






'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def 			__UpperCamelCase				(			lowercase_					:     Optional[Any]      ,					lowercase_					:     List[str]      ,					lowercase_					:     Optional[int]      ):
       """simple docstring"""




       a_	    = TaConfig.from_pretrained(lowercase__      )
       a_	    = FlaxTaForConditionalGeneration(config=lowercase__      )
       a_	    = checkpoints.load_tax_checkpoint(lowercase__      )

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

       # Encoder
       for layer_index in range(config.num_layers      ):
              a_	    = F'layers_{str(lowercase__      )}'

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

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

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

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

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

              # Assigning
              a_	    = tax_attention_key
              a_	    = tax_attention_out
              a_	    = tax_attention_query
              a_	    = tax_attention_value

              a_	    = tax_attention_layer_norm

              if split_mlp_wi:
                     a_	    = tax_mlp_wi_a
                     a_	    = tax_mlp_wi_a
              else:
                     a_	    = tax_mlp_wi

              a_	    = tax_mlp_wo
              a_	    = tax_mlp_layer_norm

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

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

       # Decoder
       for layer_index in range(config.num_decoder_layers      ):
              a_	    = F'layers_{str(lowercase__      )}'

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

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

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

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

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

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

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

              # Assigning
              a_	    = tax_attention_key
              a_	    = tax_attention_out
              a_	    = tax_attention_query
              a_	    = tax_attention_value

              a_	    = tax_pre_attention_layer_norm

              a_	    = tax_enc_dec_attention_key
              a_	    = tax_enc_dec_attention_out
              a_	    = tax_enc_dec_attention_query
              a_	    = tax_enc_dec_attention_value

              a_	    = tax_cross_layer_norm

              if split_mlp_wi:
                     a_	    = tax_mlp_wi_a
                     a_	    = tax_mlp_wi_a
              else:
                     a_	    = tax_mlp_wi

              a_	    = tax_mlp_wo

              a_	    = txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
  __lowerCAmelCase					   =       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."
  )
  __lowerCAmelCase					   =       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












'''simple docstring'''







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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    '''nystromformer'''







  def __init__( self      ,  UpperCamelCase__=30_000      ,  UpperCamelCase__=768      ,  UpperCamelCase__=12      ,  UpperCamelCase__=12      ,  UpperCamelCase__=3_072      ,  UpperCamelCase__="gelu_new"      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=510      ,  UpperCamelCase__=2      ,  UpperCamelCase__=64      ,  UpperCamelCase__=64      ,  UpperCamelCase__=65      ,  UpperCamelCase__=False      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=1e-5      ,  UpperCamelCase__=1      ,  UpperCamelCase__=0      ,  UpperCamelCase__=2      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = vocab_size
         a_	    = max_position_embeddings
         a_	    = hidden_size
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = hidden_act
         a_	    = hidden_dropout_prob
         a_	    = attention_probs_dropout_prob
         a_	    = initializer_range
         a_	    = type_vocab_size
         a_	    = segment_means_seq_len
         a_	    = num_landmarks
         a_	    = conv_kernel_size
         a_	    = inv_coeff_init_option
         a_	    = layer_norm_eps
         super().__init__(pad_token_id=UpperCamelCase__      ,  bos_token_id=UpperCamelCase__      ,  eos_token_id=UpperCamelCase__      ,  **UpperCamelCase__  )




