






"""simple docstring"""


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDeiTForImageClassification,
       TFDeiTForImageClassificationWithTeacher,
       TFDeiTForMaskedImageModeling,
       TFDeiTModel,
   )
   from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor






class 						snake_case  :





    def __init__(							self     : Optional[Any]    ,		A     : Any    ,		A     : Tuple=1_3    ,		A     : List[Any]=3_0    ,		A     : Optional[int]=2    ,		A     : Optional[int]=3    ,		A     : Any=True    ,		A     : str=True    ,		A     : Optional[Any]=3_2    ,		A     : Tuple=2    ,		A     : Any=4    ,		A     : Tuple=3_7    ,		A     : str="gelu"    ,		A     : Any=0.1    ,		A     : str=0.1    ,		A     : Union[str, Any]=1_0    ,		A     : Optional[Any]=0.02    ,		A     : Tuple=3    ,		A     : List[str]=None    ,		A     : Any=2    ,		):


      '''simple docstring'''




      a					:      str    =       parent
      a					:      Tuple    =       batch_size
      a					:      int    =       image_size
      a					:      int    =       patch_size
      a					:      Tuple    =       num_channels
      a					:      Any    =       is_training
      a					:      Any    =       use_labels
      a					:      Dict    =       hidden_size
      a					:      Optional[Any]    =       num_hidden_layers
      a					:      Optional[Any]    =       num_attention_heads
      a					:      List[str]    =       intermediate_size
      a					:      List[Any]    =       hidden_act
      a					:      Optional[Any]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Any    =       type_sequence_label_size
      a					:      List[Any]    =       initializer_range
      a					:      List[str]    =       scope
      a					:      int    =       encoder_stride

      # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
      a					:      List[Any]    =       (image_size // patch_size) ** 2
      a					:      int    =       num_patches + 2





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      int    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      a					:      str    =       None
      if self.use_labels:
        a					:      List[str]    =       ids_tensor([self.batch_size]    ,		self.type_sequence_label_size      )

      a					:      str    =       self.get_config()

      return config, pixel_values, labels





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return DeiTConfig(
          image_size=self.image_size    ,		patch_size=self.patch_size    ,		num_channels=self.num_channels    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		is_decoder=__lowerCAmelCase    ,		initializer_range=self.initializer_range    ,		encoder_stride=self.encoder_stride    ,		)





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : List[str]    ,		A     : Dict    ,		A     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       TFDeiTModel(config=__lowerCAmelCase      )
      a					:      List[Any]    =       model(__lowerCAmelCase      )
      self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Any    ,		A     : Dict    ,		A     : str      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       TFDeiTForMaskedImageModeling(config=__lowerCAmelCase      )
      a					:      Tuple    =       model(__lowerCAmelCase      )
      self.parent.assertEqual(
          result.reconstruction.shape    ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

      # test greyscale images
      a					:      Optional[Any]    =       1
      a					:      str    =       TFDeiTForMaskedImageModeling(__lowerCAmelCase      )

      a					:      Tuple    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
      a					:      Optional[Any]    =       model(__lowerCAmelCase      )
      self.parent.assertEqual(result.reconstruction.shape    ,		(self.batch_size, 1, self.image_size, self.image_size)      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : List[str]    ,		A     : List[str]    ,		A     : str      ):


      '''simple docstring'''




      a					:      List[str]    =       self.type_sequence_label_size
      a					:      str    =       TFDeiTForImageClassification(__lowerCAmelCase      )
      a					:      List[Any]    =       model(__lowerCAmelCase    ,		labels=__lowerCAmelCase      )
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)      )

      # test greyscale images
      a					:      List[Any]    =       1
      a					:      Union[str, Any]    =       TFDeiTForImageClassification(__lowerCAmelCase      )

      a					:      Any    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
      a					:      Optional[Any]    =       model(__lowerCAmelCase    ,		labels=__lowerCAmelCase      )
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)      )





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      str    =       self.prepare_config_and_inputs()
      a,					a,					a					:      Tuple    =       config_and_inputs
      a					:      int    =       {'pixel_values': pixel_values}
      return config, inputs_dict






