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

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


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class        UpperCAmelCase_					(				_a     ):
    UpperCAmelCase_      :							Optional[int]    			=				'mbart'
    UpperCAmelCase_      :							Union[str, Any]    			=				['past_key_values']
    UpperCAmelCase_      :							str    			=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




    def __init__(       self	: List[Any]   ,   lowerCAmelCase	: Union[str, Any]=50265   ,   lowerCAmelCase	: List[str]=1024   ,   lowerCAmelCase	: Optional[Any]=12   ,   lowerCAmelCase	: int=4096   ,   lowerCAmelCase	: Optional[int]=16   ,   lowerCAmelCase	: List[Any]=12   ,   lowerCAmelCase	: Optional[Any]=4096   ,   lowerCAmelCase	: Optional[Any]=16   ,   lowerCAmelCase	: List[Any]=0.0   ,   lowerCAmelCase	: Dict=0.0   ,   lowerCAmelCase	: Optional[Any]=True   ,   lowerCAmelCase	: Dict=True   ,   lowerCAmelCase	: Union[str, Any]="gelu"   ,   lowerCAmelCase	: Dict=1024   ,   lowerCAmelCase	: Union[str, Any]=0.1   ,   lowerCAmelCase	: Dict=0.0   ,   lowerCAmelCase	: List[str]=0.0   ,   lowerCAmelCase	: Optional[int]=0.02   ,   lowerCAmelCase	: Tuple=0.0   ,   lowerCAmelCase	: int=False   ,   lowerCAmelCase	: Union[str, Any]=1   ,   lowerCAmelCase	: Union[str, Any]=0   ,   lowerCAmelCase	: List[Any]=2   ,   lowerCAmelCase	: Optional[Any]=2   ,   **lowerCAmelCase	: Optional[int]   ,   )						->			Any:
      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__  		=  classifier_dropout
      a__  		=  use_cache
      a__  		=  encoder_layers
      a__  		=  scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=lowerCAmelCase   ,   bos_token_id=lowerCAmelCase   ,   eos_token_id=lowerCAmelCase   ,   is_encoder_decoder=lowerCAmelCase   ,   forced_eos_token_id=lowerCAmelCase   ,   **lowerCAmelCase   ,   )







class        UpperCAmelCase_					(				_a     ):




    @property
    def     __lowerCamelCase			(       self	: List[str] )						->			Mapping[str, Mapping[int, str]]:
      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_(lowerCAmelCase   ,   direction="inputs" )
      elif self.task == "causal-lm":
        # TODO: figure this case out.
        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(lowerCAmelCase ):
            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
    def     __lowerCamelCase			(       self	: List[Any] )						->			Mapping[str, Mapping[int, str]]:
      if self.task in ["default", "seq2seq-lm"]:
        a__  		=  super().outputs
      else:
        a__  		=  super(lowerCAmelCase   ,   self ).outputs
        if self.use_past:
          a__		,a__  		=  self.num_layers
          for i in range(lowerCAmelCase ):
            a__  		=  {0: "batch", 2: "past_sequence + sequence"}
            a__  		=  {0: "batch", 2: "past_sequence + sequence"}
      return common_outputs




    def     __lowerCamelCase			(       self	: List[str]   ,   lowerCAmelCase	: PreTrainedTokenizer   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: Optional[TensorType] = None   ,   )						->			Mapping[str, Any]:
      a__  		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )

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

      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 + 3
        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(lowerCAmelCase   ,   lowerCAmelCase )]   ,   dim=1 )

        a__  		=  []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        a__		,a__  		=  self.num_layers
        a__  		=  min(lowerCAmelCase   ,   lowerCAmelCase )
        a__  		=  max(lowerCAmelCase   ,   lowerCAmelCase ) - min_num_layers
        a__  		=  "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(lowerCAmelCase ):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(lowerCAmelCase ),
                  torch.zeros(lowerCAmelCase ),
                  torch.zeros(lowerCAmelCase ),
                  torch.zeros(lowerCAmelCase ),
              ) )
        # TODO: test this.
        a__  		=  encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(lowerCAmelCase   ,   lowerCAmelCase ):
          common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase ), torch.zeros(lowerCAmelCase )) )
      return common_inputs




    def     __lowerCamelCase			(       self	: int   ,   lowerCAmelCase	: PreTrainedTokenizer   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: Optional[TensorType] = None   ,   )						->			Mapping[str, Any]:
      a__  		=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )

