




'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
							import PIL

if is_torch_available():
							import torch


a  :				List[str]									=  logging.get_logger(__name__)






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		["pixel_values"]


				def __init__(		self    :					Optional[Any]       ,				lowercase_    :					bool = True       ,				lowercase_    :					Dict[str, int] = None       ,				lowercase_    :					PILImageResampling = PILImageResampling.BILINEAR       ,				lowercase_    :					bool = True       ,				lowercase_    :					Union[int, float] = 1 / 255       ,				lowercase_    :					bool = True       ,				lowercase_    :					Dict[str, int] = None       ,				lowercase_    :					bool = True       ,				**lowercase_    :					Union[str, Any]       ,				):
									super().__init__(**lowercase_					)
									snake_case_									=				size if size is not None else {'''shortest_edge''': 224}
									snake_case_									=				get_size_dict(lowercase_       ,				default_to_square=lowercase_					)
									snake_case_									=				crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
									snake_case_									=				get_size_dict(lowercase_       ,				param_name='''crop_size'''					)

									snake_case_									=				do_resize
									snake_case_									=				size
									snake_case_									=				resample
									snake_case_									=				do_rescale
									snake_case_									=				rescale_factor
									snake_case_									=				do_center_crop
									snake_case_									=				crop_size
									snake_case_									=				do_flip_channel_order


				def   A_ (		self    :					int       ,				lowercase_    :					np.ndarray       ,				lowercase_    :					Dict[str, int]       ,				lowercase_    :					PILImageResampling = PIL.Image.BILINEAR       ,				lowercase_    :					Optional[Union[str, ChannelDimension]] = None       ,				**lowercase_    :					Optional[int]       ,				):
									snake_case_									=				get_size_dict(lowercase_       ,				default_to_square=lowercase_					)
									if "shortest_edge" not in size:
														raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"					)
									snake_case_									=				get_resize_output_image_size(lowercase_       ,				size=size['''shortest_edge''']       ,				default_to_square=lowercase_					)
									return resize(lowercase_       ,				size=lowercase_       ,				resample=lowercase_       ,				data_format=lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					List[str]       ,				lowercase_    :					np.ndarray       ,				lowercase_    :					Dict[str, int]       ,				lowercase_    :					Optional[Union[str, ChannelDimension]] = None       ,				**lowercase_    :					Tuple       ,				):
									snake_case_									=				get_size_dict(lowercase_					)
									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()}"					)
									return center_crop(lowercase_       ,				size=(size['''height'''], size['''width'''])       ,				data_format=lowercase_       ,				**lowercase_					)


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


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					np.ndarray       ,				lowercase_    :					Optional[Union[str, ChannelDimension]] = None					):
									return flip_channel_order(lowercase_       ,				data_format=lowercase_					)


				def   A_ (		self    :					Dict       ,				lowercase_    :					ImageInput       ,				lowercase_    :					bool = None       ,				lowercase_    :					Dict[str, int] = None       ,				lowercase_    :					PILImageResampling = None       ,				lowercase_    :					bool = None       ,				lowercase_    :					float = None       ,				lowercase_    :					bool = None       ,				lowercase_    :					Dict[str, int] = None       ,				lowercase_    :					bool = None       ,				lowercase_    :					Optional[Union[str, TensorType]] = None       ,				lowercase_    :					ChannelDimension = ChannelDimension.FIRST       ,				**lowercase_    :					Dict       ,				):
									snake_case_									=				do_resize if do_resize is not None else self.do_resize
									snake_case_									=				resample if resample is not None else self.resample
									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_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_									=				(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									snake_case_									=				size if size is not None else self.size
									snake_case_									=				get_size_dict(lowercase_       ,				default_to_square=lowercase_					)
									snake_case_									=				crop_size if crop_size is not None else self.crop_size
									snake_case_									=				get_size_dict(lowercase_       ,				param_name='''crop_size'''					)

									snake_case_									=				make_list_of_images(lowercase_					)

									if not valid_images(lowercase_					):
														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_rescale and rescale_factor is None:
														raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

									if do_center_crop and crop_size is None:
														raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

									# All transformations expect numpy arrays.
									snake_case_									=				[to_numpy_array(lowercase_					) for image in images]

									if do_resize:
														snake_case_									=				[self.resize(image=lowercase_       ,				size=lowercase_       ,				resample=lowercase_					) for image in images]

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

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

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
														snake_case_									=				[self.flip_channel_order(image=lowercase_					) for image in images]

									snake_case_									=				[to_channel_dimension_format(lowercase_       ,				lowercase_					) for image in images]

									snake_case_									=				{'''pixel_values''': images}
									return BatchFeature(data=lowercase_       ,				tensor_type=lowercase_					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[Tuple] = None					):
									snake_case_									=				outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
														if len(lowercase_					) != len(lowercase_					):
																			raise ValueError(
																			    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''					)

														if is_torch_tensor(lowercase_					):
																			snake_case_									=				target_sizes.numpy()

														snake_case_									=				[]

														for idx in range(len(lowercase_					)					):
																			snake_case_									=				torch.nn.functional.interpolate(
																			    logits[idx].unsqueeze(dim=0					)       ,				size=target_sizes[idx]       ,				mode='''bilinear'''       ,				align_corners=lowercase_					)
																			snake_case_									=				resized_logits[0].argmax(dim=0					)
																			semantic_segmentation.append(lowercase_					)
									else:
														snake_case_									=				logits.argmax(dim=1					)
														snake_case_									=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

									return semantic_segmentation







'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				[
					    '''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(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_			,snake_case_									=				emb.weight.shape
					snake_case_									=				nn.Linear(__UpperCAmelCase,      __UpperCAmelCase,      bias=__UpperCAmelCase	)
					snake_case_									=				emb.weight.data
					return lin_layer


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				mam_aaa['''args''']
					snake_case_									=				mam_aaa['''model''']
					snake_case_									=				state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

					snake_case_									=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      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(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

					snake_case_									=				SpeechaTextForConditionalGeneration(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				model.model.load_state_dict(__UpperCAmelCase,      strict=__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0 and not set(__UpperCAmelCase	) <= {
					    "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:
										snake_case_									=				make_linear_from_emb(model.model.decoder.embed_tokens	)
					else:
										snake_case_									=				lm_head_weights

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  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.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

