




'''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,
    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, is_vision_available, logging


if is_vision_available():
						import PIL


a       :						Tuple								=		logging.get_logger(__name__)



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			["""pixel_values"""]





			def __init__(      self						:    Tuple      ,		a_						:    bool = True      ,		a_						:    Dict[str, int] = None      ,		a_						:    int = 0.9      ,		a_						:    PILImageResampling = PILImageResampling.BICUBIC      ,		a_						:    bool = True      ,		a_						:    Dict[str, int] = None      ,		a_						:    Union[int, float] = 1 / 255      ,		a_						:    bool = True      ,		a_						:    bool = True      ,		a_						:    Optional[Union[float, List[float]]] = None      ,		a_						:    Optional[Union[float, List[float]]] = None      ,		**a_						:    Optional[int]      ,		):



						"""simple docstring"""




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

						__snake_case			=		do_resize
						__snake_case			=		size
						__snake_case			=		crop_pct
						__snake_case			=		resample
						__snake_case			=		do_center_crop
						__snake_case			=		crop_size
						__snake_case			=		do_rescale
						__snake_case			=		rescale_factor
						__snake_case			=		do_normalize
						__snake_case			=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__snake_case			=		image_std if image_std is not None else IMAGENET_DEFAULT_STD





			def 							A (      self						:    Tuple      ,		a_						:    np.ndarray      ,		a_						:    Dict[str, int]      ,		a_						:    Optional[float] = None      ,		a_						:    PILImageResampling = PILImageResampling.BICUBIC      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    Any      ,		):



						"""simple docstring"""




						__snake_case			=		get_size_dict(a_      ,		default_to_square=a_							)
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
									raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
						if crop_pct is not None:
									if "shortest_edge" in size:
												__snake_case			=		int(size["shortest_edge"] / crop_pct							)
									elif "height" in size and "width" in size:
												if size["height"] == size["width"]:
															__snake_case			=		int(size["height"] / crop_pct							)
												else:
															__snake_case			=		(int(size["height"] / crop_pct							), int(size["width"] / crop_pct							))
									else:
												raise ValueError("Invalid size for resize: {}".format(a_							)							)

									__snake_case			=		get_resize_output_image_size(a_      ,		size=a_      ,		default_to_square=a_							)
						else:
									if "shortest_edge" in size:
												__snake_case			=		get_resize_output_image_size(a_      ,		size=size["shortest_edge"]      ,		default_to_square=a_							)
									elif "height" in size and "width" in size:
												__snake_case			=		(size["height"], size["width"])
									else:
												raise ValueError("Invalid size for resize: {}".format(a_							)							)

						return resize(a_      ,		size=a_      ,		resample=a_      ,		data_format=a_      ,		**a_							)





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



						"""simple docstring"""




						__snake_case			=		get_size_dict(a_							)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''							)
						return center_crop(a_      ,		size=(size["height"], size["width"])      ,		data_format=a_      ,		**a_							)





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



						"""simple docstring"""




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





			def 							A (      self						:    str      ,		a_						:    np.ndarray      ,		a_						:    Union[float, List[float]]      ,		a_						:    Union[float, List[float]]      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    Dict      ,		):



						"""simple docstring"""




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






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



						"""simple docstring"""




						__snake_case			=		do_resize if do_resize is not None else self.do_resize
						__snake_case			=		crop_pct if crop_pct is not None else self.crop_pct
						__snake_case			=		resample if resample is not None else self.resample
						__snake_case			=		do_center_crop if do_center_crop is not None else self.do_center_crop
						__snake_case			=		do_rescale if do_rescale is not None else self.do_rescale
						__snake_case			=		rescale_factor if rescale_factor is not None else self.rescale_factor
						__snake_case			=		do_normalize if do_normalize is not None else self.do_normalize
						__snake_case			=		image_mean if image_mean is not None else self.image_mean
						__snake_case			=		image_std if image_std is not None else self.image_std

						__snake_case			=		size if size is not None else self.size
						__snake_case			=		get_size_dict(a_      ,		default_to_square=a_							)
						__snake_case			=		crop_size if crop_size is not None else self.crop_size
						__snake_case			=		get_size_dict(a_      ,		param_name="crop_size"							)

						__snake_case			=		make_list_of_images(a_							)

						if not valid_images(a_							):
									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_pct is None:
									raise ValueError("Crop_pct 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.
						__snake_case			=		[to_numpy_array(a_							) for image in images]

						if do_resize:
									__snake_case			=		[self.resize(image=a_      ,		size=a_      ,		crop_pct=a_      ,		resample=a_							) for image in images]

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

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

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

						__snake_case			=		[to_channel_dimension_format(a_      ,		a_							) for image in images]

						__snake_case			=		{"pixel_values": images}
						return BatchFeature(data=a_      ,		tensor_type=a_							)













import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Tuple   =   emb.weight.shape
				__magic_name__		:int   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:str   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:Optional[Any]   =   mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				__magic_name__		:List[Any]   =   mam_aaa['''model''']
				remove_ignore_keys_(snake_case			)
				__magic_name__		:Tuple   =   state_dict['''encoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:List[str]   =   MaMaaaConfig(
				    vocab_size=snake_case,	max_position_embeddings=1_0_2_4,	encoder_layers=args.encoder_layers,	decoder_layers=args.decoder_layers,	encoder_attention_heads=args.encoder_attention_heads,	decoder_attention_heads=args.decoder_attention_heads,	encoder_ffn_dim=args.encoder_ffn_embed_dim,	decoder_ffn_dim=args.decoder_ffn_embed_dim,	d_model=args.encoder_embed_dim,	encoder_layerdrop=args.encoder_layerdrop,	decoder_layerdrop=args.decoder_layerdrop,	dropout=args.dropout,	attention_dropout=args.attention_dropout,	activation_dropout=args.activation_dropout,	activation_function='''relu''',	)

				__magic_name__		:int   =   state_dict['''decoder.embed_tokens.weight''']
				__magic_name__		:List[str]   =   MaMaaaForConditionalGeneration(snake_case			)
				model.model.load_state_dict(snake_case,	strict=snake_case			)
				__magic_name__		:List[str]   =   make_linear_from_emb(model.model.shared			)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""fairseq_path""", type=str, help="""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.""")
					SCREAMING_SNAKE_CASE__   :      int				=		parser.parse_args()
					SCREAMING_SNAKE_CASE__   :      Any				=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)






