
"""simple docstring"""


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


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			'''switch_transformers'''
						_lowerCamelCase:			int					=			['''past_key_values''']
						_lowerCamelCase:			int					=			{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


						def __init__(       self   :					Optional[int]     ,A_   :					Optional[int]=3_2128     ,A_   :					Optional[Any]=768     ,A_   :					Any=64     ,A_   :					Tuple=2048     ,A_   :					List[str]=64     ,A_   :					str=12     ,A_   :					Optional[int]=3     ,A_   :					Union[str, Any]=12     ,A_   :					Optional[int]=3     ,A_   :					Any=12     ,A_   :					Union[str, Any]=8     ,A_   :					List[str]=False     ,A_   :					List[str]=0.01     ,A_   :					List[str]="float32"     ,A_   :					Tuple=False     ,A_   :					Dict=32     ,A_   :					List[str]=128     ,A_   :					Tuple=0.1     ,A_   :					List[Any]=1e-6     ,A_   :					List[str]=0.0_01     ,A_   :					Tuple=0.0_01     ,A_   :					Dict=1.0     ,A_   :					int="relu"     ,A_   :					List[Any]=True     ,A_   :					Dict=False     ,A_   :					List[Any]=True     ,A_   :					int=0     ,A_   :					Optional[Any]=1     ,**A_   :					str     ,)   ->      Dict:
							A        =      vocab_size
							A        =      d_model
							A        =      d_kv
							A        =      d_ff

							A        =      num_sparse_encoder_layers

							A        =      num_layers
							A        =      (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							A        =      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:
								A        =      self.num_layers // self.num_sparse_encoder_layers
							else:
								A        =      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:
								A        =      self.num_decoder_layers // self.num_sparse_decoder_layers
							else:
								A        =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

							A        =      num_heads
							A        =      num_experts
							A        =      expert_capacity
							A        =      router_bias
							A        =      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}'					)
							A        =      router_dtype

							A        =      router_ignore_padding_tokens
							A        =      relative_attention_num_buckets
							A        =      relative_attention_max_distance

							A        =      dropout_rate
							A        =      layer_norm_epsilon
							A        =      initializer_factor
							A        =      feed_forward_proj
							A        =      use_cache
							A        =      add_router_probs

							A        =      router_z_loss_coef
							A        =      router_aux_loss_coef

							A        =      self.feed_forward_proj.split('-'					)
							A        =      act_info[-1]
							A        =      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":
								A        =      'gelu_new'

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






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

