
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[str])      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      old_name

							if "patch_embed" in old_name:
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      old_name.split(".")

														if layer == "0":
																					__UpperCamelCase			:     Optional[Any]         =      old_name.replace("0"		,						"convolution1")
														elif layer == "1":
																					__UpperCamelCase			:     List[str]         =      old_name.replace("1"		,						"batchnorm_before")
														elif layer == "3":
																					__UpperCamelCase			:     Union[str, Any]         =      old_name.replace("3"		,						"convolution2")
														else:
																					__UpperCamelCase			:     Optional[Any]         =      old_name.replace("4"		,						"batchnorm_after")

							if "network" in old_name and re.search(R"\d\.\d"		,						_lowerCamelCase):
														__UpperCamelCase			:     int         =      R"\b\d{2}\b"
														if bool(re.search(_lowerCamelCase		,						_lowerCamelCase)):
																					__UpperCamelCase			:     int         =      re.search(R"\d\.\d\d."		,						_lowerCamelCase).group()
														else:
																					__UpperCamelCase			:     List[Any]         =      re.search(R"\d\.\d."		,						_lowerCamelCase).group()
														if int(match[0]) < 6:
																					__UpperCamelCase			:     Tuple         =      old_name.replace(_lowerCamelCase		,						"")
																					__UpperCamelCase			:     Any         =      trimmed_name.replace("network"		,						match[0] + ".meta4D_layers.blocks." + match[2:-1])
																					__UpperCamelCase			:     Dict         =      "intermediate_stages." + trimmed_name
														else:
																					__UpperCamelCase			:     List[str]         =      old_name.replace(_lowerCamelCase		,						"")
																					if int(match[2]) < num_meta4D_last_stage:
																												__UpperCamelCase			:     List[str]         =      trimmed_name.replace("network"		,						"meta4D_layers.blocks." + match[2])
																					else:
																												__UpperCamelCase			:     List[Any]         =      str(int(match[2]) - num_meta4D_last_stage)
																												__UpperCamelCase			:     Union[str, Any]         =      trimmed_name.replace("network"		,						"meta3D_layers.blocks." + layer_index)
																												if "norm1" in old_name:
																																			__UpperCamelCase			:     Optional[int]         =      trimmed_name.replace("norm1"		,						"layernorm1")
																												elif "norm2" in old_name:
																																			__UpperCamelCase			:     Optional[int]         =      trimmed_name.replace("norm2"		,						"layernorm2")
																												elif "fc1" in old_name:
																																			__UpperCamelCase			:     Dict         =      trimmed_name.replace("fc1"		,						"linear_in")
																												elif "fc2" in old_name:
																																			__UpperCamelCase			:     Union[str, Any]         =      trimmed_name.replace("fc2"		,						"linear_out")

																					__UpperCamelCase			:     Dict         =      "last_stage." + trimmed_name

							elif "network" in old_name and re.search(R".\d."		,						_lowerCamelCase):
														__UpperCamelCase			:     Tuple         =      old_name.replace("network"		,						"intermediate_stages")

							if "fc" in new_name:
														__UpperCamelCase			:     Union[str, Any]         =      new_name.replace("fc"		,						"convolution")
							elif ("norm1" in new_name) and ("layernorm1" not in new_name):
														__UpperCamelCase			:     int         =      new_name.replace("norm1"		,						"batchnorm_before")
							elif ("norm2" in new_name) and ("layernorm2" not in new_name):
														__UpperCamelCase			:     List[Any]         =      new_name.replace("norm2"		,						"batchnorm_after")
							if "proj" in new_name:
														__UpperCamelCase			:     Dict         =      new_name.replace("proj"		,						"projection")
							if "dist_head" in new_name:
														__UpperCamelCase			:     List[Any]         =      new_name.replace("dist_head"		,						"distillation_classifier")
							elif "head" in new_name:
														__UpperCamelCase			:     Optional[Any]         =      new_name.replace("head"		,						"classifier")
							elif "patch_embed" in new_name:
														__UpperCamelCase			:     Dict         =      "efficientformer." + new_name
							elif new_name == "norm.weight" or new_name == "norm.bias":
														__UpperCamelCase			:     Optional[Any]         =      new_name.replace("norm"		,						"layernorm")
														__UpperCamelCase			:     List[str]         =      "efficientformer." + new_name
							else:
														__UpperCamelCase			:     int         =      "efficientformer.encoder." + new_name

							return new_name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Any)      ->					List[str]:






