
'''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()
UpperCamelCase__					:						Optional[int]											=     logging.get_logger(__name__)

UpperCamelCase__					:						List[str]											=     "Hello world! cécé herlolip"
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_  )
   roberta.eval()  # disable dropout
   _SCREAMING_SNAKE_CASE          = roberta.model.encoder.sentence_encoder
   _SCREAMING_SNAKE_CASE          = 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:
      _SCREAMING_SNAKE_CASE          = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""			,					SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE          = RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_  )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   _SCREAMING_SNAKE_CASE          = roberta_sent_encoder.embed_tokens.weight
   _SCREAMING_SNAKE_CASE          = roberta_sent_encoder.embed_positions.weight
   _SCREAMING_SNAKE_CASE          = torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
   _SCREAMING_SNAKE_CASE          = roberta_sent_encoder.emb_layer_norm.weight
   _SCREAMING_SNAKE_CASE          = roberta_sent_encoder.emb_layer_norm.bias

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

      # self attention
      _SCREAMING_SNAKE_CASE          = 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)  )
      )

      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.q_proj.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.q_proj.bias
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.k_proj.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.k_proj.bias
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.v_proj.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.v_proj.bias

      # self-attention output
      _SCREAMING_SNAKE_CASE          = layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.out_proj.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn.out_proj.bias
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn_layer_norm.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.self_attn_layer_norm.bias

      # intermediate
      _SCREAMING_SNAKE_CASE          = layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      _SCREAMING_SNAKE_CASE          = roberta_layer.fca.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.fca.bias

      # output
      _SCREAMING_SNAKE_CASE          = layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      _SCREAMING_SNAKE_CASE          = roberta_layer.fca.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.fca.bias
      _SCREAMING_SNAKE_CASE          = roberta_layer.final_layer_norm.weight
      _SCREAMING_SNAKE_CASE          = roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      _SCREAMING_SNAKE_CASE          = roberta.model.classification_heads["""mnli"""].dense.weight
      _SCREAMING_SNAKE_CASE          = roberta.model.classification_heads["""mnli"""].dense.bias
      _SCREAMING_SNAKE_CASE          = roberta.model.classification_heads["""mnli"""].out_proj.weight
      _SCREAMING_SNAKE_CASE          = roberta.model.classification_heads["""mnli"""].out_proj.bias
   else:
      # LM Head
      _SCREAMING_SNAKE_CASE          = roberta.model.encoder.lm_head.dense.weight
      _SCREAMING_SNAKE_CASE          = roberta.model.encoder.lm_head.dense.bias
      _SCREAMING_SNAKE_CASE          = roberta.model.encoder.lm_head.layer_norm.weight
      _SCREAMING_SNAKE_CASE          = roberta.model.encoder.lm_head.layer_norm.bias
      _SCREAMING_SNAKE_CASE          = roberta.model.encoder.lm_head.weight
      _SCREAMING_SNAKE_CASE          = roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   _SCREAMING_SNAKE_CASE          = roberta.encode(SCREAMING_SNAKE_CASE_  ).unsqueeze(0  )  # batch of size 1

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

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


if __name__ == "__main__":
     UpperCamelCase__					:						Dict											=     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."
     )
     UpperCamelCase__					:						Dict											=     parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )








'''simple docstring'''



from typing import Mapping

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


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 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":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






