
'''simple docstring'''



# Copyright 2022 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.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available


def A (__lowerCamelCase :Optional[int]=None						):
						if subparsers is not None:
												_lowerCAmelCase    		=					subparsers.add_parser("""env"""						)
						else:
												_lowerCAmelCase    		=					argparse.ArgumentParser("""Accelerate env command"""						)

						parser.add_argument(
						    """--config_file"""					,      default=__lowerCamelCase					,      help="""The config file to use for the default values in the launching script."""						)

						if subparsers is not None:
												parser.set_defaults(func=__lowerCamelCase						)
						return parser


def A (__lowerCamelCase :Tuple						):
						_lowerCAmelCase    		=					torch.__version__
						_lowerCAmelCase    		=					torch.cuda.is_available()
						_lowerCAmelCase    		=					is_xpu_available()
						_lowerCAmelCase    		=					is_npu_available()

						_lowerCAmelCase    		=					"""Not found"""
						# Get the default from the config file.
						if args.config_file is not None or os.path.isfile(__lowerCamelCase						):
												_lowerCAmelCase    		=					load_config_from_file(args.config_file						).to_dict()

						_lowerCAmelCase    		=					{
						    """`Accelerate` version""": version,
						    """Platform""": platform.platform(),
						    """Python version""": platform.python_version(),
						    """Numpy version""": np.__version__,
						    """PyTorch version (GPU?)""": f'{pt_version} ({pt_cuda_available})',
						    """PyTorch XPU available""": str(__lowerCamelCase						),
						    """PyTorch NPU available""": str(__lowerCamelCase						),
						    """System RAM""": f'{psutil.virtual_memory().total / 1024 ** 3:.2f} GB',
						}
						if pt_cuda_available:
												_lowerCAmelCase    		=					torch.cuda.get_device_name()

						print("""\nCopy-and-paste the text below in your GitHub issue\n"""						)
						print("""\n""".join([f'- {prop}: {val}' for prop, val in info.items()]						)						)

						print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""						)
						_lowerCAmelCase    		=					(
						    """\n""".join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]						)
						    if isinstance(__lowerCamelCase					,      __lowerCamelCase						)
						    else f'\t{accelerate_config}'
						)
						print(__lowerCamelCase						)

						_lowerCAmelCase    		=					accelerate_config

						return info


def A ():
						_lowerCAmelCase    		=					env_command_parser()
						_lowerCAmelCase    		=					parser.parse_args()
						env_command(__lowerCamelCase						)
						return 0


if __name__ == "__main__":
						raise SystemExit(main())







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 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')	)
    return token


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__											=			1_0_0_0

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

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

    SCREAMING_SNAKE_CASE__											=			SCREAMING_SNAKE_CASE__											=			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":
        SCREAMING_SNAKE_CASE__											=			[1, 2, 1_0]

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

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

    SCREAMING_SNAKE_CASE__											=			CvtForImageClassification(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
    SCREAMING_SNAKE_CASE__											=			image_size
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location=torch.device('''cpu'''	)	)

    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    SCREAMING_SNAKE_CASE__											=			[]

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

    SCREAMING_SNAKE_CASE__											=			list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(lowerCAmelCase_	)
    for i in range(len(lowerCAmelCase_	)	):
        SCREAMING_SNAKE_CASE__											=			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					:				Union[str, 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."""
  )

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