

'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__						:       int										=						logging.get_logger(__name__)




def 	_a       (				__lowerCAmelCase		:      List[Any]     ):

  """simple docstring"""

  snake_case__						:       str       						=  MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    snake_case__						:       str       						=  [1_44, 1_92, 2_40]
    snake_case__						:       int       						=  [16, 32, 64, 96, 1_28, 1_60, 6_40]
  elif "mobilevit_xs" in mobilevit_name:
    snake_case__						:       Optional[int]       						=  [96, 1_20, 1_44]
    snake_case__						:       Any       						=  [16, 32, 48, 64, 80, 96, 3_84]
  elif "mobilevit_xxs" in mobilevit_name:
    snake_case__						:       Optional[Any]       						=  [64, 80, 96]
    snake_case__						:       Optional[int]       						=  [16, 16, 24, 48, 64, 80, 3_20]
    snake_case__						:       Dict       						=  0.05
    snake_case__						:       int       						=  2.0

  if mobilevit_name.startswith('''deeplabv3_'''     ):
    snake_case__						:       Optional[Any]       						=  5_12
    snake_case__						:       List[Any]       						=  16
    snake_case__						:       Optional[Any]       						=  21
    snake_case__						:       Any       						=  '''pascal-voc-id2label.json'''
  else:
    snake_case__						:       Union[str, Any]       						=  10_00
    snake_case__						:       str       						=  '''imagenet-1k-id2label.json'''

  snake_case__						:       Tuple       						=  '''huggingface/label-files'''
  snake_case__						:       Tuple       						=  json.load(open(hf_hub_download(__lowerCAmelCase   ,							__lowerCAmelCase   ,							repo_type='''dataset'''     )   ,							'''r'''     )     )
  snake_case__						:       Optional[Any]       						=  {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
  snake_case__						:       Dict       						=  idalabel
  snake_case__						:       List[Any]       						=  {v: k for k, v in idalabel.items()}

  return config




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any=False     ):

  """simple docstring"""

  for i in range(1   ,							6     ):
    if F"""layer_{i}.""" in name:
      snake_case__						:       Dict       						=  name.replace(F"""layer_{i}."""   ,							F"""encoder.layer.{i - 1}."""     )

  if "conv_1." in name:
    snake_case__						:       List[Any]       						=  name.replace('''conv_1.'''   ,							'''conv_stem.'''     )
  if ".block." in name:
    snake_case__						:       Tuple       						=  name.replace('''.block.'''   ,							'''.'''     )

  if "exp_1x1" in name:
    snake_case__						:       List[Any]       						=  name.replace('''exp_1x1'''   ,							'''expand_1x1'''     )
  if "red_1x1" in name:
    snake_case__						:       List[Any]       						=  name.replace('''red_1x1'''   ,							'''reduce_1x1'''     )
  if ".local_rep.conv_3x3." in name:
    snake_case__						:       Dict       						=  name.replace('''.local_rep.conv_3x3.'''   ,							'''.conv_kxk.'''     )
  if ".local_rep.conv_1x1." in name:
    snake_case__						:       Tuple       						=  name.replace('''.local_rep.conv_1x1.'''   ,							'''.conv_1x1.'''     )
  if ".norm." in name:
    snake_case__						:       Union[str, Any]       						=  name.replace('''.norm.'''   ,							'''.normalization.'''     )
  if ".conv." in name:
    snake_case__						:       str       						=  name.replace('''.conv.'''   ,							'''.convolution.'''     )
  if ".conv_proj." in name:
    snake_case__						:       Optional[Any]       						=  name.replace('''.conv_proj.'''   ,							'''.conv_projection.'''     )

  for i in range(0   ,							2     ):
    for j in range(0   ,							4     ):
      if F""".{i}.{j}.""" in name:
        snake_case__						:       Optional[int]       						=  name.replace(F""".{i}.{j}."""   ,							F""".{i}.layer.{j}."""     )

  for i in range(2   ,							6     ):
    for j in range(0   ,							4     ):
      if F""".{i}.{j}.""" in name:
        snake_case__						:       int       						=  name.replace(F""".{i}.{j}."""   ,							F""".{i}."""     )
        if "expand_1x1" in name:
          snake_case__						:       Optional[int]       						=  name.replace('''expand_1x1'''   ,							'''downsampling_layer.expand_1x1'''     )
        if "conv_3x3" in name:
          snake_case__						:       Tuple       						=  name.replace('''conv_3x3'''   ,							'''downsampling_layer.conv_3x3'''     )
        if "reduce_1x1" in name:
          snake_case__						:       Optional[int]       						=  name.replace('''reduce_1x1'''   ,							'''downsampling_layer.reduce_1x1'''     )

