



'''simple docstring'''





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class __A      (  _A						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[Any]										=	'blenderbot'
 __lowerCamelCase   :	Optional[int]										=	['past_key_values']
 __lowerCamelCase   :	str										=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self			,       A=8_008			,       A=128			,       A=2			,       A=10_240			,       A=32			,       A=24			,       A=10_240			,       A=32			,       A=0.0			,       A=0.0			,       A=True			,       A=True			,       A="gelu"			,       A=2_560			,       A=0.1			,       A=0.0			,       A=0.0			,       A=0.02			,       A=1			,       A=False			,       A=0			,       A=1			,       A=2			,       A=3			,       A=2			,       **A			,       )   ->					int:


      """simple docstring"""



      _a     =  vocab_size
      _a     =  max_position_embeddings
      _a     =  d_model
      _a     =  encoder_ffn_dim
      _a     =  encoder_layers
      _a     =  encoder_attention_heads
      _a     =  decoder_ffn_dim
      _a     =  decoder_layers
      _a     =  decoder_attention_heads
      _a     =  dropout
      _a     =  attention_dropout
      _a     =  activation_dropout
      _a     =  activation_function
      _a     =  init_std
      _a     =  encoder_layerdrop
      _a     =  decoder_layerdrop
      _a     =  use_cache
      _a     =  encoder_layers
      _a     =  scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=UpperCamelCase__			,       bos_token_id=UpperCamelCase__			,       eos_token_id=UpperCamelCase__			,       is_encoder_decoder=UpperCamelCase__			,       decoder_start_token_id=UpperCamelCase__			,       encoder_no_repeat_ngram_size=UpperCamelCase__			,       forced_eos_token_id=UpperCamelCase__			,       **UpperCamelCase__			,       )






class __A      (  _A						):



 '''simple docstring'''







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


      """simple docstring"""



      if self.task in ["default", "seq2seq-lm"]:
           _a     =  OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]	)
           if self.use_past:
                _a     =  {0: '''batch'''}
                _a     =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
                _a     =  {0: '''batch''', 1: '''decoder_sequence'''}
                _a     =  {0: '''batch''', 1: '''decoder_sequence'''}
           if self.use_past:
                self.fill_with_past_key_values_(UpperCamelCase__			,       direction='''inputs'''	)
      elif self.task == "causal-lm":
           _a     =  OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]	)
           if self.use_past:
                _a  ,   _a     =  self.num_layers
                for i in range(UpperCamelCase__	):
                     _a     =  {0: '''batch''', 2: '''past_sequence + sequence'''}
                     _a     =  {0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
           _a     =  OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ]	)

      return common_inputs



 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def  a__  (self	)   ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task in ["default", "seq2seq-lm"]:
           _a     =  super().outputs
      else:
           _a     =  super(UpperCamelCase__			,       self	).outputs
           if self.use_past:
                _a  ,   _a     =  self.num_layers
                for i in range(UpperCamelCase__	):
                     _a     =  {0: '''batch''', 2: '''past_sequence + sequence'''}
                     _a     =  {0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs



 def  a__  (self			,       A			,       A = -1			,       A = -1			,       A = False			,       A = None			,       )   ->					Mapping[str, Any]:


      """simple docstring"""



      _a     =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__	)
      # Generate decoder inputs
      _a     =  seq_length if not self.use_past else 1
      _a     =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__	)
      _a     =  {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      _a     =  dict(**UpperCamelCase__			,       **UpperCamelCase__	)

      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
           else:
                import torch
           _a  ,   _a     =  common_inputs['''input_ids'''].shape
           _a     =  common_inputs['''decoder_input_ids'''].shape[1]
           _a  ,   _a     =  self.num_attention_heads
           _a     =  (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           _a     =  decoder_seq_length
           _a     =  (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
           _a     =  torch.cat(
               [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__			,       UpperCamelCase__	)]			,       dim=1	)
           _a     =  []
           _a  ,   _a     =  self.num_layers

           for _ in range(UpperCamelCase__	):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(UpperCamelCase__	),
                        torch.zeros(UpperCamelCase__	),
                        torch.zeros(UpperCamelCase__	),
                        torch.zeros(UpperCamelCase__	),
                    )	)
      return common_inputs



 def  a__  (self			,       A			,       A = -1			,       A = -1			,       A = False			,       A = None			,       )   ->					Mapping[str, Any]:


      """simple docstring"""



      _a     =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__	)

      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
           else:
                import torch
           _a  ,   _a     =  common_inputs['''input_ids'''].shape
           _a     =  seqlen
           _a  ,   _a     =  self.num_layers
           _a  ,   _a     =  self.num_attention_heads
           _a     =  (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           _a     =  common_inputs['''attention_mask'''].dtype
           _a     =  torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__			,       UpperCamelCase__			,       dtype=UpperCamelCase__	)]			,       dim=1	)
           _a     =  [
               (torch.zeros(UpperCamelCase__	), torch.zeros(UpperCamelCase__	)) for _ in range(UpperCamelCase__	)
           ]
      return common_inputs



 def  a__  (self			,       A			,       A = -1			,       A = -1			,       A = False			,       A = None			,       )   ->					Mapping[str, Any]:


      """simple docstring"""



