import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import YolosForObjectDetection, YolosModel
			from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor
class   _lowerCAmelCase		:







							def __init__(				self     :    Any	,					__snake_case     :    Optional[int]	,					__snake_case     :    int=13	,					__snake_case     :    str=[30, 30]	,					__snake_case     :    Tuple=2	,					__snake_case     :    Optional[Any]=3	,					__snake_case     :    int=True	,					__snake_case     :    Tuple=True	,					__snake_case     :    List[Any]=32	,					__snake_case     :    int=5	,					__snake_case     :    Optional[Any]=4	,					__snake_case     :    Union[str, Any]=37	,					__snake_case     :    str="gelu"	,					__snake_case     :    Tuple=0.1	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    Union[str, Any]=10	,					__snake_case     :    str=0.0_2	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Union[str, Any]=None	,					__snake_case     :    List[str]=8	,					__snake_case     :    Any=10	,					):
										lowerCamelCase						:Optional[Any]						  =     parent
										lowerCamelCase						:List[Any]						  =     batch_size
										lowerCamelCase						:Any						  =     image_size
										lowerCamelCase						:Union[str, Any]						  =     patch_size
										lowerCamelCase						:Any						  =     num_channels
										lowerCamelCase						:List[Any]						  =     is_training
										lowerCamelCase						:Optional[Any]						  =     use_labels
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:List[Any]						  =     num_hidden_layers
										lowerCamelCase						:List[str]						  =     num_attention_heads
										lowerCamelCase						:Tuple						  =     intermediate_size
										lowerCamelCase						:List[str]						  =     hidden_act
										lowerCamelCase						:List[str]						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     type_sequence_label_size
										lowerCamelCase						:Optional[int]						  =     initializer_range
										lowerCamelCase						:List[Any]						  =     num_labels
										lowerCamelCase						:Any						  =     scope
										lowerCamelCase						:Union[str, Any]						  =     n_targets
										lowerCamelCase						:Optional[Any]						  =     num_detection_tokens
										# we set the expected sequence length (which is used in several tests)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
										lowerCamelCase						:Tuple						  =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCamelCase						:str						  =     num_patches + 1 + self.num_detection_tokens







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:str						  =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

										lowerCamelCase						:List[str]						  =     None
										if self.use_labels:
													# labels is a list of Dict (each Dict being the labels for a given example in the batch)
													lowerCamelCase						:Optional[int]						  =     []
													for i in range(self.batch_size		):
																lowerCamelCase						:List[str]						  =     {}
																lowerCamelCase						:Tuple						  =     torch.randint(
																    high=self.num_labels	,					size=(self.n_targets,)	,					device=__snake_case		)
																lowerCamelCase						:List[str]						  =     torch.rand(self.n_targets	,					4	,					device=__snake_case		)
																labels.append(__snake_case		)

										lowerCamelCase						:str						  =     self.get_config()

										return config, pixel_values, labels







							def 				snake_case  (				self     :    Union[str, Any]		):
										return YolosConfig(
										    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=__snake_case	,					initializer_range=self.initializer_range	,					num_detection_tokens=self.num_detection_tokens	,					num_labels=self.num_labels	,					)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Any		):
										lowerCamelCase						:Optional[Any]						  =     YolosModel(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase						:Union[str, Any]						  =     model(__snake_case		)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,					(self.batch_size, self.expected_seq_len, self.hidden_size)		)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]		):
										lowerCamelCase						:int						  =     YolosForObjectDetection(__snake_case		)
										model.to(__snake_case		)
										model.eval()

										lowerCamelCase						:str						  =     model(pixel_values=__snake_case		)
										lowerCamelCase						:Any						  =     model(__snake_case		)