  for i in range(2   ,							5     ):
    if F""".global_rep.{i}.weight""" in name:
      snake_case__						:       int       						=  name.replace(F""".global_rep.{i}.weight"""   ,							'''.layernorm.weight'''     )
    if F""".global_rep.{i}.bias""" in name:
      snake_case__						:       Union[str, Any]       						=  name.replace(F""".global_rep.{i}.bias"""   ,							'''.layernorm.bias'''     )

  if ".global_rep." in name:
    snake_case__						:       List[str]       						=  name.replace('''.global_rep.'''   ,							'''.transformer.'''     )
  if ".pre_norm_mha.0." in name:
    snake_case__						:       Any       						=  name.replace('''.pre_norm_mha.0.'''   ,							'''.layernorm_before.'''     )
  if ".pre_norm_mha.1.out_proj." in name:
    snake_case__						:       Any       						=  name.replace('''.pre_norm_mha.1.out_proj.'''   ,							'''.attention.output.dense.'''     )
  if ".pre_norm_ffn.0." in name:
    snake_case__						:       int       						=  name.replace('''.pre_norm_ffn.0.'''   ,							'''.layernorm_after.'''     )
  if ".pre_norm_ffn.1." in name:
    snake_case__						:       Dict       						=  name.replace('''.pre_norm_ffn.1.'''   ,							'''.intermediate.dense.'''     )
  if ".pre_norm_ffn.4." in name:
    snake_case__						:       str       						=  name.replace('''.pre_norm_ffn.4.'''   ,							'''.output.dense.'''     )
  if ".transformer." in name:
    snake_case__						:       Any       						=  name.replace('''.transformer.'''   ,							'''.transformer.layer.'''     )

  if ".aspp_layer." in name:
    snake_case__						:       Optional[Any]       						=  name.replace('''.aspp_layer.'''   ,							'''.'''     )
  if ".aspp_pool." in name:
    snake_case__						:       List[Any]       						=  name.replace('''.aspp_pool.'''   ,							'''.'''     )
  if "seg_head." in name:
    snake_case__						:       Tuple       						=  name.replace('''seg_head.'''   ,							'''segmentation_head.'''     )
  if "segmentation_head.classifier.classifier." in name:
    snake_case__						:       Tuple       						=  name.replace('''segmentation_head.classifier.classifier.'''   ,							'''segmentation_head.classifier.'''     )

  if "classifier.fc." in name:
    snake_case__						:       Optional[int]       						=  name.replace('''classifier.fc.'''   ,							'''classifier.'''     )
  elif (not base_model) and ("segmentation_head." not in name):
    snake_case__						:       Optional[Any]       						=  '''mobilevit.''' + name

  return name




def 	_a       (				__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      str=False     ):

  """simple docstring"""

  if base_model:
    snake_case__						:       Union[str, Any]       						=  ''''''
  else:
    snake_case__						:       int       						=  '''mobilevit.'''

  for key in orig_state_dict.copy().keys():
    snake_case__						:       List[Any]       						=  orig_state_dict.pop(__lowerCAmelCase     )

    if key[:8] == "encoder.":
      snake_case__						:       Dict       						=  key[8:]

    if "qkv" in key:
      snake_case__						:       str       						=  key.split('''.'''     )
      snake_case__						:       List[Any]       						=  int(key_split[0][6:]     ) - 1
      snake_case__						:       List[str]       						=  int(key_split[3]     )
      snake_case__						:       List[Any]       						=  model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""     )
      snake_case__						:       List[str]       						=  layer.transformer.layer[transformer_num].attention.attention.all_head_size
      snake_case__						:       List[str]       						=  (
          F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
      )
      if "weight" in key:
        snake_case__						:       int       						=  val[:dim, :]
        snake_case__						:       List[str]       						=  val[dim : dim * 2, :]
        snake_case__						:       List[str]       						=  val[-dim:, :]
      else:
        snake_case__						:       int       						=  val[:dim]
        snake_case__						:       Optional[int]       						=  val[dim : dim * 2]
        snake_case__						:       Union[str, Any]       						=  val[-dim:]
    else:
      snake_case__						:       Tuple       						=  val

  return orig_state_dict




def 	_a       (				):

  """simple docstring"""

  snake_case__						:       Tuple       						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  snake_case__						:       Optional[Any]       						=  Image.open(requests.get(__lowerCAmelCase   ,							stream=__lowerCAmelCase     ).raw     )
  return im




