

import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[Any]  =    torch.load(snake_case_ ,   map_location='cpu'   )
    if "model" in sd.keys():
        _lowerCAmelCase       :   List[Any]  =    torch.load(snake_case_ ,   map_location='cpu'   )["model"]

    # pop unnecessary weights
    _lowerCAmelCase       :   List[str]  =    [
        "decoder.version",
        "decoder.output_projection.weight",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(snake_case_   )

    _lowerCAmelCase       :   Union[str, Any]  =    {
        "decoder.project_in_dim.weight": "decoder.project_in.weight",
        "decoder.project_out_dim.weight": "decoder.project_out.weight",
        "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
        "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            _lowerCAmelCase       :   Optional[int]  =    sd.pop(snake_case_   )

    _lowerCAmelCase       :   Any  =    list(sd.keys()   )
    for key in keys:
        if ".qkv_proj." in key:
            _lowerCAmelCase       :   int  =    sd[key]
            # We split QKV in separate Q,K,V

            _lowerCAmelCase       :   List[Any]  =    key.replace('.qkv_proj.' ,   '.q_proj.'   )
            _lowerCAmelCase       :   Any  =    key.replace('.qkv_proj.' ,   '.k_proj.'   )
            _lowerCAmelCase       :   Optional[int]  =    key.replace('.qkv_proj.' ,   '.v_proj.'   )

            _lowerCAmelCase       :   int  =    value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            _lowerCAmelCase       :   Dict  =    torch.split(snake_case_ ,   depth // 3 ,   dim=0   )

            _lowerCAmelCase       :   Union[str, Any]  =    q
            _lowerCAmelCase       :   Optional[Any]  =    k
            _lowerCAmelCase       :   Union[str, Any]  =    v
            del sd[key]

    return sd

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    load_checkpoint(snake_case_   )

    if config is not None:
        _lowerCAmelCase       :   Tuple  =    OPTConfig.from_pretrained(snake_case_   )
    else:
        _lowerCAmelCase       :   Tuple  =    OPTConfig()

    _lowerCAmelCase       :   Union[str, Any]  =    OPTModel(snake_case_   ).half().eval()
    model.load_state_dict(snake_case_   )

    # Check results
    Path(snake_case_   ).mkdir(exist_ok=snake_case_   )
    model.save_pretrained(snake_case_   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--fairseq_path""",
     type=str,
     help=(
         """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
         """ https://huggingface.co/models?other=opt_metasq"""
     ),
 )
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
 _lowerCAmelCase						=		parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








"""simple docstring"""






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 128    ,_A = 256    ,_A = 2_0_0_0.0    ,_A = 768    ,_A = 12    ,_A = 12    ,_A = 64    ,_A = 2048    ,_A = 0.1    ,):
      '''simple docstring'''

      super().__init__()

      _lowerCAmelCase       :   int  =    nn.Sequential(
          nn.Linear(_A    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,nn.Linear(d_model * 4    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,)

      _lowerCAmelCase       :   Any  =    nn.Embedding(_A    ,_A      )
      _lowerCAmelCase       :   Tuple  =    False

      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

      _lowerCAmelCase       :   int  =    nn.Dropout(p=_A      )

      _lowerCAmelCase       :   int  =    nn.ModuleList()
      for lyr_num in range(_A      ):
          # FiLM conditional T5 decoder
          _lowerCAmelCase       :   Any  =    DecoderLayer(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,d_ff=_A    ,dropout_rate=_A      )
          self.decoders.append(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    TaLayerNorm(_A      )

      _lowerCAmelCase       :   List[str]  =    nn.Dropout(p=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    torch.mul(query_input.unsqueeze(-1      )    ,key_input.unsqueeze(-2      )      )
      return mask.unsqueeze(-3      )







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      _lowerCAmelCase       :   Any  =    get_timestep_embedding(
          decoder_noise_time * self.config.max_decoder_noise_time    ,embedding_dim=self.config.d_model    ,max_period=self.config.max_decoder_noise_time    ,).to(dtype=self.dtype      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.conditioning_emb(_A      ).unsqueeze(1      )

      assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

      _lowerCAmelCase       :   str  =    decoder_input_tokens.shape[1]

      # If we want to use relative positions for audio context, we can just offset
      # this sequence by the length of encodings_and_masks.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.broadcast_to(
          torch.arange(_A    ,device=decoder_input_tokens.device      )    ,(batch, seq_length)    ,)

