


'''simple docstring'''
import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def       _lowercase							(					UpperCamelCase__							:       Dict      ):
 __A	:		Optional[Any]	  =      []
 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       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       str      ):
 __A	:		Union[str, Any]	  =      []
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
         f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
         f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
     )      )
 attention_weights.append(
     (
         f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
         f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
     )      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")      )
 attention_weights.append(
     (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")      )
 return attention_weights


def       _lowercase							(					UpperCamelCase__							:       List[Any]      ):
 __A	:		Any	  =      []
 token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')      )
 return token


def       _lowercase							(					):
 __A	:		List[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       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Any      ):
 __A	:		int	  =      'imagenet-1k-id2label.json'
 __A	:		List[str]	  =      1000

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

 __A	:		int	  =      idalabel
 __A	:		Tuple	  =      {v: k for k, v in idalabel.items()}

 __A	:		Tuple	  =      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":
  __A	:		Optional[Any]	  =      [1, 2, 10]

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

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

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

 __A	:		List[Any]	  =      OrderedDict()
 __A	:		Optional[int]	  =      []

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

 __A	:		List[Any]	  =      list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(UpperCamelCase__      )
 for i in range(len(UpperCamelCase__      )      ):
  __A	:		List[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[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_8_4,
     type=int,
     help='Input Image Size',
 )
 parser.add_argument(
     '--cvt_file_name',
     default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
     type=str,
     help='Input Image Size',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

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










'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






