from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

_lowerCamelCase					:   Optional[int]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]  ):

     if isinstance(UpperCAmelCase__      , (list, tuple)  ) and isinstance(videos[0]      , (list, tuple)  ) and is_valid_image(videos[0][0]  ):
          return videos

     elif isinstance(UpperCAmelCase__      , (list, tuple)  ) and is_valid_image(videos[0]  ):
          return [videos]

     elif is_valid_image(UpperCAmelCase__  ):
          return [[videos]]

     raise ValueError(F"Could not make batched video from {videos}"  )


class 						lowercase    (     _a     ):
       lowercase__						:       Tuple								=							["""pixel_values"""]




       def __init__(						self   :					List[str]	,							_UpperCamelCase   :					List[Any] = True	,							_UpperCamelCase   :					Tuple = None	,							_UpperCamelCase   :					str = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					int = True	,							_UpperCamelCase   :					Tuple = None	,							_UpperCamelCase   :					int = True	,							_UpperCamelCase   :					Dict = 1 / 255	,							_UpperCamelCase   :					List[str] = True	,							_UpperCamelCase   :					List[str] = True	,							_UpperCamelCase   :					Dict = None	,							_UpperCamelCase   :					int = None	,							**_UpperCamelCase   :					int	,							)					->				None:



            '''simple docstring'''

            super().__init__(**snake_case_      )
            SCREAMING_SNAKE_CASE    			=       size if size is not None else {"shortest_edge": 256}
            SCREAMING_SNAKE_CASE    			=       get_size_dict(snake_case_	,							default_to_square=snake_case_      )
            SCREAMING_SNAKE_CASE    			=       crop_size if crop_size is not None else {"height": 224, "width": 224}
            SCREAMING_SNAKE_CASE    			=       get_size_dict(snake_case_	,							param_name="crop_size"      )

            SCREAMING_SNAKE_CASE    			=       do_resize
            SCREAMING_SNAKE_CASE    			=       size
            SCREAMING_SNAKE_CASE    			=       do_center_crop
            SCREAMING_SNAKE_CASE    			=       crop_size
            SCREAMING_SNAKE_CASE    			=       resample
            SCREAMING_SNAKE_CASE    			=       do_rescale
            SCREAMING_SNAKE_CASE    			=       rescale_factor
            SCREAMING_SNAKE_CASE    			=       offset
            SCREAMING_SNAKE_CASE    			=       do_normalize
            SCREAMING_SNAKE_CASE    			=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            SCREAMING_SNAKE_CASE    			=       image_std if image_std is not None else IMAGENET_STANDARD_STD




       def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Union[str, Any] = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					List[Any] = None	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				np.ndarray:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       get_size_dict(snake_case_	,							default_to_square=snake_case_      )
            if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE    			=       get_resize_output_image_size(snake_case_	,							size["shortest_edge"]	,							default_to_square=snake_case_      )
            elif "height" in size and "width" in size:
                 SCREAMING_SNAKE_CASE    			=       (size["height"], size["width"])
            else:
                 raise ValueError(F"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"      )
            return resize(snake_case_	,							size=snake_case_	,							resample=snake_case_	,							data_format=snake_case_	,							**snake_case_      )




       def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Tuple = None	,							**_UpperCamelCase   :					List[str]	,							)					->				np.ndarray:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       get_size_dict(snake_case_      )
            if "height" not in size or "width" not in size:
                 raise ValueError(F"Size must have \'height\' and \'width\' as keys. Got {size.keys()}"      )
            return center_crop(snake_case_	,							size=(size["height"], size["width"])	,							data_format=snake_case_	,							**snake_case_      )




       def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Union[str, Any] = True	,							_UpperCamelCase   :					Optional[int] = None	,							**_UpperCamelCase   :					Dict	,							)					->				str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       image.astype(np.floataa      )
            if offset:
                 SCREAMING_SNAKE_CASE    			=       image - (scale / 2)
            return rescale(snake_case_	,							scale=snake_case_	,							data_format=snake_case_	,							**snake_case_      )




       def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Union[str, Any] = None	,							**_UpperCamelCase   :					List[str]	,							)					->				np.ndarray:



