



import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
 import torch

__snake_case		:Any 						=	logging.get_logger(__name__)





def 	__snake_case      (     ):
    return importlib.util.find_spec('''deepspeed'''							) is not None


if is_accelerate_available() and is_deepspeed_available():
 from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
 # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
 # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
 from builtins import object as DeepSpeedConfig

class        _A			(      snake_case_       ):



       def __init__(						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           set_hf_deepspeed_config(self)
           dep_version_check('''accelerate''')
           dep_version_check('''deepspeed''')
           super().__init__(lowercase_)


class        _A			(      snake_case_       ):



       def __init__(						self				:	str	,						__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           super().__init__(lowercase_)
           __a					=							None
           __a					=							[]



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           if self._dtype is None:
               raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype''')
           return self._dtype



       def 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	Dict):


           '''simple docstring'''

           __a					=							self.get_value(lowercase_)
           if val is None:
               return False
           else:
               return val == "auto"



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=None	,						__SCREAMING_SNAKE_CASE				:	List[str]=True):


           '''simple docstring'''

           __a					=							self.find_config_node(lowercase_)
           if config is None:
               return

           if config.get(lowercase_) == "auto":
               __a					=							hf_val
               return

           if not must_match:
               return

           __a					=							config.get(lowercase_)
           if ds_val is not None and ds_val != hf_val:
               self.mismatches.append(F'- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}')

       UpperCamelCase__    :       Union[str, Any]		   =		partialmethod(snake_case_ ,must_match=snake_case_       )



       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           __a					=							args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
           self.fill_match(
               '''train_micro_batch_size_per_gpu'''	,						args.per_device_train_batch_size	,						'''per_device_train_batch_size''')
           self.fill_match('''gradient_accumulation_steps'''	,						args.gradient_accumulation_steps	,						'''gradient_accumulation_steps''')
           self.fill_match('''train_batch_size'''	,						lowercase_	,						'''train_batch_size (calculated)''')
           self.fill_match('''gradient_clipping'''	,						args.max_grad_norm	,						'''max_grad_norm''')

           self.fill_match('''optimizer.params.lr'''	,						args.learning_rate	,						'''learning_rate''')
           self.fill_match('''optimizer.params.betas'''	,						[args.adam_betaa, args.adam_betaa]	,						'''adam_beta1+adam_beta2''')
           self.fill_match('''optimizer.params.eps'''	,						args.adam_epsilon	,						'''adam_epsilon''')
           self.fill_match('''optimizer.params.weight_decay'''	,						args.weight_decay	,						'''weight_decay''')

           self.fill_only('''scheduler.params.warmup_min_lr'''	,						0)  # not a trainer arg
           self.fill_match('''scheduler.params.warmup_max_lr'''	,						args.learning_rate	,						'''learning_rate''')
           # total_num_steps - will get set in trainer_config_finalize

           # fp16
           if args.fpaa or args.fpaa_full_eval:
               __a					=							'apex' if args.fpaa_backend == 'apex' else 'amp'
           else:
               __a					=							None

           if args.save_on_each_node:
               # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
               __a					=							self.config.get('''checkpoint'''	,						{})
               __a					=							args.save_on_each_node

           # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
           # any here unless the user did the work
           self.fill_match(
               '''fp16.enabled'''	,						((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')	,						'''fp16|fp16_full_eval+fp16_backend(amp)'''	,						)

           # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
           # ZeRO features
           self.fill_match('''amp.enabled'''	,						fpaa_backend == '''apex'''	,						'''fp16+fp16_backend(apex)''')
           self.fill_match('''amp.opt_level'''	,						args.fpaa_opt_level	,						'''fp16_opt_level''')

           self.fill_match('''bf16.enabled'''	,						(args.bfaa or args.bfaa_full_eval)	,						'''bf16|bf16_full_eval''')

           # deepspeed's default mode is fp16 unless there is a config that says differently
           if self.is_true('''bf16.enabled'''):
               __a					=							torch.bfloataa
           elif self.is_false('''fp16.enabled'''):
               __a					=							torch.floataa
           else:
               __a					=							torch.floataa





