'''simple docstring'''




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[str]						,					A						:				List[Any]						,					A						:				List[str]=3						,					A						:				List[str]=32						,					A						:				Optional[int]=3						,					A						:				str=10						,					A						:				Any=[10, 20, 30, 40]						,					A						:				int=[1, 1, 2, 1]						,					A						:				int=True						,					A						:				Dict=True						,					A						:				Optional[int]="relu"						,					A						:				Optional[int]=3						,					A						:				Tuple=None						,					):
             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  List[Any]							      =    image_size
             _UpperCAmelCase     :  Dict							      =    num_channels
             _UpperCAmelCase     :  List[str]							      =    embeddings_size
             _UpperCAmelCase     :  Tuple							      =    hidden_sizes
             _UpperCAmelCase     :  Optional[int]							      =    depths
             _UpperCAmelCase     :  Tuple							      =    is_training
             _UpperCAmelCase     :  str							      =    use_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  Optional[int]							      =    num_labels
             _UpperCAmelCase     :  Dict							      =    scope
             _UpperCAmelCase     :  List[Any]							      =    len(A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

             _UpperCAmelCase     :  Optional[Any]							      =    None
             if self.use_labels:
                    _UpperCAmelCase     :  Dict							      =    ids_tensor([self.batch_size]						,					self.num_labels       )

             _UpperCAmelCase     :  Tuple							      =    self.get_config()
             return config, pixel_values, labels




      def      _A	(       self						:				str       ):
             return RegNetConfig(
                 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						,					)




      def      _A	(       self						:				Optional[int]						,					A						:				Any						,					A						:				int						,					A						:				str       ):
             _UpperCAmelCase     :  int							      =    TFRegNetModel(config=A       )
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					training=A       )
             # expected last hidden states: B, C, H // 32, W // 32
             self.parent.assertEqual(
                 result.last_hidden_state.shape						,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,					)




      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[Any]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Any							      =    self.num_labels
             _UpperCAmelCase     :  str							      =    TFRegNetForImageClassification(A       )
             _UpperCAmelCase     :  Tuple							      =    model(A						,					labels=A						,					training=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)       )





      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  int							      =    self.prepare_config_and_inputs()
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    config_and_inputs
             _UpperCAmelCase     :  Optional[Any]							      =    {"pixel_values": pixel_values}
             return config, inputs_dict

@require_tf
class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      __UpperCamelCase:  Union[str, Any]						=						(
          {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      __UpperCamelCase:  Dict						=						False
      __UpperCamelCase:  Optional[int]						=						False
      __UpperCamelCase:  Any						=						False
      __UpperCamelCase:  Optional[Any]						=						False
      __UpperCamelCase:  Tuple						=						False




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Any							      =    TFRegNetModelTester(self       )
             _UpperCAmelCase     :  List[str]							      =    ConfigTester(self						,					config_class=A						,					has_text_modality=A       )




      def      _A	(       self						:				List[Any]       ):
             return




      @unittest.skip(reason="RegNet does not use inputs_embeds"       )
      def      _A	(       self						:				List[Any]       ):
             pass




      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0						,					reason="TF does not support backprop for grouped convolutions on CPU."						,					)
      @slow
      def      _A	(       self						:				Optional[Any]       ):
             super().test_keras_fit()




      @unittest.skip(reason="RegNet does not support input and output embeddings"       )
      def      _A	(       self						:				str       ):
             pass




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                    _UpperCAmelCase     :  Optional[int]							      =    ["pixel_values"]
                    self.assertListEqual(arg_names[:1]						,					A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  int							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				List[Any]       ):
             def check_hidden_states_output(A						:				Any						,					A						:				Any						,					A						:				Optional[Any]       ):
                    _UpperCAmelCase     :  Union[str, Any]							      =    model_class(A       )
                    _UpperCAmelCase     :  Optional[int]							      =    model(**self._prepare_for_class(A						,					A       )						,					training=A       )

                    _UpperCAmelCase     :  Optional[Any]							      =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                    _UpperCAmelCase     :  int							      =    self.model_tester.num_stages
                    self.assertEqual(len(A       )						,					expected_num_stages + 1       )

                    # RegNet'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 // 2, self.model_tester.image_size // 2]						,					)

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  List[str]							      =    ["basic", "bottleneck"]
             for model_class in self.all_model_classes:
                    for layer_type in layers_type:
                           _UpperCAmelCase     :  List[str]							      =    layer_type
                           _UpperCAmelCase     :  Dict							      =    True
                           check_hidden_states_output(A						,					A						,					A       )

