



'''simple docstring'''





from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase											=		logging.get_logger(__name__)






class    lowerCamelCase_       (							__snake_case	):
  """simple docstring"""




  a_				   =["""pixel_values"""]




  def __init__(					self :      Union[str, Any]     ,      _a :      bool = True     ,      _a :      Dict[str, int] = None     ,      _a :      PILImageResampling = PILImageResampling.BICUBIC     ,      _a :      bool = True     ,      _a :      Dict[str, int] = None     ,      _a :      bool = True     ,      _a :      Union[int, float] = 1 / 255     ,      _a :      bool = True     ,      _a :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,      _a :      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,      **_a :      List[str]     ,      )   ->   Optional[Any]:
    super().__init__(**_SCREAMING_SNAKE_CASE  )
    __lowerCamelCase			:			List[str]       			=					size if size is not None else {'shortest_edge': 224}
    __lowerCamelCase			:			Optional[int]       			=					get_size_dict(_SCREAMING_SNAKE_CASE     ,      default_to_square=_SCREAMING_SNAKE_CASE  )
    __lowerCamelCase			:			Dict       			=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase			:			List[str]       			=					get_size_dict(_SCREAMING_SNAKE_CASE     ,      param_name='crop_size'  )

    __lowerCamelCase			:			int       			=					do_resize
    __lowerCamelCase			:			Optional[int]       			=					size
    __lowerCamelCase			:			Any       			=					resample
    __lowerCamelCase			:			Tuple       			=					do_center_crop
    __lowerCamelCase			:			Optional[int]       			=					crop_size
    __lowerCamelCase			:			Any       			=					do_rescale
    __lowerCamelCase			:			str       			=					rescale_factor
    __lowerCamelCase			:			Union[str, Any]       			=					do_normalize
    __lowerCamelCase			:			Union[str, Any]       			=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
    __lowerCamelCase			:			Optional[Any]       			=					image_std if image_std is not None else IMAGENET_DEFAULT_STD




  def 		_lowercase	(					self :      List[str]     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      PILImageResampling = PILImageResampling.BICUBIC     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Dict     ,      )   ->   Optional[int]:
    __lowerCamelCase			:			List[Any]       			=					get_size_dict(_SCREAMING_SNAKE_CASE     ,      default_to_square=_SCREAMING_SNAKE_CASE  )
    # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
    if "shortest_edge" in size:
      __lowerCamelCase			:			List[Any]       			=					int((256 / 224) * size['shortest_edge']  )
      __lowerCamelCase			:			Dict       			=					get_resize_output_image_size(_SCREAMING_SNAKE_CASE     ,      size=_SCREAMING_SNAKE_CASE     ,      default_to_square=_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase			:			Optional[int]       			=					{'height': output_size[0], 'width': output_size[1]}
    if "height" not in size_dict or "width" not in size_dict:
      raise ValueError(
          f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'  )
    return resize(
        _SCREAMING_SNAKE_CASE     ,      size=(size_dict['height'], size_dict['width'])     ,      resample=_SCREAMING_SNAKE_CASE     ,      data_format=_SCREAMING_SNAKE_CASE     ,      **_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Dict     ,      )   ->   Tuple:
    __lowerCamelCase			:			Optional[Any]       			=					get_size_dict(_SCREAMING_SNAKE_CASE  )
    if "height" not in size or "width" not in size:
      raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'  )
    return center_crop(_SCREAMING_SNAKE_CASE     ,      size=(size['height'], size['width'])     ,      data_format=_SCREAMING_SNAKE_CASE     ,      **_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      Any     ,      _a :      np.ndarray     ,      _a :      Union[int, float]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      str     ,      )   ->   Optional[Any]:
    return rescale(_SCREAMING_SNAKE_CASE     ,      scale=_SCREAMING_SNAKE_CASE     ,      data_format=_SCREAMING_SNAKE_CASE     ,      **_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      np.ndarray     ,      _a :      Union[float, List[float]]     ,      _a :      Union[float, List[float]]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Dict     ,      )   ->   int:
    return normalize(_SCREAMING_SNAKE_CASE     ,      mean=_SCREAMING_SNAKE_CASE     ,      std=_SCREAMING_SNAKE_CASE     ,      data_format=_SCREAMING_SNAKE_CASE     ,      **_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      int     ,      _a :      ImageInput     ,      _a :      Optional[bool] = None     ,      _a :      Optional[Dict[str, int]] = None     ,      _a :      PILImageResampling = None     ,      _a :      Optional[bool] = None     ,      _a :      Optional[Dict[str, int]] = None     ,      _a :      Optional[bool] = None     ,      _a :      Optional[float] = None     ,      _a :      Optional[bool] = None     ,      _a :      Optional[Union[float, Iterable[float]]] = None     ,      _a :      Optional[Union[float, Iterable[float]]] = None     ,      _a :      Optional[TensorType] = None     ,      _a :      ChannelDimension = ChannelDimension.FIRST     ,      **_a :      int     ,      )   ->   Tuple:
    __lowerCamelCase			:			List[str]       			=					do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase			:			Union[str, Any]       			=					resample if resample is not None else self.resample
    __lowerCamelCase			:			Optional[int]       			=					do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowerCamelCase			:			Union[str, Any]       			=					do_rescale if do_rescale is not None else self.do_rescale
    __lowerCamelCase			:			Any       			=					rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase			:			List[str]       			=					do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase			:			Optional[int]       			=					image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase			:			int       			=					image_std if image_std is not None else self.image_std

    __lowerCamelCase			:			List[str]       			=					size if size is not None else self.size
    __lowerCamelCase			:			Dict       			=					get_size_dict(_SCREAMING_SNAKE_CASE     ,      default_to_square=_SCREAMING_SNAKE_CASE  )
    __lowerCamelCase			:			str       			=					crop_size if crop_size is not None else self.crop_size
    __lowerCamelCase			:			str       			=					get_size_dict(_SCREAMING_SNAKE_CASE     ,      param_name='crop_size'  )

    __lowerCamelCase			:			Tuple       			=					make_list_of_images(_SCREAMING_SNAKE_CASE  )

    if not valid_images(_SCREAMING_SNAKE_CASE  ):
      raise ValueError(
          'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
          'torch.Tensor, tf.Tensor or jax.ndarray.'  )

    if do_resize and size is None:
      raise ValueError('Size must be specified if do_resize is True.'  )

    if do_center_crop and crop_size is None:
      raise ValueError('Crop size must be specified if do_center_crop is True.'  )

    if do_rescale and rescale_factor is None:
      raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

    if do_normalize and (image_mean is None or image_std is None):
      raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

    # All transformations expect numpy arrays.
    __lowerCamelCase			:			int       			=					[to_numpy_array(_SCREAMING_SNAKE_CASE  ) for image in images]

    if do_resize:
      __lowerCamelCase			:			int       			=					[self.resize(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  ) for image in images]

    if do_center_crop:
      __lowerCamelCase			:			Dict       			=					[self.center_crop(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  ) for image in images]

    if do_rescale:
      __lowerCamelCase			:			Dict       			=					[self.rescale(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  ) for image in images]

    if do_normalize:
      __lowerCamelCase			:			Optional[int]       			=					[self.normalize(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  ) for image in images]

    __lowerCamelCase			:			List[Any]       			=					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,      _SCREAMING_SNAKE_CASE  ) for image in images]

    __lowerCamelCase			:			Union[str, Any]       			=					{'pixel_values': images}
    return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,      tensor_type=_SCREAMING_SNAKE_CASE  )














import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          '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:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

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

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  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
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

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


if __name__ == "__main__":
       __magic_name__   :  Any        =							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.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



