

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int					)	->					Optional[int]:




				'''simple docstring'''





				A__						=	[
				    "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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str					)	->					Union[str, Any]:




				'''simple docstring'''





				A__						=	list(s_dict.keys()					)
				for key in keys:
								if "transformer_layers" in key:
												A__						=	s_dict.pop(SCREAMING_SNAKE_CASE_					)
								elif "subsample" in key:
												A__						=	s_dict.pop(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple					)	->					Union[str, Any]:




				'''simple docstring'''





				A__			, A__						=	emb.weight.shape
				A__						=	nn.Linear(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				bias=SCREAMING_SNAKE_CASE_					)
				A__						=	emb.weight.data
				return lin_layer



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							List[str]					)	->					str:




				'''simple docstring'''





				A__						=	torch.load(SCREAMING_SNAKE_CASE_		,				map_location="cpu"					)
				A__						=	mam_aaa["args"]
				A__						=	mam_aaa["model"]
				A__						=	state_dict["decoder.output_projection.weight"]

				remove_ignore_keys_(SCREAMING_SNAKE_CASE_					)
				rename_keys(SCREAMING_SNAKE_CASE_					)

				A__						=	state_dict["decoder.embed_tokens.weight"].shape[0]

				A__						=	args.share_decoder_input_output_embed

				A__						=	[int(SCREAMING_SNAKE_CASE_					) for i in args.conv_kernel_sizes.split(","					)]
				A__						=	SpeechaTextConfig(
				    vocab_size=SCREAMING_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(SCREAMING_SNAKE_CASE_					)		,				conv_channels=args.conv_channels		,				conv_kernel_sizes=SCREAMING_SNAKE_CASE_		,				input_feat_per_channel=args.input_feat_per_channel		,				input_channels=args.input_channels		,				tie_word_embeddings=SCREAMING_SNAKE_CASE_		,				num_beams=5		,				max_length=2_0_0		,				use_cache=SCREAMING_SNAKE_CASE_		,				decoder_start_token_id=2		,				early_stopping=SCREAMING_SNAKE_CASE_		,				)

				A__						=	SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_					)
				A__			, A__						=	model.model.load_state_dict(SCREAMING_SNAKE_CASE_		,				strict=SCREAMING_SNAKE_CASE_					)
				if len(SCREAMING_SNAKE_CASE_					) > 0 and not set(SCREAMING_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:
								A__						=	make_linear_from_emb(model.model.decoder.embed_tokens					)
				else:
								A__						=	lm_head_weights

				model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
	lowerCAmelCase__		      =       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__		      =       parser.parse_args()
	convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


UpperCAmelCase    :     Optional[Any]   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Optional[Any]							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					b.T
							__UpperCAmelCase   :						Union[str, Any]      							=					np.sum(np.square(_UpperCamelCase							)       ,			axis=1							)
							__UpperCAmelCase   :						List[Any]      							=					np.sum(np.square(_UpperCamelCase							)       ,			axis=0							)
							__UpperCAmelCase   :						int      							=					np.matmul(_UpperCamelCase       ,			_UpperCamelCase							)
							__UpperCAmelCase   :						Tuple      							=					aa[:, None] - 2 * ab + ba[None, :]
							return d
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      Optional[Any]							)		->       Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					x.reshape(-1       ,			3							)
							__UpperCAmelCase   :						Optional[Any]      							=					squared_euclidean_distance(_UpperCamelCase       ,			_UpperCamelCase							)
							return np.argmin(_UpperCamelCase       ,			axis=1							)





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							["""pixel_values"""]






					def __init__(       self    : List[str] ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : bool = True ,       **UpperCamelCase    : int ,       ):
												'''simple docstring'''






												super().__init__(**UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					size if size is not None else {"""height""": 256, """width""": 256}
												__UpperCAmelCase   :						Tuple      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					np.array(UpperCamelCase  ) if clusters is not None else None
												__UpperCAmelCase   :						int      							=					do_resize
												__UpperCAmelCase   :						List[str]      							=					size
												__UpperCAmelCase   :						Dict      							=					resample
												__UpperCAmelCase   :						int      							=					do_normalize
												__UpperCAmelCase   :						List[Any]      							=					do_color_quantize






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Dict[str, int] ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       **UpperCamelCase    : str ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												if "height" not in size or "width" not in size:
																			raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''  )
												return resize(
												    UpperCamelCase ,       size=(size["""height"""], size["""width"""]) ,       resample=UpperCamelCase ,       data_format=UpperCamelCase ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					rescale(image=UpperCamelCase ,       scale=1 / 127.5 ,       data_format=UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					image - 1
												return image






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : ImageInput ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = None ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : Optional[Union[str, TensorType]] = None ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,       **UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					do_resize if do_resize is not None else self.do_resize
												__UpperCAmelCase   :						Any      							=					size if size is not None else self.size
												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					resample if resample is not None else self.resample
												__UpperCAmelCase   :						Any      							=					do_normalize if do_normalize is not None else self.do_normalize
												__UpperCAmelCase   :						Any      							=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												__UpperCAmelCase   :						Tuple      							=					clusters if clusters is not None else self.clusters
												__UpperCAmelCase   :						Tuple      							=					np.array(UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					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 or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""  )

												if do_color_quantize and clusters is None:
																			raise ValueError("""Clusters must be specified if do_color_quantize is True."""  )

												# All transformations expect numpy arrays.
												__UpperCAmelCase   :						Tuple      							=					[to_numpy_array(UpperCamelCase  ) for image in images]

												if do_resize:
																			__UpperCAmelCase   :						Dict      							=					[self.resize(image=UpperCamelCase ,       size=UpperCamelCase ,       resample=UpperCamelCase  ) for image in images]

												if do_normalize:
																			__UpperCAmelCase   :						Any      							=					[self.normalize(image=UpperCamelCase  ) for image in images]

												if do_color_quantize:
																			__UpperCAmelCase   :						str      							=					[to_channel_dimension_format(UpperCamelCase ,       ChannelDimension.LAST  ) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			__UpperCAmelCase   :						Optional[int]      							=					np.array(UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					color_quantize(UpperCamelCase ,       UpperCamelCase  ).reshape(images.shape[:-1]  )

																			# flatten to (batch_size, height*width)
																			__UpperCAmelCase   :						Dict      							=					images.shape[0]
																			__UpperCAmelCase   :						Optional[Any]      							=					images.reshape(UpperCamelCase ,       -1  )

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			__UpperCAmelCase   :						int      							=					list(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						List[Any]      							=					[to_channel_dimension_format(UpperCamelCase ,       UpperCamelCase  ) for image in images]

												__UpperCAmelCase   :						List[str]      							=					{"""input_ids""": images}
												return BatchFeature(data=UpperCamelCase ,       tensor_type=UpperCamelCase  )
