
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					str       						="pix2struct_text_model"
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["past_key_values"]
 SCREAMING_SNAKE_CASE_				:					Dict       						={
     "hidden_size": "hidden_size",
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }

 def __init__(						self			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]=5_02_44					, SCREAMING_SNAKE_CASE__			:			List[Any]=7_68					, SCREAMING_SNAKE_CASE__			:			Dict=64					, SCREAMING_SNAKE_CASE__			:			int=20_48					, SCREAMING_SNAKE_CASE__			:			Dict=12					, SCREAMING_SNAKE_CASE__			:			str=12					, SCREAMING_SNAKE_CASE__			:			int=32					, SCREAMING_SNAKE_CASE__			:			List[Any]=1_28					, SCREAMING_SNAKE_CASE__			:			Any=0.1					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=1e-6					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=1.0					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]="gelu_new"					, SCREAMING_SNAKE_CASE__			:			Optional[int]=0					, SCREAMING_SNAKE_CASE__			:			Tuple=False					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0					, SCREAMING_SNAKE_CASE__			:			Optional[int]=1					, SCREAMING_SNAKE_CASE__			:			int=False					, SCREAMING_SNAKE_CASE__			:			str=True					, **SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					d_kv
      UpperCamelCase						    =					d_ff
      UpperCamelCase						    =					num_layers
      UpperCamelCase						    =					num_heads
      UpperCamelCase						    =					relative_attention_num_buckets
      UpperCamelCase						    =					relative_attention_max_distance
      UpperCamelCase						    =					dropout_rate
      UpperCamelCase						    =					layer_norm_epsilon
      UpperCamelCase						    =					initializer_factor
      UpperCamelCase						    =					use_cache

      UpperCamelCase						    =					eos_token_id
      UpperCamelCase						    =					decoder_start_token_id

      # for backwards compatibility
      UpperCamelCase						    =					dense_act_fn

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE__					, eos_token_id=SCREAMING_SNAKE_CASE__					, decoder_start_token_id=SCREAMING_SNAKE_CASE__					, tie_word_embeddings=SCREAMING_SNAKE_CASE__					, is_decoder=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )






 @classmethod
 def      __lowerCAmelCase  (						cls			:			Dict					, SCREAMING_SNAKE_CASE__			:			Union[str, os.PathLike]					, **SCREAMING_SNAKE_CASE__			:			Tuple   ):






      """simple docstring"""



      cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase    ,		UpperCamelCase						    =					cls.get_config_dict(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      # get the text config dict if we are loading from Pix2StructConfig
      if config_dict.get('model_type'   ) == "pix2struct":
           UpperCamelCase						    =					config_dict['text_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(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						="pix2struct_vision_model"

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			List[Any]=7_68					, SCREAMING_SNAKE_CASE__			:			int=7_68					, SCREAMING_SNAKE_CASE__			:			Optional[int]=20_48					, SCREAMING_SNAKE_CASE__			:			str=64					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			List[Any]="gelu_new"					, SCREAMING_SNAKE_CASE__			:			Dict=1e-6					, SCREAMING_SNAKE_CASE__			:			Dict=0.0					, SCREAMING_SNAKE_CASE__			:			Dict=0.0					, SCREAMING_SNAKE_CASE__			:			Any=1e-10					, SCREAMING_SNAKE_CASE__			:			str=1.0					, SCREAMING_SNAKE_CASE__			:			Optional[int]=40_96					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=32					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=1_28					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					patch_embed_hidden_size
      UpperCamelCase						    =					d_ff
      UpperCamelCase						    =					dropout_rate
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					initializer_factor
      UpperCamelCase						    =					attention_dropout
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					dense_act_fn
      UpperCamelCase						    =					seq_len
      UpperCamelCase						    =					relative_attention_num_buckets
      UpperCamelCase						    =					relative_attention_max_distance
      UpperCamelCase						    =					d_kv






 @classmethod
 def      __lowerCAmelCase  (						cls			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Union[str, os.PathLike]					, **SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase    ,		UpperCamelCase						    =					cls.get_config_dict(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      # get the vision config dict if we are loading from Pix2StructConfig
      if config_dict.get('model_type'   ) == "pix2struct":
           UpperCamelCase						    =					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(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						="pix2struct"
 SCREAMING_SNAKE_CASE_				:					str       						=True

 def __init__(						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[int]=None					, SCREAMING_SNAKE_CASE__			:			Optional[int]=None					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=1.0					, SCREAMING_SNAKE_CASE__			:			Tuple=0.02					, SCREAMING_SNAKE_CASE__			:			Any=False					, SCREAMING_SNAKE_CASE__			:			Tuple=False					, SCREAMING_SNAKE_CASE__			:			Any=True					, **SCREAMING_SNAKE_CASE__			:			List[Any]					, ):






      """simple docstring"""



      super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE__					, is_encoder_decoder=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      if text_config is None:
           UpperCamelCase						    =					{}
           logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'   )

      if vision_config is None:
           UpperCamelCase						    =					{}
           logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'   )

      UpperCamelCase						    =					PixaStructTextConfig(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					PixaStructVisionConfig(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					self.text_config.decoder_start_token_id
      UpperCamelCase						    =					self.text_config.pad_token_id
      UpperCamelCase						    =					self.text_config.eos_token_id

      UpperCamelCase						    =					initializer_factor
      UpperCamelCase						    =					initializer_range

      UpperCamelCase						    =					self.initializer_range
      UpperCamelCase						    =					self.initializer_range

      UpperCamelCase						    =					is_vqa

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Any					, SCREAMING_SNAKE_CASE__			:			PixaStructTextConfig					, SCREAMING_SNAKE_CASE__			:			PixaStructVisionConfig					, **SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      return cls(text_config=text_config.to_dict()					, vision_config=vision_config.to_dict()					, **SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					copy.deepcopy(self.__dict__   )
      UpperCamelCase						    =					self.text_config.to_dict()
      UpperCamelCase						    =					self.vision_config.to_dict()
      UpperCamelCase						    =					self.__class__.model_type
      return output




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
 _snake_case      = 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''')
 _snake_case  ,      _snake_case      = 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''')
  _snake_case      = rh.cluster(
      name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
  )
 else:
  _snake_case      = rh.cluster(
      name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 _snake_case      = 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)