      _lowerCAmelCase       :   Any  =    self.position_encoding(_A      )

      _lowerCAmelCase       :   str  =    self.continuous_inputs_projection(_A      )
      inputs += position_encodings
      _lowerCAmelCase       :   int  =    self.dropout(_A      )

      # decoder: No padding present.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.ones(
          decoder_input_tokens.shape[:2]    ,device=decoder_input_tokens.device    ,dtype=inputs.dtype      )

      # Translate encoding masks to encoder-decoder masks.
      _lowerCAmelCase       :   Optional[Any]  =    [(x, self.encoder_decoder_mask(_A    ,_A      )) for x, y in encodings_and_masks]

      # cross attend style: concat encodings
      _lowerCAmelCase       :   Dict  =    torch.cat([x[0] for x in encodings_and_encdec_masks]    ,dim=1      )
      _lowerCAmelCase       :   Tuple  =    torch.cat([x[1] for x in encodings_and_encdec_masks]    ,dim=-1      )

      for lyr in self.decoders:
          _lowerCAmelCase       :   Tuple  =    lyr(
              _A    ,conditioning_emb=_A    ,encoder_hidden_states=_A    ,encoder_attention_mask=_A    ,)[0]

      _lowerCAmelCase       :   Any  =    self.decoder_norm(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.post_dropout(_A      )

      _lowerCAmelCase       :   int  =    self.spec_out(_A      )
      return spec_out







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    nn.ModuleList()

      # cond self attention: layer 0
      self.layer.append(
          TaLayerSelfAttentionCond(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A      )      )

      # cross attention: layer 1
      self.layer.append(
          TaLayerCrossAttention(
              d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A    ,)      )

      # Film Cond MLP + dropout: last layer
      self.layer.append(
          TaLayerFFCond(d_model=_A    ,d_ff=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A      )      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.layer[0](
          _A    ,conditioning_emb=_A    ,attention_mask=_A    ,)

      if encoder_hidden_states is not None:
          _lowerCAmelCase       :   Any  =    torch.where(encoder_attention_mask > 0    ,0    ,-1E10      ).to(
              encoder_hidden_states.dtype      )

          _lowerCAmelCase       :   str  =    self.layer[1](
              _A    ,key_value_states=_A    ,attention_mask=_A    ,)

      # Apply Film Conditional Feed Forward layer
      _lowerCAmelCase       :   Optional[Any]  =    self.layer[-1](_A    ,_A      )

      return (hidden_states,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    TaLayerNorm(_A      )
      _lowerCAmelCase       :   Any  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Dict  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )

      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.FiLMLayer(_A    ,_A      )

      # Self-attention block
      _lowerCAmelCase       :   Union[str, Any]  =    self.attention(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    hidden_states + self.dropout(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Optional[int]  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.layer_norm(_A      )
      _lowerCAmelCase       :   str  =    self.attention(
          _A    ,encoder_hidden_states=_A    ,attention_mask=attention_mask.squeeze(1      )    ,)
      _lowerCAmelCase       :   Any  =    hidden_states + self.dropout(_A      )
      return layer_output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[int]  =    TaDenseGatedActDense(d_model=_A    ,d_ff=_A    ,dropout_rate=_A      )
      _lowerCAmelCase       :   Tuple  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Any  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )
      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.film(_A    ,_A      )

      _lowerCAmelCase       :   str  =    self.DenseReluDense(_A      )
      _lowerCAmelCase       :   Tuple  =    hidden_states + self.dropout(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Any  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )
      _lowerCAmelCase       :   int  =    NewGELUActivation()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.act(self.wi_a(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    self.wi_a(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_gelu * hidden_linear
      _lowerCAmelCase       :   Dict  =    self.dropout(_A      )

      _lowerCAmelCase       :   Dict  =    self.wo(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Parameter(torch.ones(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    eps







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(torch.floataa      ).pow(2      ).mean(-1    ,keepdim=_A      )
      _lowerCAmelCase       :   List[Any]  =    hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

      # convert into half-precision if necessary
      if self.weight.dtype in [torch.floataa, torch.bfloataa]:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(self.weight.dtype      )

      return self.weight * hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.0_4_4_7_1_5 * torch.pow(_A    ,3.0      ))      ))







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,out_features * 2    ,bias=_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.scale_bias(_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    torch.chunk(_A    ,2    ,-1      )
      _lowerCAmelCase       :   List[Any]  =    x * (1 + scale) + shift
      return x