            '''simple docstring'''

            return normalize(snake_case_	,							mean=snake_case_	,							std=snake_case_	,							data_format=snake_case_	,							**snake_case_      )




       def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					List[str] = None	,							_UpperCamelCase   :					Union[str, Any] = None	,							_UpperCamelCase   :					str = None	,							_UpperCamelCase   :					Dict = None	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					List[str] = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					List[str] = None	,							_UpperCamelCase   :					str = ChannelDimension.FIRST	,							)					->				np.ndarray:



            '''simple docstring'''

            if do_resize and size is None or resample is None:
                 raise ValueError("Size and resample must be specified if do_resize is True."      )

            if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True."      )

            if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True."      )

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("Image mean and std must be specified if do_normalize is True."      )

            if offset and not do_rescale:
                 raise ValueError("For offset, do_rescale must also be set to True."      )

            # All transformations expect numpy arrays.
            SCREAMING_SNAKE_CASE    			=       to_numpy_array(snake_case_      )

            if do_resize:
                 SCREAMING_SNAKE_CASE    			=       self.resize(image=snake_case_	,							size=snake_case_	,							resample=snake_case_      )

            if do_center_crop:
                 SCREAMING_SNAKE_CASE    			=       self.center_crop(snake_case_	,							size=snake_case_      )

            if do_rescale:
                 SCREAMING_SNAKE_CASE    			=       self.rescale(image=snake_case_	,							scale=snake_case_	,							offset=snake_case_      )

            if do_normalize:
                 SCREAMING_SNAKE_CASE    			=       self.normalize(image=snake_case_	,							mean=snake_case_	,							std=snake_case_      )

            SCREAMING_SNAKE_CASE    			=       to_channel_dimension_format(snake_case_	,							snake_case_      )
            return image





       def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str = None	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					Dict = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					Union[str, Any] = None	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					List[str] = None	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					str = None	,							_UpperCamelCase   :					Tuple = None	,							_UpperCamelCase   :					List[str] = ChannelDimension.FIRST	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				PIL.Image.Image:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
            SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
            SCREAMING_SNAKE_CASE    			=       do_center_crop if do_center_crop is not None else self.do_center_crop
            SCREAMING_SNAKE_CASE    			=       do_rescale if do_rescale is not None else self.do_rescale
            SCREAMING_SNAKE_CASE    			=       rescale_factor if rescale_factor is not None else self.rescale_factor
            SCREAMING_SNAKE_CASE    			=       offset if offset is not None else self.offset
            SCREAMING_SNAKE_CASE    			=       do_normalize if do_normalize is not None else self.do_normalize
            SCREAMING_SNAKE_CASE    			=       image_mean if image_mean is not None else self.image_mean
            SCREAMING_SNAKE_CASE    			=       image_std if image_std is not None else self.image_std

            SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
            SCREAMING_SNAKE_CASE    			=       get_size_dict(snake_case_	,							default_to_square=snake_case_      )
            SCREAMING_SNAKE_CASE    			=       crop_size if crop_size is not None else self.crop_size
            SCREAMING_SNAKE_CASE    			=       get_size_dict(snake_case_	,							param_name="crop_size"      )

            if not valid_images(snake_case_      ):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray."      )

            SCREAMING_SNAKE_CASE    			=       make_batched(snake_case_      )

            SCREAMING_SNAKE_CASE    			=       [
                [
                    self._preprocess_image(
                        image=snake_case_	,							do_resize=snake_case_	,							size=snake_case_	,							resample=snake_case_	,							do_center_crop=snake_case_	,							crop_size=snake_case_	,							do_rescale=snake_case_	,							rescale_factor=snake_case_	,							offset=snake_case_	,							do_normalize=snake_case_	,							image_mean=snake_case_	,							image_std=snake_case_	,							data_format=snake_case_	,							)
                    for img in video
                ]
                for video in videos
            ]

