

import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       int,lowercase_ :       List[Any]=3,lowercase_ :       Dict=3_2,lowercase_ :       List[str]=3,lowercase_ :       List[str]=1_0,lowercase_ :       List[Any]=[8, 1_6, 3_2, 6_4],lowercase_ :       Any=[1, 1, 2, 1],lowercase_ :       Any=True,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]="relu",lowercase_ :       int=3,lowercase_ :       Tuple=None,lowercase_ :       Union[str, Any]=["stage2", "stage3", "stage4"],lowercase_ :       Tuple=[2, 3, 4],lowercase_ :       List[Any]=1,)->    int:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   num_channels
        A__          =   embeddings_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_act
        A__          =   num_labels
        A__          =   scope
        A__          =   len(lowercase_		)
        A__          =   out_features
        A__          =   out_indices
        A__          =   num_groups


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







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

        A__          =   None
        if self.use_labels:
            A__          =   ids_tensor([self.batch_size],self.num_labels		)

        A__          =   self.get_config()

        return config, pixel_values, labels


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





        '''simple docstring'''







        return BitConfig(
            num_channels=self.num_channels,embeddings_size=self.embeddings_size,hidden_sizes=self.hidden_sizes,depths=self.depths,hidden_act=self.hidden_act,num_labels=self.num_labels,out_features=self.out_features,out_indices=self.out_indices,num_groups=self.num_groups,)


    def 						snake_case__     (      self :       Dict,lowercase_ :       int,lowercase_ :       Optional[int],lowercase_ :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   BitModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Tuple,lowercase_ :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   BitForImageClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       Dict,lowercase_ :       List[str]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		),len(config.out_features		)		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		),[self.batch_size, self.hidden_sizes[1], 4, 4]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		),len(config.out_features		)		)
        self.parent.assertListEqual(model.channels,config.hidden_sizes[1:]		)

        # verify backbone works with out_features=None
        A__          =   None
        A__          =   BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		),1		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		),[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		),1		)
        self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]]		)


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





        '''simple docstring'''







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




@require_torch
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Union[str, Any]		)->    int:





        '''simple docstring'''







        A__          =   BitModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''simple docstring'''







        self.create_and_test_config_common_properties()
        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 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        return


    @unittest.skip(reason='Bit does not output attentions'		)
    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        pass


    @unittest.skip(reason='Bit does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        pass


    @unittest.skip(reason='Bit does not support input and output embeddings'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase_		)


    def 						snake_case__     (      self :       Any		)->    Any:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(config=lowercase_		)
            for name, module in model.named_modules():
                if isinstance(lowercase_,(nn.BatchNormad, nn.GroupNorm)		):
                    self.assertTrue(
                        torch.all(module.weight == 1		),msg=F'Parameter {name} of model {model_class} seems not properly initialized',)
                    self.assertTrue(
                        torch.all(module.bias == 0		),msg=F'Parameter {name} of model {model_class} seems not properly initialized',)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_ :       Any,lowercase_ :       Union[str, Any],lowercase_ :       Dict		):
            A__          =   model_class(lowercase_		)
            model.to(lowercase_		)
            model.eval()

            with torch.no_grad():
                A__          =   model(**self._prepare_for_class(lowercase_,lowercase_		)		)

            A__          =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A__          =   self.model_tester.num_stages
            self.assertEqual(len(lowercase_		),expected_num_stages + 1		)

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]		),[self.model_tester.image_size // 4, self.model_tester.image_size // 4],)

        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()
        A__          =   ['preactivation', 'bottleneck']
        for model_class in self.all_model_classes:
            for layer_type in layers_type:
                A__          =   layer_type
                A__          =   True
                check_hidden_states_output(lowercase_,lowercase_,lowercase_		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                A__          =   True

                check_hidden_states_output(lowercase_,lowercase_,lowercase_		)


    @unittest.skip(reason='Bit does not use feedforward chunking'		)
    def 						snake_case__     (      self :       int		)->    str:





        '''simple docstring'''







        pass


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


    @slow
    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__          =   BitModel.from_pretrained(lowercase_		)
            self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






    A__          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image


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







    """simple docstring"""





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





        '''simple docstring'''







        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
        )


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    Dict:





        '''simple docstring'''







        A__          =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowercase_		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='pt'		).to(lowercase_		)

        # forward pass
        with torch.no_grad():
            A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]		).to(lowercase_		)

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




@require_torch
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(BitBackbone,) if is_torch_available() else ()
    lowerCamelCase			=							BitConfig

    lowerCamelCase			=							False


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





        '''simple docstring'''







        A__          =   BitModelTester(self		)



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(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      []
       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(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Optional[Any]       	=      []
       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(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      []
       token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")				)
       return token







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Optional[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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      """imagenet-1k-id2label.json"""
       lowercase							:     List[str]       	=      1_000

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

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

       lowercase							:     List[str]       	=      CvtConfig(num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__				)

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

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

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              lowercase							:     int       	=      [2, 2, 20]
              lowercase							:     Optional[int]       	=      [3, 12, 16]
              lowercase							:     str       	=      [192, 768, 1_024]

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

       lowercase							:     Optional[Any]       	=      OrderedDict()
       lowercase							:     Tuple       	=      []

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

       lowercase							:     List[str]       	=      list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     Optional[Any]       	=      original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


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

if __name__ == "__main__":
  lowercase  :					Tuple						    =							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."""
  )

  lowercase  :					Optional[int]						    =							parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
