





'''simple docstring'''






from typing import Any, Dict, List, Union

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


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)


UpperCAmelCase_       :   List[Any]   =       Dict[str, Any]
UpperCAmelCase_       :   List[Any]   =       List[Prediction]

@add_end_docstrings(A_ )
class  UpperCAmelCase__						(       A_ ):



    def __init__(	self      :       int,*__A      :       List[Any],**__A      :       Optional[int]     ):
          super().__init__(*__A,**__A     )

          if self.framework == "tf":
                raise ValueError(f'The {self.__class__} is only available in PyTorch.'     )

          requires_backends(self,"vision"     )
          self.check_model_type(
              dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()     )     )



    def   lowerCamelCase_  (	self      :       List[str],**__A      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   {}
          if "threshold" in kwargs:
                _lowerCamelCase				:   Optional[int]								=   kwargs["""threshold"""]
          return {}, {}, postprocess_kwargs



    def __call__(	self      :       Tuple,*__A      :       Dict,**__A      :       Union[str, Any]     ):
          return super().__call__(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,__A      :       Any     ):
          _lowerCamelCase				:   List[Any]								=   load_image(__A     )
          _lowerCamelCase				:   Dict								=   torch.IntTensor([[image.height, image.width]]     )
          _lowerCamelCase				:   Dict								=   self.image_processor(images=[image],return_tensors="pt"     )
          if self.tokenizer is not None:
                _lowerCamelCase				:   Optional[Any]								=   self.tokenizer(text=inputs["words"],boxes=inputs["boxes"],return_tensors="pt"     )
          _lowerCamelCase				:   Tuple								=   target_size
          return inputs



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[str]     ):
          _lowerCamelCase				:   Tuple								=   model_inputs.pop("target_size"     )
          _lowerCamelCase				:   Tuple								=   self.model(**__A     )
          _lowerCamelCase				:   Optional[Any]								=   outputs.__class__({"target_size": target_size, **outputs}     )
          if self.tokenizer is not None:
                _lowerCamelCase				:   List[str]								=   model_inputs["""bbox"""]
          return model_outputs



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Optional[int],__A      :       int=0.9     ):
          _lowerCamelCase				:   Optional[int]								=   model_outputs["""target_size"""]
          if self.tokenizer is not None:
                # This is a LayoutLMForTokenClassification variant.
                # The OCR got the boxes and the model classified the words.
                _lowerCamelCase				:   List[str]								=   target_size[0].tolist()

                def unnormalize(__A      :       List[Any]     ):
                      return self._get_bounding_box(
                          torch.Tensor(
                              [
                                  (width * bbox[0] / 1_0_0_0),
                                  (height * bbox[1] / 1_0_0_0),
                                  (width * bbox[2] / 1_0_0_0),
                                  (height * bbox[3] / 1_0_0_0),
                              ]     )     )

                _lowerCamelCase				:   Tuple								=   model_outputs["""logits"""].squeeze(0     ).softmax(dim=-1     ).max(dim=-1     )
                _lowerCamelCase				:   Optional[Any]								=   [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
                _lowerCamelCase				:   str								=   [unnormalize(__A     ) for bbox in model_outputs["""bbox"""].squeeze(0     )]
                _lowerCamelCase				:   str								=   ["""score""", """label""", """box"""]
                _lowerCamelCase				:   Optional[int]								=   [dict(zip(__A,__A     )     ) for vals in zip(scores.tolist(),__A,__A     ) if vals[0] > threshold]
          else:
                # This is a regular ForObjectDetectionModel
                _lowerCamelCase				:   Optional[int]								=   self.image_processor.post_process_object_detection(__A,__A,__A     )
                _lowerCamelCase				:   Tuple								=   raw_annotations[0]
                _lowerCamelCase				:   Tuple								=   raw_annotation["""scores"""]
                _lowerCamelCase				:   Any								=   raw_annotation["""labels"""]
                _lowerCamelCase				:   List[Any]								=   raw_annotation["""boxes"""]

                _lowerCamelCase				:   List[Any]								=   scores.tolist()
                _lowerCamelCase				:   Union[str, Any]								=   [self.model.config.idalabel[label.item()] for label in labels]
                _lowerCamelCase				:   Optional[int]								=   [self._get_bounding_box(__A     ) for box in boxes]

                # {"scores": [...], ...} --> [{"score":x, ...}, ...]
                _lowerCamelCase				:   Tuple								=   ["""score""", """label""", """box"""]
                _lowerCamelCase				:   Optional[int]								=   [
                    dict(zip(__A,__A     )     )
                    for vals in zip(raw_annotation["scores"],raw_annotation["labels"],raw_annotation["boxes"]     )
                ]

          return annotation



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       Union[str, Any]     ):
          if self.framework != "pt":
                raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."     )
          _lowerCamelCase				:   Tuple								=   box.int().tolist()
          _lowerCamelCase				:   Union[str, Any]								=   {
              """xmin""": xmin,
              """ymin""": ymin,
              """xmax""": xmax,
              """ymax""": ymax,
          }
          return bbox

'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)







def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)
      if "model" in sd.keys():
            _lowerCamelCase				:   Optional[Any]								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]

      # pop unnecessary weights
      _lowerCamelCase				:   Optional[Any]								=   [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Union[str, Any]								=   {
          "decoder.project_in_dim.weight": "decoder.project_in.weight",
          "decoder.project_out_dim.weight": "decoder.project_out.weight",
          "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
          "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _lowerCamelCase				:   Optional[Any]								=   sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Any								=   list(sd.keys()							)
      for key in keys:
            if ".qkv_proj." in key:
                  _lowerCamelCase				:   Optional[Any]								=   sd[key]
                  # We split QKV in separate Q,K,V

                  _lowerCamelCase				:   int								=   key.replace(".qkv_proj."				,	".q_proj."							)
                  _lowerCamelCase				:   List[Any]								=   key.replace(".qkv_proj."				,	".k_proj."							)
                  _lowerCamelCase				:   Dict								=   key.replace(".qkv_proj."				,	".v_proj."							)

                  _lowerCamelCase				:   Any								=   value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch.split(_lowerCAmelCase				,	depth // 3				,	dim=0							)

                  _lowerCamelCase				:   str								=   q
                  _lowerCamelCase				:   Dict								=   k
                  _lowerCamelCase				:   int								=   v
                  del sd[key]

      return sd







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    List[str]=None							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   load_checkpoint(_lowerCAmelCase							)

      if config is not None:
            _lowerCamelCase				:   str								=   OPTConfig.from_pretrained(_lowerCAmelCase							)
      else:
            _lowerCamelCase				:   Any								=   OPTConfig()

      _lowerCamelCase				:   Optional[int]								=   OPTModel(_lowerCAmelCase							).half().eval()
      model.load_state_dict(_lowerCAmelCase							)

      # Check results
      Path(_lowerCAmelCase							).mkdir(exist_ok=_lowerCAmelCase							)
      model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   List[str]   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--fairseq_path',
     type=str,
     help=(
         'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
         ' https://huggingface.co/models?other=opt_metasq'
     ),
 )
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
 UpperCAmelCase_       :   str   =       parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)