




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowerCamelCase     :		Optional[Any]								=       logging.get_logger(__name__)

_lowerCamelCase     :		Optional[int]								=       {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class     UpperCamelCase_   (			UpperCAmelCase_					):

    '''simple docstring'''






    UpperCAmelCase__							  =				"""gptj"""
    UpperCAmelCase__							  =				{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }


    def __init__(      self				:     Any ,			UpperCAmelCase__				:     Optional[Any]=50_400 ,			UpperCAmelCase__				:     Optional[Any]=2_048 ,			UpperCAmelCase__				:     List[str]=4_096 ,			UpperCAmelCase__				:     Optional[Any]=28 ,			UpperCAmelCase__				:     List[Any]=16 ,			UpperCAmelCase__				:     List[Any]=64 ,			UpperCAmelCase__				:     int=None ,			UpperCAmelCase__				:     int="gelu_new" ,			UpperCAmelCase__				:     List[Any]=0.0 ,			UpperCAmelCase__				:     Tuple=0.0 ,			UpperCAmelCase__				:     int=0.0 ,			UpperCAmelCase__				:     List[str]=1e-5 ,			UpperCAmelCase__				:     Tuple=0.02 ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     Dict=50_256 ,			UpperCAmelCase__				:     int=50_256 ,			UpperCAmelCase__				:     Tuple=False ,			**UpperCAmelCase__				:     List[Any] ,			)						->Tuple:





           '''simple docstring'''

           A__							=   vocab_size
           A__							=   n_positions
           A__							=   n_embd
           A__							=   n_layer
           A__							=   n_head
           A__							=   n_inner
           A__							=   rotary_dim
           A__							=   activation_function
           A__							=   resid_pdrop
           A__							=   embd_pdrop
           A__							=   attn_pdrop
           A__							=   layer_norm_epsilon
           A__							=   initializer_range
           A__							=   use_cache

           A__							=   bos_token_id
           A__							=   eos_token_id

           super().__init__(
               bos_token_id=__lowercase ,			eos_token_id=__lowercase ,			tie_word_embeddings=__lowercase ,			**__lowercase)


class     UpperCamelCase_   (			UpperCAmelCase_					):

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Tuple = "default" ,			UpperCAmelCase__				:     Dict = None ,			UpperCAmelCase__				:     Any = False ,			)						->Any:





           '''simple docstring'''

           super().__init__(__lowercase ,			task=__lowercase ,			patching_specs=__lowercase ,			use_past=__lowercase)
           if not getattr(self._config ,			'''pad_token_id''' ,			__lowercase):
                  # TODO: how to do that better?
                  A__							=   0


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           A__							=   OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
           if self.use_past:
                  self.fill_with_past_key_values_(__lowercase ,			direction='''inputs''')
                  A__							=   {0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
                  A__							=   {0: '''batch''', 1: '''sequence'''}

           return common_inputs


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->int:





           '''simple docstring'''

           return self._config.n_layer


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->int:





           '''simple docstring'''

           return self._config.n_head


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     List[Any] = -1 ,			UpperCAmelCase__				:     Union[str, Any] = -1 ,			UpperCAmelCase__				:     Optional[Any] = False ,			UpperCAmelCase__				:     Tuple = None ,			)						->Mapping[str, Any]:





           '''simple docstring'''

           A__							=   super(__lowercase ,			self).generate_dummy_inputs(
               __lowercase ,			batch_size=__lowercase ,			seq_length=__lowercase ,			is_pair=__lowercase ,			framework=__lowercase)

           # We need to order the input in the way they appears in the forward()
           A__							=   OrderedDict({'''input_ids''': common_inputs['''input_ids''']})

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                         import torch

                         A__							=   common_inputs['''input_ids'''].shape
                         # Not using the same length for past_key_values
                         A__							=   seqlen + 2
                         A__							=   (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         A__							=   [
                             (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(self.num_layers)
                         ]

           A__							=   common_inputs['''attention_mask''']
           if self.use_past:
                  A__							=   ordered_inputs['''attention_mask'''].dtype
                  A__							=   torch.cat(
                      [ordered_inputs['''attention_mask'''], torch.ones(__lowercase ,			__lowercase ,			dtype=__lowercase)] ,			dim=1)

           return ordered_inputs





    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->int:





           '''simple docstring'''

           return 13








import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   AutoConfig.from_pretrained(lowercase_						)
       A__							=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_						)
       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)

       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(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''']

              # 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(lowercase_						)]['''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(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''']
              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(lowercase_						)]['''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(lowercase_						)
       print('''T5X Model was sucessfully converted!'''						)


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


