


import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[int]:
   # initialize config
   if "resnet-50" in model_name:
      UpperCAmelCase							=     ResNetConfig.from_pretrained("""microsoft/resnet-50"""   )
   elif "resnet-101" in model_name:
      UpperCAmelCase							=     ResNetConfig.from_pretrained("""microsoft/resnet-101"""   )
   else:
      raise ValueError("""Model name should include either resnet50 or resnet101"""   )

   UpperCAmelCase							=     DetrConfig(use_timm_backbone=lowerCAmelCase_  ,						backbone_config=lowerCAmelCase_   )

   # set label attributes
   UpperCAmelCase							=     """panoptic""" in model_name
   if is_panoptic:
      UpperCAmelCase							=     2_5_0
   else:
      UpperCAmelCase							=     9_1
      UpperCAmelCase							=     """huggingface/label-files"""
      UpperCAmelCase							=     """coco-detection-id2label.json"""
      UpperCAmelCase							=     json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="""dataset"""   )  ,						"""r"""   )   )
      UpperCAmelCase							=     {int(lowerCAmelCase_   ): v for k, v in idalabel.items()}
      UpperCAmelCase							=     idalabel
      UpperCAmelCase							=     {v: k for k, v in idalabel.items()}

   return config, is_panoptic
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Dict:
   # here we list all keys to be renamed (original name on the left, our name on the right)
   UpperCAmelCase							=     []

   # stem
   # fmt: off
   rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")   )
   rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")   )
   rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")   )
   rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")   )
   rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")   )
   # stages
   for stage_idx in range(len(config.backbone_config.depths   )   ):
      for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                )   )
         # 3 convs
         for i in range(3   ):
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                )   )
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                )   )
    # fmt: on

   for i in range(config.encoder_layers   ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (
              F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
              F"""encoder.layers.{i}.self_attn.out_proj.weight""",
          )   )
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")   )
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")   )
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")   )
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")   )
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")   )
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")   )
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")   )
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")   )
      rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")   )
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
              F"""decoder.layers.{i}.self_attn.out_proj.weight""",
          )   )
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")   )
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
          )   )
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
          )   )
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")   )
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")   )
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")   )
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")   )
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")   )
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")   )
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")   )
      rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")   )

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   rename_keys.extend(
       [
           ("""input_proj.weight""", """input_projection.weight"""),
           ("""input_proj.bias""", """input_projection.bias"""),
           ("""query_embed.weight""", """query_position_embeddings.weight"""),
           ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
           ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
           ("""class_embed.weight""", """class_labels_classifier.weight"""),
           ("""class_embed.bias""", """class_labels_classifier.bias"""),
           ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
           ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
           ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
           ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
           ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
           ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
       ]   )

   return rename_keys
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[Any]:
   UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
   UpperCAmelCase							=     val
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=False   )						->int:
   UpperCAmelCase							=     """"""
   if is_panoptic:
      UpperCAmelCase							=     """detr."""

   # first: transformer encoder
   for i in range(6   ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      UpperCAmelCase							=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
      UpperCAmelCase							=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase							=     in_proj_weight[:2_5_6, :]
      UpperCAmelCase							=     in_proj_bias[:2_5_6]
      UpperCAmelCase							=     in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase							=     in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase							=     in_proj_weight[-2_5_6:, :]
      UpperCAmelCase							=     in_proj_bias[-2_5_6:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6   ):
      # read in weights + bias of input projection layer of self-attention
      UpperCAmelCase							=     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
      UpperCAmelCase							=     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase							=     in_proj_weight[:2_5_6, :]
      UpperCAmelCase							=     in_proj_bias[:2_5_6]
      UpperCAmelCase							=     in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase							=     in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase							=     in_proj_weight[-2_5_6:, :]
      UpperCAmelCase							=     in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      UpperCAmelCase							=     state_dict.pop(
          F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
      UpperCAmelCase							=     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      UpperCAmelCase							=     in_proj_weight_cross_attn[:2_5_6, :]
      UpperCAmelCase							=     in_proj_bias_cross_attn[:2_5_6]
      UpperCAmelCase							=     in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      UpperCAmelCase							=     in_proj_bias_cross_attn[2_5_6:5_1_2]
      UpperCAmelCase							=     in_proj_weight_cross_attn[-2_5_6:, :]
      UpperCAmelCase							=     in_proj_bias_cross_attn[-2_5_6:]
def   _UpperCamelCase				(						)						->Optional[int]:
   UpperCAmelCase							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
   UpperCAmelCase							=     Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_   ).raw   )

   return im
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=False   )						->List[Any]:
   UpperCAmelCase				,  UpperCAmelCase							=     get_detr_config(lowerCAmelCase_   )

   # load original model from torch hub
   UpperCAmelCase							=     {
       """detr-resnet-50""": """detr_resnet50""",
       """detr-resnet-101""": """detr_resnet101""",
   }
   logger.info(F"""Converting model {model_name}..."""   )
   UpperCAmelCase							=     torch.hub.load("""facebookresearch/detr"""  ,						model_name_to_original_name[model_name]  ,						pretrained=lowerCAmelCase_   ).eval()
   UpperCAmelCase							=     detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(lowerCAmelCase_   ):
      if is_panoptic:
         UpperCAmelCase							=     """detr.""" + src
      rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowerCAmelCase_  ,						is_panoptic=lowerCAmelCase_   )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   UpperCAmelCase							=     """detr.model.""" if is_panoptic else """model."""
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("""detr"""   )
             and not key.startswith("""class_labels_classifier"""   )
             and not key.startswith("""bbox_predictor"""   )
         ):
            UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
            UpperCAmelCase							=     val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
            UpperCAmelCase							=     val
         elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
            continue
         else:
            UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
            UpperCAmelCase							=     val
      else:
         if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
            UpperCAmelCase							=     val

