
from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
			import PIL


_A  =	logging.get_logger(__name__)





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=['''pixel_values''']




				def __init__(   self							,							_UpperCamelCase = True							,							_UpperCamelCase = None							,							_UpperCamelCase = PIL.Image.BICUBIC							,							_UpperCamelCase = True							,							_UpperCamelCase = None							,							_UpperCamelCase = 1 / 255							,							_UpperCamelCase = True							,							_UpperCamelCase = True							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> None:
							super().__init__(**_UpperCamelCase	)
							lowerCAmelCase_         =      size if size is not None else {"height": 256, "width": 256}
							lowerCAmelCase_         =      get_size_dict(_UpperCamelCase	)
							lowerCAmelCase_         =      crop_size if crop_size is not None else {"height": 224, "width": 224}
							lowerCAmelCase_         =      get_size_dict(_UpperCamelCase							,							param_name="crop_size"	)

							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size
							lowerCAmelCase_         =      resample
							lowerCAmelCase_         =      do_center_crop
							lowerCAmelCase_         =      crop_size
							lowerCAmelCase_         =      do_rescale
							lowerCAmelCase_         =      rescale_factor
							lowerCAmelCase_         =      do_normalize
							lowerCAmelCase_         =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCAmelCase_         =      image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = PIL.Image.BICUBIC							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> np.ndarray:
							lowerCAmelCase_         =      get_size_dict(_UpperCamelCase	)
							if "height" not in size or "width" not in size:
										raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""	)
							return resize(
							    _UpperCamelCase							,							size=(size["height"], size["width"])							,							resample=_UpperCamelCase							,							data_format=_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> np.ndarray:
							lowerCAmelCase_         =      get_size_dict(_UpperCamelCase	)
							if "height" not in size or "width" not in size:
										raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""	)
							return center_crop(_UpperCamelCase							,							size=(size["height"], size["width"])							,							data_format=_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> Tuple:
							return rescale(_UpperCamelCase							,							scale=_UpperCamelCase							,							data_format=_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = None							,							**_UpperCamelCase							,							)							-> np.ndarray:
							return normalize(_UpperCamelCase							,							mean=_UpperCamelCase							,							std=_UpperCamelCase							,							data_format=_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase=None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = None							,							_UpperCamelCase = ChannelDimension.FIRST							,							**_UpperCamelCase							,							)							-> PIL.Image.Image:
							lowerCAmelCase_         =      do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase_         =      resample if resample is not None else self.resample
							lowerCAmelCase_         =      do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCAmelCase_         =      do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase_         =      rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase_         =      do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase_         =      image_mean if image_mean is not None else self.image_mean
							lowerCAmelCase_         =      image_std if image_std is not None else self.image_std

							lowerCAmelCase_         =      size if size is not None else self.size
							lowerCAmelCase_         =      get_size_dict(_UpperCamelCase	)
							lowerCAmelCase_         =      crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase_         =      get_size_dict(_UpperCamelCase							,							param_name="crop_size"	)

							lowerCAmelCase_         =      make_list_of_images(_UpperCamelCase	)

							if not valid_images(_UpperCamelCase	):
										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 or resample is None:
										raise ValueError("Size and resample 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_         =      [to_numpy_array(_UpperCamelCase	) for image in images]

							if do_resize:
										lowerCAmelCase_         =      [self.resize(image=_UpperCamelCase							,							size=_UpperCamelCase							,							resample=_UpperCamelCase	) for image in images]

							if do_center_crop:
										lowerCAmelCase_         =      [self.center_crop(image=_UpperCamelCase							,							size=_UpperCamelCase	) for image in images]

							if do_rescale:
										lowerCAmelCase_         =      [self.rescale(image=_UpperCamelCase							,							scale=_UpperCamelCase	) for image in images]

							if do_normalize:
										lowerCAmelCase_         =      [self.normalize(image=_UpperCamelCase							,							mean=_UpperCamelCase							,							std=_UpperCamelCase	) for image in images]

							lowerCAmelCase_         =      [to_channel_dimension_format(_UpperCamelCase							,							_UpperCamelCase	) for image in images]

							lowerCAmelCase_         =      {"pixel_values": images}
							return BatchFeature(data=_UpperCamelCase							,							tensor_type=_UpperCamelCase	)





import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_A  =	logging.get_logger(__name__)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "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_         =      sd.pop(__lowerCAmelCase							)

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

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      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_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

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

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

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


if __name__ == "__main__":
			_A  =	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.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



