
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   List[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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   int					=				[]
      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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				[]
      token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')		)
      return token


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   int					=				[]
      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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   int					=				'''imagenet-1k-id2label.json'''
      UpperCamelCase__    :   Optional[Any]					=				1_0_0_0

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

      UpperCamelCase__    :   Union[str, Any]					=				idalabel
      UpperCamelCase__    :   List[str]					=				{v: k for k, v in idalabel.items()}

      UpperCamelCase__    :   List[Any]					=				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":
            UpperCamelCase__    :   int					=				[1, 2, 1_0]

      # For depth size 21 (21 = 1+4+16)
      elif cvt_model.rsplit('''/''' ,		1		)[-1][4:6] == "21":
            UpperCamelCase__    :   List[str]					=				[1, 4, 1_6]

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            UpperCamelCase__    :   List[str]					=				[2, 2, 2_0]
            UpperCamelCase__    :   Union[str, Any]					=				[3, 1_2, 1_6]
            UpperCamelCase__    :   List[Any]					=				[1_9_2, 7_6_8, 1_0_2_4]

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

      UpperCamelCase__    :   str					=				OrderedDict()
      UpperCamelCase__    :   Any					=				[]

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

      UpperCamelCase__    :   int					=				list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(UpperCamelCase__		)
      for i in range(len(UpperCamelCase__		)		):
            UpperCamelCase__    :   Tuple					=				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__":
    lowerCamelCase      =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."
    )

    lowerCamelCase      =parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''realm'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=3_0_5_2_2						,						__SCREAMING_SNAKE_CASE=7_6_8						,						__SCREAMING_SNAKE_CASE=1_2_8						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=8						,						__SCREAMING_SNAKE_CASE=3_0_7_2						,						__SCREAMING_SNAKE_CASE="gelu_new"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-12						,						__SCREAMING_SNAKE_CASE=2_5_6						,						__SCREAMING_SNAKE_CASE=1_0						,						__SCREAMING_SNAKE_CASE=1e-3						,						__SCREAMING_SNAKE_CASE=5						,						__SCREAMING_SNAKE_CASE=3_2_0						,						__SCREAMING_SNAKE_CASE=1_3_3_5_3_7_1_8						,						__SCREAMING_SNAKE_CASE=5_0_0_0						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=2						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE						,						bos_token_id=__SCREAMING_SNAKE_CASE						,						eos_token_id=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

           # Common config
           UpperCamelCase__    :   int					=				vocab_size
           UpperCamelCase__    :   Any					=				max_position_embeddings
           UpperCamelCase__    :   List[str]					=				hidden_size
           UpperCamelCase__    :   Union[str, Any]					=				retriever_proj_size
           UpperCamelCase__    :   int					=				num_hidden_layers
           UpperCamelCase__    :   Union[str, Any]					=				num_attention_heads
           UpperCamelCase__    :   str					=				num_candidates
           UpperCamelCase__    :   int					=				intermediate_size
           UpperCamelCase__    :   Dict					=				hidden_act
           UpperCamelCase__    :   Any					=				hidden_dropout_prob
           UpperCamelCase__    :   Any					=				attention_probs_dropout_prob
           UpperCamelCase__    :   Any					=				initializer_range
           UpperCamelCase__    :   Optional[Any]					=				type_vocab_size
           UpperCamelCase__    :   List[Any]					=				layer_norm_eps

           # Reader config
           UpperCamelCase__    :   List[Any]					=				span_hidden_size
           UpperCamelCase__    :   List[Any]					=				max_span_width
           UpperCamelCase__    :   Optional[Any]					=				reader_layer_norm_eps
           UpperCamelCase__    :   Optional[Any]					=				reader_beam_size
           UpperCamelCase__    :   int					=				reader_seq_len

           # Retrieval config
           UpperCamelCase__    :   List[str]					=				num_block_records
           UpperCamelCase__    :   Union[str, Any]					=				searcher_beam_size