       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							[
               'zero_optimization.reduce_bucket_size',
               'zero_optimization.stage3_prefetch_bucket_size',
               'zero_optimization.stage3_param_persistence_threshold',
           ]
           __a					=							[x for x in hidden_size_based_keys if self.is_auto(lowercase_)]

           if len(lowercase_) > 0:
               if hasattr(model.config	,						'''hidden_size'''):
                   __a					=							model.config.hidden_size
               elif hasattr(model.config	,						'''hidden_sizes'''):
                   # if there are many hidden sizes pick the largest one
                   __a					=							max(model.config.hidden_sizes)
               else:
                   raise ValueError(
                       '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                       '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                       F'in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '
                       '''`auto` values for these keys with an integer value of your choice.''')

               self.fill_only('''zero_optimization.reduce_bucket_size'''	,						hidden_size * hidden_size)
               if self.is_zeroa():
                   # automatically assign the optimal config values based on model config
                   self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''	,						0.9 * hidden_size * hidden_size)
                   self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''	,						10 * hidden_size)

                      # scheduler
           self.fill_match('''scheduler.params.total_num_steps'''	,						lowercase_	,						'''num_training_steps (calculated)''')
           self.fill_match('''scheduler.params.warmup_num_steps'''	,						args.get_warmup_steps(lowercase_)	,						'''warmup_steps''')

           if len(self.mismatches) > 0:
               __a					=							'\n'.join(self.mismatches)
               raise ValueError(
                   '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                   F' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.')


__snake_case		:Optional[int] 						=	None





def 	__snake_case      (     _UpperCAmelCase							):
    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    __a					=							weakref.ref(UpperCamelCase_							)





def 	__snake_case      (     ):
    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
    global _hf_deepspeed_config_weak_ref
    __a					=							None





def 	__snake_case      (     ):
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False





def 	__snake_case      (     ):
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    from accelerate.utils import DummyOptim, DummyScheduler

    __a					=							hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    __a					=							None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                '''Only one optimizer can be configured.'''							)
        __a					=							DummyOptim(params=UpperCamelCase_							)
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''							)

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        __a					=							trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        __a					=							True

    __a					=							None
    if "scheduler" in config:
        __a					=							DummyScheduler(UpperCamelCase_							)
    else:
        if isinstance(UpperCamelCase_  ,					UpperCamelCase_							):
            raise ValueError(
                '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                '''Please configure a scheduler in the DeepSpeed config.'''							)
        __a					=							trainer.create_scheduler(num_training_steps=UpperCamelCase_  ,					optimizer=UpperCamelCase_							)

    return optimizer, lr_scheduler





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase=False							):
    from deepspeed.utils import logger as ds_logger

    __a					=							trainer.model
    __a					=							trainer.args

    __a					=							trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()							)

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''							)

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree('''optimizer'''							)
        hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''							)
        __a					=							None, None
        __a					=							None
    else:
        __a					=							None  # important for when deepspeed_init is used as re-init
        __a					=							list(filter(lambda _UpperCAmelCase							: p.requires_grad  ,					model.parameters()							)							)
        __a					=							deepspeed_optim_sched(
            UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase							):
    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
    # path contains what looks like a deepspeed checkpoint
    import glob

    __a					=							sorted(glob.glob(f'{checkpoint_path}/global_step*'							)							)

    if len(UpperCamelCase_							) > 0:
        logger.info(f'Attempting to resume from {checkpoint_path}'							)
        # this magically updates self.optimizer and self.lr_scheduler
        __a					=							deepspeed_engine.load_checkpoint(
            UpperCamelCase_  ,					load_optimizer_states=UpperCamelCase_  ,					load_lr_scheduler_states=UpperCamelCase_							)
        if load_path is None:
            raise ValueError(f'[deepspeed] failed to resume from checkpoint {checkpoint_path}'							)
    else:
        raise ValueError(f'Can\'t find a valid checkpoint at {checkpoint_path}'							)













"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		str:
 __A						:							Union[str, Any]      =						CLIPSegTextConfig()
 __A						:							Dict      =						CLIPSegVisionConfig(patch_size=1_6		)

 __A						:							Dict      =						True if 'refined' in model_name else False
 __A						:							Optional[int]      =						1_6 if 'rd16' in model_name else 6_4

