

'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[str]:
       A_						=      AutoConfig.from_pretrained(UpperCAmelCase__				)
       A_						=      FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCAmelCase__				)
       A_						=      checkpoints.load_tax_checkpoint(UpperCAmelCase__				)

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

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

              # 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"""]

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

              # 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_						=      flax_model.params["""encoder"""]["""block"""][str(UpperCAmelCase__				)]["""layer"""]
              A_						=      tax_attention_key
              A_						=      tax_attention_out
              A_						=      tax_attention_query
              A_						=      tax_attention_value

              A_						=      tax_attention_layer_norm

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

              A_						=      flax_model_encoder_layer_block

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

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

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

       # Decoder
       for layer_index in range(config.num_layers				):
              A_						=      F'''layers_{str(UpperCAmelCase__				)}'''

              # 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"""]
              A_						=      tax_enc_dec_attention_module["""key"""]["""kernel"""]
              A_						=      tax_enc_dec_attention_module["""out"""]["""kernel"""]
              A_						=      tax_enc_dec_attention_module["""query"""]["""kernel"""]
              A_						=      tax_enc_dec_attention_module["""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_						=      flax_model.params["""decoder"""]["""block"""][str(UpperCAmelCase__				)]["""layer"""]
              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

              A_						=      flax_model_decoder_layer_block

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

       flax_model.save_pretrained(UpperCAmelCase__				)
       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)



'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      0.01
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     A_						=      time.time()
                     locka.acquire(UpperCAmelCase__				)
                     assert time.time() - _start > timeout







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
       A_						=      """a""" * 10_00 + """.lock"""
       A_						=      FileLock(str(tmpdir / filename				)				)
       assert locka._lock_file.endswith(""".lock"""				)
       assert not locka._lock_file.endswith(UpperCAmelCase__				)
       assert len(os.path.basename(locka._lock_file				)				) <= 2_55
       A_						=      FileLock(tmpdir / filename				)
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     locka.acquire(0				)
