






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         LayoutLMvaForQuestionAnswering,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaModel,
     )
     from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Tuple ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Dict=2 ,					__UpperCAmelCase      :						Any=3 ,					__UpperCAmelCase      :						List[Any]=4 ,					__UpperCAmelCase      :						Any=2 ,					__UpperCAmelCase      :						int=7 ,					__UpperCAmelCase      :						Tuple=True ,					__UpperCAmelCase      :						Dict=True ,					__UpperCAmelCase      :						Union[str, Any]=True ,					__UpperCAmelCase      :						Dict=True ,					__UpperCAmelCase      :						Union[str, Any]=99 ,					__UpperCAmelCase      :						Tuple=36 ,					__UpperCAmelCase      :						Optional[Any]=3 ,					__UpperCAmelCase      :						List[str]=4 ,					__UpperCAmelCase      :						Union[str, Any]=37 ,					__UpperCAmelCase      :						Union[str, Any]="gelu" ,					__UpperCAmelCase      :						Any=0.1 ,					__UpperCAmelCase      :						List[str]=0.1 ,					__UpperCAmelCase      :						int=512 ,					__UpperCAmelCase      :						str=16 ,					__UpperCAmelCase      :						Optional[Any]=2 ,					__UpperCAmelCase      :						List[Any]=0.02 ,					__UpperCAmelCase      :						Any=6 ,					__UpperCAmelCase      :						Tuple=6 ,					__UpperCAmelCase      :						Optional[int]=3 ,					__UpperCAmelCase      :						int=4 ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						Tuple=1_000 ,					)    ->str:




       """simple docstring"""


       a		  =      parent
       a		  =      batch_size
       a		  =      num_channels
       a		  =      image_size
       a		  =      patch_size
       a		  =      text_seq_length
       a		  =      is_training
       a		  =      use_input_mask
       a		  =      use_token_type_ids
       a		  =      use_labels
       a		  =      vocab_size
       a		  =      hidden_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads
       a		  =      intermediate_size
       a		  =      hidden_act
       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      max_position_embeddings
       a		  =      type_vocab_size
       a		  =      type_sequence_label_size
       a		  =      initializer_range
       a		  =      coordinate_size
       a		  =      shape_size
       a		  =      num_labels
       a		  =      num_choices
       a		  =      scope
       a		  =      range_bbox

       # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
       a		  =      text_seq_length
       a		  =      (image_size // patch_size) ** 2 + 1
       a		  =      self.text_seq_length + self.image_seq_length







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       a		  =      ids_tensor([self.batch_size, self.text_seq_length] ,					self.vocab_size  )

       a		  =      ids_tensor([self.batch_size, self.text_seq_length, 4] ,					self.range_bbox  )
       # Ensure that bbox is legal
       for i in range(bbox.shape[0]  ):
        for j in range(bbox.shape[1]  ):
         if bbox[i, j, 3] < bbox[i, j, 1]:
          a		  =      bbox[i, j, 3]
          a		  =      bbox[i, j, 1]
          a		  =      t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          a		  =      bbox[i, j, 2]
          a		  =      bbox[i, j, 0]
          a		  =      t

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

       a		  =      None
       if self.use_input_mask:
        a		  =      random_attention_mask([self.batch_size, self.text_seq_length]  )

       a		  =      None
       if self.use_token_type_ids:
        a		  =      ids_tensor([self.batch_size, self.text_seq_length] ,					self.type_vocab_size  )

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

       a		  =      LayoutLMvaConfig(
           vocab_size=self.vocab_size ,					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 ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					initializer_range=self.initializer_range ,					coordinate_size=self.coordinate_size ,					shape_size=self.shape_size ,					input_size=self.image_size ,					patch_size=self.patch_size ,					)

       return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						List[str]  )    ->Dict:




       """simple docstring"""


       a		  =      LayoutLMvaModel(config=__UpperCAmelCase  )
       model.to(__UpperCAmelCase  )
       model.eval()

       # text + image
       a		  =      model(__UpperCAmelCase ,					pixel_values=__UpperCAmelCase  )
       a		  =      model(
           __UpperCAmelCase ,					bbox=__UpperCAmelCase ,					pixel_values=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase  )
       a		  =      model(__UpperCAmelCase ,					bbox=__UpperCAmelCase ,					pixel_values=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase  )
       a		  =      model(__UpperCAmelCase ,					bbox=__UpperCAmelCase ,					pixel_values=__UpperCAmelCase  )

       self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)  )

       # text only
       a		  =      model(__UpperCAmelCase  )
       self.parent.assertEqual(
           result.last_hidden_state.shape ,					(self.batch_size, self.text_seq_length, self.hidden_size)  )

       # image only
       a		  =      model(pixel_values=__UpperCAmelCase  )
       self.parent.assertEqual(
           result.last_hidden_state.shape ,					(self.batch_size, self.image_seq_length, self.hidden_size)  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Tuple  )    ->List[str]:




       """simple docstring"""


       a		  =      self.num_labels
       a		  =      LayoutLMvaForSequenceClassification(__UpperCAmelCase  )
       model.to(__UpperCAmelCase  )
       model.eval()
       a		  =      model(
           __UpperCAmelCase ,					bbox=__UpperCAmelCase ,					pixel_values=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase ,					)
       self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)  )







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Any  )    ->int:




       """simple docstring"""


       a		  =      self.num_labels
       a		  =      LayoutLMvaForTokenClassification(config=__UpperCAmelCase  )
       model.to(__UpperCAmelCase  )
       model.eval()
       a		  =      model(
           __UpperCAmelCase ,					bbox=__UpperCAmelCase ,					pixel_values=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase ,					)
       self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.text_seq_length, self.num_labels)  )







