





'''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 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @register_to_config
     def __init__(		self		,      _UpperCAmelCase = 128		,      _UpperCAmelCase = 256		,      _UpperCAmelCase = 2000.0		,      _UpperCAmelCase = 768		,      _UpperCAmelCase = 12		,      _UpperCAmelCase = 12		,      _UpperCAmelCase = 64		,      _UpperCAmelCase = 2048		,      _UpperCAmelCase = 0.1		,      ):

          '''simple docstring'''






          super().__init__()

          __A		:					Tuple						=			nn.Sequential(
              nn.Linear(_UpperCAmelCase		,      d_model * 4		,      bias=_UpperCAmelCase)		,      nn.SiLU()		,      nn.Linear(d_model * 4		,      d_model * 4		,      bias=_UpperCAmelCase)		,      nn.SiLU()		,      )

          __A		:					Optional[Any]						=			nn.Embedding(_UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					Dict						=			False

          __A		:					Any						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase		,      bias=_UpperCAmelCase)

          __A		:					Dict						=			nn.Dropout(p=_UpperCAmelCase)

          __A		:					Dict						=			nn.ModuleList()
          for lyr_num in range(_UpperCAmelCase):
               # FiLM conditional T5 decoder
               __A		:					str						=			DecoderLayer(d_model=_UpperCAmelCase		,      d_kv=_UpperCAmelCase		,      num_heads=_UpperCAmelCase		,      d_ff=_UpperCAmelCase		,      dropout_rate=_UpperCAmelCase)
               self.decoders.append(_UpperCAmelCase)

          __A		:					Dict						=			TaLayerNorm(_UpperCAmelCase)

          __A		:					Optional[int]						=			nn.Dropout(p=_UpperCAmelCase)
          __A		:					int						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase		,      bias=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A					,__A					,__A		:					Tuple						=			decoder_input_tokens.shape
          assert decoder_noise_time.shape == (batch,)

          # decoder_noise_time is in [0, 1), so rescale to expected timing range.
          __A		:					Union[str, 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)

          __A		:					List[str]						=			self.conditioning_emb(_UpperCAmelCase).unsqueeze(1)

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

          __A		:					Union[str, Any]						=			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.
          __A		:					List[Any]						=			torch.broadcast_to(
              torch.arange(_UpperCAmelCase		,      device=decoder_input_tokens.device)		,      (batch, seq_length)		,      )

          __A		:					str						=			self.position_encoding(_UpperCAmelCase)

          __A		:					List[str]						=			self.continuous_inputs_projection(_UpperCAmelCase)
          inputs += position_encodings
          __A		:					str						=			self.dropout(_UpperCAmelCase)

          # decoder: No padding present.
          __A		:					int						=			torch.ones(
              decoder_input_tokens.shape[:2]		,      device=decoder_input_tokens.device		,      dtype=inputs.dtype)

          # Translate encoding masks to encoder-decoder masks.
          __A		:					str						=			[(x, self.encoder_decoder_mask(_UpperCAmelCase		,      _UpperCAmelCase)) for x, y in encodings_and_masks]

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

          for lyr in self.decoders:
               __A		:					List[str]						=			lyr(
                   _UpperCAmelCase		,      conditioning_emb=_UpperCAmelCase		,      encoder_hidden_states=_UpperCAmelCase		,      encoder_attention_mask=_UpperCAmelCase		,      )[0]

          __A		:					Optional[Any]						=			self.decoder_norm(_UpperCAmelCase)
          __A		:					Optional[int]						=			self.post_dropout(_UpperCAmelCase)

          __A		:					Optional[Any]						=			self.spec_out(_UpperCAmelCase)
          return spec_out




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=1e-6):

          '''simple docstring'''






          super().__init__()
          __A		:					str						=			nn.ModuleList()

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

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

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.layer[0](
              _UpperCAmelCase		,      conditioning_emb=_UpperCAmelCase		,      attention_mask=_UpperCAmelCase		,      )

          if encoder_hidden_states is not None:
               __A		:					Optional[Any]						=			torch.where(encoder_attention_mask > 0		,      0		,      -1e1_0).to(
                   encoder_hidden_states.dtype)

               __A		:					Dict						=			self.layer[1](
                   _UpperCAmelCase		,      key_value_states=_UpperCAmelCase		,      attention_mask=_UpperCAmelCase		,      )

          # Apply Film Conditional Feed Forward layer
          __A		:					List[Any]						=			self.layer[-1](_UpperCAmelCase		,      _UpperCAmelCase)

          return (hidden_states,)




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A		:					int						=			TaLayerNorm(_UpperCAmelCase)
          __A		:					int						=			TaFiLMLayer(in_features=d_model * 4		,      out_features=_UpperCAmelCase)
          __A		:					Optional[Any]						=			Attention(query_dim=_UpperCAmelCase		,      heads=_UpperCAmelCase		,      dim_head=_UpperCAmelCase		,      out_bias=_UpperCAmelCase		,      scale_qk=_UpperCAmelCase)
          __A		:					Dict						=			nn.Dropout(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      ):

          '''simple docstring'''