@require_tf
class 						snake_case  (  lowerCAmelCase_       ,			lowerCAmelCase_       ,			unittest.TestCase	):
    __magic_name__   					=					(
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    __magic_name__   					=					(
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       TFDeiTModelTester(self      )
      a					:      Dict    =       ConfigTester(self    ,		config_class=__lowerCAmelCase    ,		has_text_modality=__lowerCAmelCase    ,		hidden_size=3_7      )





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      self.config_tester.run_common_tests()





    @unittest.skip(reason='DeiT does not use inputs_embeds'      )
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      pass





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a,					a					:      Any    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a					:      Tuple    =       model_class(__lowerCAmelCase      )
        self.assertIsInstance(model.get_input_embeddings()    ,		(tf.keras.layers.Layer)      )
        a					:      List[Any]    =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__lowerCAmelCase    ,		tf.keras.layers.Dense      )      )





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a,					a					:      Union[str, Any]    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a					:      Dict    =       model_class(__lowerCAmelCase      )
        a					:      Union[str, Any]    =       inspect.signature(model.call      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a					:      Optional[Any]    =       [*signature.parameters.keys()]

        a					:      Any    =       ['pixel_values']
        self.assertListEqual(arg_names[:1]    ,		__lowerCAmelCase      )





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      int    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase      )





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Dict    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase      )





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      Any    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase      )





    def     lowerCamelCase__				(							self     : str    ,		A     : List[Any]    ,		A     : Any    ,		A     : List[str]=False      ):


      '''simple docstring'''




      a					:      Any    =       super()._prepare_for_class(__lowerCAmelCase    ,		__lowerCAmelCase    ,		return_labels=__lowerCAmelCase      )

      if return_labels:
        if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
          del inputs_dict["labels"]

      return inputs_dict





    @slow
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a					:      str    =       TFDeiTModel.from_pretrained(__lowerCAmelCase      )
        self.assertIsNotNone(__lowerCAmelCase      )




def       snake_case  ():
  a					:      Optional[Any]    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
  return image






@require_tf
@require_vision
class 						snake_case  (  unittest.TestCase	):





    @cached_property
    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      return (
          DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'      )
          if is_vision_available()
          else None
      )





    @slow
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Dict    =       TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'      )

      a					:      Union[str, Any]    =       self.default_image_processor
      a					:      Dict    =       prepare_img()
      a					:      Optional[int]    =       image_processor(images=__lowerCAmelCase    ,		return_tensors='tf'      )

      # forward pass
      a					:      Union[str, Any]    =       model(**__lowerCAmelCase      )

      # verify the logits
      a					:      int    =       tf.TensorShape((1, 1_0_0_0)      )
      self.assertEqual(outputs.logits.shape    ,		__lowerCAmelCase      )

      a					:      List[str]    =       tf.constant([-1.02_66, 0.19_12, -1.28_61]      )

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












"""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       snake_case  (A_				:Dict				):







  '''simple docstring'''

  a					:      str    =       []
  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  (A_				:Any     ,      A_				:List[Any]				):







  '''simple docstring'''

  a					:      Union[str, 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  (A_				:Dict				):







  '''simple docstring'''

  a					:      int    =       []
  token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')				)
  return token




def       snake_case  ():







  '''simple docstring'''

  a					:      int    =       []
  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  (A_				:int     ,      A_				:Optional[int]     ,      A_				:Dict     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      Optional[Any]    =       'imagenet-1k-id2label.json'
  a					:      Dict    =       1_0_0_0

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

  a					:      str    =       idalabel
  a					:      Optional[int]    =       {v: k for k, v in idalabel.items()}

  a					:      Tuple    =       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":
    a					:      int    =       [1, 2, 1_0]

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

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

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

  a					:      int    =       OrderedDict()
  a					:      Any    =       []

  for idx in range(len(config.depth				)				):
    if config.cls_token[idx]:
      a					:      Dict    =       list_of_state_dict + cls_token(A_				)
    a					:      Any    =       list_of_state_dict + embeddings(A_				)
    for cnt in range(config.depth[idx]				):
      a					:      Dict    =       list_of_state_dict + attention(A_     ,      A_				)

  a					:      Any    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(A_				)
  for i in range(len(A_				)				):
    a					:      List[Any]    =       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__":
   _UpperCamelCase    :   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.'
   )

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




