






"""simple docstring"""





import copy
import os
from typing import Union

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


A__					: List[Any]=	logging.get_logger(__name__)

A__					: List[str]=	{
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						="""pix2struct_text_model"""
   a      :				Any 						=["""past_key_values"""]
   a      :				str 						={
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }

   def __init__(					self ,				snake_case_=5_0244 ,				snake_case_=768 ,				snake_case_=64 ,				snake_case_=2048 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=0.1 ,				snake_case_=1E-6 ,				snake_case_=1.0 ,				snake_case_="gelu_new" ,				snake_case_=0 ,				snake_case_=False ,				snake_case_=0 ,				snake_case_=1 ,				snake_case_=False ,				snake_case_=True ,				**snake_case_ ,				)		->       List[Any]:
       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=snake_case_ ,				eos_token_id=snake_case_ ,				decoder_start_token_id=snake_case_ ,				tie_word_embeddings=snake_case_ ,				is_decoder=snake_case_ ,				**snake_case_ ,				)







   @classmethod
   def 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				**snake_case_      )		->       "PretrainedConfig":
       cls._set_token_in_kwargs(snake_case_      )

       UpperCamelCase__       ,						UpperCamelCase__         =    cls.get_config_dict(snake_case_ ,				**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(snake_case_ ,				**snake_case_      )







class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						="""pix2struct_vision_model"""

   def __init__(					self ,				snake_case_=768 ,				snake_case_=768 ,				snake_case_=2048 ,				snake_case_=64 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_="gelu_new" ,				snake_case_=1E-6 ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=1E-10 ,				snake_case_=1.0 ,				snake_case_=4096 ,				snake_case_=32 ,				snake_case_=128 ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(**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 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				**snake_case_      )		->       "PretrainedConfig":
       cls._set_token_in_kwargs(snake_case_      )

       UpperCamelCase__       ,						UpperCamelCase__         =    cls.get_config_dict(snake_case_ ,				**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(snake_case_ ,				**snake_case_      )







class    __lowerCamelCase    (  _a  ):
   a      :				Any 						="""pix2struct"""
   a      :				Any 						=True

   def __init__(					self ,				snake_case_=None ,				snake_case_=None ,				snake_case_=1.0 ,				snake_case_=0.02 ,				snake_case_=False ,				snake_case_=False ,				snake_case_=True ,				**snake_case_ ,				)		->       List[str]:
       super().__init__(tie_word_embeddings=snake_case_ ,				is_encoder_decoder=snake_case_ ,				**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(**snake_case_      )
       UpperCamelCase__         =    PixaStructVisionConfig(**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 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				snake_case_ ,				**snake_case_      )		->       List[str]:
       return cls(text_config=text_config.to_dict() ,				vision_config=vision_config.to_dict() ,				**snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    copy.deepcopy(self.__dict__      )
       UpperCamelCase__         =    self.text_config.to_dict()
       UpperCamelCase__         =    self.vision_config.to_dict()
       UpperCamelCase__         =    self.__class__.model_type
       return output












"""simple docstring"""





# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


A__					: Optional[Any]=	"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	Dict:
    """simple docstring"""

    if subparsers is not None:
        UpperCamelCase__         =    subparsers.add_parser('tpu-config' ,	description=_description					)
    else:
        UpperCamelCase__         =    argparse.ArgumentParser('Accelerate tpu-config command' ,	description=_description					)
    # Core arguments
    UpperCamelCase__         =    parser.add_argument_group(
        'Config Arguments' ,	'Arguments that can be configured through `accelerate config`.'					)
    config_args.add_argument(
        '--config_file' ,	type=SCREAMING_SNAKE_CASE ,	default=SCREAMING_SNAKE_CASE ,	help='Path to the config file to use for accelerate.' ,	)
    config_args.add_argument(
        '--tpu_name' ,	default=SCREAMING_SNAKE_CASE ,	help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.' ,	)
    config_args.add_argument(
        '--tpu_zone' ,	default=SCREAMING_SNAKE_CASE ,	help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' ,	)
    UpperCamelCase__         =    parser.add_argument_group('TPU Arguments' ,	'Arguments for options ran inside the TPU.'					)
    pod_args.add_argument(
        '--use_alpha' ,	action='store_true' ,	help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.' ,	)
    pod_args.add_argument(
        '--command_file' ,	default=SCREAMING_SNAKE_CASE ,	help='The path to the file containing the commands to run on the pod on startup.' ,	)
    pod_args.add_argument(
        '--command' ,	action='append' ,	nargs='+' ,	help='A command to run on the pod. Can be passed multiple times.' ,	)
    pod_args.add_argument(
        '--install_accelerate' ,	action='store_true' ,	help='Whether to install accelerate on the pod. Defaults to False.' ,	)
    pod_args.add_argument(
        '--accelerate_version' ,	default='latest' ,	help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.' ,	)
    pod_args.add_argument(
        '--debug' ,	action='store_true' ,	help='If set, will print the command that would be run instead of running it.'					)

    if subparsers is not None:
        parser.set_defaults(func=SCREAMING_SNAKE_CASE					)
    return parser







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    load_config_from_file(args.config_file					)
        if not args.command_file and defaults.command_file is not None and not args.command:
            UpperCamelCase__         =    defaults.command_file
        if not args.command and defaults.commands is not None:
            UpperCamelCase__         =    defaults.commands
        if not args.tpu_name:
            UpperCamelCase__         =    defaults.tpu_name
        if not args.tpu_zone:
            UpperCamelCase__         =    defaults.tpu_zone
    if args.accelerate_version == "dev":
        UpperCamelCase__         =    'git+https://github.com/huggingface/accelerate.git'
    elif args.accelerate_version == "latest":
        UpperCamelCase__         =    'accelerate -U'
    elif isinstance(parse(args.accelerate_version					) ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    F'accelerate=={args.accelerate_version}'

    if not args.command_file and not args.command:
        raise ValueError('You must specify either a command file or a command to run on the pod.'					)

    if args.command_file:
        with open(args.command_file ,	'r'					) as f:
            UpperCamelCase__         =    [f.read().splitlines()]

    # To turn list of lists into list of strings
    if isinstance(args.command[0] ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    [line for cmd in args.command for line in cmd]
    # Default to the shared folder and install accelerate
    UpperCamelCase__         =    ['cd /usr/share']
    if args.install_accelerate:
        new_cmd += [F'pip install {args.accelerate_version}']
    new_cmd += args.command
    UpperCamelCase__         =    '; '.join(SCREAMING_SNAKE_CASE					)

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    UpperCamelCase__         =    ['gcloud']
    if args.use_alpha:
        cmd += ["alpha"]
    cmd += [
        "compute",
        "tpus",
        "tpu-vm",
        "ssh",
        args.tpu_name,
        "--zone",
        args.tpu_zone,
        "--command",
        args.command,
        "--worker",
        "all",
    ]
    if args.debug:
        print(F'Running {" ".join(SCREAMING_SNAKE_CASE					)}'					)
        return
    subprocess.run(SCREAMING_SNAKE_CASE					)
    print('Successfully setup pod.'					)







def   lowerCAmelCase_( )					->	int:
    """simple docstring"""

    UpperCamelCase__         =    tpu_command_parser()
    UpperCamelCase__         =    parser.parse_args()

    tpu_command_launcher(SCREAMING_SNAKE_CASE					)




