

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__	:Optional[Any]										=					logging.get_logger(__name__)


if is_vision_available():
							import PIL







class        lowercase	(      __A	):
							lowercase_ : Dict       =["""pixel_values"""]




							def __init__(       self     ,A__ = True     ,A__ = None     ,A__ = PILImageResampling.BICUBIC     ,A__ = True     ,A__ = None     ,A__ = True     ,A__ = 1 / 2_5_5     ,A__ = True     ,A__ = None     ,A__ = None     ,A__ = True     ,**A__     ,):
												super().__init__(**UpperCamelCase__)
												lowercase					=		size if size is not None else {'shortest_edge': 2_2_4}
												lowercase					=		get_size_dict(UpperCamelCase__     ,default_to_square=UpperCamelCase__)
												lowercase					=		crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
												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 				A__   (       self     ,A__     ,A__     ,A__ = PILImageResampling.BICUBIC     ,A__ = None     ,**A__     ,):
												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 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												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 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return rescale(UpperCamelCase__     ,scale=UpperCamelCase__     ,data_format=UpperCamelCase__     ,**UpperCamelCase__)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return normalize(UpperCamelCase__     ,mean=UpperCamelCase__     ,std=UpperCamelCase__     ,data_format=UpperCamelCase__     ,**UpperCamelCase__)





							def 				A__   (       self     ,A__     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = ChannelDimension.FIRST     ,**A__     ,):
												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__)






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    max_position_embeddings=1024				,    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'''				,    )

					lowercase					=		state_dict['''decoder.embed_tokens.weight''']
					lowercase					=		MaMaaaForConditionalGeneration(lowerCAmelCase__	)
					model.model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)
					lowercase					=		make_linear_from_emb(model.model.shared	)

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					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.")
							lowercase__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




