


"""simple docstring"""





# Copyright 2021 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 json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__UpperCAmelCase    				=      os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__UpperCAmelCase    				=      os.path.join(hf_cache_home, "accelerate")
__UpperCAmelCase    				=      os.path.join(cache_dir, "default_config.yaml")
__UpperCAmelCase    				=      os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
   __UpperCAmelCase    				=      default_yaml_config_file
else:
   __UpperCAmelCase    				=      default_json_config_file





def     lowercase__	(  lowerCamelCase     :  int				)					->     int:
  if config_file is not None:
    if not os.path.isfile(lowerCamelCase				):
      raise FileNotFoundError(
          F"The passed configuration file `{config_file}` does not exist. "
          "Please pass an existing file to `accelerate launch`, or use the the default one "
          "created through `accelerate config` and run `accelerate launch` "
          "without the `--config_file` argument."				)
  else:
    lowerCAmelCase__    :    Optional[int]								=					default_config_file
  with open(lowerCamelCase				,	"r"				,	encoding="utf-8"				) as f:
    if config_file.endswith(".json"				):
      if (
          json.load(lowerCamelCase				).get("compute_environment"				,	ComputeEnvironment.LOCAL_MACHINE				)
          == ComputeEnvironment.LOCAL_MACHINE
      ):
        lowerCAmelCase__    :    Optional[int]								=					ClusterConfig
      else:
        lowerCAmelCase__    :    List[Any]								=					SageMakerConfig
      return config_class.from_json_file(json_file=lowerCamelCase				)
    else:
      if (
          yaml.safe_load(lowerCamelCase				).get("compute_environment"				,	ComputeEnvironment.LOCAL_MACHINE				)
          == ComputeEnvironment.LOCAL_MACHINE
      ):
        lowerCAmelCase__    :    Union[str, Any]								=					ClusterConfig
      else:
        lowerCAmelCase__    :    Dict								=					SageMakerConfig
      return config_class.from_yaml_file(yaml_file=lowerCamelCase				)

@dataclass
class  lowercase_  :
      __magic_name__     :							ComputeEnvironment
      __magic_name__     :							Union[DistributedType, SageMakerDistributedType]
      __magic_name__     :							str
      __magic_name__     :							bool







      def 				_lowerCAmelCase       (      self :							List[Any]    ):
        lowerCAmelCase__    :    Optional[Any]								=					self.__dict__
        # For serialization, it's best to convert Enums to strings (or their underlying value type).
        for key, value in result.items():
          if isinstance(_lowercase		,  _lowercase    ):
            lowerCAmelCase__    :    Dict								=					value.value
          if isinstance(_lowercase		,  _lowercase    ) and not bool(_lowercase    ):
            lowerCAmelCase__    :    Union[str, Any]								=					None
        lowerCAmelCase__    :    Any								=					{k: v for k, v in result.items() if v is not None}
        return result







      @classmethod
      def 				_lowerCAmelCase       (      cls :							int		,  _lowercase :							Tuple=None    ):
        lowerCAmelCase__    :    int								=					default_json_config_file if json_file is None else json_file
        with open(_lowercase		,  "r"		,  encoding="utf-8"    ) as f:
          lowerCAmelCase__    :    Any								=					json.load(_lowercase    )
        if "compute_environment" not in config_dict:
          lowerCAmelCase__    :    List[Any]								=					ComputeEnvironment.LOCAL_MACHINE
        if "mixed_precision" not in config_dict:
          lowerCAmelCase__    :    List[Any]								=					"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
        if "fp16" in config_dict:  # Convert the config to the new format.
          del config_dict["fp16"]
        if "dynamo_backend" in config_dict:  # Convert the config to the new format.
          lowerCAmelCase__    :    Union[str, Any]								=					config_dict.pop("dynamo_backend"    )
          lowerCAmelCase__    :    Tuple								=					{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
        if "use_cpu" not in config_dict:
          lowerCAmelCase__    :    Optional[Any]								=					False
        return cls(**_lowercase    )







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							Union[str, Any]    ):
        with open(_lowercase		,  "w"		,  encoding="utf-8"    ) as f:
          lowerCAmelCase__    :    int								=					json.dumps(self.to_dict()		,  indent=2		,  sort_keys=_lowercase    ) + "\n"
          f.write(_lowercase    )