@torch.no_grad()
def 	_a       (				__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      List[str]=False     ):

  """simple docstring"""

  snake_case__						:       str       						=  get_mobilevit_config(__lowerCAmelCase     )

  # load original state_dict
  snake_case__						:       int       						=  torch.load(__lowerCAmelCase   ,							map_location='''cpu'''     )

  # load 🤗 model
  if mobilevit_name.startswith('''deeplabv3_'''     ):
    snake_case__						:       Optional[Any]       						=  MobileViTForSemanticSegmentation(__lowerCAmelCase     ).eval()
  else:
    snake_case__						:       int       						=  MobileViTForImageClassification(__lowerCAmelCase     ).eval()

  snake_case__						:       str       						=  convert_state_dict(__lowerCAmelCase   ,							__lowerCAmelCase     )
  model.load_state_dict(__lowerCAmelCase     )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  snake_case__						:       Any       						=  MobileViTImageProcessor(crop_size=config.image_size   ,							size=config.image_size + 32     )
  snake_case__						:       Optional[int]       						=  image_processor(images=prepare_img()   ,							return_tensors='''pt'''     )
  snake_case__						:       Union[str, Any]       						=  model(**__lowerCAmelCase     )
  snake_case__						:       Union[str, Any]       						=  outputs.logits

  if mobilevit_name.startswith('''deeplabv3_'''     ):
    assert logits.shape == (1, 21, 32, 32)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      snake_case__						:       List[Any]       						=  torch.tensor(
          [
              [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
              [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
              [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
          ]     )
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      snake_case__						:       int       						=  torch.tensor(
          [
              [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
              [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
              [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
          ]     )
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      snake_case__						:       Union[str, Any]       						=  torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
              [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
          ]     )
    else:
      raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""     )

    assert torch.allclose(logits[0, :3, :3, :3]   ,							__lowerCAmelCase   ,							atol=1e-4     )
  else:
    assert logits.shape == (1, 10_00)

    if mobilevit_name == "mobilevit_s":
      snake_case__						:       List[Any]       						=  torch.tensor([-0.9866, 0.2392, -1.1241]     )
    elif mobilevit_name == "mobilevit_xs":
      snake_case__						:       Optional[int]       						=  torch.tensor([-2.4761, -0.9399, -1.9587]     )
    elif mobilevit_name == "mobilevit_xxs":
      snake_case__						:       str       						=  torch.tensor([-1.9364, -1.2327, -0.4653]     )
    else:
      raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""     )

    assert torch.allclose(logits[0, :3]   ,							__lowerCAmelCase   ,							atol=1e-4     )

  Path(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
  print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""     )
  model.save_pretrained(__lowerCAmelCase     )
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
  image_processor.save_pretrained(__lowerCAmelCase     )

  if push_to_hub:
    snake_case__						:       Tuple       						=  {
        '''mobilevit_s''': '''mobilevit-small''',
        '''mobilevit_xs''': '''mobilevit-x-small''',
        '''mobilevit_xxs''': '''mobilevit-xx-small''',
        '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
        '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
        '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
    }

    print('''Pushing to the hub...'''     )
    snake_case__						:       str       						=  model_mapping[mobilevit_name]
    image_processor.push_to_hub(__lowerCAmelCase   ,							organization='''apple'''     )
    model.push_to_hub(__lowerCAmelCase   ,							organization='''apple'''     )


if __name__ == "__main__":
     lowerCAmelCase__						:       Tuple										=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--mobilevit_name""",
         default="""mobilevit_s""",
         type=str,
         help=(
             """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
             """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
         ),
     )
     parser.add_argument(
         """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
     )
     parser.add_argument(
         """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
     )

     lowerCAmelCase__						:       Optional[Any]										=						parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )






'''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


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




def 	_a       (				__lowerCAmelCase		:      Any=None     ):

		"""simple docstring"""

		if subparsers is not None:
				snake_case__						:       Tuple       						=  subparsers.add_parser('''tpu-config'''   ,							description=_description     )
		else:
				snake_case__						:       str       						=  argparse.ArgumentParser('''Accelerate tpu-config command'''   ,							description=_description     )
		# Core arguments
		snake_case__						:       Any       						=  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.'''   ,							)
		snake_case__						:       Optional[Any]       						=  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 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  None

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

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

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

		"""simple docstring"""

		snake_case__						:       Dict       						=  tpu_command_parser()
		snake_case__						:       Dict       						=  parser.parse_args()

		tpu_command_launcher(__lowerCAmelCase     )



