import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           DeiTForImageClassification,
           DeiTForImageClassificationWithTeacher,
           DeiTForMaskedImageModeling,
           DeiTModel,
       )
       from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DeiTImageProcessor


class 						lowercase    :




       def __init__(						self   :					List[str]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int=13	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Dict=2	,							_UpperCamelCase   :					Optional[int]=3	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=True	,							_UpperCamelCase   :					str=32	,							_UpperCamelCase   :					int=5	,							_UpperCamelCase   :					Dict=4	,							_UpperCamelCase   :					Dict=37	,							_UpperCamelCase   :					int="gelu"	,							_UpperCamelCase   :					Optional[int]=0.1	,							_UpperCamelCase   :					Any=0.1	,							_UpperCamelCase   :					Union[str, Any]=10	,							_UpperCamelCase   :					Tuple=0.0_2	,							_UpperCamelCase   :					Tuple=3	,							_UpperCamelCase   :					Tuple=None	,							_UpperCamelCase   :					Optional[Any]=2	,							)					->				Any:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       parent
            SCREAMING_SNAKE_CASE    			=       batch_size
            SCREAMING_SNAKE_CASE    			=       image_size
            SCREAMING_SNAKE_CASE    			=       patch_size
            SCREAMING_SNAKE_CASE    			=       num_channels
            SCREAMING_SNAKE_CASE    			=       is_training
            SCREAMING_SNAKE_CASE    			=       use_labels
            SCREAMING_SNAKE_CASE    			=       hidden_size
            SCREAMING_SNAKE_CASE    			=       num_hidden_layers
            SCREAMING_SNAKE_CASE    			=       num_attention_heads
            SCREAMING_SNAKE_CASE    			=       intermediate_size
            SCREAMING_SNAKE_CASE    			=       hidden_act
            SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
            SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
            SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
            SCREAMING_SNAKE_CASE    			=       initializer_range
            SCREAMING_SNAKE_CASE    			=       scope
            SCREAMING_SNAKE_CASE    			=       encoder_stride

            # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
            SCREAMING_SNAKE_CASE    			=       (image_size // patch_size) ** 2
            SCREAMING_SNAKE_CASE    			=       num_patches + 2




       def 	__snake_case(						self   :					str      )					->				Any:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

            SCREAMING_SNAKE_CASE    			=       None
            if self.use_labels:
                 SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )

            SCREAMING_SNAKE_CASE    			=       self.get_config()

            return config, pixel_values, labels




       def 	__snake_case(						self   :					Tuple      )					->				List[str]:



            '''simple docstring'''

            return DeiTConfig(
                image_size=self.image_size	,							patch_size=self.patch_size	,							num_channels=self.num_channels	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							is_decoder=_UpperCamelCase	,							initializer_range=self.initializer_range	,							encoder_stride=self.encoder_stride	,							)




       def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Any      )					->				Union[str, Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       DeiTModel(config=_UpperCamelCase      )
            model.to(_UpperCamelCase      )
            model.eval()
            SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
            self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




       def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Optional[int]      )					->				List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       DeiTForMaskedImageModeling(config=_UpperCamelCase      )
            model.to(_UpperCamelCase      )
            model.eval()
            SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
            self.parent.assertEqual(
                result.reconstruction.shape	,							(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

            # test greyscale images
            SCREAMING_SNAKE_CASE    			=       1
            SCREAMING_SNAKE_CASE    			=       DeiTForMaskedImageModeling(_UpperCamelCase      )
            model.to(_UpperCamelCase      )
            model.eval()

            SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
            self.parent.assertEqual(result.reconstruction.shape	,							(self.batch_size, 1, self.image_size, self.image_size)      )




       def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					int      )					->				str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.type_sequence_label_size
            SCREAMING_SNAKE_CASE    			=       DeiTForImageClassification(_UpperCamelCase      )
            model.to(_UpperCamelCase      )
            model.eval()
            SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							labels=_UpperCamelCase      )
            self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.type_sequence_label_size)      )

            # test greyscale images
            SCREAMING_SNAKE_CASE    			=       1
            SCREAMING_SNAKE_CASE    			=       DeiTForImageClassification(_UpperCamelCase      )
            model.to(_UpperCamelCase      )
            model.eval()

            SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							labels=_UpperCamelCase      )
            self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.type_sequence_label_size)      )





       def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
            (
                (
                SCREAMING_SNAKE_CASE					
            )			,   (
                SCREAMING_SNAKE_CASE					
            )			,   (
                SCREAMING_SNAKE_CASE					
            )			,   					
            )    			=       config_and_inputs
            SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
            return config, inputs_dict



@require_torch
class 						lowercase    (     lowercase_				,       lowercase_				,       unittest.TestCase     ):
       lowercase__						:       str								=							(
           (
               DeiTModel,
               DeiTForImageClassification,
               DeiTForImageClassificationWithTeacher,
               DeiTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowercase__						:       Optional[int]								=							(
           {
               '''feature-extraction''': DeiTModel,
               '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       lowercase__						:       Optional[int]								=							False
       lowercase__						:       Dict								=							False
       lowercase__						:       str								=							False




       def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       DeiTModelTester(self      )
            SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							has_text_modality=_UpperCamelCase	,							hidden_size=37      )




       def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



            '''simple docstring'''

            self.config_tester.run_common_tests()




       @unittest.skip(reason="DeiT does not use inputs_embeds"      )
       def 	__snake_case(						self   :					Any      )					->				Union[str, Any]:



