





import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
  import torch

  from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=None   ,				A_=None   ,				A_=None   ,				A_="resnet50"   ,				A_=3   ,				A_=32   ,				A_=3   ,				A_=True   ,				A_=True   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			out_indices if out_indices is not None else [4]
        SCREAMING_SNAKE_CASE__											=			stage_names
        SCREAMING_SNAKE_CASE__											=			out_features
        SCREAMING_SNAKE_CASE__											=			backbone
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			image_size
        SCREAMING_SNAKE_CASE__											=			num_channels
        SCREAMING_SNAKE_CASE__											=			use_pretrained_backbone
        SCREAMING_SNAKE_CASE__											=			is_training



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

        return config, pixel_values



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return TimmBackboneConfig(
            image_size=self.image_size   ,				num_channels=self.num_channels   ,				out_features=self.out_features   ,				out_indices=self.out_indices   ,				stage_names=self.stage_names   ,				use_pretrained_backbone=self.use_pretrained_backbone   ,				backbone=self.backbone   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimmBackbone(config=A_     )
        model.to(A_     )
        model.eval()
        with torch.no_grad():
            SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(
            result.feature_map[-1].shape   ,				(self.batch_size, model.channels[-1], 14, 14)   ,				)




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_torch
@require_timm
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	(TimmBackbone,) if is_torch_available() else ()
    lowerCamelCase__    :		Any				=	{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
    lowerCamelCase__    :		str				=	False
    lowerCamelCase__    :		Optional[int]				=	False
    lowerCamelCase__    :		Optional[Any]				=	False
    lowerCamelCase__    :		int				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimmBackboneModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				has_text_modality=A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''resnet18'''
        SCREAMING_SNAKE_CASE__											=			'''microsoft/resnet-18'''

        SCREAMING_SNAKE_CASE__											=			AutoBackbone.from_pretrained(A_   ,				use_timm_backbone=A_     )
        SCREAMING_SNAKE_CASE__											=			AutoBackbone.from_pretrained(A_     )

        self.assertEqual(len(timm_model.out_features     )   ,				len(transformers_model.out_features     )     )
        self.assertEqual(len(timm_model.stage_names     )   ,				len(transformers_model.stage_names     )     )
        self.assertEqual(timm_model.channels   ,				transformers_model.channels     )
        # Out indices are set to the last layer by default. For timm models, we don't know
        # the number of layers in advance, so we set it to (-1,), whereas for transformers
        # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
        self.assertEqual(timm_model.out_indices   ,				(-1,)     )
        self.assertEqual(transformers_model.out_indices   ,				[len(timm_model.stage_names     ) - 1]     )

        SCREAMING_SNAKE_CASE__											=			AutoBackbone.from_pretrained(A_   ,				use_timm_backbone=A_   ,				out_indices=[1, 2, 3]     )
        SCREAMING_SNAKE_CASE__											=			AutoBackbone.from_pretrained(A_   ,				out_indices=[1, 2, 3]     )

        self.assertEqual(timm_model.out_indices   ,				transformers_model.out_indices     )
        self.assertEqual(len(timm_model.out_features     )   ,				len(transformers_model.out_features     )     )
        self.assertEqual(timm_model.channels   ,				transformers_model.channels     )



    @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone initialization is managed on the timm side'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''Safetensors is not supported by timm.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self     ):





        '''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(A_     )
            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]   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			self.has_attentions

        # no need to test all models as different heads yield the same functionality
        SCREAMING_SNAKE_CASE__											=			self.all_model_classes[0]
        SCREAMING_SNAKE_CASE__											=			model_class(A_     )
        model.to(A_     )

        SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			model(**A_     )
        SCREAMING_SNAKE_CASE__											=			outputs[0][-1]

        # Encoder-/Decoder-only models
        SCREAMING_SNAKE_CASE__											=			outputs.hidden_states[0]
        hidden_states.retain_grad()

        if self.has_attentions:
            SCREAMING_SNAKE_CASE__											=			outputs.attentions[0]
            attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=A_     )

        self.assertIsNotNone(hidden_states.grad     )

        if self.has_attentions:
            self.assertIsNotNone(attentions.grad     )




    def  lowercase_					(       self     ):





        '''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(A_     )
            model.to(A_     )
            model.eval()
            SCREAMING_SNAKE_CASE__											=			model(**A_     )

            self.assertEqual(len(result.feature_maps     )   ,				len(config.out_indices     )     )
            self.assertEqual(len(model.channels     )   ,				len(config.out_indices     )     )

            # Check output of last stage is taken if out_features=None, out_indices=None
            SCREAMING_SNAKE_CASE__											=			copy.deepcopy(A_     )
            SCREAMING_SNAKE_CASE__											=			None
            SCREAMING_SNAKE_CASE__											=			model_class(A_     )
            model.to(A_     )
            model.eval()
            SCREAMING_SNAKE_CASE__											=			model(**A_     )

            self.assertEqual(len(result.feature_maps     )   ,				1     )
            self.assertEqual(len(model.channels     )   ,				1     )

            # Check backbone can be initialized with fresh weights
            SCREAMING_SNAKE_CASE__											=			copy.deepcopy(A_     )
            SCREAMING_SNAKE_CASE__											=			False
            SCREAMING_SNAKE_CASE__											=			model_class(A_     )
            model.to(A_     )
            model.eval()
            SCREAMING_SNAKE_CASE__											=			model(**A_     )







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 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')	)
    return token


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__											=			1_0_0_0

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__											=			num_labels
    SCREAMING_SNAKE_CASE__											=			json.load(open(cached_download(hf_hub_url(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	)	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE__											=			idalabel
    SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE__											=			SCREAMING_SNAKE_CASE__											=			CvtConfig(num_labels=lowerCAmelCase_ ,							idalabel=lowerCAmelCase_ ,							labelaid=lowerCAmelCase_	)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "13":
        SCREAMING_SNAKE_CASE__											=			[1, 2, 1_0]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "21":
        SCREAMING_SNAKE_CASE__											=			[1, 4, 1_6]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        SCREAMING_SNAKE_CASE__											=			[2, 2, 2_0]
        SCREAMING_SNAKE_CASE__											=			[3, 1_2, 1_6]
        SCREAMING_SNAKE_CASE__											=			[1_9_2, 7_6_8, 1_0_2_4]

    SCREAMING_SNAKE_CASE__											=			CvtForImageClassification(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
    SCREAMING_SNAKE_CASE__											=			image_size
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location=torch.device('''cpu'''	)	)

    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    SCREAMING_SNAKE_CASE__											=			[]

    for idx in range(len(config.depth	)	):
        if config.cls_token[idx]:
            SCREAMING_SNAKE_CASE__											=			list_of_state_dict + cls_token(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			list_of_state_dict + embeddings(lowerCAmelCase_	)
        for cnt in range(config.depth[idx]	):
            SCREAMING_SNAKE_CASE__											=			list_of_state_dict + attention(lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(lowerCAmelCase_	)
    for i in range(len(lowerCAmelCase_	)	):
        SCREAMING_SNAKE_CASE__											=			original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(lowerCAmelCase_	)
    model.save_pretrained(lowerCAmelCase_	)
    image_processor.save_pretrained(lowerCAmelCase_	)


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

if __name__ == "__main__":
  _A					:				Union[str, 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=3_84,
      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."""
  )

  _A					:				int				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
