




"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__A :		str      							=    {}
class 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  Any      =				'fsmt_decoder'







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=0    ,      SCREAMING_SNAKE_CASE_=0 ):
       super().__init__()
       UpperCamelCase				:   List[str]          =							vocab_size
       UpperCamelCase				:   Dict          =							bos_token_id







class 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  str      =				'fsmt'
   lowercase       :  Dict      =				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=["en", "de"]    ,      SCREAMING_SNAKE_CASE_=4_2024    ,      SCREAMING_SNAKE_CASE_=4_2024    ,      SCREAMING_SNAKE_CASE_="relu"    ,      SCREAMING_SNAKE_CASE_=1024    ,      SCREAMING_SNAKE_CASE_=200    ,      SCREAMING_SNAKE_CASE_=1024    ,      SCREAMING_SNAKE_CASE_=4096    ,      SCREAMING_SNAKE_CASE_=12    ,      SCREAMING_SNAKE_CASE_=16    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=4096    ,      SCREAMING_SNAKE_CASE_=12    ,      SCREAMING_SNAKE_CASE_=16    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.0    ,      SCREAMING_SNAKE_CASE_=0.02    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=5    ,      SCREAMING_SNAKE_CASE_=1.0    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=1    ,      SCREAMING_SNAKE_CASE_=0    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=2    ,      **SCREAMING_SNAKE_CASE_    ,      ):
       UpperCamelCase				:   str          =							langs
       UpperCamelCase				:   Tuple          =							src_vocab_size
       UpperCamelCase				:   List[Any]          =							tgt_vocab_size
       UpperCamelCase				:   Optional[int]          =							d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCamelCase				:   Union[str, Any]          =							encoder_ffn_dim
       UpperCamelCase				:   Union[str, Any]          =							encoder_layers
       UpperCamelCase				:   List[Any]          =							encoder_attention_heads
       UpperCamelCase				:   str          =							encoder_layerdrop
       UpperCamelCase				:   Optional[Any]          =							decoder_layerdrop
       UpperCamelCase				:   int          =							decoder_ffn_dim
       UpperCamelCase				:   int          =							decoder_layers
       UpperCamelCase				:   Tuple          =							decoder_attention_heads
       UpperCamelCase				:   int          =							max_position_embeddings
       UpperCamelCase				:   Dict          =							init_std  # Normal(0, this parameter)
       UpperCamelCase				:   Union[str, Any]          =							activation_function

       UpperCamelCase				:   Dict          =							DecoderConfig(vocab_size=SCREAMING_SNAKE_CASE_    ,      bos_token_id=SCREAMING_SNAKE_CASE_ )
       if "decoder" in common_kwargs:
           del common_kwargs["decoder"]

       UpperCamelCase				:   str          =							scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCamelCase				:   int          =							attention_dropout
       UpperCamelCase				:   Union[str, Any]          =							activation_dropout
       UpperCamelCase				:   Tuple          =							dropout

       UpperCamelCase				:   Dict          =							use_cache
       super().__init__(
           pad_token_id=SCREAMING_SNAKE_CASE_    ,      bos_token_id=SCREAMING_SNAKE_CASE_    ,      eos_token_id=SCREAMING_SNAKE_CASE_    ,      decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,      is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,      tie_word_embeddings=SCREAMING_SNAKE_CASE_    ,      forced_eos_token_id=SCREAMING_SNAKE_CASE_    ,      max_length=SCREAMING_SNAKE_CASE_    ,      num_beams=SCREAMING_SNAKE_CASE_    ,      length_penalty=SCREAMING_SNAKE_CASE_    ,      early_stopping=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )







   def       a_							(							self ):
       UpperCamelCase				:   str          =							copy.deepcopy(self.__dict__ )
       UpperCamelCase				:   Tuple          =							self.decoder.to_dict()
       UpperCamelCase				:   Tuple          =							self.__class__.model_type
       return output










"""simple docstring"""




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[int]          =							min(img.size    )

    if s < target_image_size:
        raise ValueError(f'min dim for image {s} < {target_image_size}'    )

    UpperCamelCase				:   Dict          =							target_image_size / s
    UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
    UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
    UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
    UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
    return img
def 					A_			(		snake_case_     :       List[Any]    ):







    '''simple docstring'''
    UpperCamelCase				:   Any          =							2.0 * x - 1.0
    return x
def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							x.detach().cpu()
    if process:
        UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
    UpperCamelCase				:   List[Any]          =							x.numpy()
    if process:
        UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
    UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
    if not x.mode == mode:
        UpperCamelCase				:   Any          =							x.convert(snake_case_    )
    return x
def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
    UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
    UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
    return x


def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
    return x.permute(2						,0						,1    ).unsqueeze(0    )




