




"""simple docstring"""



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


__lowercase		:		Dict														=							logging.get_logger(__name__)

__lowercase		:		int														=							{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					'''switch_transformers'''
    UpperCamelCase_	:					Any							=					['''past_key_values''']
    UpperCamelCase_	:					Optional[int]							=					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
    def __init__(		self							:     Optional[int]	,      A_							:     List[str]=32_128	,      A_							:     Tuple=768	,      A_							:     List[str]=64	,      A_							:     Optional[Any]=2_048	,      A_							:     Dict=64	,      A_							:     Optional[int]=12	,      A_							:     int=3	,      A_							:     Tuple=12	,      A_							:     List[str]=3	,      A_							:     List[str]=12	,      A_							:     List[Any]=8	,      A_							:     List[str]=False	,      A_							:     Dict=0.01	,      A_							:     Dict="float32"	,      A_							:     int=False	,      A_							:     Union[str, Any]=32	,      A_							:     Optional[int]=128	,      A_							:     Union[str, Any]=0.1	,      A_							:     Any=1E-6	,      A_							:     int=0.0_01	,      A_							:     Union[str, Any]=0.0_01	,      A_							:     Dict=1.0	,      A_							:     List[str]="relu"	,      A_							:     List[str]=True	,      A_							:     Optional[Any]=False	,      A_							:     Union[str, Any]=True	,      A_							:     Dict=0	,      A_							:     List[Any]=1	,      **A_							:     Dict	,      )    ->       Tuple:
         __snake_case				=							vocab_size
         __snake_case				=							d_model
         __snake_case				=							d_kv
         __snake_case				=							d_ff

         __snake_case				=							num_sparse_encoder_layers

         __snake_case				=							num_layers
         __snake_case				=							(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         __snake_case				=							num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
              __snake_case				=							self.num_layers // self.num_sparse_encoder_layers
         else:
              __snake_case				=							self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
              __snake_case				=							self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              __snake_case				=							self.num_decoder_layers  # HACK: this will create 0 sparse layers

         __snake_case				=							num_heads
         __snake_case				=							num_experts
         __snake_case				=							expert_capacity
         __snake_case				=							router_bias
         __snake_case				=							router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"    )
         __snake_case				=							router_dtype

         __snake_case				=							router_ignore_padding_tokens
         __snake_case				=							relative_attention_num_buckets
         __snake_case				=							relative_attention_max_distance

         __snake_case				=							dropout_rate
         __snake_case				=							layer_norm_epsilon
         __snake_case				=							initializer_factor
         __snake_case				=							feed_forward_proj
         __snake_case				=							use_cache
         __snake_case				=							add_router_probs

         __snake_case				=							router_z_loss_coef
         __snake_case				=							router_aux_loss_coef

         __snake_case				=							self.feed_forward_proj.split('''-'''    )
         __snake_case				=							act_info[-1]
         __snake_case				=							act_info[0] == '''gated'''

         if len(A_    ) > 1 and act_info[0] != "gated" or len(A_    ) > 2:
              raise ValueError(
                  f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                  '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                  '''\'gated-gelu\' or \'relu\''''    )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              __snake_case				=							'''gelu_new'''

         super().__init__(
             pad_token_id=A_	,      eos_token_id=A_	,      is_encoder_decoder=A_	,      **A_	,      )
"""simple docstring"""



from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __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_center_crop if do_center_crop is not None else self.do_center_crop
         __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				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __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:
              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.'''    )

         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(A_	,      A_	,      A_    ) for image in images]

         if do_center_crop:
              __snake_case				=							[self.center_crop(A_	,      A_    ) for image in images]

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      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_    )