






"""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


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







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict=None )      ->				Optional[Any]:
  if subparsers is not None:
    lowerCamelCase_         =						subparsers.add_parser('tpu-config'		,				description=_description )
  else:
    lowerCamelCase_         =						argparse.ArgumentParser('Accelerate tpu-config command'		,				description=_description )
  # Core arguments
  lowerCamelCase_         =						parser.add_argument_group(
      'Config Arguments'		,				'Arguments that can be configured through `accelerate config`.' )
  config_args.add_argument(
      '--config_file'		,				type=_lowerCamelCase		,				default=_lowerCamelCase		,				help='Path to the config file to use for accelerate.'		,				)
  config_args.add_argument(
      '--tpu_name'		,				default=_lowerCamelCase		,				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=_lowerCamelCase		,				help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'		,				)
  lowerCamelCase_         =						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=_lowerCamelCase		,				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=_lowerCamelCase )
  return parser







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				Any:
  lowerCamelCase_         =						None

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

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

  # Then send it to gcloud
  # Eventually try to use google-api-core to do this instead of subprocess
  lowerCamelCase_         =						['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(_lowerCamelCase )}''' )
    return
  subprocess.run(_lowerCamelCase )
  print('Successfully setup pod.' )







def      lowerCamelCase__       (							)      ->				Tuple:
  lowerCamelCase_         =						tpu_command_parser()
  lowerCamelCase_         =						parser.parse_args()

  tpu_command_launcher(_lowerCamelCase )












"""simple docstring"""





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class      a						(					nn.Module		):






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						list(model.children()					)[:-2]
      lowerCamelCase_         =						nn.Sequential(*__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						[json.loads(__SCREAMING_SNAKE_CASE					) for l in open(__SCREAMING_SNAKE_CASE					)]
      lowerCamelCase_         =						os.path.dirname(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						labels
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						max_seq_length

      lowerCamelCase_         =						transforms






    def __len__(       self     :							Any					)     ->		Any:
      return len(self.data					)






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

      lowerCamelCase_         =						torch.zeros(self.n_classes					)
      lowerCamelCase_         =						1

      lowerCamelCase_         =						Image.open(os.path.join(self.data_dir       ,						self.data[index]['img']					)					).convert('RGB'					)
      lowerCamelCase_         =						self.transforms(__SCREAMING_SNAKE_CASE					)

      return {
          "image_start_token": start_token,
          "image_end_token": end_token,
          "sentence": sentence,
          "image": image,
          "label": label,
      }






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,				_lowerCamelCase ) ):
    lowerCamelCase_         =						input_row['sentence']
    lowerCamelCase_         =						1

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_end_token'] for row in batch] )

  return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def      lowerCamelCase__       (							)      ->				List[str]:
  return [
      "Crime",
      "Drama",
      "Thriller",
      "Action",
      "Comedy",
      "Romance",
      "Documentary",
      "Short",
      "Mystery",
      "History",
      "Family",
      "Adventure",
      "Fantasy",
      "Sci-Fi",
      "Western",
      "Horror",
      "Sport",
      "War",
      "Music",
      "Musical",
      "Animation",
      "Biography",
      "Film-Noir",
  ]







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  return transforms.Compose(
      [
          transforms.Resize(256 ),
          transforms.CenterCrop(224 ),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,				),
      ] )




