'''simple docstring'''




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()
_A								=   logging.get_logger(__name__)

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       int     				=					['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					dct.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[Any]     				=					val

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_					:       int     				=					Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,       stream=SCREAMING_SNAKE_CASE_							).raw							)
     return im

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=True							):
     lowercase_					:       str     				=					ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          lowercase_					:       Tuple     				=					8
     # set labels if required
     if not base_model:
          lowercase_					:       Any     				=					1_000
          lowercase_					:       Tuple     				=					'huggingface/label-files'
          lowercase_					:       Union[str, Any]     				=					'imagenet-1k-id2label.json'
          lowercase_					:       int     				=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       repo_type='dataset'							)  ,       'r'							)							)
          lowercase_					:       Union[str, Any]     				=					{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
          lowercase_					:       Union[str, Any]     				=					idalabel
          lowercase_					:       str     				=					{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          lowercase_					:       Union[str, Any]     				=					384
          lowercase_					:       Optional[int]     				=					1_536
          lowercase_					:       List[Any]     				=					12
          lowercase_					:       str     				=					6

     # load original model from torch hub
     lowercase_					:       Optional[int]     				=					torch.hub.load('facebookresearch/dino:main'  ,       SCREAMING_SNAKE_CASE_							)
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowercase_					:       int     				=					original_model.state_dict()
     if base_model:
          remove_classification_head_(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					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:
          lowercase_					:       Any     				=					ViTModel(SCREAMING_SNAKE_CASE_  ,       add_pooling_layer=SCREAMING_SNAKE_CASE_							).eval()
     else:
          lowercase_					:       Optional[Any]     				=					ViTForImageClassification(SCREAMING_SNAKE_CASE_							).eval()
     model.load_state_dict(SCREAMING_SNAKE_CASE_							)

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

     if base_model:
          lowercase_					:       Any     				=					original_model(SCREAMING_SNAKE_CASE_							)
          assert torch.allclose(SCREAMING_SNAKE_CASE_  ,       outputs.last_hidden_state[:, 0, :]  ,       atol=1e-1							)
     else:
          lowercase_					:       Optional[Any]     				=					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__":
 _A								=   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)
 _A								=   parser.parse_args()
 convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
 from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=13       ,				_a=32       ,				_a=3       ,				_a=4       ,				_a=[10, 20, 30, 40]       ,				_a=[2, 2, 3, 2]       ,				_a=True       ,				_a=True       ,				_a=37       ,				_a="gelu"       ,				_a=10       ,				_a=0.02       ,				_a=["stage2", "stage3", "stage4"]       ,				_a=[2, 3, 4]       ,				_a=None       ,				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					parent
        lowercase_					:       int     				=					batch_size
        lowercase_					:       Union[str, Any]     				=					image_size
        lowercase_					:       Optional[Any]     				=					num_channels
        lowercase_					:       Any     				=					num_stages
        lowercase_					:       Optional[Any]     				=					hidden_sizes
        lowercase_					:       Any     				=					depths
        lowercase_					:       Tuple     				=					is_training
        lowercase_					:       int     				=					use_labels
        lowercase_					:       Tuple     				=					intermediate_size
        lowercase_					:       Any     				=					hidden_act
        lowercase_					:       Union[str, Any]     				=					num_labels
        lowercase_					:       Union[str, Any]     				=					initializer_range
        lowercase_					:       Optional[Any]     				=					out_features
        lowercase_					:       str     				=					out_indices
        lowercase_					:       Dict     				=					scope
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[Any]     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       int     				=					None
        if self.use_labels:
             lowercase_					:       Any     				=					ids_tensor([self.batch_size]       ,				self.num_labels				)

        lowercase_					:       str     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        return ConvNextVaConfig(
            num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=_a       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       Optional[int]     				=					ConvNextVaModel(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       List[Any]     				=					model(_a				)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Any:
        lowercase_					:       int     				=					ConvNextVaForImageClassification(_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Dict     				=					model(_a       ,				labels=_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps				)       ,				len(config.out_features				)				)
        self.parent.assertListEqual(list(result.feature_maps[0].shape				)       ,				[self.batch_size, self.hidden_sizes[1], 4, 4]				)

