"""simple docstring"""







import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     lowercase_			:       List[str]									=			XCLIPTextConfig()

     # derive patch size from model name
     lowercase_			:       Union[str, Any]									=			model_name.find('patch'   )
     lowercase_			:       Any									=			int(model_name[start_idx + len('patch'   ) : start_idx + len('patch'   ) + 2]   )
     lowercase_			:       str									=			XCLIPVisionConfig(patch_size=__SCREAMING_SNAKE_CASE  ,    num_frames=__SCREAMING_SNAKE_CASE   )

     if "large" in model_name:
          lowercase_			:       List[str]									=			7_68
          lowercase_			:       str									=			30_72
          lowercase_			:       Tuple									=			12

          lowercase_			:       int									=			10_24
          lowercase_			:       int									=			40_96
          lowercase_			:       str									=			16
          lowercase_			:       Union[str, Any]									=			24
          lowercase_			:       List[str]									=			7_68
          lowercase_			:       Optional[int]									=			30_72

     if model_name == "xclip-large-patch14-16-frames":
          lowercase_			:       List[str]									=			3_36

     lowercase_			:       List[str]									=			XCLIPConfig.from_text_vision_configs(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     if "large" in model_name:
          lowercase_			:       Tuple									=			7_68

     return config



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     # text encoder
     if name == "token_embedding.weight":
          lowercase_			:       int									=			name.replace('token_embedding.weight'  ,    'text_model.embeddings.token_embedding.weight'   )
     if name == "positional_embedding":
          lowercase_			:       Optional[Any]									=			name.replace('positional_embedding'  ,    'text_model.embeddings.position_embedding.weight'   )
     if "ln_1" in name:
          lowercase_			:       Dict									=			name.replace('ln_1'  ,    'layer_norm1'   )
     if "ln_2" in name:
          lowercase_			:       Any									=			name.replace('ln_2'  ,    'layer_norm2'   )
     if "c_fc" in name:
          lowercase_			:       List[str]									=			name.replace('c_fc'  ,    'fc1'   )
     if "c_proj" in name:
          lowercase_			:       List[Any]									=			name.replace('c_proj'  ,    'fc2'   )
     if name.startswith('transformer.resblocks'   ):
          lowercase_			:       str									=			name.replace('transformer.resblocks'  ,    'text_model.encoder.layers'   )
     if "attn.out_proj" in name and "message" not in name:
          lowercase_			:       str									=			name.replace('attn.out_proj'  ,    'self_attn.out_proj'   )
     if "ln_final" in name:
          lowercase_			:       Any									=			name.replace('ln_final'  ,    'text_model.final_layer_norm'   )
     # visual encoder
     if name == "visual.class_embedding":
          lowercase_			:       Tuple									=			name.replace('visual.class_embedding'  ,    'vision_model.embeddings.class_embedding'   )
     if name == "visual.positional_embedding":
          lowercase_			:       Optional[int]									=			name.replace('visual.positional_embedding'  ,    'vision_model.embeddings.position_embedding.weight'   )
     if name.startswith('visual.transformer.resblocks'   ):
          lowercase_			:       Optional[int]									=			name.replace('visual.transformer.resblocks'  ,    'vision_model.encoder.layers'   )
     if "visual.conv1" in name:
          lowercase_			:       List[str]									=			name.replace('visual.conv1'  ,    'vision_model.embeddings.patch_embedding'   )
     if "visual.ln_pre" in name:
          lowercase_			:       List[str]									=			name.replace('visual.ln_pre'  ,    'vision_model.pre_layernorm'   )
     if "visual.ln_post" in name:
          lowercase_			:       List[str]									=			name.replace('visual.ln_post'  ,    'vision_model.post_layernorm'   )
     if "visual.proj" in name:
          lowercase_			:       str									=			name.replace('visual.proj'  ,    'visual_projection.weight'   )
     if "text_projection" in name:
          lowercase_			:       str									=			name.replace('text_projection'  ,    'text_projection.weight'   )
     # things on top
     if "prompts_visual_proj" in name:
          lowercase_			:       List[Any]									=			name.replace('prompts_visual_proj'  ,    'prompts_visual_projection'   )
     if "prompts_visual_ln" in name:
          lowercase_			:       Any									=			name.replace('prompts_visual_ln'  ,    'prompts_visual_layernorm'   )
     # mit
     if name == "mit.positional_embedding":
          lowercase_			:       Any									=			name.replace('positional'  ,    'position'   )
     if name.startswith('mit.resblocks'   ):
          lowercase_			:       int									=			name.replace('mit.resblocks'  ,    'mit.encoder.layers'   )
     # prompts generator
     if name.startswith('prompts_generator.norm'   ):
          lowercase_			:       Any									=			name.replace('prompts_generator.norm'  ,    'prompts_generator.layernorm'   )

