






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   snake_case (UpperCAmelCase__  )	->    Optional[Any]:
  UpperCamelCase_: Union[str, Any]   				=    [
      '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   snake_case (UpperCAmelCase__  )	->    Optional[Any]:
  UpperCamelCase_: List[Any]   				=    list(s_dict.keys()  )
  for key in keys:
    if "transformer_layers" in key:
      UpperCamelCase_: List[Any]   				=    s_dict.pop(UpperCAmelCase__  )
    elif "subsample" in key:
      UpperCamelCase_: Any   				=    s_dict.pop(UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_				,UpperCamelCase_: Dict   				=    emb.weight.shape
  UpperCamelCase_: List[str]   				=    nn.Linear(UpperCAmelCase__     ,   UpperCAmelCase__     ,   bias=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    emb.weight.data
  return lin_layer

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Union[str, Any]   				=    torch.load(UpperCAmelCase__     ,   map_location='cpu'  )
  UpperCamelCase_: List[str]   				=    mam_aaa['args']
  UpperCamelCase_: str   				=    mam_aaa['model']
  UpperCamelCase_: Optional[Any]   				=    state_dict['decoder.output_projection.weight']

  remove_ignore_keys_(UpperCAmelCase__  )
  rename_keys(UpperCAmelCase__  )

  UpperCamelCase_: Optional[Any]   				=    state_dict['decoder.embed_tokens.weight'].shape[0]

  UpperCamelCase_: Dict   				=    args.share_decoder_input_output_embed

  UpperCamelCase_: int   				=    [int(UpperCAmelCase__  ) for i in args.conv_kernel_sizes.split(','  )]
  UpperCamelCase_: int   				=    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=2_0_0     ,   use_cache=UpperCAmelCase__     ,   decoder_start_token_id=2     ,   early_stopping=UpperCAmelCase__     ,   )

  UpperCamelCase_: Union[str, Any]   				=    SpeechaTextForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: List[str]   				=    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:
    UpperCamelCase_: Optional[Any]   				=    make_linear_from_emb(model.model.decoder.embed_tokens  )
  else:
    UpperCamelCase_: Optional[int]   				=    lm_head_weights

  model.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
   A_		:		Dict		     =			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_		:		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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:		int		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Any   				=    get_size_dict(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: Optional[int]   				=    do_resize
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Dict   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    do_center_crop
         UpperCamelCase_: Tuple   				=    crop_size
         UpperCamelCase_: Optional[int]   				=    size
         UpperCamelCase_: Dict   				=    resample
         UpperCamelCase_: Tuple   				=    rescale_factor
         UpperCamelCase_: Tuple   				=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size=size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           UpperCamelCase_: Any   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase							)
         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(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase							):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: str   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Dict   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: List[str]   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    resample if resample is not None else self.resample
         UpperCamelCase_: str   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Dict   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Any   				=    size if size is not None else self.size
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if not is_batched(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    [images]

         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:
           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.'							)

         # All transformations expect numpy arrays.
         UpperCamelCase_: List[str]   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: List[str]   				=    [self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: List[str]   				=    [self.center_crop(image=_lowerCamelCase  ,      size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: int   				=    [self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    [self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: Optional[int]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)