

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
  SCREAMING_SNAKE_CASE:int						      =					FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_ )
  SCREAMING_SNAKE_CASE:Tuple						      =					checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_ )

  SCREAMING_SNAKE_CASE:int						      =					"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  if config.model_type == "t5":
    SCREAMING_SNAKE_CASE:int						      =					"SelfAttention"
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    SCREAMING_SNAKE_CASE:int						      =					"LocalSelfAttention"
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    SCREAMING_SNAKE_CASE:List[Any]						      =					"TransientGlobalSelfAttention"
  else:
    raise ValueError(
        "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
        " attribute with a value from ['local', 'transient-global]." )

  # Encoder
  for layer_index in range(config.num_layers ):
    SCREAMING_SNAKE_CASE:List[Any]						      =					F'''layers_{str(SCREAMING_SNAKE_CASE_ )}'''

    # Self-Attention
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    SCREAMING_SNAKE_CASE:Any						      =					tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      SCREAMING_SNAKE_CASE:Tuple						      =					tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

    if split_mlp_wi:
      SCREAMING_SNAKE_CASE:Tuple						      =					tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      SCREAMING_SNAKE_CASE:int						      =					tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

    SCREAMING_SNAKE_CASE:Optional[int]						      =					tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    SCREAMING_SNAKE_CASE:Dict						      =					flax_model.params["encoder"]["block"][str(SCREAMING_SNAKE_CASE_ )]["layer"]
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_attention_key
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tax_attention_out
    SCREAMING_SNAKE_CASE:int						      =					tax_attention_query
    SCREAMING_SNAKE_CASE:List[str]						      =					tax_attention_value

    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      SCREAMING_SNAKE_CASE:List[str]						      =					tax_global_layer_norm

    if split_mlp_wi:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_mlp_wi_a
      SCREAMING_SNAKE_CASE:str						      =					tax_mlp_wi_a
    else:
      SCREAMING_SNAKE_CASE:Dict						      =					tax_mlp_wi

    SCREAMING_SNAKE_CASE:Dict						      =					tax_mlp_wo
    SCREAMING_SNAKE_CASE:Dict						      =					tax_mlp_layer_norm

    SCREAMING_SNAKE_CASE:List[str]						      =					flax_model_encoder_layer_block

  # Only for layer 0:
  SCREAMING_SNAKE_CASE:List[str]						      =					tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  SCREAMING_SNAKE_CASE:Dict						      =					tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    SCREAMING_SNAKE_CASE:Tuple						      =					tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_encoder_global_rel_embedding

  # Assigning
  SCREAMING_SNAKE_CASE:str						      =					tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  SCREAMING_SNAKE_CASE:int						      =					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers ):
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					F'''layers_{str(SCREAMING_SNAKE_CASE_ )}'''

    # Self-Attention
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    SCREAMING_SNAKE_CASE:Any						      =					tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    SCREAMING_SNAKE_CASE:Tuple						      =					tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

    # Encoder-Decoder-Attention
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
    SCREAMING_SNAKE_CASE:str						      =					tax_enc_dec_attention_module["key"]["kernel"]
    SCREAMING_SNAKE_CASE:List[str]						      =					tax_enc_dec_attention_module["out"]["kernel"]
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_enc_dec_attention_module["query"]["kernel"]
    SCREAMING_SNAKE_CASE:List[str]						      =					tax_enc_dec_attention_module["value"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

    # MLP
    if split_mlp_wi:
      SCREAMING_SNAKE_CASE:Optional[int]						      =					tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      SCREAMING_SNAKE_CASE:Tuple						      =					tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      SCREAMING_SNAKE_CASE:List[Any]						      =					tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    SCREAMING_SNAKE_CASE:Dict						      =					tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					flax_model.params["decoder"]["block"][str(SCREAMING_SNAKE_CASE_ )]["layer"]
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					tax_attention_key
    SCREAMING_SNAKE_CASE:str						      =					tax_attention_out
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_attention_query
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_attention_value

    SCREAMING_SNAKE_CASE:Tuple						      =					tax_pre_attention_layer_norm

    SCREAMING_SNAKE_CASE:str						      =					tax_enc_dec_attention_key
    SCREAMING_SNAKE_CASE:Dict						      =					tax_enc_dec_attention_out
    SCREAMING_SNAKE_CASE:List[Any]						      =					tax_enc_dec_attention_query
    SCREAMING_SNAKE_CASE:Dict						      =					tax_enc_dec_attention_value

    SCREAMING_SNAKE_CASE:Tuple						      =					tax_cross_layer_norm

    if split_mlp_wi:
      SCREAMING_SNAKE_CASE:List[Any]						      =					tax_mlp_wi_a
      SCREAMING_SNAKE_CASE:Optional[int]						      =					tax_mlp_wi_a
    else:
      SCREAMING_SNAKE_CASE:Dict						      =					tax_mlp_wi