     return name



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     for key in orig_state_dict.copy().keys():
          lowercase_			:       int									=			orig_state_dict.pop(__SCREAMING_SNAKE_CASE   )

          if "attn.in_proj" in key:
               lowercase_			:       Optional[Any]									=			key.split('.'   )
               if key.startswith('visual'   ):
                    lowercase_			:       Optional[Any]									=			key_split[3]
                    lowercase_			:       Optional[Any]									=			config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              lowercase_			:       Optional[int]									=			val[
                                  :dim, :
                              ]
                              lowercase_			:       List[str]									=			val[
                                  dim : dim * 2, :
                              ]
                              lowercase_			:       Optional[int]									=			val[
                                  -dim:, :
                              ]
                         else:
                              lowercase_			:       Optional[Any]									=			val[
                                  :dim
                              ]
                              lowercase_			:       Optional[int]									=			val[
                                  dim : dim * 2
                              ]
                              lowercase_			:       Union[str, Any]									=			val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              lowercase_			:       Any									=			val[
                                  :dim, :
                              ]
                              lowercase_			:       int									=			val[
                                  dim : dim * 2, :
                              ]
                              lowercase_			:       Dict									=			val[
                                  -dim:, :
                              ]
                         else:
                              lowercase_			:       Optional[Any]									=			val[:dim]
                              lowercase_			:       Any									=			val[
                                  dim : dim * 2
                              ]
                              lowercase_			:       Dict									=			val[-dim:]
               elif key.startswith('mit'   ):
                    lowercase_			:       List[str]									=			key_split[2]
                    lowercase_			:       Union[str, Any]									=			config.vision_config.mit_hidden_size
                    if "weight" in key:
                         lowercase_			:       Any									=			val[:dim, :]
                         lowercase_			:       Union[str, Any]									=			val[dim : dim * 2, :]
                         lowercase_			:       Dict									=			val[-dim:, :]
                    else:
                         lowercase_			:       Dict									=			val[:dim]
                         lowercase_			:       List[Any]									=			val[dim : dim * 2]
                         lowercase_			:       Any									=			val[-dim:]
               else:
                    lowercase_			:       Union[str, Any]									=			key_split[2]
                    lowercase_			:       Dict									=			config.text_config.hidden_size
                    if "weight" in key:
                         lowercase_			:       Optional[Any]									=			val[:dim, :]
                         lowercase_			:       int									=			val[
                             dim : dim * 2, :
                         ]
                         lowercase_			:       Tuple									=			val[-dim:, :]
                    else:
                         lowercase_			:       Union[str, Any]									=			val[:dim]
                         lowercase_			:       Union[str, Any]									=			val[
                             dim : dim * 2
                         ]
                         lowercase_			:       str									=			val[-dim:]
          else:
               lowercase_			:       Union[str, Any]									=			rename_key(__SCREAMING_SNAKE_CASE   )
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    lowercase_			:       Dict									=			val.T
               lowercase_			:       Optional[int]									=			val

     return orig_state_dict



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     if num_frames == 8:
          lowercase_			:       Union[str, Any]									=			'eating_spaghetti_8_frames.npy'
     elif num_frames == 16:
          lowercase_			:       Tuple									=			'eating_spaghetti.npy'
     elif num_frames == 32:
          lowercase_			:       str									=			'eating_spaghetti_32_frames.npy'
     lowercase_			:       Union[str, Any]									=			hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'  ,    filename=__SCREAMING_SNAKE_CASE  ,    repo_type='dataset'  ,    )
     lowercase_			:       int									=			np.load(__SCREAMING_SNAKE_CASE   )
     return list(__SCREAMING_SNAKE_CASE   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Dict=None  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]=False   ):
     lowercase_			:       int									=			{
         # fully supervised kinetics-400 checkpoints
         'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
         'xclip-base-patch32-16-frames': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
         ),
         'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
         'xclip-base-patch16-16-frames': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
         ),
         'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
         'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
         # fully supervised kinetics-600 checkpoints
         'xclip-base-patch16-kinetics-600': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
         ),
         'xclip-base-patch16-kinetics-600-16-frames': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
         ),
         'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
         # few shot
         'xclip-base-patch16-hmdb-2-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
         ),
         'xclip-base-patch16-hmdb-4-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
         ),
         'xclip-base-patch16-hmdb-8-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
         ),
         'xclip-base-patch16-hmdb-16-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
         ),
         'xclip-base-patch16-ucf-2-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
         ),
         'xclip-base-patch16-ucf-4-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
         ),
         'xclip-base-patch16-ucf-8-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
         ),
         'xclip-base-patch16-ucf-16-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
         ),
         # zero shot
         'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
     }