										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
										self.parent.assertEqual(result.pred_boxes.shape	,					(self.batch_size, self.num_detection_tokens, 4)		)

										lowerCamelCase						:int						  =     model(pixel_values=__snake_case	,					labels=__snake_case		)

										self.parent.assertEqual(result.loss.shape	,					()		)
										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
										self.parent.assertEqual(result.pred_boxes.shape	,					(self.batch_size, self.num_detection_tokens, 4)		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[Any]						  =     self.prepare_config_and_inputs()
										lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:str						  =     config_and_inputs
										lowerCamelCase						:Union[str, Any]						  =     {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_UpperCAmelCase						    =     (
							    {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
							)

							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    Dict=False		):
										lowerCamelCase						:Optional[int]						  =     super()._prepare_for_class(__snake_case	,					__snake_case	,					return_labels=__snake_case		)

										if return_labels:
													if model_class.__name__ == "YolosForObjectDetection":
																lowerCamelCase						:Dict						  =     []
																for i in range(self.model_tester.batch_size		):
																			lowerCamelCase						:Optional[Any]						  =     {}
																			lowerCamelCase						:List[Any]						  =     torch.ones(
																			    size=(self.model_tester.n_targets,)	,					device=__snake_case	,					dtype=torch.long		)
																			lowerCamelCase						:str						  =     torch.ones(
																			    self.model_tester.n_targets	,					4	,					device=__snake_case	,					dtype=torch.float		)
																			labels.append(__snake_case		)
																lowerCamelCase						:Union[str, Any]						  =     labels

										return inputs_dict







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Union[str, Any]						  =     YolosModelTester(self		)
										lowerCamelCase						:Dict						  =     ConfigTester(self	,					config_class=__snake_case	,					has_text_modality=__snake_case	,					hidden_size=37		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Optional[Any]		):
										# YOLOS does not use inputs_embeds
										pass







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[int]						  =     model_class(__snake_case		)
													self.assertIsInstance(model.get_input_embeddings()	,					(nn.Module)		)
													lowerCamelCase						:str						  =     model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__snake_case	,					nn.Linear		)		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCamelCase						:Tuple						  =     ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,					__snake_case		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase						:int						  =     True

										# in YOLOS, the seq_len is different
										lowerCamelCase						:str						  =     self.model_tester.expected_seq_len
										for model_class in self.all_model_classes:
													lowerCamelCase						:str						  =     True
													lowerCamelCase						:Tuple						  =     False
													lowerCamelCase						:Optional[int]						  =     True
													lowerCamelCase						:int						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:str						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)
													lowerCamelCase						:str						  =     outputs.attentions
													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase						:Optional[Any]						  =     True
													lowerCamelCase						:str						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:Tuple						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)
													lowerCamelCase						:Tuple						  =     outputs.attentions
													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, seq_len, seq_len]	,					)
													lowerCamelCase						:Optional[int]						  =     len(__snake_case		)

													# Check attention is always last and order is fine
													lowerCamelCase						:Union[str, Any]						  =     True
													lowerCamelCase						:List[Any]						  =     True
													lowerCamelCase						:Tuple						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:int						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Dict						  =     1
													self.assertEqual(out_len + added_hidden_states	,					len(__snake_case		)		)

													lowerCamelCase						:Dict						  =     outputs.attentions

													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, seq_len, seq_len]	,					)







							def 				snake_case  (				self     :    List[str]		):
										def check_hidden_states_output(__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple		):
													lowerCamelCase						:Union[str, Any]						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase						:Any						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Optional[Any]						  =     outputs.hidden_states

													lowerCamelCase						:Any						  =     getattr(
													    self.model_tester	,					'''expected_num_hidden_layers'''	,					self.model_tester.num_hidden_layers + 1		)
													self.assertEqual(len(__snake_case		)	,					__snake_case		)

													# YOLOS has a different seq_length
													lowerCamelCase						:List[str]						  =     self.model_tester.expected_seq_len

													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]		)	,					[seq_length, self.model_tester.hidden_size]	,					)

										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Union[str, Any]						  =     True
													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)

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

													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[Any]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*__snake_case		)