            SCREAMING_SNAKE_CASE    			=       {"pixel_values": videos}
            return BatchFeature(data=snake_case_	,							tensor_type=snake_case_      )





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict=False  ):
					SCREAMING_SNAKE_CASE    			=       []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")  )
										rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")  )
										rename_keys.append(
										    (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")  )
										rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")  )
										rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")  )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ("module.cls_token", "vit.embeddings.cls_token"),
					        ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
					        ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
					        ("module.pos_embed", "vit.embeddings.position_embeddings"),
					    ]  )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ("module.norm.weight", "layernorm.weight"),
										        ("module.norm.bias", "layernorm.bias"),
										    ]  )

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										SCREAMING_SNAKE_CASE    			=       [(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 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							str=False  ):
					for i in range(config.num_hidden_layers  ):
										if base_model:
															SCREAMING_SNAKE_CASE    			=       ""
										else:
															SCREAMING_SNAKE_CASE    			=       "vit."
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"  )
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    : config.hidden_size, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[: config.hidden_size]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    -config.hidden_size :, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[-config.hidden_size :]





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):
					# projection head is used in the self-supervised pre-training in MSN,
					# for downstream task it's not needed.
					SCREAMING_SNAKE_CASE    			=       [
					    "module.fc.fc1.weight",
					    "module.fc.fc1.bias",
					    "module.fc.bn1.weight",
					    "module.fc.bn1.bias",
					    "module.fc.bn1.running_mean",
					    "module.fc.bn1.running_var",
					    "module.fc.bn1.num_batches_tracked",
					    "module.fc.fc2.weight",
					    "module.fc.fc2.bias",
					    "module.fc.bn2.weight",
					    "module.fc.bn2.bias",
					    "module.fc.bn2.running_mean",
					    "module.fc.bn2.running_var",
					    "module.fc.bn2.num_batches_tracked",
					    "module.fc.fc3.weight",
					    "module.fc.fc3.bias",
					]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Dict  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       ViTMSNConfig()
					SCREAMING_SNAKE_CASE    			=       1_0_0_0

					SCREAMING_SNAKE_CASE    			=       "datasets/huggingface/label-files"
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(UpperCAmelCase__      , UpperCAmelCase__  )      , "r"  )  )
					SCREAMING_SNAKE_CASE    			=       {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE    			=       idalabel
					SCREAMING_SNAKE_CASE    			=       {v: k for k, v in idalabel.items()}

					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       3_8_4
										SCREAMING_SNAKE_CASE    			=       1_5_3_6
										SCREAMING_SNAKE_CASE    			=       6
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       4
					elif "l7" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       7
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1

					SCREAMING_SNAKE_CASE    			=       ViTMSNModel(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["target_encoder"]

					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(size=config.image_size  )

					remove_projection_head(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , base_model=UpperCAmelCase__  )

					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
					read_in_q_k_v(UpperCAmelCase__      , UpperCAmelCase__      , base_model=UpperCAmelCase__  )

					model.load_state_dict(UpperCAmelCase__  )
					model.eval()

					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"

					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(UpperCAmelCase__      , stream=UpperCAmelCase__  ).raw  )
					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(
					    size=config.image_size      , image_mean=UpperCAmelCase__      , image_std=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       image_processor(images=UpperCAmelCase__      , return_tensors="pt"  )

					# forward pass
					torch.manual_seed(2  )
					SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       outputs.last_hidden_state

					# The following Colab Notebook was used to generate these outputs:
					# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-1.0915, -1.4876, -1.1809]]  )
					elif "b16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[14.2889, -18.9045, 11.7281]]  )
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[41.5028, -22.8681, 45.6475]]  )
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-4.3868, 5.2932, -0.4137]]  )
					else:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-0.1792, -0.6465, 2.4263]]  )

					# verify logits
					assert torch.allclose(last_hidden_state[:, 0, :3]      , UpperCAmelCase__      , atol=1e-4  )

					print(F"Saving model 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__":
							_lowerCamelCase					:   Optional[Any]							=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
							    type=str,
							    help='''URL of the checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							_lowerCamelCase					:   Optional[Any]							=		parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