    # finally, create HuggingFace model and load state dict
   UpperCAmelCase							=     DetrForSegmentation(lowerCAmelCase_   ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_   )
   model.load_state_dict(lowerCAmelCase_   )
   model.eval()

   # verify our conversion on an image
   UpperCAmelCase							=     """coco_panoptic""" if is_panoptic else """coco_detection"""
   UpperCAmelCase							=     DetrImageProcessor(format=lowerCAmelCase_   )

   UpperCAmelCase							=     processor(images=prepare_img()  ,						return_tensors="""pt"""   )
   UpperCAmelCase							=     encoding["""pixel_values"""]

   UpperCAmelCase							=     detr(lowerCAmelCase_   )
   UpperCAmelCase							=     model(lowerCAmelCase_   )

   assert torch.allclose(outputs.logits  ,						original_outputs["""pred_logits"""]  ,						atol=1e-3   )
   assert torch.allclose(outputs.pred_boxes  ,						original_outputs["""pred_boxes"""]  ,						atol=1e-3   )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks  ,						original_outputs["""pred_masks"""]  ,						atol=1e-4   )
   print("""Looks ok!"""   )

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
      Path(lowerCAmelCase_   ).mkdir(exist_ok=lowerCAmelCase_   )
      model.save_pretrained(lowerCAmelCase_   )
      processor.save_pretrained(lowerCAmelCase_   )

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info("""Uploading PyTorch model and image processor to the hub..."""   )
      model.push_to_hub(F"""nielsr/{model_name}"""   )
      processor.push_to_hub(F"""nielsr/{model_name}"""   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     default="""detr-resnet-50""",
     type=str,
     choices=["""detr-resnet-50""", """detr-resnet-101"""],
     help="""Name of the DETR model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
 )
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
 __a     =			parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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


__a     =			logging.get_logger(__name__)

__a     =			{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''nllb-moe'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





      def __init__(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[Any]=1_2_8_1_1_2					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					Optional[int]=1_2					,       __lowerCamelCase						:					Union[str, Any]=4_0_9_6					,       __lowerCamelCase						:					List[str]=1_6					,       __lowerCamelCase						:					List[str]=1_2					,       __lowerCamelCase						:					int=4_0_9_6					,       __lowerCamelCase						:					Tuple=1_6					,       __lowerCamelCase						:					str=0.05					,       __lowerCamelCase						:					List[str]=0.05					,       __lowerCamelCase						:					List[Any]=True					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					str="relu"					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					List[str]=0.1					,       __lowerCamelCase						:					Optional[int]=0.1					,       __lowerCamelCase						:					List[Any]=0.0					,       __lowerCamelCase						:					Optional[Any]=0.02					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Tuple="float32"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=1_2_8					,       __lowerCamelCase						:					List[str]=6_4					,       __lowerCamelCase						:					List[Any]=4					,       __lowerCamelCase						:					Tuple=4					,       __lowerCamelCase						:					str=0.001					,       __lowerCamelCase						:					Optional[int]=0.001					,       __lowerCamelCase						:					Tuple="all"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=False					,       __lowerCamelCase						:					List[str]=1.0					,       __lowerCamelCase						:					Dict=0.2					,       __lowerCamelCase						:					Union[str, Any]=1					,       __lowerCamelCase						:					int=0					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					int=False					,       **__lowerCamelCase						:					str					,       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     max_position_embeddings
         UpperCAmelCase							=     d_model
         UpperCAmelCase							=     encoder_ffn_dim
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     encoder_attention_heads
         UpperCAmelCase							=     decoder_ffn_dim
         UpperCAmelCase							=     decoder_layers
         UpperCAmelCase							=     decoder_attention_heads
         UpperCAmelCase							=     dropout
         UpperCAmelCase							=     attention_dropout
         UpperCAmelCase							=     activation_dropout
         UpperCAmelCase							=     activation_function
         UpperCAmelCase							=     init_std
         UpperCAmelCase							=     encoder_layerdrop
         UpperCAmelCase							=     decoder_layerdrop
         UpperCAmelCase							=     use_cache
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase							=     router_z_loss_coef
         UpperCAmelCase							=     router_aux_loss_coef
         UpperCAmelCase							=     decoder_sparse_step
         UpperCAmelCase							=     encoder_sparse_step
         UpperCAmelCase							=     num_experts
         UpperCAmelCase							=     expert_capacity
         UpperCAmelCase							=     router_bias
         if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""       )
         UpperCAmelCase							=     router_dtype

         UpperCAmelCase							=     router_ignore_padding_tokens
         UpperCAmelCase							=     batch_prioritized_routing
         UpperCAmelCase							=     second_expert_policy
         UpperCAmelCase							=     normalize_router_prob_before_dropping
         UpperCAmelCase							=     moe_eval_capacity_token_fraction
         UpperCAmelCase							=     moe_token_dropout
         UpperCAmelCase							=     output_router_logits
         super().__init__(
             pad_token_id=__lowerCamelCase					,       bos_token_id=__lowerCamelCase					,       eos_token_id=__lowerCamelCase					,       is_encoder_decoder=__lowerCamelCase					,       decoder_start_token_id=__lowerCamelCase					,       **__lowerCamelCase					,       )



