





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, TensorType

__A							:			Optional[int]         =			logging.get_logger(__name__)

__A							:			Any         =			{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'imagegpt'
     __magic_name__   =			['past_key_values']
     __magic_name__   =			{
         'hidden_size': 'n_embd',
         'max_position_embeddings': 'n_positions',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }




     def __init__(    self				,     snake_case_=512 + 1				,     snake_case_=32 * 32				,     snake_case_=512				,     snake_case_=24				,     snake_case_=8				,     snake_case_=None				,     snake_case_="quick_gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=1E-5				,     snake_case_=0.02				,     snake_case_=True				,     snake_case_=True				,     snake_case_=False				,     snake_case_=False				,     snake_case_=False				,     **snake_case_				,     ):
         _A     =							vocab_size
         _A     =							n_positions
         _A     =							n_embd
         _A     =							n_layer
         _A     =							n_head
         _A     =							n_inner
         _A     =							activation_function
         _A     =							resid_pdrop
         _A     =							embd_pdrop
         _A     =							attn_pdrop
         _A     =							layer_norm_epsilon
         _A     =							initializer_range
         _A     =							scale_attn_weights
         _A     =							use_cache
         _A     =							scale_attn_by_inverse_layer_idx
         _A     =							reorder_and_upcast_attn
         _A     =							tie_word_embeddings

         super().__init__(tie_word_embeddings=snake_case_				,     **snake_case_							)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'sequence'}),
             ]							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = 1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = 3				,     snake_case_ = 32				,     snake_case_ = 32				,     ):
         _A     =							self._generate_dummy_images(snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							)
         _A     =							dict(preprocessor(images=snake_case_				,     return_tensors=snake_case_							)							)

         return inputs












# 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[int]         =			"Run commands across TPU VMs for initial setup before running `accelerate launch`."





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE=None				)							-> str:




    """simple docstring"""




    if subparsers is not None:
        _A     =							subparsers.add_parser('tpu-config'							,     description=_description				)
    else:
        _A     =							argparse.ArgumentParser('Accelerate tpu-config command'							,     description=_description				)
    # Core arguments
    _A     =							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.'							,     )
    _A     =							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				)							-> int:




    """simple docstring"""




    _A     =							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				):
        _A     =							load_config_from_file(args.config_file				)
        if not args.command_file and defaults.command_file is not None and not args.command:
            _A     =							defaults.command_file
        if not args.command and defaults.commands is not None:
            _A     =							defaults.commands
        if not args.tpu_name:
            _A     =							defaults.tpu_name
        if not args.tpu_zone:
            _A     =							defaults.tpu_zone
    if args.accelerate_version == "dev":
        _A     =							'git+https://github.com/huggingface/accelerate.git'
    elif args.accelerate_version == "latest":
        _A     =							'accelerate -U'
    elif isinstance(parse(args.accelerate_version				)							,     _SCREAMING_SNAKE_CASE				):
        _A     =							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:
            _A     =							[f.read().splitlines()]

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

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    _A     =							['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(       )							-> Tuple:




    """simple docstring"""




    _A     =							tpu_command_parser()
    _A     =							parser.parse_args()

    tpu_command_launcher(_SCREAMING_SNAKE_CASE				)





