






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError("""size_divisor is required for resizing"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Union[str, Any]										=   {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class  _lowercase   (   _A						):
      _a							:						int										=							'xlm'
      _a							:						List[Any]										=							{
          'hidden_size': 'emb_dim',
          'num_attention_heads': 'n_heads',
          'num_hidden_layers': 'n_layers',
          'n_words': 'vocab_size',  # For backward compatibility
      }
      def __init__(     self				,			a=3_0_1_4_5				,			a=2_0_4_8				,			a=1_2				,			a=1_6				,			a=0.1				,			a=0.1				,			a=True				,			a=False				,			a=False				,			a=False				,			a=1				,			a=True				,			a=5_1_2				,			a=2_0_4_8**-0.5				,			a=1e-12				,			a=0.02				,			a=0				,			a=1				,			a=2				,			a=3				,			a=5				,			a=True				,			a="first"				,			a=True				,			a=None				,			a=True				,			a=0.1				,			a=5				,			a=5				,			a=0				,			a=0				,			a=2				,			a=0				,			**a				,			):
       snake_case__  :    str							       =vocab_size
       snake_case__  :    List[Any]							       =emb_dim
       snake_case__  :    List[str]							       =n_layers
       snake_case__  :    Any							       =n_heads
       snake_case__  :    Any							       =dropout
       snake_case__  :    Optional[int]							       =attention_dropout
       snake_case__  :    Dict							       =gelu_activation
       snake_case__  :    Tuple							       =sinusoidal_embeddings
       snake_case__  :    Optional[int]							       =causal
       snake_case__  :    Optional[int]							       =asm
       snake_case__  :    Optional[int]							       =n_langs
       snake_case__  :    Union[str, Any]							       =use_lang_emb
       snake_case__  :    Optional[Any]							       =layer_norm_eps
       snake_case__  :    Union[str, Any]							       =bos_index
       snake_case__  :    List[Any]							       =eos_index
       snake_case__  :    Optional[Any]							       =pad_index
       snake_case__  :    Union[str, Any]							       =unk_index
       snake_case__  :    Dict							       =mask_index
       snake_case__  :    str							       =is_encoder
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Any							       =embed_init_std
       snake_case__  :    Optional[Any]							       =init_std
       snake_case__  :    str							       =summary_type
       snake_case__  :    List[Any]							       =summary_use_proj
       snake_case__  :    str							       =summary_activation
       snake_case__  :    Optional[Any]							       =summary_proj_to_labels
       snake_case__  :    Optional[int]							       =summary_first_dropout
       snake_case__  :    Optional[Any]							       =start_n_top
       snake_case__  :    List[Any]							       =end_n_top
       snake_case__  :    Union[str, Any]							       =mask_token_id
       snake_case__  :    Any							       =lang_id

       if "n_words" in kwargs:
        snake_case__  :    int							       =kwargs["""n_words"""]

       super().__init__(pad_token_id=a				,			bos_token_id=a				,			**a   )







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       if self.task == "multiple-choice":
        snake_case__  :    Any							       ={0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        snake_case__  :    Union[str, Any]							       ={0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]   )





