"""simple docstring"""


import inspect
import unittest

from transformers import ViTConfig
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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor




class  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__=13,							lowerCAmelCase__=30,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=32,							lowerCAmelCase__=5,							lowerCAmelCase__=4,							lowerCAmelCase__=37,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=10,							lowerCAmelCase__=0.02,							lowerCAmelCase__=None,							lowerCAmelCase__=2,							)      ->      Any:
            snake_case_					       =					parent
            snake_case_					       =					batch_size
            snake_case_					       =					image_size
            snake_case_					       =					patch_size
            snake_case_					       =					num_channels
            snake_case_					       =					is_training
            snake_case_					       =					use_labels
            snake_case_					       =					hidden_size
            snake_case_					       =					num_hidden_layers
            snake_case_					       =					num_attention_heads
            snake_case_					       =					intermediate_size
            snake_case_					       =					hidden_act
            snake_case_					       =					hidden_dropout_prob
            snake_case_					       =					attention_probs_dropout_prob
            snake_case_					       =					type_sequence_label_size
            snake_case_					       =					initializer_range
            snake_case_					       =					scope
            snake_case_					       =					encoder_stride

            # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
            snake_case_					       =					(image_size // patch_size) ** 2
            snake_case_					       =					num_patches + 1







       def 		a_						( self)      ->      Any:
            snake_case_					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

            snake_case_					       =					self.get_config()

            return config, pixel_values, labels







       def 		a_						( self)      ->      Union[str, Any]:
            return ViTConfig(
                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=lowerCAmelCase__,							initializer_range=self.initializer_range,							encoder_stride=self.encoder_stride,							)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Union[str, Any]:
            snake_case_					       =					ViTModel(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,							(self.batch_size, self.seq_length, self.hidden_size))







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      str:
            snake_case_					       =					ViTForMaskedImageModeling(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(
                result.reconstruction.shape,							(self.batch_size, self.num_channels, self.image_size, self.image_size))

            # test greyscale images
            snake_case_					       =					1
            snake_case_					       =					ViTForMaskedImageModeling(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()

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







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					self.type_sequence_label_size
            snake_case_					       =					ViTForImageClassification(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__,							labels=lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.type_sequence_label_size))

            # test greyscale images
            snake_case_					       =					1
            snake_case_					       =					ViTForImageClassification(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()

            snake_case_					       =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.type_sequence_label_size))







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.prepare_config_and_inputs()
            (
                (
                snake_case_
            )       ,	(
                snake_case_
            )       ,	(
                snake_case_
            )       ,	
            )					       =					config_and_inputs
            snake_case_					       =					{'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      (
           (
               ViTModel,
               ViTForImageClassification,
               ViTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_			=      (
           {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_			=      True

       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					ViTModelTester(self)
            snake_case_					       =					ConfigTester(self,							config_class=lowerCAmelCase__,							has_text_modality=lowerCAmelCase__,							hidden_size=37)







       def 		a_						( self)      ->      str:
            self.config_tester.run_common_tests()







       @unittest.skip(reason='ViT does not use inputs_embeds')
       def 		a_						( self)      ->      List[str]:
            pass







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_       ,	snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()

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







       def 		a_						( self)      ->      List[str]:
            snake_case_       ,	snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case_					       =					['pixel_values']
                 self.assertListEqual(arg_names[:1],							lowerCAmelCase__)







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__)







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)







       @slow
       def 		a_						( self)      ->      str:
            for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case_					       =					ViTModel.from_pretrained(lowerCAmelCase__)
                 self.assertIsNotNone(lowerCAmelCase__)







def 					UpperCAmelCase    (		)				->      List[Any]:
     snake_case_					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
     return image




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







       @cached_property
       def 		a_						( self)      ->      Dict:
            return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') if is_vision_available() else None







       @slow
       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(lowerCAmelCase__)

            snake_case_					       =					self.default_image_processor
            snake_case_					       =					prepare_img()
            snake_case_					       =					image_processor(images=lowerCAmelCase__,							return_tensors='pt').to(lowerCAmelCase__)

            # forward pass
            with torch.no_grad():
                 snake_case_					       =					model(**lowerCAmelCase__)

            # verify the logits
            snake_case_					       =					torch.Size((1, 1000))
            self.assertEqual(outputs.logits.shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor([-0.2744, 0.8215, -0.0836]).to(lowerCAmelCase__)

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







       @slow
       def 		a_						( self)      ->      List[Any]:
            # ViT models have an `interpolate_pos_encoding` argument in their forward method,
            # allowing to interpolate the pre-trained position embeddings in order to use
            # the model on higher resolutions. The DINO model by Facebook AI leverages this
            # to visualize self-attention on higher resolution images.
            snake_case_					       =					ViTModel.from_pretrained('facebook/dino-vits8').to(lowerCAmelCase__)

            snake_case_					       =					ViTImageProcessor.from_pretrained('facebook/dino-vits8',							size=480)
            snake_case_					       =					prepare_img()
            snake_case_					       =					image_processor(images=lowerCAmelCase__,							return_tensors='pt')
            snake_case_					       =					inputs.pixel_values.to(lowerCAmelCase__)

            # forward pass
            with torch.no_grad():
                 snake_case_					       =					model(lowerCAmelCase__,							interpolate_pos_encoding=lowerCAmelCase__)