							@slow
							def 				snake_case  (				self     :    Dict		):
										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase						:Tuple						  =     YolosModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
			return image


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







							@cached_property
							def 				snake_case  (				self     :    Tuple		):
										return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None







							@slow
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Union[str, Any]						  =     YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     self.default_image_processor
										lowerCamelCase						:str						  =     prepare_img()
										lowerCamelCase						:Dict						  =     image_processor(images=__snake_case	,					return_tensors='''pt'''		).to(__snake_case		)

										# forward pass
										with torch.no_grad():
													lowerCamelCase						:Optional[Any]						  =     model(inputs.pixel_values		)

										# verify outputs
										lowerCamelCase						:int						  =     torch.Size((1, 100, 92)		)
										self.assertEqual(outputs.logits.shape	,					__snake_case		)

										lowerCamelCase						:Any						  =     torch.tensor(
										    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]	,					device=__snake_case	,					)
										lowerCamelCase						:Any						  =     torch.tensor(
										    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]	,					device=__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)

										# verify postprocessing
										lowerCamelCase						:List[str]						  =     image_processor.post_process_object_detection(
										    __snake_case	,					threshold=0.3	,					target_sizes=[image.size[::-1]]		)[0]
										lowerCamelCase						:List[str]						  =     torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(__snake_case		)
										lowerCamelCase						:str						  =     [75, 75, 17, 63, 17]
										lowerCamelCase						:Tuple						  =     torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(__snake_case		)

										self.assertEqual(len(results['''scores''']		)	,					5		)
										self.assertTrue(torch.allclose(results['''scores''']	,					__snake_case	,					atol=1e-4		)		)
										self.assertSequenceEqual(results['''labels'''].tolist()	,					__snake_case		)
										self.assertTrue(torch.allclose(results['''boxes'''][0, :]	,					__snake_case		)		)




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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A__           =							logging.get_logger(__name__)





def  _lowerCamelCase					(   a_				: str						,			a_				: str=False):
			lowerCamelCase						:Optional[int]						  =     []

			# fmt: off
			# stem:
			rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token'''))
			rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings'''))

			rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''))
			rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''))

			# backbone
			rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight'''))
			rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight'''))
			rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias'''))

			for stage_idx in range(len(config.backbone_config.depths)):
						for layer_idx in range(config.backbone_config.depths[stage_idx]):
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias"))

						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight"))
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight"))
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias"))

			# transformer encoder
			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"))

			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"
						lowerCamelCase						:List[Any]						  =     [(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'''),
						    ])
			# fmt: on

			return rename_keys





def  _lowerCamelCase					(   a_				: Any						,			a_				: Any						,			a_				: int=False):
			for i in range(config.num_hidden_layers):
						if base_model:
									lowerCamelCase						:Union[str, Any]						  =     ''''''
						else:
									lowerCamelCase						:Optional[int]						  =     '''vit.'''
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCamelCase						:Optional[Any]						  =     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						:Any						  =     in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase						:Tuple						  =     in_proj_bias[: config.hidden_size]
						lowerCamelCase						:int						  =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase						:Tuple						  =     in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCamelCase						:Optional[Any]						  =     in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase						:List[Any]						  =     in_proj_bias[-config.hidden_size :]





def  _lowerCamelCase					(   a_				: int):
			lowerCamelCase						:Any						  =     ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a_						,			a_)





def  _lowerCamelCase					(   a_				: int						,			a_				: Any						,			a_				: Tuple):
			lowerCamelCase						:Optional[Any]						  =     dct.pop(a_)
			lowerCamelCase						:str						  =     val





