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

from packaging import version


if TYPE_CHECKING:
      from ... import PreTrainedTokenizer, TensorType

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


A   :     Union[str, Any]       =				logging.get_logger(__name__)

A   :     Optional[int]       =				{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''bloom'''
  lowerCamelCase__   =  ['''past_key_values''']
  lowerCamelCase__   =  {
      '''num_hidden_layers''': '''n_layer''',
      '''num_attention_heads''': '''n_head''',
  }







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Tuple=250_880     ,   __magic_name__       :				Union[str, Any]=64     ,   __magic_name__       :				List[str]=2     ,   __magic_name__       :				Union[str, Any]=8     ,   __magic_name__       :				Optional[Any]=1e-5     ,   __magic_name__       :				List[Any]=0.02     ,   __magic_name__       :				Any=True     ,   __magic_name__       :				Dict=1     ,   __magic_name__       :				Optional[int]=2     ,   __magic_name__       :				Optional[Any]=False     ,   __magic_name__       :				Dict=0.0     ,   __magic_name__       :				Optional[Any]=0.0     ,   __magic_name__       :				Union[str, Any]=1     ,   __magic_name__       :				Tuple=False     ,   **__magic_name__       :				str     ,   )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     vocab_size
        # Backward compatibility with n_embed kwarg
        SCREAMING_SNAKE_CASE_      =     kwargs.pop("n_embed"     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     hidden_size if n_embed is None else n_embed
        SCREAMING_SNAKE_CASE_      =     n_layer
        SCREAMING_SNAKE_CASE_      =     n_head
        SCREAMING_SNAKE_CASE_      =     layer_norm_epsilon
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     use_cache
        SCREAMING_SNAKE_CASE_      =     pretraining_tp
        SCREAMING_SNAKE_CASE_      =     apply_residual_connection_post_layernorm
        SCREAMING_SNAKE_CASE_      =     hidden_dropout
        SCREAMING_SNAKE_CASE_      =     attention_dropout

        SCREAMING_SNAKE_CASE_      =     bos_token_id
        SCREAMING_SNAKE_CASE_      =     eos_token_id
        SCREAMING_SNAKE_CASE_      =     slow_but_exact

        super().__init__(bos_token_id=__magic_name__     ,   eos_token_id=__magic_name__     ,   **__magic_name__      )



class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  version.parse('''1.12'''    )







  def __init__(							self       :				Union[str, Any]     ,   __magic_name__       :				PretrainedConfig     ,   __magic_name__       :				str = "default"     ,   __magic_name__       :				List[PatchingSpec] = None     ,   __magic_name__       :				bool = False     ,   )     ->		List[Any]:
        super().__init__(__magic_name__     ,   task=__magic_name__     ,   patching_specs=__magic_name__     ,   use_past=__magic_name__      )
        if not getattr(self._config     ,   "pad_token_id"     ,   __magic_name__      ):
              # TODO: how to do that better?
              SCREAMING_SNAKE_CASE_      =     0







  @property
  def       __A   (							self       :				List[Any]      )     ->		Mapping[str, Mapping[int, str]]:
        SCREAMING_SNAKE_CASE_      =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}      )
        if self.use_past:
              # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
              self.fill_with_past_key_values_(__magic_name__     ,   direction="inputs"     ,   inverted_values_shape=__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     {0: "batch", 1: "past_sequence + sequence"}
        else:
              SCREAMING_SNAKE_CASE_      =     {0: "batch", 1: "sequence"}

        return common_inputs







  @property
  def       __A   (							self       :				Optional[int]      )     ->		int:
        return self._config.n_layer







  @property
  def       __A   (							self       :				Optional[int]      )     ->		int:
        return self._config.n_head







  @property
  def       __A   (							self       :				Optional[int]      )     ->		float:
        return 1e-3







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				"PreTrainedTokenizer"     ,   __magic_name__       :				int = -1     ,   __magic_name__       :				int = -1     ,   __magic_name__       :				bool = False     ,   __magic_name__       :				Optional["TensorType"] = None     ,   )     ->		Mapping[str, Any]:
        SCREAMING_SNAKE_CASE_      =     super(__magic_name__     ,   self      ).generate_dummy_inputs(
            __magic_name__     ,   batch_size=__magic_name__     ,   seq_length=__magic_name__     ,   is_pair=__magic_name__     ,   framework=__magic_name__      )

        # We need to order the input in the way they appears in the forward()
        SCREAMING_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

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

        SCREAMING_SNAKE_CASE_      =     common_inputs["attention_mask"]
        if self.use_past:
              SCREAMING_SNAKE_CASE_      =     ordered_inputs["attention_mask"].dtype
              SCREAMING_SNAKE_CASE_      =     torch.cat(
                  [ordered_inputs["attention_mask"], torch.ones(__magic_name__     ,   __magic_name__     ,   dtype=__magic_name__      )]     ,   dim=1      )

        return ordered_inputs







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        return 13



import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					encoder="bert"   ,					max_pos=5_1_2   ,					enc_layers=6   ,					enc_hidden_size=5_1_2   ,					enc_heads=8   ,					enc_ff_size=5_1_2   ,					enc_dropout=0.2   ,					dec_layers=6   ,					dec_hidden_size=7_6_8   ,					dec_heads=8   ,					dec_ff_size=2_0_4_8   ,					dec_dropout=0.2   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     BertAbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" ) )
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info("convert the model" )
      new_model.bert.load_state_dict(original.bert.state_dict() )
      new_model.decoder.load_state_dict(original.decoder.state_dict() )
      new_model.generator.load_state_dict(original.generator.state_dict() )

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info("Make sure that the models' outputs are identical" )
      SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("bert-base-uncased" )

      # prepare the model inputs
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample éàalj'-." )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample 3 éàalj'-." )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0

      # forward pass
      SCREAMING_SNAKE_CASE_      =     encoder_input_ids
      SCREAMING_SNAKE_CASE_      =     decoder_input_ids
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      SCREAMING_SNAKE_CASE_      =     original(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     original.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )
      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )

      SCREAMING_SNAKE_CASE_      =     torch.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3 )
      if are_identical:
            logging.info("all weights are equal up to 1e-3" )
      else:
            raise ValueError("the weights are different. The new model is likely different from the original one." )

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info("saving the model's state dictionary" )
      torch.save(
          new_model.state_dict()   ,					"./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )


if __name__ == "__main__":
      A   :     Any       =				argparse.ArgumentParser()
      parser.add_argument(
          "--bertabs_checkpoint_path",
          default=None,
          type=str,
          required=True,
          help="Path the official PyTorch dump.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the output PyTorch model.",
      )
      A   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


