from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
		from .deepspeed import (
		    DeepSpeedEngineWrapper,
		    DeepSpeedOptimizerWrapper,
		    DeepSpeedSchedulerWrapper,
		    DummyOptim,
		    DummyScheduler,
		    HfDeepSpeedConfig,
		)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					'huggingface/label-files'
 UpperCamelCase_					:						int =					'imagenet-1k-id2label.json'
 UpperCamelCase_					:						int =					json.load(open(hf_hub_download(lowerCamelCase  ,  lowerCamelCase  ,  repo_type='dataset'	)  ,  'r'	)	)
 UpperCamelCase_					:						str =					{int(lowerCamelCase	): v for k, v in idalabel.items()}
 UpperCamelCase_					:						Optional[int] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						str =					'std_conv' if 'bit' in model_name else False

 # note that when using BiT as backbone for ViT-hybrid checkpoints,
 # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
 # config.conv_layer = "std_conv_same"
 UpperCamelCase_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     int	):
 if "stem.conv" in name:
  UpperCamelCase_					:						str =					name.replace('stem.conv'  ,  'bit.embedder.convolution'	)
 if "blocks" in name:
  UpperCamelCase_					:						str =					name.replace('blocks'  ,  'layers'	)
 if "head.fc" in name:
  UpperCamelCase_					:						Dict =					name.replace('head.fc'  ,  'classifier.1'	)
 if name.startswith('norm'	):
  UpperCamelCase_					:						List[str] =					'bit.' + name
 if "bit" not in name and "classifier" not in name:
  UpperCamelCase_					:						int =					'bit.encoder.' + name

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					get_config(lowerCamelCase	)

 # load original model from timm
 UpperCamelCase_					:						Dict =					create_model(lowerCamelCase  ,  pretrained=lowerCamelCase	)
 timm_model.eval()

 # load state_dict of original model
 UpperCamelCase_					:						str =					timm_model.state_dict()
 for key in state_dict.copy().keys():
  UpperCamelCase_					:						Tuple =					state_dict.pop(lowerCamelCase	)
  UpperCamelCase_					:						str =					val.squeeze() if 'head' in key else val

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

 UpperCamelCase_					:						str =					{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 UpperCamelCase_					:						Tuple =					BitImageProcessor(
     do_resize=lowerCamelCase  ,  size={'shortest_edge': timm_transforms[0].size}  ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,  do_center_crop=lowerCamelCase  ,  crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}  ,  do_normalize=lowerCamelCase  ,  image_mean=timm_transforms[-1].mean.tolist()  ,  image_std=timm_transforms[-1].std.tolist()  ,  )

 UpperCamelCase_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					processor(lowerCamelCase  ,  return_tensors='pt'	).pixel_values

 # verify pixel values
 assert torch.allclose(lowerCamelCase  ,  lowerCamelCase	)

 # verify logits
 with torch.no_grad():
  UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					outputs.logits

 print('Logits:'  ,  logits[0, :3]	)
 print('Predicted class:'  ,  model.config.idalabel[logits.argmax(-1	).item()]	)
 UpperCamelCase_					:						List[Any] =					timm_model(lowerCamelCase	)
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(lowerCamelCase  ,  outputs.logits  ,  atol=1e-3	)
 print('Looks ok!'	)

 if pytorch_dump_folder_path is not None:
  Path(lowerCamelCase	).mkdir(exist_ok=lowerCamelCase	)
  print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"	)
  model.save_pretrained(lowerCamelCase	)
  processor.save_pretrained(lowerCamelCase	)

 if push_to_hub:
  print(F"Pushing model {model_name} and processor to the hub"	)
  model.push_to_hub(F"ybelkada/{model_name}"	)
  processor.push_to_hub(F"ybelkada/{model_name}"	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='resnetv2_50x1_bitm',
       type=str,
       help='Name of the BiT timm model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether to push the model to the hub.',
   )

   a_    					=       parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



