




import torch

from transformers import AutoTokenizer


class       SCREAMING_SNAKE_CASE_							(					A__      ):



 """simple docstring"""


 def __init__(				self		:	Union[str, Any]				,    lowerCAmelCase		:	Dict  )  ->					List[str]:







      """simple docstring"""







      __UpperCamelCase				:  Tuple								=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )






 def 				lowerCamelCase__    (				self		:	str				,    lowerCAmelCase		:	Union[str, Any]  )  ->					str:







      """simple docstring"""







      if isinstance(_lowerCAmelCase				,    _lowerCAmelCase  ) and all(isinstance(_x				,    _lowerCAmelCase  ) for _x in x  ):
           __UpperCamelCase				:  Dict								=			None
           for l in x:
                __UpperCamelCase				:  Optional[int]								=			self.tokenizer(
                    _lowerCAmelCase				,    padding="""max_length"""				,    max_length=384				,    truncation=_lowerCAmelCase				,    return_tensors="""pt"""				,    )
                __UpperCamelCase				:  Any								=			torch.tensor([len(_lowerCAmelCase  )]  )
                if d is not None:
                     for k in d.keys():
                          __UpperCamelCase				:  Optional[Any]								=			torch.cat((d[k], t[k])				,    0  )
                else:
                     __UpperCamelCase				:  Optional[Any]								=			t

           __UpperCamelCase				:  List[str]								=			torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""  )  )
           __UpperCamelCase				:  List[Any]								=			torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""  )  )

      elif isinstance(_lowerCAmelCase				,    _lowerCAmelCase  ) and all(isinstance(_x				,    _lowerCAmelCase  ) for _x in x  ):
           __UpperCamelCase				:  Any								=			self.tokenizer(
               _lowerCAmelCase				,    padding="""max_length"""				,    max_length=384				,    truncation=_lowerCAmelCase				,    return_tensors="""pt"""				,    )

      else:
           raise Exception(
               """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
               """ strings` for supports are supported."""  )

      return d






 def 				lowerCamelCase__    (				self		:	Optional[int]				,    lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	Optional[int]				,    lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	Dict=0.70  )  ->					Any:







      """simple docstring"""







      __UpperCamelCase				:  List[Any]								=			[]
      for idx in range(len(W_query["""input_ids"""]  )  ):
           __UpperCamelCase				:  int								=			range(p_start.shape[1]  )

           __UpperCamelCase				:  str								=			[]
           for start_id in start_indexes:
                for end_id in end_indexes:
                     if start_id < end_id:
                          output.append(
                              (
                                  start_id,
                                  end_id,
                                  p_start[idx][start_id].item(),
                                  p_end[idx][end_id].item(),
                              )  )

           output.sort(key=lambda lowerCAmelCase  : (tup[2] * tup[3])				,    reverse=_lowerCAmelCase  )
           __UpperCamelCase				:  Union[str, Any]								=			[]
           for k in range(len(_lowerCAmelCase  )  ):
                if output[k][2] * output[k][3] >= thresh:
                     __UpperCamelCase			, __UpperCamelCase				:  Tuple								=			output[k][0], output[k][1]
                     __UpperCamelCase				:  Tuple								=			self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]  )
                     temp.append((decoded, output[k][2] * output[k][3])  )

           final_outputs.append(_lowerCAmelCase  )

      return final_outputs












from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Any			  =		'mobilevit'






	def __init__(				self		:	int				,    lowerCAmelCase		:	Dict=3				,    lowerCAmelCase		:	Dict=256				,    lowerCAmelCase		:	Any=2				,    lowerCAmelCase		:	Any=[144, 192, 240]				,    lowerCAmelCase		:	int=[16, 32, 64, 96, 128, 160, 640]				,    lowerCAmelCase		:	List[Any]=4				,    lowerCAmelCase		:	Tuple=2.0				,    lowerCAmelCase		:	Tuple=4.0				,    lowerCAmelCase		:	List[str]="silu"				,    lowerCAmelCase		:	Tuple=3				,    lowerCAmelCase		:	Tuple=32				,    lowerCAmelCase		:	Any=0.1				,    lowerCAmelCase		:	List[str]=0.0				,    lowerCAmelCase		:	Union[str, Any]=0.1				,    lowerCAmelCase		:	List[str]=0.02				,    lowerCAmelCase		:	Optional[Any]=1E-5				,    lowerCAmelCase		:	Union[str, Any]=True				,    lowerCAmelCase		:	List[str]=256				,    lowerCAmelCase		:	List[Any]=[6, 12, 18]				,    lowerCAmelCase		:	Tuple=0.1				,    lowerCAmelCase		:	List[str]=255				,    **lowerCAmelCase		:	Union[str, Any]				,    )  ->					Optional[Any]:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase  )

						__UpperCamelCase				:  Tuple								=			num_channels
						__UpperCamelCase				:  Union[str, Any]								=			image_size
						__UpperCamelCase				:  List[Any]								=			patch_size
						__UpperCamelCase				:  Optional[int]								=			hidden_sizes
						__UpperCamelCase				:  Union[str, Any]								=			neck_hidden_sizes
						__UpperCamelCase				:  List[str]								=			num_attention_heads
						__UpperCamelCase				:  Union[str, Any]								=			mlp_ratio
						__UpperCamelCase				:  Tuple								=			expand_ratio
						__UpperCamelCase				:  Optional[Any]								=			hidden_act
						__UpperCamelCase				:  int								=			conv_kernel_size
						__UpperCamelCase				:  Any								=			output_stride
						__UpperCamelCase				:  int								=			hidden_dropout_prob
						__UpperCamelCase				:  Union[str, Any]								=			attention_probs_dropout_prob
						__UpperCamelCase				:  Dict								=			classifier_dropout_prob
						__UpperCamelCase				:  Any								=			initializer_range
						__UpperCamelCase				:  List[str]								=			layer_norm_eps
						__UpperCamelCase				:  Union[str, Any]								=			qkv_bias

						# decode head attributes for semantic segmentation
						__UpperCamelCase				:  Any								=			aspp_out_channels
						__UpperCamelCase				:  Union[str, Any]								=			atrous_rates
						__UpperCamelCase				:  Union[str, Any]								=			aspp_dropout_prob
						__UpperCamelCase				:  List[str]								=			semantic_loss_ignore_index






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Optional[Any]			  =		version.parse('1.11'      )






	@property
	def 				lowerCamelCase__    (				self		:	Any  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						if self.task == "image-classification":
											return OrderedDict([("""logits""", {0: """batch"""})]  )
						else:
											return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					float:







						"""simple docstring"""







						return 1E-4






