


'''simple docstring'''

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase							:   List[str]  						=	logging.get_logger(__name__)


if is_vision_available():
 import PIL
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							["""pixel_values"""]



      def __init__(self    :    Any   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Union[int, float] = 1 / 255   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    bool = True   ,						**UpperCamelCase    :    Any   ,						):

             '''simple docstring'''







             super().__init__(**UpperCamelCase )
             lowercase__      			= size if size is not None else {'''shortest_edge''': 224}
             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase )
             lowercase__      			= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase   ,						param_name='''crop_size''' )

             lowercase__      			= do_resize
             lowercase__      			= size
             lowercase__      			= resample
             lowercase__      			= do_center_crop
             lowercase__      			= crop_size
             lowercase__      			= do_rescale
             lowercase__      			= rescale_factor
             lowercase__      			= do_normalize
             lowercase__      			= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             lowercase__      			= image_std if image_std is not None else OPENAI_CLIP_STD
             lowercase__      			= do_convert_rgb



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Dict[str, int]   ,						UpperCamelCase    :    PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase )
             if "shortest_edge" not in size:
                    raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
             lowercase__      			= get_resize_output_image_size(UpperCamelCase   ,						size=size['''shortest_edge''']   ,						default_to_square=UpperCamelCase )
             return resize(UpperCamelCase   ,						size=UpperCamelCase   ,						resample=UpperCamelCase   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Dict[str, int]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= get_size_dict(UpperCamelCase )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
             return center_crop(UpperCamelCase   ,						size=(size['''height'''], size['''width'''])   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Union[int, float]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







             return rescale(UpperCamelCase   ,						scale=UpperCamelCase   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Union[float, List[float]]   ,						UpperCamelCase    :    Union[float, List[float]]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             return normalize(UpperCamelCase   ,						mean=UpperCamelCase   ,						std=UpperCamelCase   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )






      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    ImageInput   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    PILImageResampling = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    int = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    float = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Optional[Union[str, TensorType]] = None   ,						UpperCamelCase    :    Optional[ChannelDimension] = ChannelDimension.FIRST   ,						**UpperCamelCase    :    Optional[int]   ,						):

             '''simple docstring'''







             lowercase__      			= do_resize if do_resize is not None else self.do_resize
             lowercase__      			= size if size is not None else self.size
             lowercase__      			= get_size_dict(UpperCamelCase   ,						param_name='''size'''   ,						default_to_square=UpperCamelCase )
             lowercase__      			= resample if resample is not None else self.resample
             lowercase__      			= do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__      			= crop_size if crop_size is not None else self.crop_size
             lowercase__      			= get_size_dict(UpperCamelCase   ,						param_name='''crop_size'''   ,						default_to_square=UpperCamelCase )
             lowercase__      			= do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      			= rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      			= do_normalize if do_normalize is not None else self.do_normalize
             lowercase__      			= image_mean if image_mean is not None else self.image_mean
             lowercase__      			= image_std if image_std is not None else self.image_std
             lowercase__      			= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             lowercase__      			= 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:
                    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.''' )

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    lowercase__      			= [convert_to_rgb(UpperCamelCase ) for image in images]

             # All transformations expect numpy arrays.
             lowercase__      			= [to_numpy_array(UpperCamelCase ) for image in images]

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

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

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

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

             lowercase__      			= [to_channel_dimension_format(UpperCamelCase   ,						UpperCamelCase ) for image in images]

             lowercase__      			= {'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase   ,						tensor_type=UpperCamelCase )








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




