




'''simple docstring'''
import copy
from typing import Dict, Optional

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


A							:      Optional[int] 	=       logging.get_logger(__name__)

A							:      List[str] 	=       {
    """shi-labs/oneformer_ade20k_swin_tiny""": (
        """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""oneformer"""
       snake_case_ :     Any    =					{"""hidden_size""": """hidden_dim"""}

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 255					,		lowerCamelCase__ = 150					,		lowerCamelCase__ = 0.1					,		lowerCamelCase__ = 2.0					,		lowerCamelCase__ = 5.0					,		lowerCamelCase__ = 5.0					,		lowerCamelCase__ = 0.5					,		lowerCamelCase__ = 0.07					,		lowerCamelCase__ = 1_2544					,		lowerCamelCase__ = 3.0					,		lowerCamelCase__ = 0.75					,		lowerCamelCase__ = 0.02					,		lowerCamelCase__ = 1.0					,		lowerCamelCase__ = 1E-0_5					,		lowerCamelCase__ = False					,		lowerCamelCase__ = True					,		lowerCamelCase__ = True					,		lowerCamelCase__ = [4, 8, 16, 32]					,		lowerCamelCase__ = 77					,		lowerCamelCase__ = 256					,		lowerCamelCase__ = 77					,		lowerCamelCase__ = 6					,		lowerCamelCase__ = 4_9408					,		lowerCamelCase__ = 2					,		lowerCamelCase__ = 16					,		lowerCamelCase__ = 256					,		lowerCamelCase__ = 256					,		lowerCamelCase__ = 256					,		lowerCamelCase__ = 1024					,		lowerCamelCase__ = "GN"					,		lowerCamelCase__ = 6					,		lowerCamelCase__ = 10					,		lowerCamelCase__ = True					,		lowerCamelCase__ = 8					,		lowerCamelCase__ = 0.1					,		lowerCamelCase__ = 2048					,		lowerCamelCase__ = False					,		lowerCamelCase__ = False					,		lowerCamelCase__ = 2					,		lowerCamelCase__ = 4					,		**lowerCamelCase__					,		)    ->					int:

           """simple docstring"""



           if backbone_config is None:
               logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""     )
               __lowercase							=						CONFIG_MAPPING["""swin"""](
                   image_size=224					,		in_channels=3					,		patch_size=4					,		embed_dim=96					,		depths=[2, 2, 6, 2]					,		num_heads=[3, 6, 12, 24]					,		window_size=7					,		drop_path_rate=0.3					,		use_absolute_embeddings=lowerCamelCase__					,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					,		)
           elif isinstance(lowerCamelCase__					,		lowerCamelCase__     ):
               __lowercase							=						backbone_config.get("""model_type"""     )
               __lowercase							=						CONFIG_MAPPING[backbone_model_type]
               __lowercase							=						config_class.from_dict(lowerCamelCase__     )

           __lowercase							=						backbone_config

           __lowercase							=						ignore_value
           __lowercase							=						num_queries
           __lowercase							=						no_object_weight
           __lowercase							=						class_weight
           __lowercase							=						mask_weight
           __lowercase							=						dice_weight
           __lowercase							=						contrastive_weight
           __lowercase							=						contrastive_temperature
           __lowercase							=						train_num_points
           __lowercase							=						oversample_ratio
           __lowercase							=						importance_sample_ratio
           __lowercase							=						init_std
           __lowercase							=						init_xavier_std
           __lowercase							=						layer_norm_eps
           __lowercase							=						is_training
           __lowercase							=						use_auxiliary_loss
           __lowercase							=						output_auxiliary_logits
           __lowercase							=						strides
           __lowercase							=						task_seq_len
           __lowercase							=						text_encoder_width
           __lowercase							=						text_encoder_context_length
           __lowercase							=						text_encoder_num_layers
           __lowercase							=						text_encoder_vocab_size
           __lowercase							=						text_encoder_proj_layers
           __lowercase							=						text_encoder_n_ctx
           __lowercase							=						conv_dim
           __lowercase							=						mask_dim
           __lowercase							=						hidden_dim
           __lowercase							=						encoder_feedforward_dim
           __lowercase							=						norm
           __lowercase							=						encoder_layers
           __lowercase							=						decoder_layers
           __lowercase							=						use_task_norm
           __lowercase							=						num_attention_heads
           __lowercase							=						dropout
           __lowercase							=						dim_feedforward
           __lowercase							=						pre_norm
           __lowercase							=						enforce_input_proj
           __lowercase							=						query_dec_layers
           __lowercase							=						common_stride
           __lowercase							=						decoder_layers

           super().__init__(**lowerCamelCase__     )




       def     snake_case__			(    self     )    ->					Dict[str, any]:

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )
           __lowercase							=						self.backbone_config.to_dict()
           __lowercase							=						self.__class__.model_type
           return output










'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 				snake_case_  (   a__		:	Optional[int]    ,a__		:	str    ,a__		:	int			):



    """simple docstring"""




    __lowercase							=						TaConfig.from_pretrained(a__			)
    __lowercase							=						FlaxTaForConditionalGeneration(config=a__			)
    __lowercase							=						checkpoints.load_tax_checkpoint(a__			)

    __lowercase							=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

        # Self-Attention
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

        if split_mlp_wi:
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_attention_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo
        __lowercase							=						tax_mlp_layer_norm

    # Only for layer 0:
    __lowercase							=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    __lowercase							=						tax_encoder_rel_embedding

    # Assigning
    __lowercase							=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    __lowercase							=						tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

        # Self-Attention
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

        # Encoder-Decoder-Attention
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
            """kernel"""
        ]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_pre_attention_layer_norm

        __lowercase							=						tax_enc_dec_attention_key
        __lowercase							=						tax_enc_dec_attention_out
        __lowercase							=						tax_enc_dec_attention_query
        __lowercase							=						tax_enc_dec_attention_value

        __lowercase							=						tax_cross_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo

        __lowercase							=						txa_mlp_layer_norm

    # Decoder Normalization
    __lowercase							=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
    __lowercase							=						txa_decoder_norm

    # Only for layer 0:
    __lowercase							=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    __lowercase							=						tax_decoder_rel_embedding

    # Token Embeddings
    __lowercase							=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    __lowercase							=						txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        __lowercase							=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
  A							:      Optional[Any] 	=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  A							:      Dict 	=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