            '''simple docstring'''

            pass




       def 	__snake_case(						self   :					Optional[int]      )					->				Tuple:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
                 self.assertIsInstance(model.get_input_embeddings()	,							(nn.Module)      )
                 SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_UpperCamelCase	,							nn.Linear      )      )




       def 	__snake_case(						self   :					Tuple      )					->				str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
                 self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




       def 	__snake_case(						self   :					Optional[Any]      )					->				int:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_UpperCamelCase      )




       def 	__snake_case(						self   :					List[str]      )					->				Union[str, Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCamelCase      )




       def 	__snake_case(						self   :					Dict      )					->				str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )




       def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[Any]=False      )					->				Dict:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       super()._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )

            if return_labels:
                 if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                      del inputs_dict["labels"]

            return inputs_dict




       def 	__snake_case(						self   :					Optional[int]      )					->				List[Any]:



            '''simple docstring'''

            if not self.model_tester.is_training:
                 return

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()
            SCREAMING_SNAKE_CASE    			=       True

            for model_class in self.all_model_classes:
                 # DeiTForImageClassificationWithTeacher supports inference-only
                 if (
                     model_class in get_values(_UpperCamelCase      )
                     or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                 ):
                      continue
                 SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
                 model.to(_UpperCamelCase      )
                 model.train()
                 SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )
                 SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      ).loss
                 loss.backward()




       def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()
            if not self.model_tester.is_training:
                 return

            SCREAMING_SNAKE_CASE    			=       False
            SCREAMING_SNAKE_CASE    			=       True

            for model_class in self.all_model_classes:
                 if model_class in get_values(_UpperCamelCase      ) or not model_class.supports_gradient_checkpointing:
                      continue
                 # DeiTForImageClassificationWithTeacher supports inference-only
                 if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                      continue
                 SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
                 model.gradient_checkpointing_enable()
                 model.to(_UpperCamelCase      )
                 model.train()
                 SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )
                 SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      ).loss
                 loss.backward()




       def 	__snake_case(						self   :					List[Any]      )					->				Dict:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

            SCREAMING_SNAKE_CASE    			=       [
                {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
                {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
                {"title": "regression", "num_labels": 1, "dtype": torch.float},
            ]

            for model_class in self.all_model_classes:
                 if (
                     model_class
                     not in [
                         *get_values(_UpperCamelCase      ),
                         *get_values(_UpperCamelCase      ),
                     ]
                     or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                 ):
                      continue

                 for problem_type in problem_types:
                      with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"      ):
                           SCREAMING_SNAKE_CASE    			=       problem_type["title"]
                           SCREAMING_SNAKE_CASE    			=       problem_type["num_labels"]

                           SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
                           model.to(_UpperCamelCase      )
                           model.train()

                           SCREAMING_SNAKE_CASE    			=       self._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )

                           if problem_type["num_labels"] > 1:
                                SCREAMING_SNAKE_CASE    			=       inputs["labels"].unsqueeze(1      ).repeat(1	,							problem_type["num_labels"]      )

                           SCREAMING_SNAKE_CASE    			=       inputs["labels"].to(problem_type["dtype"]      )

                           # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                           # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                           # they have the same size." which is a symptom something in wrong for the regression problem.
                           # See https://github.com/huggingface/transformers/issues/11780
                           with warnings.catch_warnings(record=_UpperCamelCase      ) as warning_list:
                                SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      ).loss
                           for w in warning_list:
                                if "Using a target size that is different to the input size" in str(w.message      ):
                                     raise ValueError(
                                         F"Something is going wrong in the regression problem: intercepted {w.message}"      )

                           loss.backward()





       @slow
       def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



            '''simple docstring'''

            for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE    			=       DeiTModel.from_pretrained(_UpperCamelCase      )
                 self.assertIsNotNone(_UpperCamelCase      )





def 	__lowerCamelCase							():
     SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
     return image


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




       @cached_property
       def 	__snake_case(						self   :					Tuple      )					->				Optional[int]:



            '''simple docstring'''

            return (
                DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
                if is_vision_available()
                else None
            )




       @slow
       def 	__snake_case(						self   :					Optional[int]      )					->				Any:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      ).to(
                _UpperCamelCase      )

            SCREAMING_SNAKE_CASE    			=       self.default_image_processor
            SCREAMING_SNAKE_CASE    			=       prepare_img()
            SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      ).to(_UpperCamelCase      )

            # forward pass
            with torch.no_grad():
                 SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )

            # verify the logits
            SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_000)      )
            self.assertEqual(outputs.logits.shape	,							_UpperCamelCase      )

            SCREAMING_SNAKE_CASE    			=       torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]      ).to(_UpperCamelCase      )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )





       @slow
       @require_accelerate
       @require_torch_gpu
       def 	__snake_case(						self   :					List[str]      )					->				Any:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       DeiTModel.from_pretrained(
                "facebook/deit-base-distilled-patch16-224"	,							torch_dtype=torch.floataa	,							device_map="auto"      )
            SCREAMING_SNAKE_CASE    			=       self.default_image_processor

            SCREAMING_SNAKE_CASE    			=       prepare_img()
            SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      )
            SCREAMING_SNAKE_CASE    			=       inputs.pixel_values.to(_UpperCamelCase      )

            # forward pass to make sure inference works in fp16
            with torch.no_grad():
                 SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )





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)




