



"""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


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


if is_vision_available():
     import PIL




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			Dict							=		["""pixel_values"""]

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

     snake_case_ :     List[Any]				=	do_resize
     snake_case_ :     int				=	size
     snake_case_ :     List[str]				=	resample
     snake_case_ :     List[str]				=	do_center_crop
     snake_case_ :     Union[str, Any]				=	crop_size
     snake_case_ :     str				=	do_rescale
     snake_case_ :     int				=	rescale_factor
     snake_case_ :     str				=	do_normalize
     snake_case_ :     List[str]				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     snake_case_ :     List[Any]				=	image_std if image_std is not None else OPENAI_CLIP_STD
     snake_case_ :     int				=	do_convert_rgb

 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Dict[str, int]  ,     lowercase_		:       PILImageResampling = PILImageResampling.BICUBIC  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       Dict  ,     ):
     snake_case_ :     List[Any]				=	get_size_dict(lowercase_  ,     default_to_square=lowercase_				)
     if "shortest_edge" not in size:
         raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
     snake_case_ :     Optional[Any]				=	get_resize_output_image_size(lowercase_  ,     size=size['''shortest_edge''']  ,     default_to_square=lowercase_				)
     return resize(lowercase_  ,     size=lowercase_  ,     resample=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Dict[str, int]  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       Tuple  ,     ):
     snake_case_ :     List[Any]				=	get_size_dict(lowercase_				)
     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(lowercase_  ,     size=(size['''height'''], size['''width'''])  ,     data_format=lowercase_  ,     **lowercase_				)

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Union[int, float]  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       Optional[Any]  ,     ):
     return rescale(lowercase_  ,     scale=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Union[float, List[float]]  ,     lowercase_		:       Union[float, List[float]]  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       int  ,     ):
     return normalize(lowercase_  ,     mean=lowercase_  ,     std=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)






 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       ImageInput  ,     lowercase_		:       bool = None  ,     lowercase_		:       Dict[str, int] = None  ,     lowercase_		:       PILImageResampling = None  ,     lowercase_		:       bool = None  ,     lowercase_		:       int = None  ,     lowercase_		:       bool = None  ,     lowercase_		:       float = None  ,     lowercase_		:       bool = None  ,     lowercase_		:       Optional[Union[float, List[float]]] = None  ,     lowercase_		:       Optional[Union[float, List[float]]] = None  ,     lowercase_		:       bool = None  ,     lowercase_		:       Optional[Union[str, TensorType]] = None  ,     lowercase_		:       Optional[ChannelDimension] = ChannelDimension.FIRST  ,     **lowercase_		:       Any  ,     ):
     snake_case_ :     Union[str, Any]				=	do_resize if do_resize is not None else self.do_resize
     snake_case_ :     List[Any]				=	size if size is not None else self.size
     snake_case_ :     List[Any]				=	get_size_dict(lowercase_  ,     param_name='''size'''  ,     default_to_square=lowercase_				)
     snake_case_ :     Optional[Any]				=	resample if resample is not None else self.resample
     snake_case_ :     List[Any]				=	do_center_crop if do_center_crop is not None else self.do_center_crop
     snake_case_ :     Tuple				=	crop_size if crop_size is not None else self.crop_size
     snake_case_ :     List[str]				=	get_size_dict(lowercase_  ,     param_name='''crop_size'''  ,     default_to_square=lowercase_				)
     snake_case_ :     Dict				=	do_rescale if do_rescale is not None else self.do_rescale
     snake_case_ :     str				=	rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_ :     int				=	do_normalize if do_normalize is not None else self.do_normalize
     snake_case_ :     Tuple				=	image_mean if image_mean is not None else self.image_mean
     snake_case_ :     str				=	image_std if image_std is not None else self.image_std
     snake_case_ :     Dict				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     snake_case_ :     int				=	make_list_of_images(lowercase_				)

     if not valid_images(lowercase_				):
         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:
         snake_case_ :     Optional[int]				=	[convert_to_rgb(lowercase_				) for image in images]

     # All transformations expect numpy arrays.
     snake_case_ :     Union[str, Any]				=	[to_numpy_array(lowercase_				) for image in images]

     if do_resize:
         snake_case_ :     Tuple				=	[self.resize(image=lowercase_  ,     size=lowercase_  ,     resample=lowercase_				) for image in images]

     if do_center_crop:
         snake_case_ :     Any				=	[self.center_crop(image=lowercase_  ,     size=lowercase_				) for image in images]

     if do_rescale:
         snake_case_ :     Optional[int]				=	[self.rescale(image=lowercase_  ,     scale=lowercase_				) for image in images]

     if do_normalize:
         snake_case_ :     Union[str, Any]				=	[self.normalize(image=lowercase_  ,     mean=lowercase_  ,     std=lowercase_				) for image in images]

     snake_case_ :     Optional[int]				=	[to_channel_dimension_format(lowercase_  ,     lowercase_				) for image in images]

     snake_case_ :     List[str]				=	{'''pixel_values''': images}
     return BatchFeature(data=lowercase_  ,     tensor_type=lowercase_				)










"""simple docstring"""

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_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.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





