





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


UpperCAmelCase__  :	List[Any]     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[Any]     ={
    '''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 					__A   (  a			):
				__A            = """mobilevit"""


				def __init__(						self      ,       UpperCAmelCase_=3      ,       UpperCAmelCase_=256      ,       UpperCAmelCase_=2      ,       UpperCAmelCase_=[144, 192, 240]      ,       UpperCAmelCase_=[16, 32, 64, 96, 128, 160, 640]      ,       UpperCAmelCase_=4      ,       UpperCAmelCase_=2.0      ,       UpperCAmelCase_=4.0      ,       UpperCAmelCase_="silu"      ,       UpperCAmelCase_=3      ,       UpperCAmelCase_=32      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.0      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.0_2      ,       UpperCAmelCase_=1E-5      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=256      ,       UpperCAmelCase_=[6, 12, 18]      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=255      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)

								lowerCamelCase		       =num_channels
								lowerCamelCase		       =image_size
								lowerCamelCase		       =patch_size
								lowerCamelCase		       =hidden_sizes
								lowerCamelCase		       =neck_hidden_sizes
								lowerCamelCase		       =num_attention_heads
								lowerCamelCase		       =mlp_ratio
								lowerCamelCase		       =expand_ratio
								lowerCamelCase		       =hidden_act
								lowerCamelCase		       =conv_kernel_size
								lowerCamelCase		       =output_stride
								lowerCamelCase		       =hidden_dropout_prob
								lowerCamelCase		       =attention_probs_dropout_prob
								lowerCamelCase		       =classifier_dropout_prob
								lowerCamelCase		       =initializer_range
								lowerCamelCase		       =layer_norm_eps
								lowerCamelCase		       =qkv_bias

								# decode head attributes for semantic segmentation
								lowerCamelCase		       =aspp_out_channels
								lowerCamelCase		       =atrous_rates
								lowerCamelCase		       =aspp_dropout_prob
								lowerCamelCase		       =semantic_loss_ignore_index




class 					__A   (  a			):
				__A            = version.parse("""1.11"""			)


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


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


				@property
				def _snake_case				(						self							):
								return 1E-4







import torch

from transformers import AutoTokenizer

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				List[str]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							int		=  AutoTokenizer.from_pretrained(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Any]					):

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

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

							elif isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							List[str]		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		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       :				int  ,		snake_case_       :				List[str]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				List[Any]=0.70					):
							UpperCamelCase_:							int		=  []
							for idx in range(len(W_query["""input_ids"""]					)					):
											UpperCamelCase_:							List[str]		=  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 snake_case_					: (tup[2] * tup[3])  ,		reverse=snake_case_					)
											UpperCamelCase_:							Optional[int]		=  []
											for k in range(len(snake_case_					)					):
															if output[k][2] * output[k][3] >= thresh:
																			UpperCamelCase_,      UpperCamelCase_:							Tuple		=  output[k][0], output[k][1]
																			UpperCamelCase_:							Any		=  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(snake_case_					)

							return final_outputs