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

                           check_hidden_states_output(A						,					A						,					A       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    self.model_tester.prepare_config_and_inputs_for_common()

             def check_equivalence(A						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				Optional[Any]						,					A						:				str={}       ):
                    _UpperCAmelCase     :  Tuple							      =    model(A						,					return_dict=A						,					**A       )
                    _UpperCAmelCase     :  Any							      =    model(A						,					return_dict=A						,					**A       ).to_tuple()

                    def recursive_check(A						:				Tuple						,					A						:				Tuple       ):
                           if isinstance(A						,					(List, Tuple)       ):
                                  for tuple_iterable_value, dict_iterable_value in zip(A						,					A       ):
                                         recursive_check(A						,					A       )
                           elif tuple_object is None:
                                  return
                           else:
                                  self.assertTrue(
                                      all(tf.equal(A						,					A       )       )						,					msg=(
                                          "Tuple and dict output are not equal. Difference:"
                                          F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"""
                                      )						,					)

                           recursive_check(A						,					A       )

             for model_class in self.all_model_classes:
                    _UpperCAmelCase     :  Tuple							      =    model_class(A       )

                    _UpperCAmelCase     :  Optional[Any]							      =    self._prepare_for_class(A						,					A       )
                    _UpperCAmelCase     :  str							      =    self._prepare_for_class(A						,					A       )
                    check_equivalence(A						,					A						,					A       )

                    _UpperCAmelCase     :  Optional[int]							      =    self._prepare_for_class(A						,					A						,					return_labels=A       )
                    _UpperCAmelCase     :  Optional[int]							      =    self._prepare_for_class(A						,					A						,					return_labels=A       )
                    check_equivalence(A						,					A						,					A       )

                    _UpperCAmelCase     :  Optional[Any]							      =    self._prepare_for_class(A						,					A       )
                    _UpperCAmelCase     :  Tuple							      =    self._prepare_for_class(A						,					A       )
                    check_equivalence(A						,					A						,					A						,					{"output_hidden_states": True}       )

                    _UpperCAmelCase     :  int							      =    self._prepare_for_class(A						,					A						,					return_labels=A       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    self._prepare_for_class(A						,					A						,					return_labels=A       )
                    check_equivalence(A						,					A						,					A						,					{"output_hidden_states": True}       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*A       )





      @slow
      def      _A	(       self						:				List[str]       ):
             for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _UpperCAmelCase     :  Any							      =    TFRegNetModel.from_pretrained(A       )
                    self.assertIsNotNone(A       )

def 	UpperCamelCase_							(					)    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image


@require_tf
@require_vision
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @cached_property
      def      _A	(       self						:				Optional[int]       ):
             return (
                 AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
                 if is_vision_available()
                 else None
             )





      @slow
      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Dict							      =    TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

             _UpperCAmelCase     :  Tuple							      =    self.default_image_processor
             _UpperCAmelCase     :  int							      =    prepare_img()
             _UpperCAmelCase     :  Optional[Any]							      =    image_processor(images=A						,					return_tensors="tf"       )

             # forward pass
             _UpperCAmelCase     :  Optional[Any]							      =    model(**A						,					training=A       )

             # verify the logits
             _UpperCAmelCase     :  Union[str, Any]							      =    tf.TensorShape((1, 1000)       )
             self.assertEqual(outputs.logits.shape						,					A       )

             _UpperCAmelCase     :  Optional[Any]							      =    tf.constant([-0.4_180, -1.5_051, -3.4_836]       )

             tf.debugging.assert_near(outputs.logits[0, :3]						,					A						,					atol=1E-4       )








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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    lowerCamelCase__    :      Optional[int]					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Tuple					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Union[str, Any]					 =		[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
    return token


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      str					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    lowerCamelCase__    :      Tuple					 =		'imagenet-1k-id2label.json'
    lowerCamelCase__    :      Union[str, Any]					 =		1000

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

    lowerCamelCase__    :      Tuple					 =		idalabel
    lowerCamelCase__    :      List[Any]					 =		{v: k for k, v in idalabel.items()}

    lowerCamelCase__    :      List[str]					 =		CvtConfig(num_labels=_UpperCAmelCase						,	idalabel=_UpperCAmelCase						,	labelaid=_UpperCAmelCase )

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

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowerCamelCase__    :      Optional[Any]					 =		[2, 2, 20]
        lowerCamelCase__    :      Optional[int]					 =		[3, 12, 16]
        lowerCamelCase__    :      str					 =		[192, 768, 1024]

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

    lowerCamelCase__    :      Optional[int]					 =		OrderedDict()
    lowerCamelCase__    :      Tuple					 =		[]

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

    lowerCamelCase__    :      int					 =		list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase )
    for i in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      str					 =		original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase )
    model.save_pretrained(_UpperCAmelCase )
    image_processor.save_pretrained(_UpperCAmelCase )


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

if __name__ == "__main__":
    _UpperCAmelCase				:       List[str]	   =				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."""
    )

    _UpperCAmelCase				:       List[str]	   =				parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





