


'''simple docstring'''




# Copyright 2021 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 json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


_a      :      int       				=      os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_a      :      int       				=      os.path.join(hf_cache_home, "accelerate")
_a      :      List[Any]       				=      os.path.join(cache_dir, "default_config.yaml")
_a      :      str       				=      os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
       _a      :      int       				=      default_yaml_config_file
else:
       _a      :      Optional[int]       				=      default_json_config_file





def      _a		(lowercase__ :      int	) -> Any:






     """simple docstring"""
     if config_file is not None:
          if not os.path.isfile(lowercase__	):
               raise FileNotFoundError(
                   f'The passed configuration file `{config_file}` does not exist. '
                   'Please pass an existing file to `accelerate launch`, or use the the default one '
                   'created through `accelerate config` and run `accelerate launch` '
                   'without the `--config_file` argument.'	)
     else:
          __snake_case    			=	default_config_file
     with open(lowercase__       ,      'r'       ,      encoding='utf-8'	) as f:
          if config_file.endswith('.json'	):
               if (
                   json.load(lowercase__	).get('compute_environment'       ,      ComputeEnvironment.LOCAL_MACHINE	)
                   == ComputeEnvironment.LOCAL_MACHINE
               ):
                    __snake_case    			=	ClusterConfig
               else:
                    __snake_case    			=	SageMakerConfig
               return config_class.from_json_file(json_file=lowercase__	)
          else:
               if (
                   yaml.safe_load(lowercase__	).get('compute_environment'       ,      ComputeEnvironment.LOCAL_MACHINE	)
                   == ComputeEnvironment.LOCAL_MACHINE
               ):
                    __snake_case    			=	ClusterConfig
               else:
                    __snake_case    			=	SageMakerConfig
               return config_class.from_yaml_file(yaml_file=lowercase__	)




@dataclass
class  _lowercase   :
   _SCREAMING_SNAKE_CASE     :     ComputeEnvironment
   _SCREAMING_SNAKE_CASE     :     Union[DistributedType, SageMakerDistributedType]
   _SCREAMING_SNAKE_CASE     :     str
   _SCREAMING_SNAKE_CASE     :     bool
   def a     (	self					:			Any			)   ->       Optional[int]:
        __snake_case    			=	self.__dict__
        # For serialization, it's best to convert Enums to strings (or their underlying value type).
        for key, value in result.items():
             if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	value.value
             if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) and not bool(SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	None
        __snake_case    			=	{k: v for k, v in result.items() if v is not None}
        return result
   @classmethod
   def a     (	cls					:			int					,      SCREAMING_SNAKE_CASE_					:			List[str]=None			)   ->       Union[str, Any]:
        __snake_case    			=	default_json_config_file if json_file is None else json_file
        with open(SCREAMING_SNAKE_CASE_					,      'r'					,      encoding='utf-8'			) as f:
             __snake_case    			=	json.load(SCREAMING_SNAKE_CASE_			)
        if "compute_environment" not in config_dict:
             __snake_case    			=	ComputeEnvironment.LOCAL_MACHINE
        if "mixed_precision" not in config_dict:
             __snake_case    			=	"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
        if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
        if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             __snake_case    			=	config_dict.pop('dynamo_backend'			)
             __snake_case    			=	{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
        if "use_cpu" not in config_dict:
             __snake_case    			=	False
        return cls(**SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Tuple			)   ->       int:






        with open(SCREAMING_SNAKE_CASE_					,      'w'					,      encoding='utf-8'			) as f:
             __snake_case    			=	json.dumps(self.to_dict()					,      indent=2					,      sort_keys=SCREAMING_SNAKE_CASE_			) + "\n"
             f.write(SCREAMING_SNAKE_CASE_			)
   @classmethod
   def a     (	cls					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]=None			)   ->       List[Any]:
        __snake_case    			=	default_yaml_config_file if yaml_file is None else yaml_file
        with open(SCREAMING_SNAKE_CASE_					,      'r'					,      encoding='utf-8'			) as f:
             __snake_case    			=	yaml.safe_load(SCREAMING_SNAKE_CASE_			)
        if "compute_environment" not in config_dict:
             __snake_case    			=	ComputeEnvironment.LOCAL_MACHINE

