

'''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


__UpperCAmelCase    				=     'Run commands across TPU VMs for initial setup before running `accelerate launch`.'







def 	_snake_case		(    SCREAMING_SNAKE_CASE=None				)       ->      Dict:

     """simple docstring"""







     if subparsers is not None:
          _lowerCAmelCase   :					str        =    subparsers.add_parser("tpu-config"		,							description=_description				)
     else:
          _lowerCAmelCase   :					int        =    argparse.ArgumentParser("Accelerate tpu-config command"		,							description=_description				)
     # Core arguments
     _lowerCAmelCase   :					Tuple        =    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."		,							)
     _lowerCAmelCase   :					Tuple        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    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				):
          _lowerCAmelCase   :					str        =    load_config_from_file(args.config_file				)
          if not args.command_file and defaults.command_file is not None and not args.command:
               _lowerCAmelCase   :					str        =    defaults.command_file
          if not args.command and defaults.commands is not None:
               _lowerCAmelCase   :					Any        =    defaults.commands
          if not args.tpu_name:
               _lowerCAmelCase   :					List[str]        =    defaults.tpu_name
          if not args.tpu_zone:
               _lowerCAmelCase   :					int        =    defaults.tpu_zone
     if args.accelerate_version == "dev":
          _lowerCAmelCase   :					Optional[Any]        =    "git+https://github.com/huggingface/accelerate.git"
     elif args.accelerate_version == "latest":
          _lowerCAmelCase   :					List[Any]        =    "accelerate -U"
     elif isinstance(parse(args.accelerate_version				)		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					str        =    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   :					Union[str, Any]        =    [f.read().splitlines()]

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

     # Then send it to gcloud
     # Eventually try to use google-api-core to do this instead of subprocess
     _lowerCAmelCase   :					Optional[int]        =    ["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 	_snake_case		(    )       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    tpu_command_parser()
     _lowerCAmelCase   :					int        =    parser.parse_args()

     tpu_command_launcher(SCREAMING_SNAKE_CASE				)









import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    {}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.cmd.mim_head.cls.predictions"		,							"mmm_image_head"				)
          _lowerCAmelCase   :					int        =    key.replace("heads.cmd.mlm_head.cls.predictions"		,							"mmm_text_head"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.itm_head.cls"		,							"itm_head"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("heads.cmd.itm_head.pooler"		,							"itm_head.pooler"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.clip_head.logit_scale"		,							"flava.logit_scale"				)
          _lowerCAmelCase   :					List[Any]        =    key.replace("heads.fairseq_mlm.cls.predictions"		,							"mlm_head"				)
          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.imagenet.mim_head.cls.predictions"		,							"mim_head"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("mm_text_projection"		,							"flava.text_to_mm_projection"				)
          _lowerCAmelCase   :					int        =    key.replace("mm_image_projection"		,							"flava.image_to_mm_projection"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("image_encoder.module"		,							"flava.image_model"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("text_encoder.module"		,							"flava.text_model"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("mm_encoder.module.encoder.cls_token"		,							"flava.multimodal_model.cls_token"				)
          _lowerCAmelCase   :					Dict        =    key.replace("mm_encoder.module"		,							"flava.multimodal_model"				)
          _lowerCAmelCase   :					Dict        =    key.replace("text_projection"		,							"flava.text_projection"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("image_projection"		,							"flava.image_projection"				)

          _lowerCAmelCase   :					List[Any]        =    value.float()

     for key, value in codebook_state_dict.items():
          _lowerCAmelCase   :					int        =    value

     return upgrade







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None				)       ->      Tuple:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					Dict        =    FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					Dict        =    FlavaConfig()

     _lowerCAmelCase   :					List[Any]        =    FlavaForPreTraining(SCREAMING_SNAKE_CASE				).eval()

     _lowerCAmelCase   :					Optional[int]        =    convert_dalle_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							save_checkpoint=SCREAMING_SNAKE_CASE				)

     if os.path.exists(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Dict        =    torch.load(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)
     else:
          _lowerCAmelCase   :					Tuple        =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)

     _lowerCAmelCase   :					Optional[int]        =    upgrade_state_dict(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    hf_model.state_dict()
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				) + count_parameters(SCREAMING_SNAKE_CASE				)

     assert torch.allclose(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							atol=1e-3				)

     hf_model.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
  parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  __UpperCAmelCase    				=     parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