      @classmethod
      def 				_lowerCAmelCase       (      cls :							str		,  _lowercase :							Union[str, Any]=None    ):
        lowerCAmelCase__    :    Union[str, Any]								=					default_yaml_config_file if yaml_file is None else yaml_file
        with open(_lowercase		,  "r"		,  encoding="utf-8"    ) as f:
          lowerCAmelCase__    :    Optional[int]								=					yaml.safe_load(_lowercase    )
        if "compute_environment" not in config_dict:
          lowerCAmelCase__    :    Dict								=					ComputeEnvironment.LOCAL_MACHINE

        if "mixed_precision" not in config_dict:
          lowerCAmelCase__    :    Tuple								=					"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
        if isinstance(config_dict["mixed_precision"]		,  _lowercase    ) and not config_dict["mixed_precision"]:
          lowerCAmelCase__    :    Tuple								=					"no"
        if "fp16" in config_dict:  # Convert the config to the new format.
          del config_dict["fp16"]
        if "dynamo_backend" in config_dict:  # Convert the config to the new format.
          lowerCAmelCase__    :    Tuple								=					config_dict.pop("dynamo_backend"    )
          lowerCAmelCase__    :    Optional[int]								=					{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
        if "use_cpu" not in config_dict:
          lowerCAmelCase__    :    Union[str, Any]								=					False
        return cls(**_lowercase    )







      def 				_lowerCAmelCase       (      self :							str		,  _lowercase :							Dict    ):
        with open(_lowercase		,  "w"		,  encoding="utf-8"    ) as f:
          yaml.safe_dump(self.to_dict()		,  _lowercase    )







      def 				_lowerCAmelCase       (      self :							Union[str, Any]    ):
        if isinstance(self.compute_environment		,  _lowercase    ):
          lowerCAmelCase__    :    Optional[int]								=					ComputeEnvironment(self.compute_environment    )
        if isinstance(self.distributed_type		,  _lowercase    ):
          if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
            lowerCAmelCase__    :    Union[str, Any]								=					SageMakerDistributedType(self.distributed_type    )
          else:
            lowerCAmelCase__    :    Tuple								=					DistributedType(self.distributed_type    )
        if self.dynamo_config is None:
          lowerCAmelCase__    :    List[str]								=					{}

@dataclass
class  lowercase_  ( a_						):
      __magic_name__     :							int
      __magic_name__     :							int          =				0
      __magic_name__     :							int          =				1
      __magic_name__     :							Optional[str]          =				None
      __magic_name__     :							Optional[str]          =				None
      __magic_name__     :							Optional[int]          =				None
      __magic_name__     :							Optional[str]          =				"static"
      __magic_name__     :							Optional[bool]          =				False
      __magic_name__     :							str          =				"main"

      # args for deepspeed_plugin
      __magic_name__     :							dict          =				None
      # args for fsdp
      __magic_name__     :							dict          =				None
      # args for megatron_lm
      __magic_name__     :							dict          =				None
      # args for ipex
      __magic_name__     :							dict          =				None
      # args for TPU
      __magic_name__     :							bool          =				False

      # args for TPU pods
      __magic_name__     :							str          =				None
      __magic_name__     :							str          =				None
      __magic_name__     :							bool          =				False
      __magic_name__     :							bool          =				False
      __magic_name__     :							str          =				None
      __magic_name__     :							List[str]          =				None
      __magic_name__     :							List[str]          =				None
      __magic_name__     :							List[str]          =				None

      # args for dynamo
      __magic_name__     :							dict          =				None







      def 				_lowerCAmelCase       (      self :							Union[str, Any]    ):
        if self.deepspeed_config is None:
          lowerCAmelCase__    :    Dict								=					{}
        if self.fsdp_config is None:
          lowerCAmelCase__    :    Optional[int]								=					{}
        if self.megatron_lm_config is None:
          lowerCAmelCase__    :    List[str]								=					{}
        if self.ipex_config is None:
          lowerCAmelCase__    :    List[str]								=					{}
        return super().__post_init__()

