =       "huggingface/label-files"
										__lowerCAmelCase				   =       "imagenet-1k-id2label.json"
										__lowerCAmelCase				   =       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      repo_type="dataset"     )      ,      "r"     )     )
										__lowerCAmelCase				   =       {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
										__lowerCAmelCase				   =       idalabel
										__lowerCAmelCase				   =       {v: k for k, v in idalabel.items()}
					# size of the architecture
					if model_name in ["dino_vits8", "dino_vits16"]:
										__lowerCAmelCase				   =       3_84
										__lowerCAmelCase				   =       15_36
										__lowerCAmelCase				   =       12
										__lowerCAmelCase				   =       6

					# load original model from torch hub
					__lowerCAmelCase				   =       torch.hub.load("facebookresearch/dino:main"      ,      SCREAMING_SNAKE_CASE_     )
					original_model.eval()

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

					# load HuggingFace model
					if base_model:
										__lowerCAmelCase				   =       ViTModel(SCREAMING_SNAKE_CASE_      ,      add_pooling_layer=SCREAMING_SNAKE_CASE_     ).eval()
					else:
										__lowerCAmelCase				   =       ViTForImageClassification(SCREAMING_SNAKE_CASE_     ).eval()
					model.load_state_dict(SCREAMING_SNAKE_CASE_     )

					# Check outputs on an image, prepared by ViTImageProcessor
					__lowerCAmelCase				   =       ViTImageProcessor()
					__lowerCAmelCase				   =       image_processor(images=prepare_img()      ,      return_tensors="pt"     )
					__lowerCAmelCase				   =       encoding["pixel_values"]
					__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_     )

					if base_model:
										__lowerCAmelCase				   =       original_model(SCREAMING_SNAKE_CASE_     )
										assert torch.allclose(SCREAMING_SNAKE_CASE_      ,      outputs.last_hidden_state[:, 0, :]      ,      atol=1E-1     )
					else:
										__lowerCAmelCase				   =       original_model(SCREAMING_SNAKE_CASE_     )
										assert logits.shape == outputs.logits.shape
										assert torch.allclose(SCREAMING_SNAKE_CASE_      ,      outputs.logits      ,      atol=1E-3     )

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


if __name__ == "__main__":
					UpperCamelCase__    			=			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)
					UpperCamelCase__    			=			parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)








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      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       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." ,						__lowerCamelCase ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       Dict				    = 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__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				Any:

            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(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = max([len(__lowerCamelCase) for t in text])

                          # Pad all batch samples to max number of text queries
                          for t in text:
                                 if len(__lowerCamelCase) != max_num_queries:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
                                 encodings.append(__lowerCamelCase)
                   else:
                          raise TypeError("Input text should be a string, a list of strings or a nested list of strings")

                   if return_tensors == "np":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[Any]				    = 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      :       Optional[int]				    = 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      :       Optional[Any]				    = torch.cat([encoding["input_ids"] for encoding in encodings] ,						dim=0)
                          _A      :       Union[str, 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      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

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

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

            if query_images is not None:
                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       List[str]				    = self.image_processor(
                       __lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase).pixel_values
                   _A      :       Union[str, Any]				    = query_pixel_values

            if images is not None:
                   _A      :       int				    = self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)

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


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				int:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,						__lowerCamelCase ,						)
            return self.image_processor_class


     @property
     def  _lowerCamelCase (						self)			->				List[str]:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__lowerCamelCase ,						)
            return self.image_processor
