


'''simple docstring'''




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 ,					snake_case__ ,					snake_case__=13 ,					snake_case__=[30, 30] ,					snake_case__=2 ,					snake_case__=3 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=32 ,					snake_case__=5 ,					snake_case__=4 ,					snake_case__=37 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=10 ,					snake_case__=0.0_2 ,					snake_case__=3 ,					snake_case__=None ,					snake_case__=8 ,					snake_case__=10 ,					):
		lowerCAmelCase					:    str      							= parent
		lowerCAmelCase					:    Tuple      							= batch_size
		lowerCAmelCase					:    List[Any]      							= image_size
		lowerCAmelCase					:    List[Any]      							= patch_size
		lowerCAmelCase					:    Dict      							= num_channels
		lowerCAmelCase					:    int      							= is_training
		lowerCAmelCase					:    Optional[Any]      							= use_labels
		lowerCAmelCase					:    Optional[int]      							= hidden_size
		lowerCAmelCase					:    List[Any]      							= num_hidden_layers
		lowerCAmelCase					:    int      							= num_attention_heads
		lowerCAmelCase					:    Any      							= intermediate_size
		lowerCAmelCase					:    Union[str, Any]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[int]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= type_sequence_label_size
		lowerCAmelCase					:    Any      							= initializer_range
		lowerCAmelCase					:    str      							= num_labels
		lowerCAmelCase					:    Any      							= scope
		lowerCAmelCase					:    Dict      							= n_targets
		lowerCAmelCase					:    Tuple      							= 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					:    List[Any]      							= (image_size[1] // patch_size) * (image_size[0] // patch_size)
		lowerCAmelCase					:    int      							= num_patches + 1 + self.num_detection_tokens



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]    )

		lowerCAmelCase					:    int      							= 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					:    Tuple      							= {}
				lowerCAmelCase					:    Dict      							= 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					:    Any      							= self.get_config()

		return config, pixel_values, labels



	def 				lowercase     (					self    ):
		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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= YolosModel(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[Any]      							= model(snake_case__    )
		self.parent.assertEqual(
		    result.last_hidden_state.shape ,					(self.batch_size, self.expected_seq_len, self.hidden_size)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= YolosForObjectDetection(snake_case__    )
		model.to(snake_case__    )
		model.eval()

		lowerCAmelCase					:    Union[str, Any]      							= model(pixel_values=snake_case__    )
		lowerCAmelCase					:    Dict      							= 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					:    str      							= 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 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= self.prepare_config_and_inputs()
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Optional[int]      							= config_and_inputs
		lowerCAmelCase					:    Dict      							= {'pixel_values': pixel_values}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	_lowerCamelCase   :					str			      = (
	    {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
	)

	_lowerCamelCase   :					Optional[int]			      = False
	_lowerCamelCase   :					Dict			      = False
	_lowerCamelCase   :					Optional[Any]			      = False
	_lowerCamelCase   :					Union[str, Any]			      = False



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__=False    ):
		lowerCAmelCase					:    Any      							= super()._prepare_for_class(snake_case__ ,					snake_case__ ,					return_labels=snake_case__    )

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

		return inputs_dict



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= YolosModelTester(self    )
		lowerCAmelCase					:    Optional[Any]      							= ConfigTester(self ,					config_class=snake_case__ ,					has_text_modality=snake_case__ ,					hidden_size=37    )



	def 				lowercase     (					self    ):
		self.config_tester.run_common_tests()



	def 				lowercase     (					self    ):
		# YOLOS does not use inputs_embeds
		pass



	def 				lowercase     (					self    ):
		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]      							= model_class(snake_case__    )
			self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)    )
			lowerCAmelCase					:    Optional[Any]      							= model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(snake_case__ ,					nn.Linear    )    )



	def 				lowercase     (					self    ):
		lowerCAmelCase							,   lowerCAmelCase					:    str      							= self.model_tester.prepare_config_and_inputs_for_common()

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

			lowerCAmelCase					:    Any      							= ['pixel_values']
			self.assertListEqual(arg_names[:1] ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs_for_common()
		lowerCAmelCase					:    Union[str, Any]      							= True

		# in YOLOS, the seq_len is different
		lowerCAmelCase					:    Any      							= self.model_tester.expected_seq_len
		for model_class in self.all_model_classes:
			lowerCAmelCase					:    List[str]      							= True
			lowerCAmelCase					:    Optional[Any]      							= False
			lowerCAmelCase					:    int      							= True
			lowerCAmelCase					:    Dict      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.eval()
			with torch.no_grad():
				lowerCAmelCase					:    Optional[Any]      							= model(**self._prepare_for_class(snake_case__ ,					snake_case__    )    )
			lowerCAmelCase					:    Any      							= 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					:    str      							= True
			lowerCAmelCase					:    List[str]      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.eval()
			with torch.no_grad():
				lowerCAmelCase					:    Optional[int]      							= model(**self._prepare_for_class(snake_case__ ,					snake_case__    )    )
			lowerCAmelCase					:    Any      							= 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					:    Tuple      							= True
			lowerCAmelCase					:    Any      							= True
			lowerCAmelCase					:    Any      							= model_class(snake_case__    )
			model.to(snake_case__    )
			model.eval()
			with torch.no_grad():
				lowerCAmelCase					:    List[str]      							= model(**self._prepare_for_class(snake_case__ ,					snake_case__    )    )

			lowerCAmelCase					:    Dict      							= 1
			self.assertEqual(out_len + added_hidden_states ,					len(snake_case__    )    )

			lowerCAmelCase					:    Optional[Any]      							= 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 				lowercase     (					self    ):
		def check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Optional[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					:    Optional[Any]      							= outputs.hidden_states

			lowerCAmelCase					:    Dict      							= 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					:    Union[str, Any]      							= self.model_tester.expected_seq_len

