




"""simple docstring"""







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''longt5'''
   A_										=					['''past_key_values''']
   A_										=					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







   def __init__(		self					,				lowerCamelCase_=3_2_1_2_8					,				lowerCamelCase_=5_1_2					,				lowerCamelCase_=6_4					,				lowerCamelCase_=2_0_4_8					,				lowerCamelCase_=6					,				lowerCamelCase_=None					,				lowerCamelCase_=8					,				lowerCamelCase_=1_2_7					,				lowerCamelCase_=1_6					,				lowerCamelCase_=3_2					,				lowerCamelCase_=1_2_8					,				lowerCamelCase_=0.1					,				lowerCamelCase_=1e-6					,				lowerCamelCase_=1.0					,				lowerCamelCase_="relu"					,				lowerCamelCase_=True					,				lowerCamelCase_="local"					,				lowerCamelCase_=True					,				lowerCamelCase_=0					,				lowerCamelCase_=1					,				**lowerCamelCase_					,				)							->      Optional[int]:
       UpperCamelCase										=  vocab_size
       UpperCamelCase										=  d_model
       UpperCamelCase										=  d_kv
       UpperCamelCase										=  d_ff
       UpperCamelCase										=  num_layers
       # default = symmetry
       UpperCamelCase										=  num_decoder_layers if num_decoder_layers is not None else self.num_layers
       UpperCamelCase										=  num_heads
       UpperCamelCase										=  local_radius
       UpperCamelCase										=  global_block_size
       UpperCamelCase										=  relative_attention_num_buckets
       UpperCamelCase										=  relative_attention_max_distance
       UpperCamelCase										=  dropout_rate
       UpperCamelCase										=  layer_norm_epsilon
       UpperCamelCase										=  initializer_factor
       UpperCamelCase										=  feed_forward_proj
       UpperCamelCase										=  encoder_attention_type
       UpperCamelCase										=  use_cache

       UpperCamelCase										=  self.feed_forward_proj.split('''-''')
       UpperCamelCase										=  act_info[-1]
       UpperCamelCase										=  act_info[0] == '''gated'''

       if len(lowerCamelCase_) > 1 and act_info[0] != "gated" or len(lowerCamelCase_) > 2:
           raise ValueError(
               F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\'''')

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
           UpperCamelCase										=  '''gelu_new'''

       super().__init__(
           pad_token_id=lowerCamelCase_					,				eos_token_id=lowerCamelCase_					,				is_encoder_decoder=lowerCamelCase_					,				**lowerCamelCase_					,				)

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   @property
   def      UpperCAmelCase__ (		self)							->      Mapping[str, Mapping[int, str]]:
       UpperCamelCase										=  {
           '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
           '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
       }
       if self.use_past:
           UpperCamelCase										=  '''past_encoder_sequence + sequence'''
           UpperCamelCase										=  {0: '''batch'''}
           UpperCamelCase										=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
           UpperCamelCase										=  {0: '''batch''', 1: '''decoder_sequence'''}
           UpperCamelCase										=  {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
           self.fill_with_past_key_values_(lowerCamelCase_					,				direction='''inputs''')

       return common_inputs







   @property
   def      UpperCAmelCase__ (		self)							->      int:
       return 1_3





"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )