

"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[
						    "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       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	list(s_dict.keys()   )
						for key in keys:
												if "transformer_layers" in key:
																		SCREAMING_SNAKE_CASE_:			List[Any]  		=	s_dict.pop(__snake_case   )
												elif "subsample" in key:
																		SCREAMING_SNAKE_CASE_:			str  		=	s_dict.pop(__snake_case   )







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	emb.weight.shape
						SCREAMING_SNAKE_CASE_:			Dict  		=	nn.Linear(__snake_case    ,     __snake_case    ,     bias=__snake_case   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	emb.weight.data
						return lin_layer







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.load(__snake_case    ,     map_location="cpu"   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	mam_aaa["args"]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	mam_aaa["model"]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	state_dict["decoder.output_projection.weight"]

						remove_ignore_keys_(__snake_case   )
						rename_keys(__snake_case   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	state_dict["decoder.embed_tokens.weight"].shape[0]

						SCREAMING_SNAKE_CASE_:			Tuple  		=	args.share_decoder_input_output_embed

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[int(__snake_case   ) for i in args.conv_kernel_sizes.split(","   )]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	SpeechaTextConfig(
						    vocab_size=__snake_case    ,     max_source_positions=args.max_source_positions    ,     max_target_positions=args.max_target_positions    ,     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    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="relu"    ,     num_conv_layers=len(__snake_case   )    ,     conv_channels=args.conv_channels    ,     conv_kernel_sizes=__snake_case    ,     input_feat_per_channel=args.input_feat_per_channel    ,     input_channels=args.input_channels    ,     tie_word_embeddings=__snake_case    ,     num_beams=5    ,     max_length=2_00    ,     use_cache=__snake_case    ,     decoder_start_token_id=2    ,     early_stopping=__snake_case    ,     )

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	SpeechaTextForConditionalGeneration(__snake_case   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	model.model.load_state_dict(__snake_case    ,     strict=__snake_case   )
						if len(__snake_case   ) > 0 and not set(__snake_case   ) <= {
						    "encoder.embed_positions.weights",
						    "decoder.embed_positions.weights",
						}:
												raise ValueError(
												    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
												    f" but all the following weights are missing {missing}"   )

						if tie_embeds:
												SCREAMING_SNAKE_CASE_:			str  		=	make_linear_from_emb(model.model.decoder.embed_tokens   )
						else:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	lm_head_weights

						model.save_pretrained(__snake_case   )


if __name__ == "__main__":
						lowerCAmelCase							:  str            =						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						lowerCAmelCase							:  Any            =						parser.parse_args()
						convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)









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 convert_to_rgb, 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


if is_vision_available():
						import PIL


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



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															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_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:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

									if do_resize:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__) for image in images]

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






