





'''simple docstring'''







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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
   import torch

if is_vision_available():
   import PIL


_lowercase       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : np.ndarray					,   a    : Union[int, Iterable[int]]					,   a    : bool					,   a    : int     ) ->Tuple[int, int]:
     def constraint_to_multiple_of(a    : Any					,   a    : Optional[Any]					,   a    : List[Any]=0					,   a    : Union[str, Any]=None     ):
          snake_case				=			round(val / multiple     ) * multiple

          if max_val is not None and x > max_val:
               snake_case				=			math.floor(val / multiple     ) * multiple

          if x < min_val:
               snake_case				=			math.ceil(val / multiple     ) * multiple

          return x

     snake_case				=			(output_size, output_size) if isinstance(a					,   a     ) else output_size

     snake_case				=			get_image_size(a     )
     snake_case				=			output_size

     # determine new height and width
     snake_case				=			output_height / input_height
     snake_case				=			output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width     ) < abs(1 - scale_height     ):
               # fit width
               snake_case				=			scale_width
          else:
               # fit height
               snake_case				=			scale_height

     snake_case				=			constraint_to_multiple_of(scale_height * input_height					,   multiple=a     )
     snake_case				=			constraint_to_multiple_of(scale_width * input_width					,   multiple=a     )

     return (new_height, new_width)






class 			_lowercase						(       UpperCamelCase_					):
     _UpperCAmelCase										=					['''pixel_values''']







     def __init__( self    ,   A__ = True    ,   A__ = None    ,   A__ = PILImageResampling.BILINEAR    ,   A__ = False    ,   A__ = 1    ,   A__ = True    ,   A__ = 1 / 2_55    ,   A__ = True    ,   A__ = None    ,   A__ = None    ,   **A__    ,   ) ->	None:
          super().__init__(**__a    )
          snake_case				=			size if size is not None else {'height': 3_84, 'width': 3_84}
          snake_case				=			get_size_dict(__a    )
          snake_case				=			do_resize
          snake_case				=			size
          snake_case				=			keep_aspect_ratio
          snake_case				=			ensure_multiple_of
          snake_case				=			resample
          snake_case				=			do_rescale
          snake_case				=			rescale_factor
          snake_case				=			do_normalize
          snake_case				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case				=			image_std if image_std is not None else IMAGENET_STANDARD_STD







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__ = False    ,   A__ = 1    ,   A__ = PILImageResampling.BICUBIC    ,   A__ = None    ,   **A__    ,   ) ->	np.ndarray:
          snake_case				=			get_size_dict(__a    )
          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()}"""    )
          snake_case				=			get_resize_output_image_size(
              __a    ,   output_size=(size['''height'''], size['''width'''])    ,   keep_aspect_ratio=__a    ,   multiple=__a    ,   )
          return resize(__a    ,   size=__a    ,   resample=__a    ,   data_format=__a    ,   **__a    )







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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__ = None    ,   **A__    ,   ) ->	np.ndarray:
          return normalize(__a    ,   mean=__a    ,   std=__a    ,   data_format=__a    ,   **__a    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = ChannelDimension.FIRST    ,   **A__    ,   ) ->	PIL.Image.Image:
          snake_case				=			do_resize if do_resize is not None else self.do_resize
          snake_case				=			size if size is not None else self.size
          snake_case				=			get_size_dict(__a    )
          snake_case				=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
          snake_case				=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
          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_normalize if do_normalize is not None else self.do_normalize
          snake_case				=			image_mean if image_mean is not None else self.image_mean
          snake_case				=			image_std if image_std is not None else self.image_std

          snake_case				=			make_list_of_images(__a    )

          if not valid_images(__a    ):
               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.'''    )

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

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

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

          if do_normalize:
               snake_case				=			[self.normalize(image=__a    ,   mean=__a    ,   std=__a    ) for image in images]

          snake_case				=			[to_channel_dimension_format(__a    ,   __a    ) for image in images]

          snake_case				=			{'pixel_values': images}
          return BatchFeature(data=__a    ,   tensor_type=__a    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	str:
          snake_case				=			outputs.logits

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

               if is_torch_tensor(__a    ):
                    snake_case				=			target_sizes.numpy()

               snake_case				=			[]

               for idx in range(len(__a    )    ):
                    snake_case				=			torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0    )    ,   size=target_sizes[idx]    ,   mode='''bilinear'''    ,   align_corners=__a    )
                    snake_case				=			resized_logits[0].argmax(dim=0    )
                    semantic_segmentation.append(__a    )
          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    __UpperCamelCase	(	a    : Optional[int]     ) ->Dict:
 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(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->int:
 snake_case				=			list(s_dict.keys()     )
 for key in keys:
  if "transformer_layers" in key:
   snake_case				=			s_dict.pop(a     )
  elif "subsample" in key:
   snake_case				=			s_dict.pop(a     )

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Optional[int]:
 snake_case				,		snake_case				=			emb.weight.shape
 snake_case				=			nn.Linear(a					,   a					,   bias=a     )
 snake_case				=			emb.weight.data
 return lin_layer

def    __UpperCamelCase	(	a    : Any					,   a    : Tuple     ) ->Tuple:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 snake_case				=			mam_aaa['''args''']
 snake_case				=			mam_aaa['''model''']
 snake_case				=			state_dict['''decoder.output_projection.weight''']

 remove_ignore_keys_(a     )
 rename_keys(a     )

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

 snake_case				=			args.share_decoder_input_output_embed

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

 snake_case				=			SpeechaTextForConditionalGeneration(a     )
 snake_case				,		snake_case				=			model.model.load_state_dict(a					,   strict=a     )
 if len(a     ) > 0 and not set(a     ) <= {
     "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(a     )


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