        if "mixed_precision" not in config_dict:
             __snake_case    			=	"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
        if isinstance(config_dict['mixed_precision']					,      SCREAMING_SNAKE_CASE_			) and not config_dict["mixed_precision"]:
             __snake_case    			=	"no"
        if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
        if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             __snake_case    			=	config_dict.pop('dynamo_backend'			)
             __snake_case    			=	{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
        if "use_cpu" not in config_dict:
             __snake_case    			=	False
        return cls(**SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			Dict			)   ->       List[str]:






        with open(SCREAMING_SNAKE_CASE_					,      'w'					,      encoding='utf-8'			) as f:
             yaml.safe_dump(self.to_dict()					,      SCREAMING_SNAKE_CASE_			)





   def a     (	self					:			Tuple			)   ->       Union[str, Any]:






        if isinstance(self.compute_environment					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	ComputeEnvironment(self.compute_environment			)
        if isinstance(self.distributed_type					,      SCREAMING_SNAKE_CASE_			):
             if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                  __snake_case    			=	SageMakerDistributedType(self.distributed_type			)
             else:
                  __snake_case    			=	DistributedType(self.distributed_type			)
        if self.dynamo_config is None:
             __snake_case    			=	{}




@dataclass
class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     int
   _SCREAMING_SNAKE_CASE     :     int									=  0
   _SCREAMING_SNAKE_CASE     :     int									=  1
   _SCREAMING_SNAKE_CASE     :     Optional[str]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[str]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[str]									=  "static"
   _SCREAMING_SNAKE_CASE     :     Optional[bool]									=  False
   _SCREAMING_SNAKE_CASE     :     str									=  "main"

   # args for deepspeed_plugin
   _SCREAMING_SNAKE_CASE     :     dict									=  None
   # args for fsdp
   _SCREAMING_SNAKE_CASE     :     dict									=  None
   # args for megatron_lm
   _SCREAMING_SNAKE_CASE     :     dict									=  None
   # args for ipex
   _SCREAMING_SNAKE_CASE     :     dict									=  None
   # args for TPU
   _SCREAMING_SNAKE_CASE     :     bool									=  False

   # args for TPU pods
   _SCREAMING_SNAKE_CASE     :     str									=  None
   _SCREAMING_SNAKE_CASE     :     str									=  None
   _SCREAMING_SNAKE_CASE     :     bool									=  False
   _SCREAMING_SNAKE_CASE     :     bool									=  False
   _SCREAMING_SNAKE_CASE     :     str									=  None
   _SCREAMING_SNAKE_CASE     :     List[str]									=  None
   _SCREAMING_SNAKE_CASE     :     List[str]									=  None
   _SCREAMING_SNAKE_CASE     :     List[str]									=  None

   # args for dynamo
   _SCREAMING_SNAKE_CASE     :     dict									=  None
   def a     (	self					:			str			)   ->       Tuple:






        if self.deepspeed_config is None:
             __snake_case    			=	{}
        if self.fsdp_config is None:
             __snake_case    			=	{}
        if self.megatron_lm_config is None:
             __snake_case    			=	{}
        if self.ipex_config is None:
             __snake_case    			=	{}
        return super().__post_init__()




@dataclass
class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     str
   _SCREAMING_SNAKE_CASE     :     str
   _SCREAMING_SNAKE_CASE     :     Optional[str]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[str]									=  None
   _SCREAMING_SNAKE_CASE     :     str									=  "us-east-1"
   _SCREAMING_SNAKE_CASE     :     int									=  1
   _SCREAMING_SNAKE_CASE     :     str									=  "all"
   _SCREAMING_SNAKE_CASE     :     str									=  F'accelerate-sagemaker-{num_machines}'
   _SCREAMING_SNAKE_CASE     :     str									=  SAGEMAKER_PYTORCH_VERSION
   _SCREAMING_SNAKE_CASE     :     str									=  SAGEMAKER_TRANSFORMERS_VERSION
   _SCREAMING_SNAKE_CASE     :     str									=  SAGEMAKER_PYTHON_VERSION
   _SCREAMING_SNAKE_CASE     :     str									=  None
   _SCREAMING_SNAKE_CASE     :     str									=  None
   _SCREAMING_SNAKE_CASE     :     dict									=  None
   _SCREAMING_SNAKE_CASE     :     dict									=  None








'''simple docstring'''