      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						Union[str, Any]  )    ->str:




       """simple docstring"""


       a		  =      LayoutLMvaForQuestionAnswering(config=__UpperCAmelCase  )
       model.to(__UpperCAmelCase  )
       model.eval()
       a		  =      model(
           __UpperCAmelCase ,					bbox=__UpperCAmelCase ,					pixel_values=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					start_positions=__UpperCAmelCase ,					end_positions=__UpperCAmelCase ,					)
       self.parent.assertEqual(result.start_logits.shape ,					(self.batch_size, self.seq_length)  )
       self.parent.assertEqual(result.end_logits.shape ,					(self.batch_size, self.seq_length)  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[int]:




       """simple docstring"""


       a		  =      self.prepare_config_and_inputs()
       (
           (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     
       )		  =      config_and_inputs
       a		  =      {
           '''input_ids''': input_ids,
           '''bbox''': bbox,
           '''pixel_values''': pixel_values,
           '''token_type_ids''': token_type_ids,
           '''attention_mask''': input_mask,
       }
       return config, inputs_dict






@require_torch
class 		lowercase_					(	lowercase ,  lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  False
      __snake_case       =  False
      __snake_case       =  False

      __snake_case       =  (
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __snake_case       =  (
          {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
          if is_torch_available()
          else {}
      )







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




       """simple docstring"""


       return True







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




       """simple docstring"""


       a		  =      LayoutLMvaModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase ,					hidden_size=37  )







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						str=False  )    ->Optional[int]:




       """simple docstring"""


       a		  =      copy.deepcopy(__UpperCAmelCase  )
       if model_class in get_values(__UpperCAmelCase  ):
        a		  =      {
            k: v.unsqueeze(1  ).expand(-1 ,					self.model_tester.num_choices ,					-1  ).contiguous()
            if isinstance(__UpperCAmelCase ,					torch.Tensor  ) and v.ndim > 1
            else v
            for k, v in inputs_dict.items()
        }
       if return_labels:
        if model_class in get_values(__UpperCAmelCase  ):
         a		  =      torch.ones(self.model_tester.batch_size ,					dtype=torch.long ,					device=__UpperCAmelCase  )
        elif model_class in get_values(__UpperCAmelCase  ):
         a		  =      torch.zeros(
             self.model_tester.batch_size ,					dtype=torch.long ,					device=__UpperCAmelCase  )
         a		  =      torch.zeros(
             self.model_tester.batch_size ,					dtype=torch.long ,					device=__UpperCAmelCase  )
        elif model_class in [
            *get_values(__UpperCAmelCase  ),
        ]:
         a		  =      torch.zeros(
             self.model_tester.batch_size ,					dtype=torch.long ,					device=__UpperCAmelCase  )
        elif model_class in [
            *get_values(__UpperCAmelCase  ),
        ]:
         a		  =      torch.zeros(
             (self.model_tester.batch_size, self.model_tester.text_seq_length) ,					dtype=torch.long ,					device=__UpperCAmelCase ,					)

       return inputs_dict







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




       """simple docstring"""


       self.config_tester.run_common_tests()







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




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase  )







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




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        a		  =      type
        self.model_tester.create_and_check_model(*__UpperCAmelCase  )







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




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase  )







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




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase  )







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




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase  )







      @slow
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		  =      LayoutLMvaModel.from_pretrained(__UpperCAmelCase  )
        self.assertIsNotNone(__UpperCAmelCase  )
def 						_a    (      )							->			List[Any]:
 a		  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
 return image






@require_torch
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      @cached_property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase  ) if is_vision_available() else None







      @slow
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Dict:




       """simple docstring"""


       a		  =      LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''  ).to(__UpperCAmelCase  )

       a		  =      self.default_image_processor
       a		  =      prepare_img()
       a		  =      image_processor(images=__UpperCAmelCase ,					return_tensors='''pt'''  ).pixel_values.to(__UpperCAmelCase  )

       a		  =      torch.tensor([[1, 2]]  )
       a		  =      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]  ).unsqueeze(0  )

       # forward pass
       a		  =      model(
           input_ids=input_ids.to(__UpperCAmelCase  ) ,					bbox=bbox.to(__UpperCAmelCase  ) ,					pixel_values=pixel_values.to(__UpperCAmelCase  ) ,					)

       # verify the logits
       a		  =      torch.Size((1, 199, 768)  )
       self.assertEqual(outputs.last_hidden_state.shape ,					__UpperCAmelCase  )

       a		  =      torch.tensor(
           [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]  ).to(__UpperCAmelCase  )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,					__UpperCAmelCase ,					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 						_a    (      a :List[Any] )							->			Optional[int]:
 a		  =      []
 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 						_a    (      a :List[Any]	,							a :Optional[int] )							->			Dict:
 a		  =      []
 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 						_a    (      a :Any )							->			List[Any]:
 a		  =      []
 token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
 return token
def 						_a    (      )							->			Optional[int]:
 a		  =      []
 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 						_a    (      a :Tuple	,							a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Optional[int]:
 a		  =      '''imagenet-1k-id2label.json'''
 a		  =      1_000

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

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

 a		  =      a		  =      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		  =      [1, 2, 10]

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

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

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

 a		  =      OrderedDict()
 a		  =      []

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

 a		  =      list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(a )
 for i in range(len(a ) ):
  a		  =      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__				   =		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__				   =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