      _a     =  compute_effective_axis_dimension(
          UpperCamelCase__			,       fixed_dimension=OnnxConfig.default_fixed_batch			,       num_token_to_add=0	)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      _a     =  tokenizer.num_special_tokens_to_add(UpperCamelCase__	)
      _a     =  compute_effective_axis_dimension(
          UpperCamelCase__			,       fixed_dimension=OnnxConfig.default_fixed_sequence			,       num_token_to_add=UpperCamelCase__	)

      # Generate dummy inputs according to compute batch and sequence
      _a     =  [''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
      _a     =  dict(tokenizer(UpperCamelCase__			,       return_tensors=UpperCamelCase__	)	)
      return common_inputs



 def  a__  (self			,       A			,       A = -1			,       A = -1			,       A = False			,       A = None			,       )   ->					Mapping[str, Any]:


      """simple docstring"""



      if self.task in ["default", "seq2seq-lm"]:
           _a     =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               UpperCamelCase__			,       batch_size=UpperCamelCase__			,       seq_length=UpperCamelCase__			,       is_pair=UpperCamelCase__			,       framework=UpperCamelCase__	)

      elif self.task == "causal-lm":
           _a     =  self._generate_dummy_inputs_for_causal_lm(
               UpperCamelCase__			,       batch_size=UpperCamelCase__			,       seq_length=UpperCamelCase__			,       is_pair=UpperCamelCase__			,       framework=UpperCamelCase__	)
      else:
           _a     =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               UpperCamelCase__			,       batch_size=UpperCamelCase__			,       seq_length=UpperCamelCase__			,       is_pair=UpperCamelCase__			,       framework=UpperCamelCase__	)

      return common_inputs



 def  a__  (self			,       A			,       A			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      if self.task in ["default", "seq2seq-lm"]:
           _a     =  super()._flatten_past_key_values_(UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__	)
      else:
           _a     =  super(UpperCamelCase__			,       self	)._flatten_past_key_values_(
               UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__			,       UpperCamelCase__	)



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      if direction not in ["inputs", "outputs"]:
           raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''	)

      _a     =  '''past_key_values''' if direction == '''inputs''' else '''present'''
      _a  ,   _a     =  self.num_layers

      _a     =  '''past_encoder_sequence'''
      _a     =  '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(UpperCamelCase__	):
           _a     =  {0: '''batch''', 2: decoder_sequence}
           _a     =  {0: '''batch''', 2: decoder_sequence}
           _a     =  {0: '''batch''', 2: encoder_sequence}
           _a     =  {0: '''batch''', 2: encoder_sequence}











'''simple docstring'''





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
lowercase_			=  logging.get_logger(__name__)

lowercase_			=  "Hello world! cécé herlolip"




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a     =  os.path.split(__A)
     _a     =  DataaVecTextModel.from_pretrained(
         __A  ,     checkpoint_file=__A)
     dataavec.eval()  # disable dropout
     _a     =  dataavec.models[0]
     _a     =  dataavec_model.encoder.sentence_encoder
     _a     =  DataaVecTextConfig(
         vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,     hidden_size=dataavec_model.args.encoder_embed_dim  ,     num_hidden_layers=dataavec_model.args.encoder_layers  ,     num_attention_heads=dataavec_model.args.encoder_attention_heads  ,     intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,     max_position_embeddings=514  ,     type_vocab_size=1  ,     layer_norm_eps=1e-5  ,     )
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''  ,     __A)

     _a     =  DataaVecTextForSequenceClassification(__A) if classification_head else DataaVecTextForMaskedLM(__A)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     _a     =  dataavec_sent_encoder.embed_tokens.weight
     _a     =  dataavec_sent_encoder.embed_positions.weight
     _a     =  torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
     _a     =  dataavec_sent_encoder.layernorm_embedding.weight
     _a     =  dataavec_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers):
          # Encoder: start of layer
          _a     =  model.dataavec_text.encoder.layer[i]
          _a     =  dataavec_sent_encoder.layers[i]

          # self attention
          _a     =  layer.attention.self
          assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )

          _a     =  dataavec_layer.self_attn.q_proj.weight
          _a     =  dataavec_layer.self_attn.q_proj.bias
          _a     =  dataavec_layer.self_attn.k_proj.weight
          _a     =  dataavec_layer.self_attn.k_proj.bias
          _a     =  dataavec_layer.self_attn.v_proj.weight
          _a     =  dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          _a     =  layer.attention.output
          assert (
              self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
          ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
          _a     =  dataavec_layer.self_attn.out_proj.weight
          _a     =  dataavec_layer.self_attn.out_proj.bias
          _a     =  dataavec_layer.self_attn_layer_norm.weight
          _a     =  dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _a     =  layer.intermediate
          assert (
              intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias

          # output
          _a     =  layer.output
          assert (
              bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias
          _a     =  dataavec_layer.final_layer_norm.weight
          _a     =  dataavec_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].dense.weight
          _a     =  dataavec.model.classification_heads['''mnli'''].dense.bias
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          _a     =  dataavec_model.encoder.lm_head.dense.weight
          _a     =  dataavec_model.encoder.lm_head.dense.bias
          _a     =  dataavec_model.encoder.lm_head.layer_norm.weight
          _a     =  dataavec_model.encoder.lm_head.layer_norm.bias
          _a     =  dataavec_model.encoder.lm_head.weight
          _a     =  dataavec_model.encoder.lm_head.bias

     # Let's check that we get the same results.
     _a     =  dataavec.encode(__A).unsqueeze(0)  # batch of size 1

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

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


if __name__ == "__main__":
  lowercase_			=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--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."
  )
  lowercase_			=  parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



