


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=2      , __snake_case=True      , __snake_case=False      , __snake_case=1_0      , __snake_case=3      , __snake_case=3_2 * 8      , __snake_case=3_2 * 8      , __snake_case=4      , __snake_case=6_4      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    is_training
         snake_case					=    use_auxiliary_loss
         snake_case					=    num_queries
         snake_case					=    num_channels
         snake_case					=    min_size
         snake_case					=    max_size
         snake_case					=    num_labels
         snake_case					=    hidden_dim
         snake_case					=    hidden_dim
    def 		a_      (	self   ):
         snake_case					=    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
             __snake_case   )

         snake_case					=    torch.ones([self.batch_size, self.min_size, self.max_size]      , device=__snake_case   )

         snake_case					=    (
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      , device=__snake_case   ) > 0.5
         ).float()
         snake_case					=    (torch.rand((self.batch_size, self.num_labels)      , device=__snake_case   ) > 0.5).long()

         snake_case					=    self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerConfig(
             hidden_size=self.hidden_dim      , )
         snake_case					=    self.num_queries
         snake_case					=    self.num_labels

         snake_case					=    [1, 1, 1, 1]
         snake_case					=    self.num_channels

         snake_case					=    6_4
         snake_case					=    1_2_8
         snake_case					=    self.hidden_dim
         snake_case					=    self.hidden_dim
         snake_case					=    self.hidden_dim
         return config
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()
         snake_case					=    {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
         return config, inputs_dict
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    output.encoder_hidden_states
         snake_case					=    output.pixel_decoder_hidden_states
         snake_case					=    output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(__snake_case   )      , len(config.backbone_config.depths   )   )
         self.parent.assertTrue(len(__snake_case   )      , len(config.backbone_config.depths   )   )
         self.parent.assertTrue(len(__snake_case   )      , config.decoder_layers   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case=False   ):
         with torch.no_grad():
              snake_case					=    MaskaFormerModel(config=__snake_case   )
              model.to(__snake_case   )
              model.eval()

              snake_case					=    model(pixel_values=__snake_case      , pixel_mask=__snake_case   )
              snake_case					=    model(__snake_case      , output_hidden_states=__snake_case   )

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape      , (self.batch_size, self.num_queries, self.hidden_dim)      , )
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
         self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

         if output_hidden_states:
              self.check_output_hidden_state(__snake_case      , __snake_case   )




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    MaskaFormerForUniversalSegmentation(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         def comm_check_on_output(__snake_case   ):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
              self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape      , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)      , )
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape      , (self.batch_size, self.num_queries, self.num_labels + 1)   )

         with torch.no_grad():
              snake_case					=    model(pixel_values=__snake_case      , pixel_mask=__snake_case   )
              snake_case					=    model(__snake_case   )

              comm_check_on_output(__snake_case   )

              snake_case					=    model(
                  pixel_values=__snake_case      , pixel_mask=__snake_case      , mask_labels=__snake_case      , class_labels=__snake_case   )

         comm_check_on_output(__snake_case   )

         self.parent.assertTrue(result.loss is not None   )
         self.parent.assertEqual(result.loss.shape      , torch.Size([1]   )   )






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    __magic_name__        =		{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , has_text_modality=__snake_case   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__snake_case      , **__snake_case      , output_hidden_states=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case   )
    @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Mask2Former is not a generative model'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Mask2Former does not use token embeddings'''   )
    def 		a_      (	self   ):
         pass
    @require_torch_multi_gpu
    @unittest.skip(
        reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case					=    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]      , __snake_case   )
    @slow
    def 		a_      (	self   ):
         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              snake_case					=    MaskaFormerModel.from_pretrained(__snake_case   )
              self.assertIsNotNone(__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    (self.model_tester.min_size,) * 2
         snake_case					=    {
             '''pixel_values''': torch.randn((2, 3, *size)      , device=__snake_case   ),
             '''mask_labels''': torch.randn((2, 1_0, *size)      , device=__snake_case   ),
             '''class_labels''': torch.zeros(2      , 1_0      , device=__snake_case   ).long(),
         }
         snake_case					=    self.model_tester.get_config()

         snake_case					=    MaskaFormerForUniversalSegmentation(__snake_case   ).to(__snake_case   )
         snake_case					=    model(**__snake_case   )
         self.assertTrue(outputs.loss is not None   )
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__snake_case      , **__snake_case      , output_hidden_states=__snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case					=    model_class(__snake_case   ).to(__snake_case   )
              snake_case					=    model(**__snake_case      , output_attentions=__snake_case   )
              self.assertTrue(outputs.attentions is not None   )
    def 		a_      (	self   ):
         if not self.model_tester.is_training:
              return

