




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Dict   	=		FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Tuple   	=		checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_       )

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

       if config.model_type == "t5":
              UpperCamelCase    :	Dict   	=		'''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              UpperCamelCase    :	List[str]   	=		'''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              UpperCamelCase    :	Optional[int]   	=		'''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       ):
              UpperCamelCase    :	Tuple   	=		F"""layers_{str(SCREAMING_SNAKE_CASE_       )}"""

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

              # Layer Normalization
              UpperCamelCase    :	Any   	=		tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     UpperCamelCase    :	str   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     UpperCamelCase    :	Union[str, Any]   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     UpperCamelCase    :	Optional[int]   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              UpperCamelCase    :	List[Any]   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              UpperCamelCase    :	List[str]   	=		tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              UpperCamelCase    :	Union[str, Any]   	=		flax_model.params['''encoder''']['''block'''][str(SCREAMING_SNAKE_CASE_       )]['''layer''']
              UpperCamelCase    :	str   	=		tax_attention_key
              UpperCamelCase    :	int   	=		tax_attention_out
              UpperCamelCase    :	Dict   	=		tax_attention_query
              UpperCamelCase    :	Dict   	=		tax_attention_value

              UpperCamelCase    :	int   	=		tax_attention_layer_norm

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

              if split_mlp_wi:
                     UpperCamelCase    :	Tuple   	=		tax_mlp_wi_a
                     UpperCamelCase    :	Optional[Any]   	=		tax_mlp_wi_a
              else:
                     UpperCamelCase    :	Optional[Any]   	=		tax_mlp_wi

              UpperCamelCase    :	Tuple   	=		tax_mlp_wo
              UpperCamelCase    :	Dict   	=		tax_mlp_layer_norm

              UpperCamelCase    :	Tuple   	=		flax_model_encoder_layer_block

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

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

       # Assigning
       UpperCamelCase    :	Any   	=		tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       UpperCamelCase    :	List[Any]   	=		tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers       ):
              UpperCamelCase    :	Any   	=		F"""layers_{str(SCREAMING_SNAKE_CASE_       )}"""

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

              # Layer Normalization
              UpperCamelCase    :	Union[str, Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

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

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

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

              UpperCamelCase    :	Optional[Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              UpperCamelCase    :	int   	=		tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

              UpperCamelCase    :	int   	=		tax_pre_attention_layer_norm

              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_key
              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_out
              UpperCamelCase    :	Optional[int]   	=		tax_enc_dec_attention_query
              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_value

              UpperCamelCase    :	List[str]   	=		tax_cross_layer_norm

              if split_mlp_wi:
                     UpperCamelCase    :	str   	=		tax_mlp_wi_a
                     UpperCamelCase    :	Optional[Any]   	=		tax_mlp_wi_a
              else:
                     UpperCamelCase    :	List[str]   	=		tax_mlp_wi

              UpperCamelCase    :	Tuple   	=		tax_mlp_wo

              UpperCamelCase    :	int   	=		txa_mlp_layer_norm

              UpperCamelCase    :	List[Any]   	=		flax_model_decoder_layer_block

       # Decoder Normalization
       UpperCamelCase    :	Optional[Any]   	=		tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       UpperCamelCase    :	Optional[Any]   	=		txa_decoder_norm

       # Only for layer 0:
       UpperCamelCase    :	int   	=		tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       UpperCamelCase    :	Union[str, Any]   	=		tax_decoder_rel_embedding

       # Token Embeddings
       UpperCamelCase    :	int   	=		tax_model['''target''']['''token_embedder''']['''embedding''']
       UpperCamelCase    :	int   	=		txa_token_embeddings

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

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


if __name__ == "__main__":
  __UpperCAmelCase : List[Any] 			=				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."
  )
  __UpperCAmelCase : Optional[Any] 			=				parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





import copy
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


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

__UpperCAmelCase : List[str] 			=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					"conditional_detr"
       __UpperCamelCase      :     Optional[Any]							=					["past_key_values"]
       __UpperCamelCase      :     Union[str, Any]							=					{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=300 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="relu" ,					__SCREAMING_SNAKE_CASE=256 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1.0 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="sine" ,					__SCREAMING_SNAKE_CASE="resnet50" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.25 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              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.'''						)
                            UpperCamelCase    :	str   	=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
                     elif isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		backbone_config.get('''model_type'''						)
                            UpperCamelCase    :	Optional[Any]   	=		CONFIG_MAPPING[backbone_model_type]
                            UpperCamelCase    :	Any   	=		config_class.from_dict(__SCREAMING_SNAKE_CASE						)

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






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.encoder_attention_heads






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.d_model






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		copy.deepcopy(self.__dict__						)
              if self.backbone_config is not None:
                     UpperCamelCase    :	List[Any]   	=		self.backbone_config.to_dict()
              UpperCamelCase    :	List[Any]   	=		self.__class__.model_type
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					version.parse("1.11")






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                      ('''pixel_mask''', {0: '''batch'''}),
                  ]						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 1e-5






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 12