@dataclass
class  lowercase_  ( a_						):
      __magic_name__     :							str
      __magic_name__     :							str
      __magic_name__     :							Optional[str]          =				None
      __magic_name__     :							Optional[str]          =				None
      __magic_name__     :							str          =				"us-east-1"
      __magic_name__     :							int          =				1
      __magic_name__     :							str          =				"all"
      __magic_name__     :							str          =				f'''accelerate-sagemaker-{num_machines}'''
      __magic_name__     :							str          =				SAGEMAKER_PYTORCH_VERSION
      __magic_name__     :							str          =				SAGEMAKER_TRANSFORMERS_VERSION
      __magic_name__     :							str          =				SAGEMAKER_PYTHON_VERSION
      __magic_name__     :							str          =				None
      __magic_name__     :							str          =				None
      __magic_name__     :							dict          =				None
      __magic_name__     :							dict          =				None




"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    				=      logging.get_logger(__name__)





def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  Union[str, Any]=False				,	lowerCamelCase     :  Optional[int]=False				)					->     Union[str, Any]:
  lowerCAmelCase__    :    str								=					"backbone." if is_semantic else ""

  lowerCAmelCase__    :    Optional[Any]								=					[]
  for i in range(config.num_hidden_layers				):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")				)
    rename_keys.append(
        (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")				)
    rename_keys.append(
        (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")				)
    rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")				)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")				)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (F"{prefix}cls_token", "beit.embeddings.cls_token"),
          (F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
          (F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
      ]				)

  if has_lm_head:
    # mask token + shared relative position bias + layernorm
    rename_keys.extend(
        [
            ("mask_token", "beit.embeddings.mask_token"),
            (
                "rel_pos_bias.relative_position_bias_table",
                "beit.encoder.relative_position_bias.relative_position_bias_table",
            ),
            (
                "rel_pos_bias.relative_position_index",
                "beit.encoder.relative_position_bias.relative_position_index",
            ),
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
        ]				)
  elif is_semantic:
    # semantic segmentation classification heads
    rename_keys.extend(
        [
            ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
            ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
            ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
            ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
        ]				)
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("fc_norm.weight", "beit.pooler.layernorm.weight"),
            ("fc_norm.bias", "beit.pooler.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]				)

  return rename_keys





def     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Dict=False				,	lowerCamelCase     :  Optional[int]=False				)					->     Tuple:
  for i in range(config.num_hidden_layers				):
    lowerCAmelCase__    :    Dict								=					"backbone." if is_semantic else ""
    # queries, keys and values
    lowerCAmelCase__    :    str								=					state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"				)
    lowerCAmelCase__    :    str								=					state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"				)
    lowerCAmelCase__    :    Optional[Any]								=					state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"				)

    lowerCAmelCase__    :    Union[str, Any]								=					in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase__    :    Dict								=					q_bias
    lowerCAmelCase__    :    str								=					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase__    :    Optional[int]								=					in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase__    :    Optional[Any]								=					v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCAmelCase__    :    Any								=					state_dict.pop(F"{prefix}blocks.{i}.gamma_1"				)
    lowerCAmelCase__    :    Optional[int]								=					state_dict.pop(F"{prefix}blocks.{i}.gamma_2"				)

    lowerCAmelCase__    :    Tuple								=					gamma_a
    lowerCAmelCase__    :    Any								=					gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      lowerCAmelCase__    :    int								=					state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"				)
      lowerCAmelCase__    :    Optional[int]								=					state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"				)

      lowerCAmelCase__    :    Any								=					table
      lowerCAmelCase__    :    int								=					index





def     lowercase__	(  lowerCamelCase     :  int				,	lowerCamelCase     :  Any				,	lowerCamelCase     :  Any				)					->     Dict:
  lowerCAmelCase__    :    str								=					dct.pop(lowerCamelCase				)
  lowerCAmelCase__    :    Union[str, Any]								=					val





def     lowercase__	(  )					->     Optional[int]:
  lowerCAmelCase__    :    Union[str, Any]								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase__    :    List[str]								=					Image.open(requests.get(lowerCamelCase				,	stream=lowerCamelCase				).raw				)
  return im