          __A		:					List[str]						=			self.layer_norm(_UpperCAmelCase)

          if conditioning_emb is not None:
               __A		:					List[Any]						=			self.FiLMLayer(_UpperCAmelCase		,      _UpperCAmelCase)

          # Self-attention block
          __A		:					Any						=			self.attention(_UpperCAmelCase)

          __A		:					Any						=			hidden_states + self.dropout(_UpperCAmelCase)

          return hidden_states




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A		:					Any						=			Attention(query_dim=_UpperCAmelCase		,      heads=_UpperCAmelCase		,      dim_head=_UpperCAmelCase		,      out_bias=_UpperCAmelCase		,      scale_qk=_UpperCAmelCase)
          __A		:					List[str]						=			TaLayerNorm(_UpperCAmelCase		,      eps=_UpperCAmelCase)
          __A		:					List[Any]						=			nn.Dropout(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			self.layer_norm(_UpperCAmelCase)
          __A		:					str						=			self.attention(
              _UpperCAmelCase		,      encoder_hidden_states=_UpperCAmelCase		,      attention_mask=attention_mask.squeeze(1)		,      )
          __A		:					List[Any]						=			hidden_states + self.dropout(_UpperCAmelCase)
          return layer_output




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A		:					Any						=			TaDenseGatedActDense(d_model=_UpperCAmelCase		,      d_ff=_UpperCAmelCase		,      dropout_rate=_UpperCAmelCase)
          __A		:					List[Any]						=			TaFiLMLayer(in_features=d_model * 4		,      out_features=_UpperCAmelCase)
          __A		:					int						=			TaLayerNorm(_UpperCAmelCase		,      eps=_UpperCAmelCase)
          __A		:					Optional[int]						=			nn.Dropout(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=None):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.layer_norm(_UpperCAmelCase)
          if conditioning_emb is not None:
               __A		:					Optional[int]						=			self.film(_UpperCAmelCase		,      _UpperCAmelCase)

          __A		:					Optional[int]						=			self.DenseReluDense(_UpperCAmelCase)
          __A		:					Any						=			hidden_states + self.dropout(_UpperCAmelCase)
          return hidden_states




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A		:					List[str]						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase		,      bias=_UpperCAmelCase)
          __A		:					List[Any]						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase		,      bias=_UpperCAmelCase)
          __A		:					Optional[int]						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase		,      bias=_UpperCAmelCase)
          __A		:					Tuple						=			nn.Dropout(_UpperCAmelCase)
          __A		:					Tuple						=			NewGELUActivation()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			self.act(self.wi_a(_UpperCAmelCase))
          __A		:					List[Any]						=			self.wi_a(_UpperCAmelCase)
          __A		:					int						=			hidden_gelu * hidden_linear
          __A		:					List[Any]						=			self.dropout(_UpperCAmelCase)

          __A		:					int						=			self.wo(_UpperCAmelCase)
          return hidden_states




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=1e-6):

          '''simple docstring'''






          super().__init__()
          __A		:					Optional[int]						=			nn.Parameter(torch.ones(_UpperCAmelCase))
          __A		:					Optional[Any]						=			eps


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			hidden_states.to(torch.floataa).pow(2).mean(-1		,      keepdim=_UpperCAmelCase)
          __A		:					str						=			hidden_states * torch.rsqrt(variance + self.variance_epsilon)

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

          return self.weight * hidden_states




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(_UpperCAmelCase		,      3.0))))




class 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A		:					Tuple						=			nn.Linear(_UpperCAmelCase		,      out_features * 2		,      bias=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			self.scale_bias(_UpperCAmelCase)
          __A					,__A		:					str						=			torch.chunk(_UpperCAmelCase		,      2		,      -1)
          __A		:					Optional[Any]						=			x * (1 + scale) + shift
          return x






'''simple docstring'''




import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : str         =       [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

     if k.startswith('encoder'     ):
          __A		:					Any						=			k.replace('.attn'					,				'.self_attn'     )
          __A		:					Any						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          __A		:					Tuple						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'encoder_attn_layer_norm'     )
          __A		:					int						=			k.replace('norm3'					,				'final_layer_norm'     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					Optional[int]						=			[
         'model.encoder.layernorm_embedding.weight',
         'model.encoder.layernorm_embedding.bias',
         'model.decoder.layernorm_embedding.weight',
         'model.decoder.layernorm_embedding.bias',
     ]
     for k in keys:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[Any]     )	->	int:
     __A		:					List[str]						=			torch.load(__snake_case					,				map_location='cpu'     )
     __A		:					Tuple						=			model['model']
     __A		:					str						=			BlenderbotConfig.from_json_file(__snake_case     )
     __A		:					int						=			BlenderbotForConditionalGeneration(__snake_case     )
     __A		:					List[Any]						=			m.model.state_dict().keys()
     __A		:					Optional[int]						=			[]
     __A		:					Optional[int]						=			{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __A		:					Union[str, Any]						=			rename_state_dict_key(__snake_case     )
          if new_k not in valid_keys:
               failures.append([k, new_k]     )
          else:
               __A		:					Optional[Any]						=			v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__snake_case     )
     m.model.load_state_dict(__snake_case					,				strict=__snake_case     )
     m.half()
     m.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
  parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
  parser.add_argument(
      '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)