





import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:



  class 					__snake_case	:


      '''simple docstring'''







      @staticmethod
      def  lowercase_					(       *A_   ,				**A_     ):





          '''simple docstring'''

          pass



@is_pipeline_test
@require_vision
@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			pipeline(
            '''zero-shot-object-detection'''   ,				model='''hf-internal-testing/tiny-random-owlvit-object-detection'''     )

        SCREAMING_SNAKE_CASE__											=			[
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]
        return object_detector, examples



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			object_detector(examples[0]   ,				threshold=0.0     )

        SCREAMING_SNAKE_CASE__											=			len(A_     )
        self.assertGreater(A_   ,				0     )
        self.assertEqual(
            A_   ,				[
                {
                    '''score''': ANY(A_     ),
                    '''label''': ANY(A_     ),
                    '''box''': {'''xmin''': ANY(A_     ), '''ymin''': ANY(A_     ), '''xmax''': ANY(A_     ), '''ymax''': ANY(A_     )},
                }
                for i in range(A_     )
            ]   ,				)



    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @require_torch
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			pipeline(
            '''zero-shot-object-detection'''   ,				model='''hf-internal-testing/tiny-random-owlvit-object-detection'''     )

        SCREAMING_SNAKE_CASE__											=			object_detector(
            '''./tests/fixtures/tests_samples/COCO/000000039769.png'''   ,				candidate_labels=['''cat''', '''remote''', '''couch''']   ,				threshold=0.64   ,				)

        self.assertEqual(
            nested_simplify(A_   ,				decimals=4     )   ,				[
                {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
                {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
                {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
            ]   ,				)

        SCREAMING_SNAKE_CASE__											=			object_detector(
            [
                {
                    '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                }
            ]   ,				threshold=0.64   ,				)

        self.assertEqual(
            nested_simplify(A_   ,				decimals=4     )   ,				[
                [
                    {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                    {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                    {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                    {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                    {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                    {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                    {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
                    {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
                    {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
                ]
            ]   ,				)



    @require_torch
    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			pipeline('''zero-shot-object-detection'''     )

        SCREAMING_SNAKE_CASE__											=			object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''   ,				candidate_labels=['''cat''', '''remote''', '''couch''']   ,				)
        self.assertEqual(
            nested_simplify(A_   ,				decimals=4     )   ,				[
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
                {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
                {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
                {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
            ]   ,				)

        SCREAMING_SNAKE_CASE__											=			object_detector(
            [
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
            ]   ,				)
        self.assertEqual(
            nested_simplify(A_   ,				decimals=4     )   ,				[
                [
                    {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                    {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
                    {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
                    {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
                    {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
                ],
                [
                    {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                    {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
                    {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
                    {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
                    {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
                ],
            ]   ,				)



    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @require_torch
    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			0.2
        SCREAMING_SNAKE_CASE__											=			pipeline('''zero-shot-object-detection'''     )

        SCREAMING_SNAKE_CASE__											=			object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''   ,				candidate_labels=['''cat''', '''remote''', '''couch''']   ,				threshold=A_   ,				)
        self.assertEqual(
            nested_simplify(A_   ,				decimals=4     )   ,				[
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
                {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
            ]   ,				)




    @require_torch
    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			pipeline('''zero-shot-object-detection'''     )

        SCREAMING_SNAKE_CASE__											=			object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''   ,				candidate_labels=['''cat''', '''remote''', '''couch''']   ,				top_k=A_   ,				)
        self.assertEqual(
            nested_simplify(A_   ,				decimals=4     )   ,				[
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
            ]   ,				)







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)
