"""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 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()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



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

     lowercase_			:       Optional[Any]									=			'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"
     lowercase_			:       Tuple									=			BitConfig(
         conv_layer=__SCREAMING_SNAKE_CASE  ,    num_labels=10_00  ,    idalabel=__SCREAMING_SNAKE_CASE  ,    labelaid=__SCREAMING_SNAKE_CASE  ,    )

     return config



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     if "stem.conv" in name:
          lowercase_			:       str									=			name.replace('stem.conv'  ,    'bit.embedder.convolution'   )
     if "blocks" in name:
          lowercase_			:       List[str]									=			name.replace('blocks'  ,    'layers'   )
     if "head.fc" in name:
          lowercase_			:       int									=			name.replace('head.fc'  ,    'classifier.1'   )
     if name.startswith('norm'   ):
          lowercase_			:       Any									=			'bit.' + name
     if "bit" not in name and "classifier" not in name:
          lowercase_			:       List[Any]									=			'bit.encoder.' + name

     return name



def     lowercase__( ):
     lowercase_			:       Any									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_			:       Tuple									=			Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,    stream=__SCREAMING_SNAKE_CASE   ).raw   )
     return im



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Dict=False   ):
     lowercase_			:       Any									=			get_config(__SCREAMING_SNAKE_CASE   )

     # load original model from timm
     lowercase_			:       List[Any]									=			create_model(__SCREAMING_SNAKE_CASE  ,    pretrained=__SCREAMING_SNAKE_CASE   )
     timm_model.eval()

     # load state_dict of original model
     lowercase_			:       List[str]									=			timm_model.state_dict()
     for key in state_dict.copy().keys():
          lowercase_			:       Optional[Any]									=			state_dict.pop(__SCREAMING_SNAKE_CASE   )
          lowercase_			:       Any									=			val.squeeze() if 'head' in key else val

     # load HuggingFace model
     lowercase_			:       Union[str, Any]									=			BitForImageClassification(__SCREAMING_SNAKE_CASE   )
     model.eval()
     model.load_state_dict(__SCREAMING_SNAKE_CASE   )

     # create image processor
     lowercase_			:       Union[str, Any]									=			create_transform(**resolve_data_config({}  ,    model=__SCREAMING_SNAKE_CASE   )   )
     lowercase_			:       Optional[Any]									=			transform.transforms

     lowercase_			:       Optional[int]									=			{
         'bilinear': PILImageResampling.BILINEAR,
         'bicubic': PILImageResampling.BICUBIC,
         'nearest': PILImageResampling.NEAREST,
     }

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

     lowercase_			:       Optional[Any]									=			prepare_img()
     lowercase_			:       Optional[int]									=			transform(__SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
     lowercase_			:       Any									=			processor(__SCREAMING_SNAKE_CASE  ,    return_tensors='pt'   ).pixel_values

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

     # verify logits
     with torch.no_grad():
          lowercase_			:       Tuple									=			model(__SCREAMING_SNAKE_CASE   )
          lowercase_			:       Union[str, Any]									=			outputs.logits

     print('Logits:'  ,    logits[0, :3]   )
     print('Predicted class:'  ,    model.config.idalabel[logits.argmax(-1   ).item()]   )
     lowercase_			:       Dict									=			timm_model(__SCREAMING_SNAKE_CASE   )
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(__SCREAMING_SNAKE_CASE  ,    outputs.logits  ,    atol=1E-3   )
     print('Looks ok!'   )

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

     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__":
       __SCREAMING_SNAKE_CASE								=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.",
       )

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


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'switch_transformers'
   lowercase							=						['past_key_values']
   lowercase							=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
   def __init__(    self  ,__UpperCamelCase=3_2128  ,__UpperCamelCase=768  ,__UpperCamelCase=64  ,__UpperCamelCase=2048  ,__UpperCamelCase=64  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=8  ,__UpperCamelCase=False  ,__UpperCamelCase=0.01  ,__UpperCamelCase="float32"  ,__UpperCamelCase=False  ,__UpperCamelCase=32  ,__UpperCamelCase=128  ,__UpperCamelCase=0.1  ,__UpperCamelCase=1e-6  ,__UpperCamelCase=0.001  ,__UpperCamelCase=0.001  ,__UpperCamelCase=1.0  ,__UpperCamelCase="relu"  ,__UpperCamelCase=True  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,**__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       List[str]									=			vocab_size
        lowercase_			:       Optional[Any]									=			d_model
        lowercase_			:       Dict									=			d_kv
        lowercase_			:       Dict									=			d_ff

        lowercase_			:       str									=			num_sparse_encoder_layers

        lowercase_			:       List[str]									=			num_layers
        lowercase_			:       int									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase_			:       Optional[Any]									=			num_sparse_decoder_layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_encoder_layers > 0:
             lowercase_			:       Tuple									=			self.num_layers // self.num_sparse_encoder_layers
        else:
             lowercase_			:       Union[str, Any]									=			self.num_layers  # HACK: this will create 0 sparse layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_decoder_layers > 0:
             lowercase_			:       Union[str, Any]									=			self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
             lowercase_			:       List[Any]									=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

        lowercase_			:       List[Any]									=			num_heads
        lowercase_			:       Dict									=			num_experts
        lowercase_			:       List[str]									=			expert_capacity
        lowercase_			:       Optional[Any]									=			router_bias
        lowercase_			:       Optional[Any]									=			router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''    )
        lowercase_			:       Optional[Any]									=			router_dtype

        lowercase_			:       Union[str, Any]									=			router_ignore_padding_tokens
        lowercase_			:       Any									=			relative_attention_num_buckets
        lowercase_			:       List[Any]									=			relative_attention_max_distance

        lowercase_			:       str									=			dropout_rate
        lowercase_			:       Any									=			layer_norm_epsilon
        lowercase_			:       Tuple									=			initializer_factor
        lowercase_			:       str									=			feed_forward_proj
        lowercase_			:       List[Any]									=			use_cache
        lowercase_			:       str									=			add_router_probs

        lowercase_			:       Tuple									=			router_z_loss_coef
        lowercase_			:       int									=			router_aux_loss_coef

        lowercase_			:       List[str]									=			self.feed_forward_proj.split('-'    )
        lowercase_			:       List[str]									=			act_info[-1]
        lowercase_			:       Optional[Any]									=			act_info[0] == 'gated'

        if len(__UpperCamelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase    ) > 2:
             raise ValueError(
                 f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                 'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                 '\'gated-gelu\' or \'relu\''    )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
             lowercase_			:       Union[str, Any]									=			'gelu_new'

        super().__init__(
            pad_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase  ,is_encoder_decoder=__UpperCamelCase  ,**__UpperCamelCase  ,)

