'''simple docstring'''

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__a: Dict          =   logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class 			UpperCAmelCase  (						SCREAMING_SNAKE_CASE__					):





       '''simple docstring'''



       def __init__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  List[str]:
        super().__init__(*__lowerCAmelCase	,   **__lowerCAmelCase )
        requires_backends(self	,   '''vision''' )
        self.check_model_type(
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None )  ->  str:
        lowercase__				:    Tuple  =					{}
        lowercase__				:    str  =					{}

        if prompt is not None:
         lowercase__				:    Union[str, Any]  =					prompt

        if generate_kwargs is not None:
         lowercase__				:    Optional[Any]  =					generate_kwargs
        if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
          lowercase__				:    List[str]  =					{}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
          raise ValueError(
              '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
              ''' please use only one''' )
         lowercase__				:    Union[str, Any]  =					max_new_tokens
        return preprocess_params, forward_kwargs, {}

       def __call__(						self	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  Optional[Any]:
        return super().__call__(__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  Tuple:
        lowercase__				:    str  =					load_image(__lowerCAmelCase )

        if prompt is not None:
         if not isinstance(__lowerCAmelCase	,   __lowerCAmelCase ):
          raise ValueError(
              F"""Received an invalid text input, got - {type(__lowerCAmelCase )} - but expected a single string. """
              '''Note also that one single text can be provided for conditional image to text generation.''' )

         lowercase__				:    List[Any]  =					self.model.config.model_type

         if model_type == "git":
          lowercase__				:    Dict  =					self.image_processor(images=__lowerCAmelCase	,   return_tensors=self.framework )
          lowercase__				:    Tuple  =					self.tokenizer(text=__lowerCAmelCase	,   add_special_tokens=__lowerCAmelCase ).input_ids
          lowercase__				:    Any  =					[self.tokenizer.cls_token_id] + input_ids
          lowercase__				:    Optional[int]  =					torch.tensor(__lowerCAmelCase ).unsqueeze(0 )
          model_inputs.update({'''input_ids''': input_ids} )

         elif model_type == "pix2struct":
          lowercase__				:    Optional[Any]  =					self.image_processor(images=__lowerCAmelCase	,   header_text=__lowerCAmelCase	,   return_tensors=self.framework )

         elif model_type != "vision-encoder-decoder":
          # vision-encoder-decoder does not support conditional generation
          lowercase__				:    Dict  =					self.image_processor(images=__lowerCAmelCase	,   return_tensors=self.framework )
          lowercase__				:    Optional[int]  =					self.tokenizer(__lowerCAmelCase	,   return_tensors=self.framework )
          model_inputs.update(__lowerCAmelCase )

         else:
          raise ValueError(F"""Model type {model_type} does not support conditional text generation""" )

        else:
         lowercase__				:    Union[str, Any]  =					self.image_processor(images=__lowerCAmelCase	,   return_tensors=self.framework )

        if self.model.config.model_type == "git" and prompt is None:
         lowercase__				:    int  =					None

        return model_inputs

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  int:
        # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
        # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
        if (
            "input_ids" in model_inputs
            and isinstance(model_inputs['''input_ids''']	,   __lowerCAmelCase )
            and all(x is None for x in model_inputs['''input_ids'''] )
        ):
         lowercase__				:    List[str]  =					None

        if generate_kwargs is None:
         lowercase__				:    Optional[Any]  =					{}
        # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
        #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
        #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
        #  in the `_prepare_model_inputs` method.
        lowercase__				:    Any  =					model_inputs.pop(self.model.main_input_name )
        lowercase__				:    str  =					self.model.generate(__lowerCAmelCase	,   **__lowerCAmelCase	,   **__lowerCAmelCase )
        return model_outputs


       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Optional[Any]:
        lowercase__				:    Tuple  =					[]
        for output_ids in model_outputs:
         lowercase__				:    List[str]  =					{
             '''generated_text''': self.tokenizer.decode(
                 __lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   )
         }
         records.append(__lowerCAmelCase )
        return records


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,
      )


