


"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def lowerCAmelCase_   (					UpperCamelCase__						:			Any					, UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Tuple			):






 """simple docstring"""







 __lowercase						=						AutoConfig.from_pretrained(UpperCamelCase_			)
 __lowercase						=						FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase_			)
 __lowercase						=						checkpoints.load_tax_checkpoint(UpperCamelCase_			)

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

 if config.model_type == "t5":
  __lowercase						=						"""SelfAttention"""
 if config.model_type == "longt5" and config.encoder_attention_type == "local":
  __lowercase						=						"""LocalSelfAttention"""
 elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  __lowercase						=						"""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			):
  __lowercase						=						f'''layers_{str(UpperCamelCase_			)}'''

  # 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"""]

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   __lowercase						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

  # 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						=						flax_model.params["""encoder"""]["""block"""][str(UpperCamelCase_			)]["""layer"""]
  __lowercase						=						tax_attention_key
  __lowercase						=						tax_attention_out
  __lowercase						=						tax_attention_query
  __lowercase						=						tax_attention_value

  __lowercase						=						tax_attention_layer_norm

  # Global input layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
   __lowercase						=						tax_global_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

  __lowercase						=						flax_model_encoder_layer_block

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

 # Side/global relative position_bias + layer norm
 if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
  __lowercase						=						tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
  __lowercase						=						tax_encoder_global_rel_embedding

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

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

  # 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"""]
  __lowercase						=						tax_enc_dec_attention_module["""key"""]["""kernel"""]
  __lowercase						=						tax_enc_dec_attention_module["""out"""]["""kernel"""]
  __lowercase						=						tax_enc_dec_attention_module["""query"""]["""kernel"""]
  __lowercase						=						tax_enc_dec_attention_module["""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						=						flax_model.params["""decoder"""]["""block"""][str(UpperCamelCase_			)]["""layer"""]
  __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

  __lowercase						=						flax_model_decoder_layer_block

 # 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 and LongT5 checkpoints)
 if "logits_dense" in tax_model["target"]["decoder"]:
  __lowercase						=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