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase						:Tuple						  =     Image.open(requests.get(a_						,			stream=a_).raw)
			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: Optional[Any]						,			a_				: Optional[Any]=False):
			lowerCamelCase						:Optional[int]						  =     BitConfig(
			    global_padding='''same'''						,			layer_type='''bottleneck'''						,			depths=(3, 4, 9)						,			out_features=['''stage3''']						,			embedding_dynamic_padding=a_						,			)
			lowerCamelCase						:Optional[int]						  =     ViTHybridConfig(backbone_config=a_						,			image_size=3_84						,			num_labels=10_00)
			lowerCamelCase						:List[Any]						  =     False

			# load original model from timm
			lowerCamelCase						:List[str]						  =     timm.create_model(a_						,			pretrained=a_)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase						:List[str]						  =     timm_model.state_dict()
			if base_model:
						remove_classification_head_(a_)
			lowerCamelCase						:Tuple						  =     create_rename_keys(a_						,			a_)
			for src, dest in rename_keys:
						rename_key(a_						,			a_						,			a_)
			read_in_q_k_v(a_						,			a_						,			a_)

			lowerCamelCase						:List[str]						  =     '''huggingface/label-files'''
			lowerCamelCase						:Any						  =     '''imagenet-1k-id2label.json'''
			lowerCamelCase						:List[Any]						  =     json.load(open(hf_hub_download(a_						,			a_						,			repo_type='''dataset''')						,			'''r'''))
			lowerCamelCase						:Optional[Any]						  =     {int(a_): v for k, v in idalabel.items()}
			lowerCamelCase						:Optional[int]						  =     idalabel
			lowerCamelCase						:Union[str, Any]						  =     {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						lowerCamelCase						:Optional[Any]						  =     ViTHybridModel(a_).eval()
			else:
						lowerCamelCase						:Dict						  =     ViTHybridForImageClassification(a_).eval()
			model.load_state_dict(a_)

			# create image processor
			lowerCamelCase						:Dict						  =     create_transform(**resolve_data_config({}						,			model=a_))
			lowerCamelCase						:str						  =     transform.transforms

			lowerCamelCase						:int						  =     {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			lowerCamelCase						:Any						  =     ViTHybridImageProcessor(
			    do_resize=a_						,			size={'''shortest_edge''': timm_transforms[0].size}						,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,			do_center_crop=a_						,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}						,			do_normalize=a_						,			image_mean=timm_transforms[-1].mean.tolist()						,			image_std=timm_transforms[-1].std.tolist()						,			)

			lowerCamelCase						:Dict						  =     prepare_img()
			lowerCamelCase						:str						  =     transform(a_).unsqueeze(0)
			lowerCamelCase						:str						  =     processor(a_						,			return_tensors='''pt''').pixel_values

			# verify pixel values
			assert torch.allclose(a_						,			a_)

			# verify logits
			with torch.no_grad():
						lowerCamelCase						:Optional[int]						  =     model(a_)
						lowerCamelCase						:Union[str, Any]						  =     outputs.logits

			print('''Predicted class:'''						,			logits.argmax(-1).item())
			if base_model:
						lowerCamelCase						:Union[str, Any]						  =     timm_model.forward_features(a_)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(a_						,			outputs.pooler_output						,			atol=1e-3)
			else:
						lowerCamelCase						:List[str]						  =     timm_model(a_)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(a_						,			outputs.logits						,			atol=1e-3)
			print('''Looks ok!''')

			if pytorch_dump_folder_path is not None:
						Path(a_).mkdir(exist_ok=a_)
						print(F"Saving model {vit_name} to {pytorch_dump_folder_path}")
						model.save_pretrained(a_)
						print(F"Saving processor to {pytorch_dump_folder_path}")
						processor.save_pretrained(a_)

			if push_to_hub:
						print(F"Pushing model and processor to the hub {vit_name}")
						model.push_to_hub(F"ybelkada/{vit_name}")
						processor.push_to_hub(F"ybelkada/{vit_name}")


if __name__ == "__main__":
			A__           =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--vit_name""",
			    default="""vit_base_r50_s16_384""",
			    type=str,
			    help="""Name of the hybrid 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."""
			)
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
			)

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



