
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					List[str]       ):
       snake_case			:  Optional[int]     							= AutoConfig.from_pretrained(__lowerCamelCase       )
       snake_case			:  Any     							= FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase       )
       snake_case			:  Dict     							= checkpoints.load_tax_checkpoint(__lowerCamelCase       )

       snake_case			:  Union[str, Any]     							= "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

       if config.model_type == "t5":
              snake_case			:  Optional[Any]     							= "SelfAttention"
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              snake_case			:  int     							= "LocalSelfAttention"
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              snake_case			:  List[str]     							= "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			:  Tuple     							= f"""layers_{str(__lowerCamelCase       )}"""

              # Self-Attention
              snake_case			:  Tuple     							= tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
              snake_case			:  Optional[Any]     							= tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
              snake_case			:  Dict     							= tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
              snake_case			:  str     							= 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			:  Union[str, Any]     							= tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

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

              if split_mlp_wi:
                     snake_case			:  Dict     							= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
                     snake_case			:  Any     							= 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			:  int     							= tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

              # Layer Normalization
              snake_case			:  Tuple     							= tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

              # Assigning
              snake_case			:  Optional[Any]     							= flax_model.params["encoder"]["block"][str(__lowerCamelCase       )]["layer"]
              snake_case			:  Tuple     							= tax_attention_key
              snake_case			:  str     							= tax_attention_out
              snake_case			:  Dict     							= tax_attention_query
              snake_case			:  Union[str, Any]     							= tax_attention_value

              snake_case			:  Tuple     							= tax_attention_layer_norm

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

              if split_mlp_wi:
                     snake_case			:  Optional[int]     							= tax_mlp_wi_a
                     snake_case			:  Union[str, Any]     							= tax_mlp_wi_a
              else:
                     snake_case			:  List[str]     							= tax_mlp_wi

              snake_case			:  Union[str, Any]     							= tax_mlp_wo
              snake_case			:  Optional[int]     							= tax_mlp_layer_norm

              snake_case			:  List[str]     							= flax_model_encoder_layer_block

       # Only for layer 0:
       snake_case			:  Any     							= tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
       snake_case			:  List[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			:  Optional[Any]     							= tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
              snake_case			:  Any     							= tax_encoder_global_rel_embedding

       # Assigning
       snake_case			:  int     							= tax_model["target"]["encoder"]["encoder_norm"]["scale"]
       snake_case			:  List[Any]     							= tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers       ):
              snake_case			:  List[str]     							= f"""layers_{str(__lowerCamelCase       )}"""

              # Self-Attention
              snake_case			:  str     							= tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
              snake_case			:  str     							= tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
              snake_case			:  int     							= tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
              snake_case			:  Tuple     							= tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

              # Layer Normalization
              snake_case			:  Dict     							= 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			:  str     							= tax_enc_dec_attention_module["key"]["kernel"]
              snake_case			:  Optional[Any]     							= tax_enc_dec_attention_module["out"]["kernel"]
              snake_case			:  List[Any]     							= tax_enc_dec_attention_module["query"]["kernel"]
              snake_case			:  int     							= 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			:  Optional[int]     							= tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
                     snake_case			:  List[Any]     							= tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
              else:
                     snake_case			:  List[str]     							= tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

              snake_case			:  Tuple     							= tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

              # Layer Normalization
              snake_case			:  str     							= tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

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

              snake_case			:  Optional[Any]     							= tax_pre_attention_layer_norm

              snake_case			:  List[Any]     							= tax_enc_dec_attention_key
              snake_case			:  Union[str, Any]     							= tax_enc_dec_attention_out
              snake_case			:  int     							= tax_enc_dec_attention_query
              snake_case			:  int     							= tax_enc_dec_attention_value

              snake_case			:  Optional[Any]     							= tax_cross_layer_norm

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

              snake_case			:  Optional[Any]     							= tax_mlp_wo

              snake_case			:  Optional[int]     							= txa_mlp_layer_norm

              snake_case			:  Tuple     							= flax_model_decoder_layer_block

       # Decoder Normalization
       snake_case			:  Dict     							= tax_model["target"]["decoder"]["decoder_norm"]["scale"]
       snake_case			:  Union[str, Any]     							= txa_decoder_norm

       # Only for layer 0:
       snake_case			:  Optional[int]     							= tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
       snake_case			:  Optional[Any]     							= tax_decoder_rel_embedding

       # Token Embeddings
       snake_case			:  Any     							= tax_model["target"]["token_embedder"]["embedding"]
       snake_case			:  int     							= txa_token_embeddings

       # LM Head (only in v1.1 and LongT5 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              snake_case			:  Optional[int]     							= tax_model["target"]["decoder"]["logits_dense"]["kernel"]

       flax_model.save_pretrained(__lowerCamelCase       )
       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 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."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--bert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained BERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