							'''simple docstring'''





							for key in checkpoint.copy().keys():
														__UpperCamelCase			:     Union[str, Any]         =      checkpoint.pop(_lowerCamelCase)
														__UpperCamelCase			:     Union[str, Any]         =      val

							return checkpoint




def 			_SCREAMING_SNAKE_CASE      (	)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      "http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase			:     Dict         =      Image.open(requests.get(_lowerCamelCase		,						stream=_lowerCamelCase).raw)

							return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Path		,						_lowerCamelCase				:    Path		,						_lowerCamelCase				:    Path		,						_lowerCamelCase				:    bool)      ->					Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      torch.load(_lowerCamelCase		,						map_location="cpu")["model"]
							__UpperCamelCase			:     int         =      EfficientFormerConfig.from_json_file(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase)
							__UpperCamelCase			:     int         =      "_".join(checkpoint_path.split("/")[-1].split(".")[0].split("_")[:-1])

							__UpperCamelCase			:     Union[str, Any]         =      config.depths[-1] - config.num_metaad_blocks + 1
							__UpperCamelCase			:     List[Any]         =      convert_torch_checkpoint(_lowerCamelCase		,						_lowerCamelCase)

							model.load_state_dict(_lowerCamelCase)
							model.eval()

							__UpperCamelCase			:     int         =      {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							# prepare image
							__UpperCamelCase			:     List[str]         =      prepare_img()
							__UpperCamelCase			:     Optional[int]         =      256
							__UpperCamelCase			:     Tuple         =      224
							__UpperCamelCase			:     List[Any]         =      EfficientFormerImageProcessor(
							    size={"shortest_edge": image_size}		,						crop_size={"height": crop_size, "width": crop_size}		,						resample=pillow_resamplings["bicubic"]		,						)
							__UpperCamelCase			:     Union[str, Any]         =      processor(images=_lowerCamelCase		,						return_tensors="pt").pixel_values

							# original processing pipeline
							__UpperCamelCase			:     Union[str, Any]         =      Compose(
							    [
							        Resize(_lowerCamelCase		,						interpolation=pillow_resamplings["bicubic"]),
							        CenterCrop(_lowerCamelCase),
							        ToTensor(),
							        Normalize(_lowerCamelCase		,						_lowerCamelCase),
							    ])
							__UpperCamelCase			:     Any         =      image_transforms(_lowerCamelCase).unsqueeze(0)

							assert torch.allclose(_lowerCamelCase		,						_lowerCamelCase)

							__UpperCamelCase			:     Tuple         =      model(_lowerCamelCase)
							__UpperCamelCase			:     int         =      outputs.logits

							__UpperCamelCase			:     str         =      (1, 1_000)

							if "l1" in model_name:
														__UpperCamelCase			:     Optional[Any]         =      torch.Tensor(
														    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8])
														assert torch.allclose(logits[0, :10]		,						_lowerCamelCase		,						atol=1e-3)
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														__UpperCamelCase			:     Dict         =      torch.Tensor(
														    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7])
														assert torch.allclose(logits[0, :10]		,						_lowerCamelCase		,						atol=1e-3)
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														__UpperCamelCase			:     Union[str, Any]         =      torch.Tensor(
														    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8])
														assert logits.shape == expected_shape
							else:
														raise ValueError(
														    F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7')

							# Save Checkpoints
							Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
							model.save_pretrained(_lowerCamelCase)
							print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}')
							processor.save_pretrained(_lowerCamelCase)
							print(F'Processor successfuly saved at {pytorch_dump_path}')

							if push_to_hub:
														print("Pushing model to the hub...")

														model.push_to_hub(
														    repo_id=F'Bearnardd/{pytorch_dump_path}'		,						commit_message="Add model"		,						use_temp_dir=_lowerCamelCase		,						)
														processor.push_to_hub(
														    repo_id=F'Bearnardd/{pytorch_dump_path}'		,						commit_message="Add image processor"		,						use_temp_dir=_lowerCamelCase		,						)


if __name__ == "__main__":
		lowercase						:      Union[str, Any]									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--pytorch_model_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to EfficientFormer pytorch checkpoint.',
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help='The json file for EfficientFormer model config.',
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)

		parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
		parser.add_argument(
		    '--no-push_to_hub',
		    dest='push_to_hub',
		    action='store_false',
		    help='Do not push model and image processor to the hub',
		)
		parser.set_defaults(push_to_hub=True)

		lowercase						:      int									= parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)

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