



"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 	snake_case_  (				A_ :		Tuple,    A_ :		Optional[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    str         =			AutoConfig.from_pretrained(A_						)
       _lowerCamelCase						:    Any         =			FlaxAutoModelForSeqaSeqLM.from_config(config=A_						)
       _lowerCamelCase						:    Optional[Any]         =			checkpoints.load_tax_checkpoint(A_						)

       _lowerCamelCase						:    int         =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       if config.model_type == "t5":
              _lowerCamelCase						:    Dict         =			'''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              _lowerCamelCase						:    List[Any]         =			'''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              _lowerCamelCase						:    Any         =			'''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						):
              _lowerCamelCase						:    int         =			F'''layers_{str(A_						)}'''

              # Self-Attention
              _lowerCamelCase						:    int         =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              _lowerCamelCase						:    str         =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              _lowerCamelCase						:    Any         =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              _lowerCamelCase						:    List[Any]         =			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":
                     _lowerCamelCase						:    List[str]         =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

              # Layer Normalization
              _lowerCamelCase						:    Dict         =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     _lowerCamelCase						:    str         =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     _lowerCamelCase						:    Tuple         =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     _lowerCamelCase						:    Optional[int]         =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              _lowerCamelCase						:    Dict         =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              _lowerCamelCase						:    str         =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              _lowerCamelCase						:    List[Any]         =			flax_model.params['''encoder''']['''block'''][str(A_						)]['''layer''']
              _lowerCamelCase						:    Optional[int]         =			tax_attention_key
              _lowerCamelCase						:    Optional[Any]         =			tax_attention_out
              _lowerCamelCase						:    Union[str, Any]         =			tax_attention_query
              _lowerCamelCase						:    Union[str, Any]         =			tax_attention_value

              _lowerCamelCase						:    str         =			tax_attention_layer_norm

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

              if split_mlp_wi:
                     _lowerCamelCase						:    Optional[int]         =			tax_mlp_wi_a
                     _lowerCamelCase						:    str         =			tax_mlp_wi_a
              else:
                     _lowerCamelCase						:    Optional[int]         =			tax_mlp_wi

              _lowerCamelCase						:    List[Any]         =			tax_mlp_wo
              _lowerCamelCase						:    Optional[int]         =			tax_mlp_layer_norm

              _lowerCamelCase						:    Any         =			flax_model_encoder_layer_block

       # Only for layer 0:
       _lowerCamelCase						:    Union[str, Any]         =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       _lowerCamelCase						:    str         =			tax_encoder_rel_embedding

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              _lowerCamelCase						:    List[Any]         =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
              _lowerCamelCase						:    Optional[int]         =			tax_encoder_global_rel_embedding

       # Assigning
       _lowerCamelCase						:    Optional[Any]         =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       _lowerCamelCase						:    Optional[Any]         =			tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers						):
              _lowerCamelCase						:    List[Any]         =			F'''layers_{str(A_						)}'''

              # Self-Attention
              _lowerCamelCase						:    Optional[int]         =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              _lowerCamelCase						:    str         =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              _lowerCamelCase						:    List[Any]         =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              _lowerCamelCase						:    Any         =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              _lowerCamelCase						:    Optional[Any]         =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              _lowerCamelCase						:    Any         =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
              _lowerCamelCase						:    List[Any]         =			tax_enc_dec_attention_module['''key''']['''kernel''']
              _lowerCamelCase						:    Dict         =			tax_enc_dec_attention_module['''out''']['''kernel''']
              _lowerCamelCase						:    Dict         =			tax_enc_dec_attention_module['''query''']['''kernel''']
              _lowerCamelCase						:    List[str]         =			tax_enc_dec_attention_module['''value''']['''kernel''']

              # Layer Normalization
              _lowerCamelCase						:    int         =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     _lowerCamelCase						:    int         =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     _lowerCamelCase						:    str         =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     _lowerCamelCase						:    List[str]         =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              _lowerCamelCase						:    Dict         =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              _lowerCamelCase						:    int         =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

              _lowerCamelCase						:    List[str]         =			tax_pre_attention_layer_norm

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

              _lowerCamelCase						:    int         =			tax_cross_layer_norm

              if split_mlp_wi:
                     _lowerCamelCase						:    List[str]         =			tax_mlp_wi_a
                     _lowerCamelCase						:    Tuple         =			tax_mlp_wi_a
              else:
                     _lowerCamelCase						:    str         =			tax_mlp_wi

              _lowerCamelCase						:    Optional[Any]         =			tax_mlp_wo

              _lowerCamelCase						:    Dict         =			txa_mlp_layer_norm

              _lowerCamelCase						:    Optional[Any]         =			flax_model_decoder_layer_block