     lowercase_			:       List[str]									=			model_to_url[model_name]
     lowercase_			:       Dict									=			8
     if "16-frames" in model_name:
          lowercase_			:       List[str]									=			16
     elif "shot" in model_name:
          lowercase_			:       Tuple									=			32

     lowercase_			:       str									=			get_xclip_config(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[Any]									=			XCLIPModel(__SCREAMING_SNAKE_CASE   )
     model.eval()

     if "drive" in checkpoint_url:
          lowercase_			:       List[str]									=			'pytorch_model.bin'
          gdown.cached_download(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    quiet=__SCREAMING_SNAKE_CASE   )
          lowercase_			:       Union[str, Any]									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )['model']
     else:
          lowercase_			:       Optional[int]									=			torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE   )['model']

     lowercase_			:       Optional[Any]									=			convert_state_dict(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     lowercase_			:       Optional[Any]									=			XCLIPModel(__SCREAMING_SNAKE_CASE   )
     lowercase_							,						lowercase_			:       Dict									=			model.load_state_dict(__SCREAMING_SNAKE_CASE  ,    strict=__SCREAMING_SNAKE_CASE   )
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     lowercase_			:       Tuple									=			3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
     lowercase_			:       Optional[Any]									=			VideoMAEImageProcessor(size=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'   )
     lowercase_			:       List[Any]									=			CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'   )
     lowercase_			:       Tuple									=			XCLIPProcessor(image_processor=__SCREAMING_SNAKE_CASE  ,    tokenizer=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       Union[str, Any]									=			prepare_video(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[str]									=			processor(
         text=['playing sports', 'eating spaghetti', 'go shopping']  ,    videos=__SCREAMING_SNAKE_CASE  ,    return_tensors='pt'  ,    padding=__SCREAMING_SNAKE_CASE   )

     print('Shape of pixel values:'  ,    inputs.pixel_values.shape   )

     with torch.no_grad():
          lowercase_			:       Dict									=			model(**__SCREAMING_SNAKE_CASE   )

     # Verify outputs
     lowercase_			:       Any									=			outputs.logits_per_video
     lowercase_			:       int									=			logits_per_video.softmax(dim=1   )
     print('Probs:'  ,    __SCREAMING_SNAKE_CASE   )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          lowercase_			:       str									=			torch.tensor([[0.0019, 0.9951, 0.0030]]   )
     elif model_name == "xclip-base-patch32-16-frames":
          lowercase_			:       int									=			torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]   )
     elif model_name == "xclip-base-patch16":
          lowercase_			:       Any									=			torch.tensor([[0.0083, 0.9681, 0.0236]]   )
     elif model_name == "xclip-base-patch16-16-frames":
          lowercase_			:       List[str]									=			torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]   )
     elif model_name == "xclip-large-patch14":
          lowercase_			:       str									=			torch.tensor([[0.0062, 0.9864, 0.0075]]   )
     elif model_name == "xclip-large-patch14-16-frames":
          lowercase_			:       List[str]									=			torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]   )
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          lowercase_			:       List[str]									=			torch.tensor([[0.0555, 0.8914, 0.0531]]   )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          lowercase_			:       Tuple									=			torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]   )
     elif model_name == "xclip-large-patch14-kinetics-600":
          lowercase_			:       List[str]									=			torch.tensor([[0.0036, 0.9920, 0.0045]]   )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          lowercase_			:       List[Any]									=			torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]   )
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          lowercase_			:       Union[str, Any]									=			torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]   )
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          lowercase_			:       Tuple									=			torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]   )
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          lowercase_			:       Dict									=			torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]   )
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          lowercase_			:       List[Any]									=			torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]   )
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          lowercase_			:       int									=			torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]   )
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          lowercase_			:       str									=			torch.tensor([[0.0027, 0.9904, 0.0070]]   )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          lowercase_			:       str									=			torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]   )
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          lowercase_			:       Optional[int]									=			torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-03]]   )
     else:
          raise ValueError(F'''Model name {model_name} not supported'''   )
     assert torch.allclose(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    atol=1E-3   )
     print('Looks ok!'   )

     if pytorch_dump_folder_path is not None:
          print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
          model.save_pretrained(__SCREAMING_SNAKE_CASE   )

     if push_to_hub:
          print('Pushing model, processor and slow tokenizer files to the hub...'   )
          model.push_to_hub(__SCREAMING_SNAKE_CASE  ,    organization='nielsr'   )
          processor.push_to_hub(__SCREAMING_SNAKE_CASE  ,    organization='nielsr'   )
          slow_tokenizer.push_to_hub(__SCREAMING_SNAKE_CASE  ,    organization='nielsr'   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="xclip-base-patch32",
           type=str,
           help="Name of the model.",
       )
       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 or not to push the converted model to the 🤗 hub."
       )

       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_xclip_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  ,)

