





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _UpperCAmelCase ):
						UpperCAmelCase_ =			["""pixel_values"""]


						def __init__(self   ,							__a = True   ,							__a = 32   ,							__a=PILImageResampling.BILINEAR   ,							__a = True   ,							**__a   ,							) ->   Tuple:
								UpperCamelCase   =	do_resize
								UpperCamelCase   =	do_rescale
								UpperCamelCase   =	size_divisor
								UpperCamelCase   =	resample
								super().__init__(**lowercase__		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a = None   ,							**__a		) ->   List[Any]:
								UpperCamelCase   =	get_image_size(lowercase__		)
								# Rounds the height and width down to the closest multiple of size_divisor
								UpperCamelCase   =	height // size_divisor * size_divisor
								UpperCamelCase   =	width // size_divisor * size_divisor
								UpperCamelCase   =	resize(lowercase__   ,							(new_h, new_w)   ,							resample=lowercase__   ,							data_format=lowercase__   ,							**lowercase__		)
								return image


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a = None   ,							**__a		) ->   Union[str, Any]:
								return rescale(image=lowercase__   ,							scale=lowercase__   ,							data_format=lowercase__   ,							**lowercase__		)




						def     snake_case_   (self   ,							__a   ,							__a = None   ,							__a = None   ,							__a=None   ,							__a = None   ,							__a = None   ,							__a = ChannelDimension.FIRST   ,							**__a   ,							) ->   List[str]:
								UpperCamelCase   =	do_resize if do_resize is not None else self.do_resize
								UpperCamelCase   =	do_rescale if do_rescale is not None else self.do_rescale
								UpperCamelCase   =	size_divisor if size_divisor is not None else self.size_divisor
								UpperCamelCase   =	resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
										raise ValueError("size_divisor is required for resizing"		)

								UpperCamelCase   =	make_list_of_images(lowercase__		)

								if not valid_images(lowercase__		):
										raise ValueError("Invalid image(s)"		)

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

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

								if do_rescale:
										UpperCamelCase   =	[self.rescale(lowercase__   ,							scale=1 / 2_55		) for image in images]

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

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











"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	MaMaaaConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	max_position_embeddings=1_024  ,	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"  ,	)

		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.shared    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			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.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






