



"""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__				):
      UpperCAmelCase_      			=							[]
      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__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							[]
      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__				):
      UpperCAmelCase_      			=							[]
      token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")				)
      return token





def        a__     (				):
      UpperCAmelCase_      			=							[]
      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__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							"""imagenet-1k-id2label.json"""
      UpperCAmelCase_      			=							1000

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

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

      UpperCAmelCase_      			=							CvtConfig(num_labels=lowercase__     ,      idalabel=lowercase__     ,      labelaid=lowercase__				)

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

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

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

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

      UpperCAmelCase_      			=							OrderedDict()
      UpperCAmelCase_      			=							[]

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

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

      model.load_state_dict(lowercase__				)
      model.save_pretrained(lowercase__				)
      image_processor.save_pretrained(lowercase__				)


# 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=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."""
  )

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









"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase       	=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase       	=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

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