@torch.no_grad()
def     lowercase__	(  lowerCamelCase     :  Optional[int]				,	lowerCamelCase     :  Optional[int]				)					->     int:
  lowerCAmelCase__    :    str								=					BeitConfig()
  lowerCAmelCase__    :    Any								=					False
  lowerCAmelCase__    :    Optional[Any]								=					False
  lowerCAmelCase__    :    Optional[Any]								=					"huggingface/label-files"
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    List[str]								=					True
    lowerCAmelCase__    :    Union[str, Any]								=					True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    lowerCAmelCase__    :    List[Any]								=					True
    lowerCAmelCase__    :    Dict								=					2_1_8_4_1
    lowerCAmelCase__    :    str								=					"imagenet-22k-id2label.json"
    lowerCAmelCase__    :    Union[str, Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    Tuple								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    # this dataset contains 21843 labels but the model only has 21841
    # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
    del idalabel[9_2_0_5]
    del idalabel[1_5_0_2_7]
    lowerCAmelCase__    :    str								=					idalabel
    lowerCAmelCase__    :    str								=					{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    Optional[int]								=					1_0_0_0
    lowerCAmelCase__    :    List[Any]								=					"imagenet-1k-id2label.json"
    lowerCAmelCase__    :    List[str]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    Optional[Any]								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__    :    List[Any]								=					idalabel
    lowerCAmelCase__    :    Union[str, Any]								=					{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      lowerCAmelCase__    :    str								=					3_8_4
    if "512" in checkpoint_url:
      lowerCAmelCase__    :    Union[str, Any]								=					5_1_2
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    lowerCAmelCase__    :    int								=					True
    lowerCAmelCase__    :    Tuple								=					1_5_0
    lowerCAmelCase__    :    Dict								=					"ade20k-id2label.json"
    lowerCAmelCase__    :    Union[str, Any]								=					json.load(open(hf_hub_download(lowerCamelCase				,	lowerCamelCase				,	repo_type="dataset"				)				,	"r"				)				)
    lowerCAmelCase__    :    str								=					{int(lowerCamelCase				): v for k, v in idalabel.items()}
    lowerCAmelCase__    :    List[Any]								=					idalabel
    lowerCAmelCase__    :    Any								=					{v: k for k, v in idalabel.items()}
    lowerCAmelCase__    :    Any								=					6_4_0
    lowerCAmelCase__    :    Dict								=					True
  else:
    raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"				)

  # size of the architecture
  if "base" in checkpoint_url:
    pass
  elif "large" in checkpoint_url:
    lowerCAmelCase__    :    Optional[int]								=					1_0_2_4
    lowerCAmelCase__    :    int								=					4_0_9_6
    lowerCAmelCase__    :    Optional[Any]								=					2_4
    lowerCAmelCase__    :    Optional[int]								=					1_6
    if "ade20k" in checkpoint_url:
      lowerCAmelCase__    :    Optional[int]								=					6_4_0
      lowerCAmelCase__    :    Tuple								=					[7, 1_1, 1_5, 2_3]
  else:
    raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase__    :    List[str]								=					torch.hub.load_state_dict_from_url(lowerCamelCase				,	map_location="cpu"				,	check_hash=lowerCamelCase				)
  lowerCAmelCase__    :    Tuple								=					state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

  lowerCAmelCase__    :    Optional[Any]								=					create_rename_keys(lowerCamelCase				,	has_lm_head=lowerCamelCase				,	is_semantic=lowerCamelCase				)
  for src, dest in rename_keys:
    rename_key(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  read_in_q_k_v(lowerCamelCase				,	lowerCamelCase				,	has_lm_head=lowerCamelCase				,	is_semantic=lowerCamelCase				)
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      lowerCAmelCase__    :    Dict								=					state_dict.pop(lowerCamelCase				)
      if key.startswith("backbone.fpn"				):
        lowerCAmelCase__    :    Any								=					key.replace("backbone.fpn"				,	"fpn"				)
      lowerCAmelCase__    :    Any								=					val

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    lowerCAmelCase__    :    Union[str, Any]								=					BeitForMaskedImageModeling(lowerCamelCase				)
  elif "ade20k" in checkpoint_url:
    lowerCAmelCase__    :    List[str]								=					BeitForSemanticSegmentation(lowerCamelCase				)
  else:
    lowerCAmelCase__    :    int								=					BeitForImageClassification(lowerCamelCase				)
  model.eval()
  model.load_state_dict(lowerCamelCase				)