    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_mlp_wo

    SCREAMING_SNAKE_CASE:int						      =					txa_mlp_layer_norm

    SCREAMING_SNAKE_CASE:Dict						      =					flax_model_decoder_layer_block

  # Decoder Normalization
  SCREAMING_SNAKE_CASE:List[str]						      =					tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  SCREAMING_SNAKE_CASE:Tuple						      =					txa_decoder_norm

  # Only for layer 0:
  SCREAMING_SNAKE_CASE:List[Any]						      =					tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  SCREAMING_SNAKE_CASE:Dict						      =					tax_decoder_rel_embedding

  # Token Embeddings
  SCREAMING_SNAKE_CASE:List[Any]						      =					tax_model["target"]["token_embedder"]["embedding"]
  SCREAMING_SNAKE_CASE:Any						      =					txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tax_model["target"]["decoder"]["logits_dense"]["kernel"]

  flax_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
  print("T5X Model was sucessfully converted!" )


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
    )
    parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
    parser.add_argument(
        "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
    )
    A_       						=							parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''simple docstring'''

import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_       						=							{
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     List[Any]       =					'''mask2former'''
   _A       :     str       =					['''swin''']
   _A       :     Tuple       =					{'''hidden_size''': '''hidden_dim'''}
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Dict] = None     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 1_024     ,SCREAMING_SNAKE_CASE__			:      str = "relu"     ,SCREAMING_SNAKE_CASE__			:      int = 6     ,SCREAMING_SNAKE_CASE__			:      int = 10     ,SCREAMING_SNAKE_CASE__			:      int = 8     ,SCREAMING_SNAKE_CASE__			:      float = 0.0     ,SCREAMING_SNAKE_CASE__			:      int = 2_048     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      int = 4     ,SCREAMING_SNAKE_CASE__			:      int = 255     ,SCREAMING_SNAKE_CASE__			:      int = 100     ,SCREAMING_SNAKE_CASE__			:      float = 0.1     ,SCREAMING_SNAKE_CASE__			:      float = 2.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      int = 12_544     ,SCREAMING_SNAKE_CASE__			:      float = 3.0     ,SCREAMING_SNAKE_CASE__			:      float = 0.75     ,SCREAMING_SNAKE_CASE__			:      float = 0.02     ,SCREAMING_SNAKE_CASE__			:      float = 1.0     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      List[int] = [4, 8, 16, 32]     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					CONFIG_MAPPING["swin"](
           image_size=224     ,in_channels=3     ,patch_size=4     ,embed_dim=96     ,depths=[2, 2, 18, 2]     ,num_heads=[3, 6, 12, 24]     ,window_size=7     ,drop_path_rate=0.3     ,use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)

     if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					backbone_config.pop("model_type"							)
       SCREAMING_SNAKE_CASE:List[str]						      =					CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE:List[str]						      =					config_class.from_dict(SCREAMING_SNAKE_CASE__							)

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
           F'''Supported model types: {",".join(self.backbones_supported							)}'''							)

     SCREAMING_SNAKE_CASE:List[str]						      =					backbone_config
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					mask_feature_size
     SCREAMING_SNAKE_CASE:str						      =					hidden_dim
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_feedforward_dim
     SCREAMING_SNAKE_CASE:Tuple						      =					activation_function
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_layers
     SCREAMING_SNAKE_CASE:List[Any]						      =					decoder_layers
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_attention_heads
     SCREAMING_SNAKE_CASE:Dict						      =					dropout
     SCREAMING_SNAKE_CASE:List[str]						      =					dim_feedforward
     SCREAMING_SNAKE_CASE:int						      =					pre_norm
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					enforce_input_projection
     SCREAMING_SNAKE_CASE:Any						      =					common_stride
     SCREAMING_SNAKE_CASE:int						      =					ignore_value
     SCREAMING_SNAKE_CASE:List[Any]						      =					num_queries
     SCREAMING_SNAKE_CASE:Dict						      =					no_object_weight
     SCREAMING_SNAKE_CASE:str						      =					class_weight
     SCREAMING_SNAKE_CASE:Tuple						      =					mask_weight
     SCREAMING_SNAKE_CASE:Optional[int]						      =					dice_weight
     SCREAMING_SNAKE_CASE:int						      =					train_num_points
     SCREAMING_SNAKE_CASE:str						      =					oversample_ratio
     SCREAMING_SNAKE_CASE:str						      =					importance_sample_ratio
     SCREAMING_SNAKE_CASE:str						      =					init_std
     SCREAMING_SNAKE_CASE:Any						      =					init_xavier_std
     SCREAMING_SNAKE_CASE:List[Any]						      =					use_auxiliary_loss
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_strides
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output_auxiliary_logits
     SCREAMING_SNAKE_CASE:Dict						      =					decoder_layers

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return cls(
         backbone_config=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)







   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:str						      =					self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.__class__.model_type
     return output