       # Decoder Normalization
       _lowerCamelCase						:    Tuple         =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       _lowerCamelCase						:    Optional[Any]         =			txa_decoder_norm

       # Only for layer 0:
       _lowerCamelCase						:    Optional[Any]         =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       _lowerCamelCase						:    List[str]         =			tax_decoder_rel_embedding

       # Token Embeddings
       _lowerCamelCase						:    Tuple         =			tax_model['''target''']['''token_embedder''']['''embedding''']
       _lowerCamelCase						:    str         =			txa_token_embeddings

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

       flax_model.save_pretrained(A_						)
       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 argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowerCAmelCase__      =    logging.get_logger(__name__)
logging.set_verbosity_info()






def 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





       if "xprophetnet" in prophetnet_checkpoint_path:
              _lowerCamelCase						:    Any         =			XLMProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)
       else:
              _lowerCamelCase						:    str         =			ProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			ProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)

       _lowerCamelCase						:    Optional[Any]         =			['''key_proj''', '''value_proj''', '''query_proj''']

       _lowerCamelCase						:    List[Any]         =			{
           '''self_attn''': '''ngram_self_attn''',
           '''cross_attn''': '''encoder_attn''',
           '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
           '''feed_forward_layer_norm''': '''final_layer_norm''',
           '''feed_forward''': '''''',
           '''intermediate''': '''fc1''',
           '''output''': '''fc2''',
           '''key_proj''': '''k_proj''',
           '''query_proj''': '''q_proj''',
           '''value_proj''': '''v_proj''',
           '''word_embeddings''': '''embed_tokens''',
           '''embeddings_layer_norm''': '''emb_layer_norm''',
           '''relative_pos_embeddings''': '''relative_linear''',
           '''ngram_embeddings''': '''ngram_input_embed''',
           '''position_embeddings''': '''embed_positions''',
       }

       for key in loading_info["missing_keys"]:
              _lowerCamelCase						:    Union[str, Any]         =			key.split('''.'''						)

              if attributes[0] == "lm_head":
                     _lowerCamelCase						:    str         =			prophet
                     _lowerCamelCase						:    List[Any]         =			prophet_old
              else:
                     _lowerCamelCase						:    Optional[int]         =			prophet.prophetnet
                     _lowerCamelCase						:    Optional[Any]         =			prophet_old.model

              _lowerCamelCase						:    Any         =			False
              for attribute in attributes:
                     if attribute in mapping:
                            _lowerCamelCase						:    Optional[int]         =			mapping[attribute]
                            if not hasattr(A_,    A_						) and len(A_						) > 0:
                                   _lowerCamelCase						:    int         =			attribute
                     elif hasattr(A_,    A_						):
                            _lowerCamelCase						:    int         =			attribute

                     if attribute == "weight":
                            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                            _lowerCamelCase						:    Optional[int]         =			old_model.weight
                            logger.info(F'''{attribute} is initialized.'''						)
                            _lowerCamelCase						:    List[str]         =			True
                            break
                     elif attribute == "bias":
                            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                            _lowerCamelCase						:    int         =			old_model.bias
                            logger.info(F'''{attribute} is initialized'''						)
                            _lowerCamelCase						:    Union[str, Any]         =			True
                            break
                     elif attribute in special_keys and hasattr(A_,    '''in_proj_weight'''						):
                            _lowerCamelCase						:    Tuple         =			old_model.in_proj_weight.shape[0] // 3
                            _lowerCamelCase						:    List[Any]         =			getattr(A_,    A_						)
                            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                            if attribute == "query_proj":
                                   _lowerCamelCase						:    Optional[Any]         =			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]						)
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_bias[:embed_dim]						)

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    Dict         =			True
                            break
                     elif attribute == "position_embeddings":
                            assert (
                                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                            ), "Hidden size has to match"
                            assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
                            _lowerCamelCase						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

                     if attribute.isdigit():
                            _lowerCamelCase						:    Optional[int]         =			model[int(A_						)]
                            _lowerCamelCase						:    List[Any]         =			old_model[int(A_						)]
                     else:
                            _lowerCamelCase						:    List[str]         =			getattr(A_,    A_						)

                            if old_attribute == "":
                                   _lowerCamelCase						:    str         =			old_model
                            else:
                                   if not hasattr(A_,    A_						):
                                          raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
                                   _lowerCamelCase						:    Optional[int]         =			getattr(A_,    A_						)

              if not is_key_init:
                     raise ValueError(F'''{key} was not correctly initialized!'''						)

       print(F'''Saving model to {pytorch_dump_folder_path}'''						)
       prophet.save_pretrained(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