            # verify the logits
            snake_case_					       =					torch.Size((1, 3601, 384))
            self.assertEqual(outputs.last_hidden_state.shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor(
                [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]).to(lowerCAmelCase__)

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







       @slow
       @require_accelerate
       @require_torch_gpu
       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					ViTModel.from_pretrained('facebook/dino-vits8',							torch_dtype=torch.floataa,							device_map='auto')
            snake_case_					       =					self.default_image_processor

            snake_case_					       =					prepare_img()
            snake_case_					       =					image_processor(images=lowerCAmelCase__,							return_tensors='pt')
            snake_case_					       =					inputs.pixel_values.to(lowerCAmelCase__)

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









import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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






def   a			(						lowerCamelCase_		,   lowerCamelCase_=False				):






				'''simple docstring'''
				lowercase__    =			[]
				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'''),
								        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
								        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
								    ]				)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								lowercase__    =			[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''				) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('''norm.weight''', '''vit.layernorm.weight'''),
								        ('''norm.bias''', '''vit.layernorm.bias'''),
								        ('''head.weight''', '''classifier.weight'''),
								        ('''head.bias''', '''classifier.bias'''),
								    ]				)

				return rename_keys






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=False				):






				'''simple docstring'''
				for i in range(config.num_hidden_layers				):
								if base_model:
												lowercase__    =			''''''
								else:
												lowercase__    =			'''vit.'''
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowercase__    =			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
								lowercase__    =			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
								# next, add query, keys and values (in that order) to the state dict
								lowercase__    =			in_proj_weight[
								    : config.hidden_size, :
								]
								lowercase__    =			in_proj_bias[: config.hidden_size]
								lowercase__    =			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowercase__    =			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowercase__    =			in_proj_weight[
								    -config.hidden_size :, :
								]
								lowercase__    =			in_proj_bias[-config.hidden_size :]






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase_		,   lowerCamelCase_				)






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			dct.pop(lowerCamelCase_				)
				lowercase__    =			val






def   a			(						):






				'''simple docstring'''
				lowercase__    =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__    =			Image.open(requests.get(lowerCamelCase_		,   stream=lowerCamelCase_				).raw				)
				return im






@torch.no_grad()
def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			ViTConfig()
				lowercase__    =			False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								lowercase__    =			True
								lowercase__    =			int(vit_name[-12:-10]				)
								lowercase__    =			int(vit_name[-9:-6]				)
				else:
								lowercase__    =			1000
								lowercase__    =			'''huggingface/label-files'''
								lowercase__    =			'''imagenet-1k-id2label.json'''
								lowercase__    =			json.load(open(hf_hub_download(lowerCamelCase_		,   lowerCamelCase_		,   repo_type='''dataset'''				)		,   '''r'''				)				)
								lowercase__    =			{int(lowerCamelCase_				): v for k, v in idalabel.items()}
								lowercase__    =			idalabel
								lowercase__    =			{v: k for k, v in idalabel.items()}
								lowercase__    =			int(vit_name[-6:-4]				)
								lowercase__    =			int(vit_name[-3:]				)
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith('''tiny'''				):
												lowercase__    =			192
												lowercase__    =			768
												lowercase__    =			12
												lowercase__    =			3
								elif vit_name[9:].startswith('''small'''				):
												lowercase__    =			384
												lowercase__    =			1536
												lowercase__    =			12
												lowercase__    =			6
								else:
												pass
				else:
								if vit_name[4:].startswith('''small'''				):
												lowercase__    =			768
												lowercase__    =			2304
												lowercase__    =			8
												lowercase__    =			8
								elif vit_name[4:].startswith('''base'''				):
												pass
								elif vit_name[4:].startswith('''large'''				):
												lowercase__    =			1024
												lowercase__    =			4096
												lowercase__    =			24
												lowercase__    =			16
								elif vit_name[4:].startswith('''huge'''				):
												lowercase__    =			1280
												lowercase__    =			5120
												lowercase__    =			32
												lowercase__    =			16

    # load original model from timm
				lowercase__    =			timm.create_model(lowerCamelCase_		,   pretrained=lowerCamelCase_				)
				timm_model.eval()

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

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowercase__    =			ViTModel(lowerCamelCase_				).eval()
				else:
								lowercase__    =			ViTForImageClassification(lowerCamelCase_				).eval()
				model.load_state_dict(lowerCamelCase_				)

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								lowercase__    =			DeiTImageProcessor(size=config.image_size				)
				else:
								lowercase__    =			ViTImageProcessor(size=config.image_size				)
				lowercase__    =			image_processor(images=prepare_img()		,   return_tensors='''pt'''				)
				lowercase__    =			encoding['''pixel_values''']
				lowercase__    =			model(lowerCamelCase_				)

				if base_model:
								lowercase__    =			timm_model.forward_features(lowerCamelCase_				)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(lowerCamelCase_		,   outputs.pooler_output		,   atol=1e-3				)
				else:
								lowercase__    =			timm_model(lowerCamelCase_				)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCamelCase_		,   outputs.logits		,   atol=1e-3				)

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


if __name__ == "__main__":
							A__	:			int			   = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--vit_name',
							    default='vit_base_patch16_224',
							    type=str,
							    help='Name of the ViT timm model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)

							A__	:			str			   = parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




