





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_					:   Tuple 							=						logging.get_logger(__name__)






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






def 							_lowerCAmelCase (  _a   :					Optional[Any]			)	->			Optional[Any]:
     lowerCAmelCase_					:       Optional[Any]    =		["""head.weight""", """head.bias"""]
     for k in ignore_keys:
          state_dict.pop(_a					,   _a			)






def 							_lowerCAmelCase (  _a   :					Any					,   _a   :					List[Any]					,   _a   :					int			)	->			Dict:
     lowerCAmelCase_					:       Optional[Any]    =		dct.pop(_a			)
     lowerCAmelCase_					:       Dict    =		val






def 							_lowerCAmelCase (  )	->			int:
     lowerCAmelCase_					:       str    =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     lowerCAmelCase_					:       List[Any]    =		Image.open(requests.get(_a					,   stream=_a			).raw			)
     return im






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

     # load original model from torch hub
     lowerCAmelCase_					:       List[str]    =		torch.hub.load("""facebookresearch/dino:main"""					,   _a			)
     original_model.eval()

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

     # load HuggingFace model
     if base_model:
          lowerCAmelCase_					:       Dict    =		ViTModel(_a					,   add_pooling_layer=_a			).eval()
     else:
          lowerCAmelCase_					:       str    =		ViTForImageClassification(_a			).eval()
     model.load_state_dict(_a			)

     # Check outputs on an image, prepared by ViTImageProcessor
     lowerCAmelCase_					:       Tuple    =		ViTImageProcessor()
     lowerCAmelCase_					:       int    =		image_processor(images=prepare_img()					,   return_tensors="""pt"""			)
     lowerCAmelCase_					:       List[str]    =		encoding["""pixel_values"""]
     lowerCAmelCase_					:       List[str]    =		model(_a			)

     if base_model:
          lowerCAmelCase_					:       Optional[int]    =		original_model(_a			)
          assert torch.allclose(_a					,   outputs.last_hidden_state[:, 0, :]					,   atol=1E-1			)
     else:
          lowerCAmelCase_					:       Tuple    =		original_model(_a			)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(_a					,   outputs.logits					,   atol=1E-3			)

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


if __name__ == "__main__":
      UpperCAmelCase_					:   Optional[Any] 							=						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_					:   int 							=						parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


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

UpperCAmelCase_					:   Any 							=						{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """perceiver"""







  def __init__(	self    ,						_lowercase=256    ,						_lowercase=1_280    ,						_lowercase=768    ,						_lowercase=1    ,						_lowercase=26    ,						_lowercase=8    ,						_lowercase=8    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase="kv"    ,						_lowercase=1    ,						_lowercase=1    ,						_lowercase="gelu"    ,						_lowercase=0.1    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=True    ,						_lowercase=262    ,						_lowercase=2_048    ,						_lowercase=56    ,						_lowercase=[368, 496]    ,						_lowercase=16    ,						_lowercase=1_920    ,						_lowercase=16    ,						_lowercase=[1, 16, 224, 224]    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)

       lowerCAmelCase_					:       Optional[int]    =		num_latents
       lowerCAmelCase_					:       List[str]    =		d_latents
       lowerCAmelCase_					:       int    =		d_model
       lowerCAmelCase_					:       Dict    =		num_blocks
       lowerCAmelCase_					:       Union[str, Any]    =		num_self_attends_per_block
       lowerCAmelCase_					:       List[str]    =		num_self_attention_heads
       lowerCAmelCase_					:       List[str]    =		num_cross_attention_heads
       lowerCAmelCase_					:       List[Any]    =		qk_channels
       lowerCAmelCase_					:       Optional[Any]    =		v_channels
       lowerCAmelCase_					:       Optional[Any]    =		cross_attention_shape_for_attention
       lowerCAmelCase_					:       Optional[int]    =		self_attention_widening_factor
       lowerCAmelCase_					:       int    =		cross_attention_widening_factor
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       int    =		attention_probs_dropout_prob
       lowerCAmelCase_					:       int    =		initializer_range
       lowerCAmelCase_					:       Optional[Any]    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[Any]    =		use_query_residual
       # masked language modeling attributes
       lowerCAmelCase_					:       Tuple    =		vocab_size
       lowerCAmelCase_					:       List[Any]    =		max_position_embeddings
       # image classification attributes
       lowerCAmelCase_					:       List[Any]    =		image_size
       # flow attributes
       lowerCAmelCase_					:       Dict    =		train_size
       # multimodal autoencoding attributes
       lowerCAmelCase_					:       Optional[Any]    =		num_frames
       lowerCAmelCase_					:       int    =		audio_samples_per_frame
       lowerCAmelCase_					:       Any    =		samples_per_patch
       lowerCAmelCase_					:       Any    =		output_shape





class      lowercase__    (							__A  ):







  @property
  def 	UpperCAmelCase__		(	self						):
       if self.task == "multiple-choice":
            lowerCAmelCase_					:       Optional[Any]    =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_					:       Optional[int]    =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""inputs""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return 1e-4







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 3    ,						_lowercase = 40    ,						_lowercase = 40    ,						):
       # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

       if isinstance(_lowercase    ,						_lowercase						):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_					:       Union[str, Any]    =		compute_effective_axis_dimension(
                _lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch    ,						num_token_to_add=0						)
            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            lowerCAmelCase_					:       int    =		preprocessor.num_special_tokens_to_add(_lowercase						)
            lowerCAmelCase_					:       int    =		compute_effective_axis_dimension(
                _lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_sequence    ,						num_token_to_add=_lowercase						)
            # Generate dummy inputs according to compute batch and sequence
            lowerCAmelCase_					:       Optional[int]    =		[""" """.join(["""a"""]						) * seq_length] * batch_size
            lowerCAmelCase_					:       Optional[Any]    =		dict(preprocessor(_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Optional[Any]    =		inputs.pop("""input_ids"""						)
            return inputs
       elif isinstance(_lowercase    ,						_lowercase						) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_					:       Any    =		compute_effective_axis_dimension(_lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch						)
            lowerCAmelCase_					:       str    =		self._generate_dummy_images(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       List[str]    =		dict(preprocessor(images=_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Tuple    =		inputs.pop("""pixel_values"""						)
            return inputs
       else:
            raise ValueError(
                """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""						)
