"""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 				a_     (      _lowerCAmelCase  : int     ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	[]
       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_     (      _lowerCAmelCase  : str   , _lowerCAmelCase  : Optional[int]     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : Union[str, Any]     ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	[]
       token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')     )
       return token





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	[]
       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_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : Optional[Any]     ):





       '''simple docstring'''

       lowercase__			:		Any       =	'imagenet-1k-id2label.json'
       lowercase__			:		int       =	1000

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

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

       lowercase__			:		Tuple       =	CvtConfig(num_labels=_SCREAMING_SNAKE_CASE   , idalabel=_SCREAMING_SNAKE_CASE   , labelaid=_SCREAMING_SNAKE_CASE     )

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

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

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

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

       lowercase__			:		List[Any]       =	OrderedDict()
       lowercase__			:		List[str]       =	[]

       for idx in range(len(config.depth     )     ):
              if config.cls_token[idx]:
                     lowercase__			:		Dict       =	list_of_state_dict + cls_token(_SCREAMING_SNAKE_CASE     )
              lowercase__			:		Tuple       =	list_of_state_dict + embeddings(_SCREAMING_SNAKE_CASE     )
              for cnt in range(config.depth[idx]     ):
                     lowercase__			:		Any       =	list_of_state_dict + attention(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE     )

       lowercase__			:		str       =	list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(_SCREAMING_SNAKE_CASE     )
       for i in range(len(_SCREAMING_SNAKE_CASE     )     ):
              lowercase__			:		List[str]       =	original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(_SCREAMING_SNAKE_CASE     )
       model.save_pretrained(_SCREAMING_SNAKE_CASE     )
       image_processor.save_pretrained(_SCREAMING_SNAKE_CASE     )


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

if __name__ == "__main__":
       _UpperCamelCase   :						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."
       )

       _UpperCamelCase   :						Union[str, Any]      =    parser.parse_args()
       convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE_						   =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


