





'''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        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      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        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      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        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")  )
      return token
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      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        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							"""imagenet-1k-id2label.json"""
      _lowerCAmelCase												=							10_00

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

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

      _lowerCAmelCase												=							_lowerCAmelCase												=							CvtConfig(num_labels=lowerCAmelCase							,     idalabel=lowerCAmelCase							,     labelaid=lowerCAmelCase  )

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

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

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            _lowerCAmelCase												=							[2, 2, 20]
            _lowerCAmelCase												=							[3, 12, 16]
            _lowerCAmelCase												=							[1_92, 7_68, 10_24]

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

      _lowerCAmelCase												=							OrderedDict()
      _lowerCAmelCase												=							[]

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

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

      model.load_state_dict(lowerCAmelCase  )
      model.save_pretrained(lowerCAmelCase  )
      image_processor.save_pretrained(lowerCAmelCase  )


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

if __name__ == "__main__":
      A__    :       Optional[int]              =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.'''
      )

      A__    :       str              =parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)













'''simple docstring'''






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__    :       str              =5_00_00
A__    :       Optional[int]              =50_00

A__       ,						A__    :       Optional[int]              =os.path.split(__file__)
A__    :       Tuple              =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(0							,     len(lowerCAmelCase  )							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i : i + batch_size]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(0							,     lowerCAmelCase							,     lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i : i + batch_size]
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							{"""num examples""": SPEED_TEST_N_EXAMPLES}
      _lowerCAmelCase												=							[
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
      ]

      _lowerCAmelCase												=							[
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print("""generating dataset"""  )
            _lowerCAmelCase												=							datasets.Features(
                {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
            _lowerCAmelCase												=							generate_example_dataset(
                os.path.join(lowerCAmelCase							,     """dataset.arrow"""  )							,     lowerCAmelCase							,     num_examples=lowerCAmelCase							,     seq_shapes={"""list""": (1_00,)}							,     )
            print("""first set of iterations"""  )
            for func, kwargs in functions:
                  print(func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(lowerCAmelCase							,     **lowerCAmelCase  )

            print("""shuffling dataset"""  )
            _lowerCAmelCase												=							dataset.shuffle()
            print("""Second set of iterations (after shuffling"""  )
            for func, kwargs in functions_shuffled:
                  print("""shuffled """							,     func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(
                      lowerCAmelCase							,     **lowerCAmelCase  )

      with open(lowerCAmelCase							,     """wb"""  ) as f:
            f.write(json.dumps(lowerCAmelCase  ).encode("""utf-8"""  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()