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

		lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    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					:    Dict      							= True

			check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_object_detection(*snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowerCAmelCase					:    List[Any]      							= YolosModel.from_pretrained(snake_case__    )
			self.assertIsNotNone(snake_case__    )


def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image






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



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



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= YolosForObjectDetection.from_pretrained('hustvl/yolos-small'    ).to(snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= self.default_image_processor
		lowerCAmelCase					:    List[Any]      							= prepare_img()
		lowerCAmelCase					:    List[Any]      							= image_processor(images=snake_case__ ,					return_tensors='pt'    ).to(snake_case__    )

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

		# verify outputs
		lowerCAmelCase					:    Optional[Any]      							= 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					:    Dict      							= 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					:    Tuple      							= image_processor.post_process_object_detection(
		    snake_case__ ,					threshold=0.3 ,					target_sizes=[image.size[::-1]]    )[0]
		lowerCAmelCase					:    Dict      							= 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					:    int      							= [75, 75, 17, 63, 17]
		lowerCAmelCase					:    str      							= 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__    )    )




'''simple docstring'''




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 			__UpperCamelCase    (    _A    :				Dict				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
	        F"stage{idx}.patch_embed.proj.weight",
	    )				)
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
	        F"stage{idx}.patch_embed.proj.bias",
	    )				)
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
	        F"stage{idx}.patch_embed.norm.weight",
	    )				)
	embed.append(
	    (
	        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
	        F"stage{idx}.patch_embed.norm.bias",
	    )				)
	return embed

def 			__UpperCamelCase    (    _A    :				List[Any]					,   _A    :				Dict				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= []
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
	        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
	        F"stage{idx}.blocks.{cnt}.attn.proj.weight",
	    )				)
	attention_weights.append(
	    (
	        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
	        F"stage{idx}.blocks.{cnt}.attn.proj.bias",
	    )				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")				)
	attention_weights.append(
	    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")				)
	return attention_weights

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= []
	token.append((F"cvt.encoder.stages.{idx}.cls_token", 'stage2.cls_token')				)
	return token

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= []
	head.append(('layernorm.weight', 'norm.weight')				)
	head.append(('layernorm.bias', 'norm.bias')				)
	head.append(('classifier.weight', 'head.weight')				)
	head.append(('classifier.bias', 'head.bias')				)
	return head

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Optional[Any]					,   _A    :				Dict					,   _A    :				str				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    Tuple      							= 10_00

	lowerCAmelCase					:    str      							= 'huggingface/label-files'
	lowerCAmelCase					:    List[Any]      							= num_labels
	lowerCAmelCase					:    Any      							= json.load(open(cached_download(hf_hub_url(_A					,   _A					,   repo_type='dataset'				)				)					,   'r'				)				)
	lowerCAmelCase					:    List[str]      							= {int(_A				): v for k, v in idalabel.items()}

	lowerCAmelCase					:    List[str]      							= idalabel
	lowerCAmelCase					:    str      							= {v: k for k, v in idalabel.items()}

	lowerCAmelCase					:    int      							= CvtConfig(num_labels=_A					,   idalabel=_A					,   labelaid=_A				)

	# For depth size 13 (13 = 1+2+10)
	if cvt_model.rsplit('/'					,   1				)[-1][4:6] == "13":
		lowerCAmelCase					:    List[str]      							= [1, 2, 10]

	# For depth size 21 (21 = 1+4+16)
	elif cvt_model.rsplit('/'					,   1				)[-1][4:6] == "21":
		lowerCAmelCase					:    Tuple      							= [1, 4, 16]

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		lowerCAmelCase					:    Any      							= [2, 2, 20]
		lowerCAmelCase					:    List[str]      							= [3, 12, 16]
		lowerCAmelCase					:    List[Any]      							= [1_92, 7_68, 10_24]

	lowerCAmelCase					:    Union[str, Any]      							= CvtForImageClassification(_A				)
	lowerCAmelCase					:    str      							= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
	lowerCAmelCase					:    Optional[Any]      							= image_size
	lowerCAmelCase					:    List[Any]      							= torch.load(_A					,   map_location=torch.device('cpu'				)				)

	lowerCAmelCase					:    str      							= OrderedDict()
	lowerCAmelCase					:    int      							= []

	for idx in range(len(config.depth				)				):
		if config.cls_token[idx]:
			lowerCAmelCase					:    List[str]      							= list_of_state_dict + cls_token(_A				)
		lowerCAmelCase					:    Optional[Any]      							= list_of_state_dict + embeddings(_A				)
		for cnt in range(config.depth[idx]				):
			lowerCAmelCase					:    List[Any]      							= list_of_state_dict + attention(_A					,   _A				)

	lowerCAmelCase					:    List[str]      							= list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(_A				)
	for i in range(len(_A				)				):
		lowerCAmelCase					:    Tuple      							= original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(_A				)
	model.save_pretrained(_A				)
	image_processor.save_pretrained(_A				)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
				_lowerCAmelCase  :		Optional[Any]		     =	argparse.ArgumentParser()
				parser.add_argument(
				    '--cvt_model',
				    default='cvt-w24',
				    type=str,
				    help='Name of the cvt model you\'d like to convert.',
				)
				parser.add_argument(
				    '--image_size',
				    default=384,
				    type=int,
				    help='Input Image Size',
				)
				parser.add_argument(
				    '--cvt_file_name',
				    default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
				    type=str,
				    help='Input Image Size',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				_lowerCAmelCase  :		str		     =	parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
