





'''simple docstring'''







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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowercase       						=			logging.get_logger(__name__)


_lowercase       						=			{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''codegen'''
     _UpperCAmelCase										=					{
         '''max_position_embeddings''': '''n_positions''',
         '''hidden_size''': '''n_embd''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }







     def __init__( self    ,   A__=5_04_00    ,   A__=20_48    ,   A__=20_48    ,   A__=40_96    ,   A__=28    ,   A__=16    ,   A__=64    ,   A__=None    ,   A__="gelu_new"    ,   A__=0.0    ,   A__=0.0    ,   A__=0.0    ,   A__=1e-5    ,   A__=0.0_2    ,   A__=True    ,   A__=5_02_56    ,   A__=5_02_56    ,   A__=False    ,   **A__    ,   ) ->	List[Any]:
      snake_case				=			vocab_size
      snake_case				=			n_ctx
      snake_case				=			n_positions
      snake_case				=			n_embd
      snake_case				=			n_layer
      snake_case				=			n_head
      snake_case				=			n_inner
      snake_case				=			rotary_dim
      snake_case				=			activation_function
      snake_case				=			resid_pdrop
      snake_case				=			embd_pdrop
      snake_case				=			attn_pdrop
      snake_case				=			layer_norm_epsilon
      snake_case				=			initializer_range
      snake_case				=			use_cache

      snake_case				=			bos_token_id
      snake_case				=			eos_token_id

      super().__init__(
          bos_token_id=A__    ,   eos_token_id=A__    ,   tie_word_embeddings=A__    ,   **A__    )




class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__ = "default"    ,   A__ = None    ,   A__ = False    ,   ) ->	str:
      super().__init__(A__    ,   task=A__    ,   patching_specs=A__    ,   use_past=A__    )
      if not getattr(self._config    ,   '''pad_token_id'''    ,   A__    ):
       # TODO: how to do that better?
       snake_case				=			0







     @property
     def     UpperCamelCase			( self    ) ->	Mapping[str, Mapping[int, str]]:
      snake_case				=			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}    )
      if self.use_past:
       self.fill_with_past_key_values_(A__    ,   direction='''inputs'''    )
       snake_case				=			{0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
       snake_case				=			{0: '''batch''', 1: '''sequence'''}

      return common_inputs







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







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







     def     UpperCamelCase			( self    ,   A__    ,   A__ = -1    ,   A__ = -1    ,   A__ = False    ,   A__ = None    ,   ) ->	Mapping[str, Any]:
      snake_case				=			super(A__    ,   self    ).generate_dummy_inputs(
          A__    ,   batch_size=A__    ,   seq_length=A__    ,   is_pair=A__    ,   framework=A__    )

      # We need to order the input in the way they appears in the forward()
      snake_case				=			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}    )

      # Need to add the past_keys
      if self.use_past:
       if not is_torch_available():
        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
       else:
        import torch

        snake_case				,		snake_case				=			common_inputs['''input_ids'''].shape
        # Not using the same length for past_key_values
        snake_case				=			seqlen + 2
        snake_case				=			(
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        snake_case				=			[
            (torch.zeros(A__    ), torch.zeros(A__    )) for _ in range(self.num_layers    )
        ]

      snake_case				=			common_inputs['''attention_mask''']
      if self.use_past:
       snake_case				=			ordered_inputs['''attention_mask'''].dtype
       snake_case				=			torch.cat(
           [ordered_inputs['''attention_mask'''], torch.ones(A__    ,   A__    ,   dtype=A__    )]    ,   dim=1    )

      return ordered_inputs







     @property
     def     UpperCamelCase			( self    ) ->	int:
      return 13








'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Dict:
 snake_case				=			[
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->int:
 snake_case				=			list(s_dict.keys()     )
 for key in keys:
  if "transformer_layers" in key:
   snake_case				=			s_dict.pop(a     )
  elif "subsample" in key:
   snake_case				=			s_dict.pop(a     )

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Optional[int]:
 snake_case				,		snake_case				=			emb.weight.shape
 snake_case				=			nn.Linear(a					,   a					,   bias=a     )
 snake_case				=			emb.weight.data
 return lin_layer

def    __UpperCamelCase	(	a    : Any					,   a    : Tuple     ) ->Tuple:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 snake_case				=			mam_aaa['''args''']
 snake_case				=			mam_aaa['''model''']
 snake_case				=			state_dict['''decoder.output_projection.weight''']

 remove_ignore_keys_(a     )
 rename_keys(a     )

 snake_case				=			state_dict['''decoder.embed_tokens.weight'''].shape[0]

 snake_case				=			args.share_decoder_input_output_embed

 snake_case				=			[int(a     ) for i in args.conv_kernel_sizes.split(''','''     )]
 snake_case				=			SpeechaTextConfig(
     vocab_size=a					,   max_source_positions=args.max_source_positions					,   max_target_positions=args.max_target_positions					,   encoder_layers=args.encoder_layers					,   decoder_layers=args.decoder_layers					,   encoder_attention_heads=args.encoder_attention_heads					,   decoder_attention_heads=args.decoder_attention_heads					,   encoder_ffn_dim=args.encoder_ffn_embed_dim					,   decoder_ffn_dim=args.decoder_ffn_embed_dim					,   d_model=args.encoder_embed_dim					,   dropout=args.dropout					,   attention_dropout=args.attention_dropout					,   activation_dropout=args.activation_dropout					,   activation_function='''relu'''					,   num_conv_layers=len(a     )					,   conv_channels=args.conv_channels					,   conv_kernel_sizes=a					,   input_feat_per_channel=args.input_feat_per_channel					,   input_channels=args.input_channels					,   tie_word_embeddings=a					,   num_beams=5					,   max_length=200					,   use_cache=a					,   decoder_start_token_id=2					,   early_stopping=a					,   )

 snake_case				=			SpeechaTextForConditionalGeneration(a     )
 snake_case				,		snake_case				=			model.model.load_state_dict(a					,   strict=a     )
 if len(a     ) > 0 and not set(a     ) <= {
     "encoder.embed_positions.weights",
     "decoder.embed_positions.weights",
 }:
  raise ValueError(
      '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
      f""" but all the following weights are missing {missing}"""     )

 if tie_embeds:
  snake_case				=			make_linear_from_emb(model.model.decoder.embed_tokens     )
 else:
  snake_case				=			lm_head_weights

 model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   _lowercase       						=			parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

