






'''simple docstring'''
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser(
      description=(
          'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
          ' Distillation'
      )
  )
  parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
  parser.add_argument('--model_name', default='roberta-large', type=str)
  parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
  parser.add_argument('--vocab_transform', action='store_true')
  a_					=	parser.parse_args()

  if args.model_type == "roberta":
    a_					=	RobertaForMaskedLM.from_pretrained(args.model_name)
    a_					=	'roberta'
  elif args.model_type == "gpt2":
    a_					=	GPTaLMHeadModel.from_pretrained(args.model_name)
    a_					=	'transformer'

  a_					=	model.state_dict()
  a_					=	{}

  # Embeddings #
  if args.model_type == "gpt2":
    for param_name in ["wte.weight", "wpe.weight"]:
      a_					=	state_dict[F'''{prefix}.{param_name}''']
  else:
    for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
      a_					=	F'''{prefix}.embeddings.{w}.weight'''
      a_					=	state_dict[param_name]
    for w in ["weight", "bias"]:
      a_					=	F'''{prefix}.embeddings.LayerNorm.{w}'''
      a_					=	state_dict[param_name]

    # Transformer Blocks #
  a_					=	0
  for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
    if args.model_type == "gpt2":
      for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
        for w in ["weight", "bias"]:
          a_					=	state_dict[
              F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
          ]
      a_					=	state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
    else:
      for layer in [
          "attention.self.query",
          "attention.self.key",
          "attention.self.value",
          "attention.output.dense",
          "attention.output.LayerNorm",
          "intermediate.dense",
          "output.dense",
          "output.LayerNorm",
      ]:
        for w in ["weight", "bias"]:
          a_					=	state_dict[
              F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
          ]
    std_idx += 1

  # Language Modeling Head ###s
  if args.model_type == "roberta":
    for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
      a_					=	state_dict[F'''{layer}''']
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        a_					=	state_dict[F'''lm_head.dense.{w}''']
        a_					=	state_dict[F'''lm_head.layer_norm.{w}''']
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      a_					=	state_dict[F'''{prefix}.ln_f.{w}''']
    a_					=	state_dict['lm_head.weight']

  print(F'''N layers selected for distillation: {std_idx}''')
  print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

  print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
  torch.save(compressed_sd, args.dump_checkpoint)







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''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							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

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

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Any													=tax_encoder_rel_embedding

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

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

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

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

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

      flax_model.save_pretrained(UpperCamelCase__							)
      print('''T5X Model was sucessfully converted!'''							)


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