


'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       Any							)  ->	Any:
     __lowerCamelCase     :			str              =	TaConfig.from_pretrained(UpperCAmelCase_							)
     __lowerCamelCase     :			Union[str, Any]              =	FlaxTaForConditionalGeneration(config=UpperCAmelCase_							)
     __lowerCamelCase     :			Tuple              =	checkpoints.load_tax_checkpoint(UpperCAmelCase_							)

     __lowerCamelCase     :			str              =	'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

     # Encoder
     for layer_index in range(config.num_layers							):
          __lowerCamelCase     :			Optional[Any]              =	F'layers_{str(UpperCAmelCase_							)}'

          # Self-Attention
          __lowerCamelCase     :			Tuple              =	tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
          __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
          __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
          __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

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

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

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

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

          # Assigning
          __lowerCamelCase     :			int              =	tax_attention_key
          __lowerCamelCase     :			Dict              =	tax_attention_out
          __lowerCamelCase     :			int              =	tax_attention_query
          __lowerCamelCase     :			Dict              =	tax_attention_value

          __lowerCamelCase     :			Tuple              =	tax_attention_layer_norm

          if split_mlp_wi:
               __lowerCamelCase     :			Optional[int]              =	tax_mlp_wi_a
               __lowerCamelCase     :			List[Any]              =	tax_mlp_wi_a
          else:
               __lowerCamelCase     :			Tuple              =	tax_mlp_wi

          __lowerCamelCase     :			str              =	tax_mlp_wo
          __lowerCamelCase     :			Union[str, Any]              =	tax_mlp_layer_norm

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

     # Assigning
     __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder']['encoder_norm']['scale']
     __lowerCamelCase     :			str              =	tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          __lowerCamelCase     :			Optional[Any]              =	F'layers_{str(UpperCAmelCase_							)}'

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

          # Layer Normalization
          __lowerCamelCase     :			Tuple              =	tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

          # MLP
          if split_mlp_wi:
               __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
               __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
          else:
               __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

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

          # Layer Normalization
          __lowerCamelCase     :			Tuple              =	tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          __lowerCamelCase     :			Any              =	tax_attention_key
          __lowerCamelCase     :			int              =	tax_attention_out
          __lowerCamelCase     :			List[str]              =	tax_attention_query
          __lowerCamelCase     :			Tuple              =	tax_attention_value

          __lowerCamelCase     :			int              =	tax_pre_attention_layer_norm

          __lowerCamelCase     :			Any              =	tax_enc_dec_attention_key
          __lowerCamelCase     :			str              =	tax_enc_dec_attention_out
          __lowerCamelCase     :			Dict              =	tax_enc_dec_attention_query
          __lowerCamelCase     :			List[str]              =	tax_enc_dec_attention_value

          __lowerCamelCase     :			Dict              =	tax_cross_layer_norm

          if split_mlp_wi:
               __lowerCamelCase     :			Union[str, Any]              =	tax_mlp_wi_a
               __lowerCamelCase     :			Dict              =	tax_mlp_wi_a
          else:
               __lowerCamelCase     :			str              =	tax_mlp_wi

          __lowerCamelCase     :			Optional[int]              =	tax_mlp_wo

          __lowerCamelCase     :			int              =	txa_mlp_layer_norm

     # Decoder Normalization
     __lowerCamelCase     :			Union[str, Any]              =	tax_model['target']['decoder']['decoder_norm']['scale']
     __lowerCamelCase     :			List[str]              =	txa_decoder_norm

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

     # Token Embeddings
     __lowerCamelCase     :			Optional[int]              =	tax_model['target']['token_embedder']['embedding']
     __lowerCamelCase     :			Dict              =	txa_token_embeddings

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

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


if __name__ == "__main__":
      A__	:      str				=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
      parser.add_argument(
          """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
      )
      A__	:      str				=						parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










'''simple docstring'''



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
      raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
A__	:      List[str]				=						logging.get_logger(__name__)

A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
     __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
     roberta.eval()  # disable dropout
     __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
     __lowerCamelCase     :			Optional[Any]              =	RobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,					hidden_size=roberta.args.encoder_embed_dim   ,					num_hidden_layers=roberta.args.encoder_layers   ,					num_attention_heads=roberta.args.encoder_attention_heads   ,					intermediate_size=roberta.args.encoder_ffn_embed_dim   ,					max_position_embeddings=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
     if classification_head:
          __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'   ,					UpperCAmelCase_							)

     __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
     __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
     __lowerCamelCase     :			Tuple              =	torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
     __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
     __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias

     for i in range(config.num_hidden_layers							):
          # Encoder: start of layer
          __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
          __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]

          # self attention
          __lowerCamelCase     :			BertSelfAttention              =	layer.attention.self
          assert (
              roberta_layer.self_attn.k_proj.weight.data.shape
              == roberta_layer.self_attn.q_proj.weight.data.shape
              == roberta_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)							)
          )

          __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
          __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias

          # self-attention output
          __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
          __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias

          # intermediate
          __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
          __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias

          # output
          __lowerCamelCase     :			BertOutput              =	layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase     :			str              =	roberta_layer.fca.weight
          __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
          __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
          __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
          __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
          __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
          __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
     else:
          # LM Head
          __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
          __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
          __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
          __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
          __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
          __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1

     __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
     if classification_head:
          __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
     else:
          __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
     print(our_output.shape   ,					their_output.shape							)
     __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
     print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
     __lowerCamelCase     :			List[str]              =	torch.allclose(UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=1e-3							)
     print('Do both models output the same tensors?'   ,					'🔥' if success else '💩'							)
     if not success:
          raise Exception('Something went wRoNg'							)

     pathlib.Path(UpperCAmelCase_							).mkdir(parents=UpperCAmelCase_   ,					exist_ok=UpperCAmelCase_							)
     print(F'Saving model to {pytorch_dump_folder_path}'							)
     model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__	:      Optional[Any]				=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--roberta_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."""
      )
      parser.add_argument(
          """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
      )
      A__	:      Optional[Any]				=						parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )






