






"""simple docstring"""
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''instructblip_vision_model'''





	def __init__(   self      :				int			,			lowerCAmelCase_      :				Optional[Any]=1_408			,			lowerCAmelCase_      :				Union[str, Any]=6_144			,			lowerCAmelCase_      :				Optional[Any]=39			,			lowerCAmelCase_      :				Any=16			,			lowerCAmelCase_      :				List[str]=224			,			lowerCAmelCase_      :				List[Any]=14			,			lowerCAmelCase_      :				Optional[int]="gelu"			,			lowerCAmelCase_      :				int=1e-6			,			lowerCAmelCase_      :				Optional[Any]=0.0			,			lowerCAmelCase_      :				str=1e-10			,			lowerCAmelCase_      :				Any=True			,			**lowerCAmelCase_      :				int			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)

				UpperCAmelCase_  :				Any      =      hidden_size
				UpperCAmelCase_  :				Union[str, Any]      =      intermediate_size
				UpperCAmelCase_  :				Any      =      num_hidden_layers
				UpperCAmelCase_  :				Dict      =      num_attention_heads
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Union[str, Any]      =      image_size
				UpperCAmelCase_  :				List[Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      attention_dropout
				UpperCAmelCase_  :				Any      =      layer_norm_eps
				UpperCAmelCase_  :				Optional[int]      =      hidden_act
				UpperCAmelCase_  :				Optional[Any]      =      qkv_bias





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, os.PathLike]			,			**lowerCAmelCase_      :				Dict		)		->    "PretrainedConfig":
				cls._set_token_in_kwargs(lowerCAmelCase_		)

				UpperCAmelCase_  ,			UpperCAmelCase_  :				Tuple      =      cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_		)

				# get the vision config dict if we are loading from InstructBlipConfig
				if config_dict.get("model_type"		) == "instructblip":
							UpperCAmelCase_  :				str      =      config_dict["vision_config"]

				if "model_type" in config_dict and hasattr(cls			,			"model_type"		) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
							    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

				return cls.from_dict(lowerCAmelCase_			,			**lowerCAmelCase_		)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''instructblip_qformer'''





	def __init__(   self      :				str			,			lowerCAmelCase_      :				int=30_522			,			lowerCAmelCase_      :				List[Any]=768			,			lowerCAmelCase_      :				Dict=12			,			lowerCAmelCase_      :				Union[str, Any]=12			,			lowerCAmelCase_      :				Dict=3_072			,			lowerCAmelCase_      :				List[str]="gelu"			,			lowerCAmelCase_      :				Any=0.1			,			lowerCAmelCase_      :				str=0.1			,			lowerCAmelCase_      :				int=512			,			lowerCAmelCase_      :				Tuple=0.0_2			,			lowerCAmelCase_      :				List[str]=1e-12			,			lowerCAmelCase_      :				str=0			,			lowerCAmelCase_      :				List[Any]="absolute"			,			lowerCAmelCase_      :				Dict=2			,			lowerCAmelCase_      :				List[str]=1_408			,			**lowerCAmelCase_      :				int			,			)		->    Any:
				super().__init__(pad_token_id=lowerCAmelCase_			,			**lowerCAmelCase_		)

				UpperCAmelCase_  :				Tuple      =      vocab_size
				UpperCAmelCase_  :				Any      =      hidden_size
				UpperCAmelCase_  :				Any      =      num_hidden_layers
				UpperCAmelCase_  :				Tuple      =      num_attention_heads
				UpperCAmelCase_  :				Union[str, Any]      =      hidden_act
				UpperCAmelCase_  :				Optional[Any]      =      intermediate_size
				UpperCAmelCase_  :				Any      =      hidden_dropout_prob
				UpperCAmelCase_  :				Tuple      =      attention_probs_dropout_prob
				UpperCAmelCase_  :				Optional[int]      =      max_position_embeddings
				UpperCAmelCase_  :				Optional[int]      =      initializer_range
				UpperCAmelCase_  :				Optional[Any]      =      layer_norm_eps
				UpperCAmelCase_  :				Any      =      position_embedding_type
				UpperCAmelCase_  :				int      =      cross_attention_frequency
				UpperCAmelCase_  :				Optional[Any]      =      encoder_hidden_size





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, os.PathLike]			,			**lowerCAmelCase_      :				List[str]		)		->    "PretrainedConfig":
				cls._set_token_in_kwargs(lowerCAmelCase_		)

				UpperCAmelCase_  ,			UpperCAmelCase_  :				int      =      cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_		)

				# get the qformer config dict if we are loading from InstructBlipConfig
				if config_dict.get("model_type"		) == "instructblip":
							UpperCAmelCase_  :				Tuple      =      config_dict["qformer_config"]

				if "model_type" in config_dict and hasattr(cls			,			"model_type"		) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
							    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

				return cls.from_dict(lowerCAmelCase_			,			**lowerCAmelCase_		)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''instructblip'''
	__magic_name__			   =       True





	def __init__(   self      :				str			,			lowerCAmelCase_      :				List[str]=None			,			lowerCAmelCase_      :				Union[str, Any]=None			,			lowerCAmelCase_      :				int=None			,			lowerCAmelCase_      :				Optional[Any]=32			,			**lowerCAmelCase_      :				Optional[int]		)		->    Any:
				super().__init__(**lowerCAmelCase_		)

				if vision_config is None:
							UpperCAmelCase_  :				List[str]      =      {}
							logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."		)

				if qformer_config is None:
							UpperCAmelCase_  :				Dict      =      {}
							logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."		)

				if text_config is None:
							UpperCAmelCase_  :				List[Any]      =      {}
							logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."		)

				UpperCAmelCase_  :				Tuple      =      InstructBlipVisionConfig(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      InstructBlipQFormerConfig(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      text_config["model_type"] if "model_type" in text_config else "opt"
				UpperCAmelCase_  :				Optional[Any]      =      CONFIG_MAPPING[text_model_type](**lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      self.text_config.tie_word_embeddings
				UpperCAmelCase_  :				List[Any]      =      self.text_config.is_encoder_decoder

				UpperCAmelCase_  :				Dict      =      num_query_tokens
				UpperCAmelCase_  :				Union[str, Any]      =      self.vision_config.hidden_size
				UpperCAmelCase_  :				int      =      self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
				UpperCAmelCase_  :				Union[str, Any]      =      1.0
				UpperCAmelCase_  :				Any      =      0.0_2





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Optional[Any]			,			lowerCAmelCase_      :				InstructBlipVisionConfig			,			lowerCAmelCase_      :				InstructBlipQFormerConfig			,			lowerCAmelCase_      :				PretrainedConfig			,			**lowerCAmelCase_      :				str			,			)		->    Dict:
				return cls(
				    vision_config=vision_config.to_dict()			,			qformer_config=qformer_config.to_dict()			,			text_config=text_config.to_dict()			,			**lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				UpperCAmelCase_  :				Dict      =      copy.deepcopy(self.__dict__		)
				UpperCAmelCase_  :				Union[str, Any]      =      self.vision_config.to_dict()
				UpperCAmelCase_  :				Tuple      =      self.qformer_config.to_dict()
				UpperCAmelCase_  :				str      =      self.text_config.to_dict()
				UpperCAmelCase_  :				Optional[Any]      =      self.__class__.model_type
				return output











"""simple docstring"""
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowerCamelCase_       			=   argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowerCamelCase_			,	lowerCamelCase_       			=   parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowerCamelCase_       			=   args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'pip install -r transformers/examples/{example_dir}/requirements.txt'])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



