



import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command

class 					_A (			unittest.TestCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		inspect.getfile(accelerate.test_utils					)
       UpperCamelCase__						:					Tuple		      =		os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ['''scripts''', '''test_cli.py''']					)

       UpperCamelCase__						:					List[str]		      =		['''accelerate''', '''launch''']
       UpperCamelCase__						:					int		      =		Path.home() / '''.cache/huggingface/accelerate'''
       UpperCamelCase__						:					List[str]		      =		'''default_config.yaml'''
       UpperCamelCase__						:					Union[str, Any]		      =		config_folder / config_file
       UpperCamelCase__						:					int		      =		config_folder / '''_default_config.yaml'''

       UpperCamelCase__						:					Any		      =		Path('''tests/test_configs'''					)



       @classmethod
       def 	_lowerCamelCase    (	cls							:  Optional[int]):


           '''simple docstring'''

           if cls.config_path.is_file():
               cls.config_path.rename(cls.changed_path)



       @classmethod
       def 	_lowerCamelCase    (	cls							:  Optional[int]):


           '''simple docstring'''

           if cls.changed_path.is_file():
               cls.changed_path.rename(cls.config_path)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.base_cmd
           if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
               cmd += ["--multi_gpu"]
           execute_subprocess_async(cmd + [self.test_file_path]  ,	env=os.environ.copy())



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           for config in sorted(self.test_config_path.glob('''**/*.yaml''')):
               with self.subTest(config_file=__SCREAMING_SNAKE_CASE):
                   execute_subprocess_async(
                       self.base_cmd + ['''--config_file''', str(__SCREAMING_SNAKE_CASE), self.test_file_path]  ,	env=os.environ.copy())





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           execute_subprocess_async(['''accelerate''', '''test''']  ,	env=os.environ.copy())


class 					_A (			unittest.TestCase					):
       UpperCamelCase__						:					Tuple		      =		'''test-tpu'''
       UpperCamelCase__						:					Union[str, Any]		      =		'''us-central1-a'''
       UpperCamelCase__						:					List[Any]		      =		'''ls'''
       UpperCamelCase__						:					Union[str, Any]		      =		['''accelerate''', '''tpu-config''']
       UpperCamelCase__						:					Any		      =		'''cd /usr/share'''
       UpperCamelCase__						:					str		      =		'''tests/test_samples/test_command_file.sh'''
       UpperCamelCase__						:					Any		      =		'''Running gcloud compute tpus tpu-vm ssh'''



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd
               + ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug''']  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd
               + [
                   '''--config_file''',
                   '''tests/test_configs/0_12_0.yaml''',
                   '''--command''',
                   self.command,
                   '''--tpu_zone''',
                   self.tpu_zone,
                   '''--tpu_name''',
                   self.tpu_name,
                   '''--debug''',
               ]  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']  ,	return_stdout=__SCREAMING_SNAKE_CASE)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug''']  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd
               + [
                   '''--config_file''',
                   '''tests/test_configs/latest.yaml''',
                   '''--command''',
                   self.command,
                   '''--command''',
                   '''echo "Hello World"''',
                   '''--debug''',
               ]  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd
               + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd
               + [
                   '''--config_file''',
                   '''tests/test_configs/0_12_0.yaml''',
                   '''--command_file''',
                   self.command_file,
                   '''--tpu_zone''',
                   self.tpu_zone,
                   '''--tpu_name''',
                   self.tpu_name,
                   '''--debug''',
               ]  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)





       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       run_command(
               self.cmd
               + [
                   '''--config_file''',
                   '''tests/test_configs/latest.yaml''',
                   '''--install_accelerate''',
                   '''--accelerate_version''',
                   '''12.0.0''',
                   '''--debug''',
               ]  ,	return_stdout=__SCREAMING_SNAKE_CASE  ,	)
           self.assertIn(
               F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all'  ,	__SCREAMING_SNAKE_CASE  ,	)










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			(    _UpperCAmelCase       ):
    __a		 =       []
    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			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       []
    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			(    _UpperCAmelCase       ):
    __a		 =       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')       )
    return token





def   __snake_case			(    ):
    __a		 =       []
    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			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       '''imagenet-1k-id2label.json'''
    __a		 =       1000

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

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

    __a		 =       __a		 =       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":
        __a		 =       [1, 2, 10]

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

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

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

    __a		 =       OrderedDict()
    __a		 =       []

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

    __a		 =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase       )
    for i in range(len(_UpperCAmelCase       )       ):
        __a		 =       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__":
 __snake_case		:str   	=  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.'''
 )

 __snake_case		:Dict   	=  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