        # verify channels
        self.parent.assertEqual(len(model.channels				)       ,				len(config.out_features				)				)
        self.parent.assertListEqual(model.channels       ,				config.hidden_sizes[1:]				)

        # verify backbone works with out_features=None
        lowercase_					:       int     				=					None
        lowercase_					:       List[str]     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps				)       ,				1				)
        self.parent.assertListEqual(list(result.feature_maps[0].shape				)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

        # verify channels
        self.parent.assertEqual(len(model.channels				)       ,				1				)
        self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Union[str, Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       str     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'pixel_values': pixel_values}
        return config, inputs_dict





   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Optional[Any]     				=					config_and_inputs
        lowercase_					:       Dict     				=					{'pixel_values': pixel_values, 'labels': labels}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       List[str]     =    (
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   A							:       Optional[int]     =    (
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A							:       Optional[int]     =    False
   A							:       str     =    False
   A							:       int     =    False
   A							:       Dict     =    False
   A							:       Optional[int]     =    False
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					ConvNextVaModelTester(self				)
        lowercase_					:       List[str]     				=					ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     int:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return
   @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        pass
   @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass
   @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Dict     				=					True

             if model_class.__name__ in [
                 *get_values(_a				),
                 *get_values(_a				),
             ]:
                  continue

             lowercase_					:       str     				=					model_class(_a				)
             model.to(_a				)
             model.train()
             lowercase_					:       Tuple     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       List[Any]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Optional[int]     				=					False
             lowercase_					:       Union[str, Any]     				=					True

             if (
                 model_class.__name__
                 in [*get_values(_a				), *get_values(_a				)]
                 or not model_class.supports_gradient_checkpointing
             ):
                  continue

             lowercase_					:       Union[str, Any]     				=					model_class(_a				)
             model.to(_a				)
             model.gradient_checkpointing_enable()
             model.train()
             lowercase_					:       List[Any]     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       Optional[int]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_				,lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Any     				=					model_class(_a				)
             lowercase_					:       Any     				=					inspect.signature(model.forward				)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_					:       int     				=					[*signature.parameters.keys()]

             lowercase_					:       List[str]     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        def check_hidden_states_output(_a       ,				_a       ,				_a				):
             lowercase_					:       Tuple     				=					model_class(_a				)
             model.to(_a				)
             model.eval()

             with torch.no_grad():
                  lowercase_					:       str     				=					model(**self._prepare_for_class(_a       ,				_a				)				)

             lowercase_					:       str     				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase_					:       Optional[int]     				=					self.model_tester.num_stages
             self.assertEqual(len(_a				)       ,				expected_num_stages + 1				)

             # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]				)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

        lowercase_				,lowercase_					:       Optional[int]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Optional[Any]     				=					True
             check_hidden_states_output(_a       ,				_a       ,				_a				)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_					:       int     				=					True

             check_hidden_states_output(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Any:
        for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       Optional[int]     				=					ConvNextVaModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






@require_torch
@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   @cached_property
   def 		_lowerCamelCase (self				)    ->     str:
        return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'				) if is_vision_available() else None





   @slow
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[Any]     				=					ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'				).to(_a				)

        lowercase_					:       Any     				=					self.default_image_processor
        lowercase_					:       Union[str, Any]     				=					prepare_img()
        lowercase_					:       List[str]     				=					preprocessor(images=_a       ,				return_tensors='pt'				).to(_a				)

        # forward pass
        with torch.no_grad():
             lowercase_					:       List[Any]     				=					model(**_a				)

        # verify the logits
        lowercase_					:       str     				=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)

        lowercase_					:       Tuple     				=					torch.tensor([0.99_96, 0.19_66, -0.43_86]				).to(_a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				_a       ,				atol=1e-4				)				)




