


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      lowercase			(				A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		str      							=			[
      "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      lowercase			(				A_       )->      Optional[Any]:
  '''simple docstring'''







  a						:		Tuple      							=			list(s_dict.keys()       )
  for key in keys:
    if "transformer_layers" in key:
      a						:		Union[str, Any]      							=			s_dict.pop(A_       )
    elif "subsample" in key:
      a						:		Optional[Any]      							=			s_dict.pop(A_       )


def      lowercase			(				A_       )->      Optional[int]:
  '''simple docstring'''







  a							,		a						:		int      							=			emb.weight.shape
  a						:		List[str]      							=			nn.Linear(A_  ,   A_  ,   bias=A_       )
  a						:		List[Any]      							=			emb.weight.data
  return lin_layer


def      lowercase			(				A_  ,   A_       )->      Dict:
  '''simple docstring'''







  a						:		Optional[Any]      							=			torch.load(A_  ,   map_location="cpu"       )
  a						:		Union[str, Any]      							=			mam_aaa["args"]
  a						:		Tuple      							=			mam_aaa["model"]
  a						:		Tuple      							=			state_dict["decoder.output_projection.weight"]

  remove_ignore_keys_(A_       )
  rename_keys(A_       )

  a						:		str      							=			state_dict["decoder.embed_tokens.weight"].shape[0]

  a						:		List[Any]      							=			args.share_decoder_input_output_embed

  a						:		str      							=			[int(A_       ) for i in args.conv_kernel_sizes.split(","       )]
  a						:		Optional[int]      							=			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_  ,   )

  a						:		Dict      							=			SpeechaTextForConditionalGeneration(A_       )
  a							,		a						:		List[Any]      							=			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:
    a						:		Union[str, Any]      							=			make_linear_from_emb(model.model.decoder.embed_tokens       )
  else:
    a						:		Union[str, Any]      							=			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)













"""simple docstring"""





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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'longformer'





      def __init__(       self      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 1      ,						lowercase = 0      ,						lowercase = 2      ,						lowercase = 30_522      ,						lowercase = 768      ,						lowercase = 12      ,						lowercase = 12      ,						lowercase = 3_072      ,						lowercase = "gelu"      ,						lowercase = 0.1      ,						lowercase = 0.1      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 0.02      ,						lowercase = 1e-12      ,						lowercase = False      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(pad_token_id=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       attention_window
            lowerCAmelCase  						=       sep_token_id
            lowerCAmelCase  						=       bos_token_id
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       layer_norm_eps
            lowerCAmelCase  						=       onnx_export







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase = "default"      ,						lowercase = None )		->     Tuple:
            super().__init__(lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       True





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  lowerCAmelCase  						=       {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""global_attention_mask""", dynamic_axis),
                ] )





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            lowerCAmelCase  						=       super().outputs
            if self.task == "default":
                  lowerCAmelCase  						=       {0: """batch"""}
            return outputs





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-4





      @property
      def     _snake_case			(       self )		->     int:
            # needs to be >= 14 to support tril operator
            return max(super().default_onnx_opset      ,						14 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = -1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       super().generate_dummy_inputs(
                preprocessor=lowercase      ,						batch_size=lowercase      ,						seq_length=lowercase      ,						is_pair=lowercase      ,						framework=lowercase )
            import torch

            # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
            # makes the export fail randomly
            lowerCAmelCase  						=       torch.zeros_like(inputs["""input_ids"""] )
            # make every second token global
            lowerCAmelCase  						=       1

            return inputs