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_a      :      int       				=      logging.get_logger(__name__)





def      _a		(lowercase__ :      int	) -> Union[str, Any]:






     """simple docstring"""
     __snake_case    			=	MobileNetVaConfig(layer_norm_eps=0.0_01	)

     if "quant" in model_name:
          raise ValueError('Quantized models are not supported.'	)

     __snake_case    			=	re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'       ,      lowercase__	)
     if matches:
          __snake_case    			=	float(matches[1]	)
          __snake_case    			=	int(matches[2]	)

     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	8
          __snake_case    			=	2_1
          __snake_case    			=	'pascal-voc-id2label.json'
     else:
          # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
          # of the usual 1000. The first class (index 0) is "background".
          __snake_case    			=	1_0_0_1
          __snake_case    			=	'imagenet-1k-id2label.json'

     __snake_case    			=	'huggingface/label-files'
     __snake_case    			=	json.load(open(hf_hub_download(lowercase__       ,      lowercase__       ,      repo_type='dataset'	)       ,      'r'	)	)

     if config.num_labels == 1_0_0_1:
          __snake_case    			=	{int(lowercase__	) + 1: v for k, v in idalabel.items()}
          __snake_case    			=	'background'
     else:
          __snake_case    			=	{int(lowercase__	): v for k, v in idalabel.items()}

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

     return config





def      _a		() -> Dict:






     """simple docstring"""
     __snake_case    			=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __snake_case    			=	Image.open(requests.get(lowercase__       ,      stream=lowercase__	).raw	)
     return im





@torch.no_grad()
def      _a		(lowercase__ :      str       ,      lowercase__ :      Optional[int]       ,      lowercase__ :      Optional[Any]       ,      lowercase__ :      Dict=False	) -> Any:






     """simple docstring"""
     __snake_case    			=	get_mobilenet_va_config(lowercase__	)

     # Load 🤗 model
     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	MobileNetVaForSemanticSegmentation(lowercase__	).eval()
     else:
          __snake_case    			=	MobileNetVaForImageClassification(lowercase__	).eval()

     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(lowercase__       ,      lowercase__       ,      lowercase__	)

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     __snake_case    			=	MobileNetVaImageProcessor(
         crop_size={'width': config.image_size, 'height': config.image_size}       ,      size={'shortest_edge': config.image_size + 3_2}       ,      )
     __snake_case    			=	image_processor(images=prepare_img()       ,      return_tensors='pt'	)
     __snake_case    			=	model(**lowercase__	)
     __snake_case    			=	outputs.logits

     if model_name.startswith('deeplabv3_'	):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               __snake_case    			=	torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]	)

          else:
               raise ValueError(f'Unknown model name: {model_name}'	)

          assert torch.allclose(logits[0, :3, :3, :3]       ,      lowercase__       ,      atol=1e-4	)
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               __snake_case    			=	torch.tensor([0.01_81, -1.00_15, 0.46_88]	)
          elif model_name == "mobilenet_v2_1.0_224":
               __snake_case    			=	torch.tensor([0.24_45, -1.19_93, 0.19_05]	)
          elif model_name == "mobilenet_v2_0.75_160":
               __snake_case    			=	torch.tensor([0.24_82, 0.41_36, 0.66_69]	)
          elif model_name == "mobilenet_v2_0.35_96":
               __snake_case    			=	torch.tensor([0.14_51, -0.46_24, 0.71_92]	)
          else:
               __snake_case    			=	None

          if expected_logits is not None:
               assert torch.allclose(logits[0, :3]       ,      lowercase__       ,      atol=1e-4	)

     Path(lowercase__	).mkdir(exist_ok=lowercase__	)
     print(f'Saving model {model_name} to {pytorch_dump_folder_path}'	)
     model.save_pretrained(lowercase__	)
     print(f'Saving image processor to {pytorch_dump_folder_path}'	)
     image_processor.save_pretrained(lowercase__	)

     if push_to_hub:
          print('Pushing to the hub...'	)
          __snake_case    			=	'google/' + model_name
          image_processor.push_to_hub(lowercase__	)
          model.push_to_hub(lowercase__	)


if __name__ == "__main__":
       _a      :      List[str]       				=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="mobilenet_v2_1.0_224",
           type=str,
           help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
       )
       parser.add_argument(
           "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       _a      :      str       				=      parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