  # Check outputs on an image
  if is_semantic:
    lowerCAmelCase__    :    Optional[Any]								=					BeitImageProcessor(size=config.image_size				,	do_center_crop=lowerCamelCase				)
    lowerCAmelCase__    :    str								=					load_dataset("hf-internal-testing/fixtures_ade20k"				,	split="test"				)
    lowerCAmelCase__    :    Union[str, Any]								=					Image.open(ds[0]["file"]				)
  else:
    lowerCAmelCase__    :    Optional[int]								=					BeitImageProcessor(
        size=config.image_size				,	resample=PILImageResampling.BILINEAR				,	do_center_crop=lowerCamelCase				)
    lowerCAmelCase__    :    List[Any]								=					prepare_img()

  lowerCAmelCase__    :    Optional[int]								=					image_processor(images=lowerCamelCase				,	return_tensors="pt"				)
  lowerCAmelCase__    :    Union[str, Any]								=					encoding["pixel_values"]

  lowerCAmelCase__    :    Optional[Any]								=					model(lowerCamelCase				)
  lowerCAmelCase__    :    Optional[int]								=					outputs.logits

  # verify logits
  lowerCAmelCase__    :    Union[str, Any]								=					torch.Size([1, 1_0_0_0]				)
  if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"				):
    lowerCAmelCase__    :    Any								=					torch.Size([1, 1_9_6, 8_1_9_2]				)
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"				):
    lowerCAmelCase__    :    Any								=					torch.Size([1, 1_9_6, 8_1_9_2]				)
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"				):
    lowerCAmelCase__    :    Dict								=					torch.Size([1, 2_1_8_4_1]				)
    lowerCAmelCase__    :    Optional[int]								=					torch.tensor([2.22_88, 2.46_71, 0.73_95]				)
    lowerCAmelCase__    :    Any								=					2_3_9_7
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"				):
    lowerCAmelCase__    :    Tuple								=					torch.Size([1, 2_1_8_4_1]				)
    lowerCAmelCase__    :    List[str]								=					torch.tensor([1.68_81, -0.27_87, 0.59_01]				)
    lowerCAmelCase__    :    int								=					2_3_9_6
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"				):
    lowerCAmelCase__    :    Tuple								=					torch.tensor([0.12_41, 0.07_98, -0.65_69]				)
    lowerCAmelCase__    :    Tuple								=					2_8_5
  elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    List[Any]								=					torch.tensor([-1.23_85, -1.09_87, -1.01_08]				)
    lowerCAmelCase__    :    Union[str, Any]								=					2_8_1
  elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    Optional[Any]								=					torch.tensor([-1.53_03, -0.94_84, -0.31_47]				)
    lowerCAmelCase__    :    Optional[Any]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([0.46_10, -0.09_28, 0.20_86]				)
    lowerCAmelCase__    :    Any								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    str								=					torch.tensor([-0.48_04, 0.62_57, -0.18_37]				)
    lowerCAmelCase__    :    Any								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    int								=					torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]				)
    lowerCAmelCase__    :    Optional[int]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor([-0.30_62, 0.72_61, 0.48_52]				)
    lowerCAmelCase__    :    Optional[Any]								=					7_6_1
  elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"				):
    lowerCAmelCase__    :    Union[str, Any]								=					(1, 1_5_0, 1_6_0, 1_6_0)
    lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor(
        [
            [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
            [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
            [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
        ]				)
  elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"				):
    lowerCAmelCase__    :    Dict								=					(1, 1_5_0, 1_6_0, 1_6_0)
    lowerCAmelCase__    :    List[Any]								=					torch.tensor(
        [
            [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
            [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
            [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
        ]				)
  else:
    raise ValueError("Can't verify logits as model is not supported"				)

  assert logits.shape == expected_shape, "Shape of logits not as expected"
  if not has_lm_head:
    if is_semantic:
      assert torch.allclose(
          logits[0, :3, :3, :3]				,	lowerCamelCase				,	atol=1e-3				), "First elements of logits not as expected"
    else:
      print("Predicted class idx:"				,	logits.argmax(-1				).item()				)
      assert torch.allclose(
          logits[0, :3]				,	lowerCamelCase				,	atol=1e-3				), "First elements of logits not as expected"
      assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

  Path(lowerCamelCase				).mkdir(exist_ok=lowerCamelCase				)
  print(F"Saving model 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 __name__ == "__main__":
   __UpperCAmelCase    				=      argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_url",
       default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
       type=str,
       help="URL to the original PyTorch checkpoint (.pth file).",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
   )
   __UpperCAmelCase    				=      parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