 __A						:							Tuple      =						CLIPSegConfig.from_text_vision_configs(
     UpperCamelCase_  ,						UpperCamelCase_  ,						use_complex_transposed_convolution=UpperCamelCase_  ,						reduce_dim=UpperCamelCase_  ,						)
 return config




def 					a			(  UpperCamelCase_				:					List[str]		)	->		Dict:
 # update prefixes
 if "clip_model" in name:
  __A						:							str      =						name.replace('clip_model'  ,						'clip'		)
 if "transformer" in name:
  if "visual" in name:
   __A						:							List[str]      =						name.replace('visual.transformer'  ,						'vision_model'		)
  else:
   __A						:							Dict      =						name.replace('transformer'  ,						'text_model'		)
 if "resblocks" in name:
  __A						:							List[str]      =						name.replace('resblocks'  ,						'encoder.layers'		)
 if "ln_1" in name:
  __A						:							Dict      =						name.replace('ln_1'  ,						'layer_norm1'		)
 if "ln_2" in name:
  __A						:							Any      =						name.replace('ln_2'  ,						'layer_norm2'		)
 if "c_fc" in name:
  __A						:							List[Any]      =						name.replace('c_fc'  ,						'fc1'		)
 if "c_proj" in name:
  __A						:							int      =						name.replace('c_proj'  ,						'fc2'		)
 if "attn" in name and "self" not in name:
  __A						:							Dict      =						name.replace('attn'  ,						'self_attn'		)
 # text encoder
 if "token_embedding" in name:
  __A						:							Dict      =						name.replace('token_embedding'  ,						'text_model.embeddings.token_embedding'		)
 if "positional_embedding" in name and "visual" not in name:
  __A						:							int      =						name.replace('positional_embedding'  ,						'text_model.embeddings.position_embedding.weight'		)
 if "ln_final" in name:
  __A						:							Optional[Any]      =						name.replace('ln_final'  ,						'text_model.final_layer_norm'		)
 # vision encoder
 if "visual.class_embedding" in name:
  __A						:							Tuple      =						name.replace('visual.class_embedding'  ,						'vision_model.embeddings.class_embedding'		)
 if "visual.conv1" in name:
  __A						:							List[Any]      =						name.replace('visual.conv1'  ,						'vision_model.embeddings.patch_embedding'		)
 if "visual.positional_embedding" in name:
  __A						:							str      =						name.replace('visual.positional_embedding'  ,						'vision_model.embeddings.position_embedding.weight'		)
 if "visual.ln_pre" in name:
  __A						:							Any      =						name.replace('visual.ln_pre'  ,						'vision_model.pre_layrnorm'		)
 if "visual.ln_post" in name:
  __A						:							str      =						name.replace('visual.ln_post'  ,						'vision_model.post_layernorm'		)
 # projection layers
 if "visual.proj" in name:
  __A						:							Any      =						name.replace('visual.proj'  ,						'visual_projection.weight'		)
 if "text_projection" in name:
  __A						:							int      =						name.replace('text_projection'  ,						'text_projection.weight'		)
 # decoder
 if "trans_conv" in name:
  __A						:							int      =						name.replace('trans_conv'  ,						'transposed_convolution'		)
 if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
  __A						:							List[Any]      =						'decoder.' + name
 if "blocks" in name:
  __A						:							Optional[Any]      =						name.replace('blocks'  ,						'decoder.layers'		)
 if "linear1" in name:
  __A						:							int      =						name.replace('linear1'  ,						'mlp.fc1'		)
 if "linear2" in name:
  __A						:							Tuple      =						name.replace('linear2'  ,						'mlp.fc2'		)
 if "norm1" in name and "layer_" not in name:
  __A						:							List[Any]      =						name.replace('norm1'  ,						'layer_norm1'		)
 if "norm2" in name and "layer_" not in name:
  __A						:							Optional[Any]      =						name.replace('norm2'  ,						'layer_norm2'		)

 return name




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict		)	->		Any:
 for key in orig_state_dict.copy().keys():
  __A						:							str      =						orig_state_dict.pop(UpperCamelCase_		)

  if key.startswith('clip_model'		) and "attn.in_proj" in key:
   __A						:							Any      =						key.split('.'		)
   if "visual" in key:
    __A						:							Optional[int]      =						int(key_split[4]		)
    __A						:							Dict      =						config.vision_config.hidden_size
    __A						:							str      =						'vision_model'
   else:
    __A						:							Union[str, Any]      =						int(key_split[3]		)
    __A						:							List[str]      =						config.text_config.hidden_size
    __A						:							Union[str, Any]      =						'text_model'