         snake_case					=    self.all_model_classes[1]
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()

         snake_case					=    model_class(__snake_case   )
         model.to(__snake_case   )
         model.train()

         snake_case					=    model(__snake_case      , mask_labels=__snake_case      , class_labels=__snake_case   ).loss
         loss.backward()




    def 		a_      (	self   ):
         snake_case					=    self.all_model_classes[1]
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         snake_case					=    True
         snake_case					=    True

         snake_case					=    model_class(__snake_case   ).to(__snake_case   )
         model.train()

         snake_case					=    model(__snake_case      , mask_labels=__snake_case      , class_labels=__snake_case   )

         snake_case					=    outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         snake_case					=    outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         snake_case					=    outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         snake_case					=    outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=__snake_case   )

         self.assertIsNotNone(encoder_hidden_states.grad   )
         self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
         self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
         self.assertIsNotNone(attentions.grad   )






_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   1e-4
def       UpperCAmelCase__						():



     """simple docstring"""






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

@require_vision
@slow
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @cached_property
    def 		a_      (	self   ):
         return "facebook/mask2former-swin-small-coco-instance"
    @cached_property
    def 		a_      (	self   ):
         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(__snake_case   )
         snake_case					=    self.default_image_processor
         snake_case					=    prepare_img()
         snake_case					=    image_processor(__snake_case      , return_tensors='''pt'''   ).to(__snake_case   )
         snake_case					=    inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
         # check size
         self.assertEqual(__snake_case      , (1, 3, 3_8_4, 3_8_4)   )

         with torch.no_grad():
              snake_case					=    model(**__snake_case   )

         snake_case					=    torch.tensor(
             [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]   ).to(__snake_case   )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]      , __snake_case      , atol=__snake_case   )   )

         snake_case					=    torch.tensor(
             [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]   ).to(__snake_case   )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      , __snake_case      , atol=__snake_case   )   )

         snake_case					=    torch.tensor(
             [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]   ).to(__snake_case   )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]      , __snake_case      , atol=__snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(__snake_case   ).eval()
         snake_case					=    self.default_image_processor
         snake_case					=    prepare_img()
         snake_case					=    image_processor(__snake_case      , return_tensors='''pt'''   ).to(__snake_case   )
         snake_case					=    inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
         # check size
         self.assertEqual(__snake_case      , (1, 3, 3_8_4, 3_8_4)   )

         with torch.no_grad():
              snake_case					=    model(**__snake_case   )
         # masks_queries_logits
         snake_case					=    outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape      , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
         snake_case					=    [
             [-8.7839, -9.0056, -8.8121],
             [-7.4104, -7.0313, -6.5401],
             [-6.6105, -6.3427, -6.4675],
         ]
         snake_case					=    torch.tensor(__snake_case   ).to(__snake_case   )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      , __snake_case      , atol=__snake_case   )   )
         # class_queries_logits
         snake_case					=    outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape      , (1, model.config.num_queries, model.config.num_labels + 1)   )
         snake_case					=    torch.tensor(
             [
                 [1.8324, -8.0835, -4.1922],
                 [0.8450, -9.0050, -3.6053],
                 [0.3045, -7.7293, -3.0275],
             ]   ).to(__snake_case   )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      , __snake_case      , atol=__snake_case   )   )




    def 		a_      (	self   ):
         snake_case					=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(__snake_case   ).eval()
         snake_case					=    self.default_image_processor

         snake_case					=    image_processor(
             [np.zeros((3, 8_0_0, 1_3_3_3)   ), np.zeros((3, 8_0_0, 1_3_3_3)   )]      , segmentation_maps=[np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   ), np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   )]      , return_tensors='''pt'''      , )

         snake_case					=    inputs['''pixel_values'''].to(__snake_case   )
         snake_case					=    [el.to(__snake_case   ) for el in inputs['''mask_labels''']]
         snake_case					=    [el.to(__snake_case   ) for el in inputs['''class_labels''']]

         with torch.no_grad():
              snake_case					=    model(**__snake_case   )

         self.assertTrue(outputs.loss is not None   )








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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
     return token
def       UpperCAmelCase__						():



     """simple docstring"""






     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    10_00

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

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

     snake_case					=    snake_case					=    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":
          snake_case					=    [1, 2, 10]

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

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          snake_case					=    [2, 2, 20]
          snake_case					=    [3, 12, 16]
          snake_case					=    [1_92, 7_68, 10_24]

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

     snake_case					=    OrderedDict()
     snake_case					=    []

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

     snake_case					=    list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCamelCase_    )
     for i in range(len(UpperCamelCase_    )    ):
          snake_case					=    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__":
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   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."
 )

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