      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
        # Not using the same length for past_key_values
        a__  		=  seqlen + 2
        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(lowerCAmelCase   ,   lowerCAmelCase   ,   dtype=lowerCAmelCase )]   ,   dim=1 )
        a__  		=  [
            (torch.zeros(lowerCAmelCase ), torch.zeros(lowerCAmelCase )) for _ in range(lowerCAmelCase )
        ]
      return common_inputs




    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: PreTrainedTokenizer   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: Optional[TensorType] = None   ,   )						->			Mapping[str, Any]:
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      a__  		=  compute_effective_axis_dimension(
          lowerCAmelCase   ,   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(lowerCAmelCase )
      a__  		=  compute_effective_axis_dimension(
          lowerCAmelCase   ,   fixed_dimension=OnnxConfig.default_fixed_sequence   ,   num_token_to_add=lowerCAmelCase )

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




    def     __lowerCamelCase			(       self	: Optional[Any]   ,   lowerCAmelCase	: PreTrainedTokenizer   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: bool = False   ,   lowerCAmelCase	: Optional[TensorType] = None   ,   )						->			Mapping[str, Any]:
      if self.task in ["default", "seq2seq-lm"]:
        a__  		=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            lowerCAmelCase   ,   batch_size=lowerCAmelCase   ,   seq_length=lowerCAmelCase   ,   is_pair=lowerCAmelCase   ,   framework=lowerCAmelCase )

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

      return common_inputs




    def     __lowerCamelCase			(       self	: Optional[Any]   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: Optional[int] )						->			List[str]:
      if self.task in ["default", "seq2seq-lm"]:
        a__  		=  super()._flatten_past_key_values_(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )
      else:
        a__  		=  super(lowerCAmelCase   ,   self )._flatten_past_key_values_(
            lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )


import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_SCREAMING_SNAKE_CASE     =     get_logger(__name__)
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  if text.startswith(lowerCamelCase__       ):
    return text[len(lowerCamelCase__       ) :]
  return text  # or whatever
def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  return {remove_prefix(lowerCamelCase__			,					"model."       ): v for k, v in sd.items()}
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  a__  		=  {}
  for k in state_dicts[0].keys():
    a__  		=  [sd[k] for sd in state_dicts]
    a__  		=  sum(lowerCamelCase__       ) / len(lowerCamelCase__       )
    assert isinstance(lowerCamelCase__			,					torch.Tensor       )
    a__  		=  new_t
  return new_sd
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->None:

  '''simple docstring'''




  a__  		=  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )
  if os.path.isfile(lowerCamelCase__       ):
    a__  		=  [pl_ckpt_path]
  else:
    assert os.path.isdir(lowerCamelCase__       )
    a__  		=  list(Path(lowerCamelCase__       ).glob("*.ckpt"       )       )
    assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

  if len(lowerCamelCase__       ) > 1:
    logger.info(f"averaging the weights of {ckpt_files}"       )

  a__  		=  [sanitize(torch.load(lowerCamelCase__			,					map_location="cpu"       )["state_dict"]       ) for x in ckpt_files]
  a__  		=  average_state_dicts(lowerCamelCase__       )

  a__		,a__  		=  hf_model.load_state_dict(lowerCamelCase__			,					strict=lowerCamelCase__       )
  assert not missing, f"missing keys: {missing}"
  hf_model.save_pretrained(lowerCamelCase__       )
  try:
    a__  		=  AutoTokenizer.from_pretrained(lowerCamelCase__       )
    tok.save_pretrained(lowerCamelCase__       )
  except Exception:
    pass
    # dont copy tokenizer if cant


if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)