   if "weight" in key:
    __A						:							List[Any]      =						val[:dim, :]
    __A						:							Optional[Any]      =						val[
        dim : dim * 2, :
    ]
    __A						:							List[str]      =						val[-dim:, :]
   else:
    __A						:							Any      =						val[:dim]
    __A						:							Tuple      =						val[dim : dim * 2]
    __A						:							Dict      =						val[-dim:]
  elif "self_attn" in key and "out_proj" not in key:
   __A						:							Any      =						key.split('.'		)
   __A						:							Optional[int]      =						int(key_split[1]		)
   __A						:							int      =						config.reduce_dim
   if "weight" in key:
    __A						:							Tuple      =						val[:dim, :]
    __A						:							Optional[int]      =						val[dim : dim * 2, :]
    __A						:							List[Any]      =						val[-dim:, :]
   else:
    __A						:							List[Any]      =						val[:dim]
    __A						:							Union[str, Any]      =						val[dim : dim * 2]
    __A						:							Union[str, Any]      =						val[-dim:]
  else:
   __A						:							List[Any]      =						rename_key(UpperCamelCase_		)
   if "visual_projection" in new_name or "text_projection" in new_name:
    __A						:							Dict      =						val.T
   __A						:							int      =						val

 return orig_state_dict




def 					a			(  )	->		Dict:
 __A						:							List[Any]      =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A						:							Any      =						Image.open(requests.get(UpperCamelCase_  ,						stream=UpperCamelCase_		).raw		)
 return image




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int		)	->		Optional[int]:
 __A						:							str      =						get_clipseg_config(UpperCamelCase_		)
 __A						:							int      =						CLIPSegForImageSegmentation(UpperCamelCase_		)
 model.eval()

 __A						:							Optional[int]      =						torch.load(UpperCamelCase_  ,						map_location='cpu'		)

 # remove some keys
 for key in state_dict.copy().keys():
  if key.startswith('model'		):
   state_dict.pop(UpperCamelCase_  ,						UpperCamelCase_		)

    # rename some keys
 __A						:							int      =						convert_state_dict(UpperCamelCase_  ,						UpperCamelCase_		)
 __A     ,       __A						:							Tuple      =						model.load_state_dict(UpperCamelCase_  ,						strict=UpperCamelCase_		)

 if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase_		)		)
 if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  raise ValueError(F"Unexpected keys: {unexpected_keys}"		)

 __A						:							Optional[Any]      =						ViTImageProcessor(size=3_5_2		)
 __A						:							List[Any]      =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		)
 __A						:							Optional[int]      =						CLIPSegProcessor(image_processor=UpperCamelCase_  ,						tokenizer=UpperCamelCase_		)

 __A						:							List[str]      =						prepare_img()
 __A						:							Optional[Any]      =						['a glass', 'something to fill', 'wood', 'a jar']

 __A						:							Optional[Any]      =						processor(text=UpperCamelCase_  ,						images=[image] * len(UpperCamelCase_		)  ,						padding='max_length'  ,						return_tensors='pt'		)

 with torch.no_grad():
  __A						:							int      =						model(**UpperCamelCase_		)

 # verify values
 __A						:							Union[str, Any]      =						torch.tensor([0.1110, -0.1882, 0.1645]		)
 __A						:							List[Any]      =						torch.tensor([0.2692, -0.7197, -0.1328]		)
 if model_name == "clipseg-rd64-refined":
  __A						:							int      =						torch.tensor(
      [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]		)
 elif model_name == "clipseg-rd64":
  __A						:							Optional[Any]      =						torch.tensor(
      [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]		)
 elif model_name == "clipseg-rd16":
  __A						:							Optional[Any]      =						torch.tensor(
      [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]		)
 else:
  raise ValueError(F"Model name {model_name} not supported."		)

 assert torch.allclose(outputs.logits[0, :3, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.pooled_output[0, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 print('Looks ok!'		)

 if pytorch_dump_folder_path is not None:
  print(F"Saving model and processor to {pytorch_dump_folder_path}"		)
  model.save_pretrained(UpperCamelCase_		)
  processor.save_pretrained(UpperCamelCase_		)

 if push_to_hub:
  print(F"Pushing model and processor for {model_name} to the hub"		)
  model.push_to_hub(F"CIDAS/{model_name}"		)
  processor.push_to_hub(F"CIDAS/{model_name}"		)


if __name__ == "__main__":
   _UpperCamelCase :    str      =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""clipseg-rd64""",
       type=str,
       choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
       help=(
           """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
           """ reduce dimension)"""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""",
       default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
       type=str,
       help=(
           """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
           """ the decoder weights."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, 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."""
   )

   _UpperCamelCase :    Tuple      =			parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





