






"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__					:						Optional[Any]												=			logging.get_logger(__name__)

lowercase__					:						str												=			{
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""table-transformer"""
 _lowerCAmelCase     :    Optional[Any]	      =	["""past_key_values"""]
 _lowerCAmelCase     :    List[Any]	      =	{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }

 def __init__(  self	: Any						,  lowercase_	: Any=True						,  lowercase_	: Optional[int]=None						,  lowercase_	: Dict=3						,  lowercase_	: Any=100						,  lowercase_	: int=6						,  lowercase_	: Union[str, Any]=2048						,  lowercase_	: Union[str, Any]=8						,  lowercase_	: Any=6						,  lowercase_	: List[str]=2048						,  lowercase_	: Tuple=8						,  lowercase_	: str=0.0						,  lowercase_	: str=0.0						,  lowercase_	: Any=True						,  lowercase_	: Union[str, Any]="relu"						,  lowercase_	: List[Any]=256						,  lowercase_	: Dict=0.1						,  lowercase_	: Optional[int]=0.0						,  lowercase_	: Union[str, Any]=0.0						,  lowercase_	: int=0.02						,  lowercase_	: Union[str, Any]=1.0						,  lowercase_	: Union[str, Any]=False						,  lowercase_	: Optional[Any]="sine"						,  lowercase_	: Union[str, Any]="resnet50"						,  lowercase_	: Tuple=True						,  lowercase_	: Any=False						,  lowercase_	: int=1						,  lowercase_	: Tuple=5						,  lowercase_	: int=2						,  lowercase_	: Optional[Any]=1						,  lowercase_	: Dict=1						,  lowercase_	: Optional[Any]=5						,  lowercase_	: int=2						,  lowercase_	: Any=0.1						,  **lowercase_	: List[str]						,  ):
     if backbone_config is not None and use_timm_backbone:
         raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

     if not use_timm_backbone:
         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
             snake_case_				:  Dict       =			CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
         elif isinstance(lowercase_						,  lowercase_       ):
             snake_case_				:  Optional[int]       =			backbone_config.get('''model_type'''       )
             snake_case_				:  Dict       =			CONFIG_MAPPING[backbone_model_type]
             snake_case_				:  List[str]       =			config_class.from_dict(lowercase_       )
         # set timm attributes to None
         snake_case_				:  List[Any]       =			None, None, None

     snake_case_				:  Tuple       =			use_timm_backbone
     snake_case_				:  int       =			backbone_config
     snake_case_				:  Tuple       =			num_channels
     snake_case_				:  Any       =			num_queries
     snake_case_				:  str       =			d_model
     snake_case_				:  List[str]       =			encoder_ffn_dim
     snake_case_				:  int       =			encoder_layers
     snake_case_				:  str       =			encoder_attention_heads
     snake_case_				:  Union[str, Any]       =			decoder_ffn_dim
     snake_case_				:  Optional[Any]       =			decoder_layers
     snake_case_				:  int       =			decoder_attention_heads
     snake_case_				:  List[str]       =			dropout
     snake_case_				:  List[str]       =			attention_dropout
     snake_case_				:  Optional[Any]       =			activation_dropout
     snake_case_				:  int       =			activation_function
     snake_case_				:  Tuple       =			init_std
     snake_case_				:  int       =			init_xavier_std
     snake_case_				:  List[str]       =			encoder_layerdrop
     snake_case_				:  Union[str, Any]       =			decoder_layerdrop
     snake_case_				:  List[Any]       =			encoder_layers
     snake_case_				:  Optional[Any]       =			auxiliary_loss
     snake_case_				:  List[str]       =			position_embedding_type
     snake_case_				:  int       =			backbone
     snake_case_				:  str       =			use_pretrained_backbone
     snake_case_				:  Union[str, Any]       =			dilation
     # Hungarian matcher
     snake_case_				:  List[str]       =			class_cost
     snake_case_				:  List[Any]       =			bbox_cost
     snake_case_				:  List[str]       =			giou_cost
     # Loss coefficients
     snake_case_				:  Dict       =			mask_loss_coefficient
     snake_case_				:  Dict       =			dice_loss_coefficient
     snake_case_				:  int       =			bbox_loss_coefficient
     snake_case_				:  List[str]       =			giou_loss_coefficient
     snake_case_				:  List[str]       =			eos_coefficient
     super().__init__(is_encoder_decoder=lowercase_						,  **lowercase_       )

 @property
 def 		_snake_case      (  self	: Dict       ):
     return self.encoder_attention_heads






 @property
 def 		_snake_case      (  self	: Optional[int]       ):
     return self.d_model





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[str, Any]	      =	version.parse("""1.11""")

 @property
 def 		_snake_case      (  self	: Dict       ):
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ]       )

 @property
 def 		_snake_case      (  self	: Union[str, Any]       ):
     return 1E-5






 @property
 def 		_snake_case      (  self	: Dict       ):
     return 12










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''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					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			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":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

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

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

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

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			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.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





