

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					Optional[Any]		 =   {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Tuple     					=				'''dpt'''




	def __init__(     self						:     Tuple					,      __A						:     int=768					,      __A						:     List[str]=12					,      __A						:     Tuple=12					,      __A						:     List[str]=3072					,      __A						:     List[str]="gelu"					,      __A						:     str=0.0					,      __A						:     Optional[Any]=0.0					,      __A						:     List[str]=0.0_2					,      __A						:     Dict=1e-1_2					,      __A						:     List[Any]=384					,      __A						:     Dict=16					,      __A						:     Union[str, Any]=3					,      __A						:     Any=False					,      __A						:     Tuple=True					,      __A						:     List[Any]=[2, 5, 8, 11]					,      __A						:     Union[str, Any]="project"					,      __A						:     Tuple=[4, 2, 1, 0.5]					,      __A						:     Any=[96, 192, 384, 768]					,      __A						:     Dict=256					,      __A						:     Optional[int]=-1					,      __A						:     Any=False					,      __A						:     Dict=True					,      __A						:     Optional[int]=0.4					,      __A						:     Any=255					,      __A						:     Any=0.1					,      __A						:     Dict=[1, 1024, 24, 24]					,      __A						:     Dict=[0, 1]					,      __A						:     int=None					,      **__A						:     Dict					,      ):
		super().__init__(**__A							)

		__A :			List[str]		    =			hidden_size
		__A :			List[Any]		    =			is_hybrid

		if self.is_hybrid:
			if backbone_config is None:
				logger.info("""Initializing the config with a `BiT` backbone."""							)
				__A :			Union[str, Any]		    =			{
				    """global_padding""": """same""",
				    """layer_type""": """bottleneck""",
				    """depths""": [3, 4, 9],
				    """out_features""": ["""stage1""", """stage2""", """stage3"""],
				    """embedding_dynamic_padding""": True,
				}
				__A :			List[str]		    =			BitConfig(**__A							)
			elif isinstance(__A					,      __A							):
				logger.info("""Initializing the config with a `BiT` backbone."""							)
				__A :			int		    =			BitConfig(**__A							)
			elif isinstance(__A					,      __A							):
				__A :			Optional[Any]		    =			backbone_config
			else:
				raise ValueError(
				    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""							)

			__A :			Optional[Any]		    =			backbone_featmap_shape
			__A :			Optional[int]		    =			neck_ignore_stages

			if readout_type != "project":
				raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""							)
		else:
			__A :			str		    =			None
			__A :			Union[str, Any]		    =			None
			__A :			List[str]		    =			[]

		__A :			Union[str, Any]		    =			num_hidden_layers
		__A :			Dict		    =			num_attention_heads
		__A :			Union[str, Any]		    =			intermediate_size
		__A :			Dict		    =			hidden_act
		__A :			Tuple		    =			hidden_dropout_prob
		__A :			Optional[int]		    =			attention_probs_dropout_prob
		__A :			Optional[int]		    =			initializer_range
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			List[str]		    =			image_size
		__A :			Optional[int]		    =			patch_size
		__A :			int		    =			num_channels
		__A :			Tuple		    =			qkv_bias
		__A :			Tuple		    =			backbone_out_indices
		if readout_type not in ["ignore", "add", "project"]:
			raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""							)
		__A :			Optional[int]		    =			readout_type
		__A :			List[str]		    =			reassemble_factors
		__A :			Dict		    =			neck_hidden_sizes
		__A :			Dict		    =			fusion_hidden_size
		__A :			Dict		    =			head_in_index
		__A :			Dict		    =			use_batch_norm_in_fusion_residual
		# auxiliary head attributes (semantic segmentation)
		__A :			int		    =			use_auxiliary_head
		__A :			List[Any]		    =			auxiliary_loss_weight
		__A :			List[Any]		    =			semantic_loss_ignore_index
		__A :			List[str]		    =			semantic_classifier_dropout




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			List[Any]		    =			copy.deepcopy(self.__dict__							)

		if output["backbone_config"] is not None:
			__A :			Dict		    =			self.backbone_config.to_dict()

		__A :			Optional[int]		    =			self.__class__.model_type
		return output








from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__A			:					List[Any]						=			{'UserAgent': UserAgent().random}





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   script.contents[0]
    SCREAMING_SNAKE_CASE										=   json.loads(data[data.find("{\"config\""       ) : -1]       )
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			List[str]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   f"https://www.instagram.com/{username}/"
       SCREAMING_SNAKE_CASE										=   self.get_json()





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   requests.get(self.url     , headers=__lowerCamelCase			).text
       SCREAMING_SNAKE_CASE										=   BeautifulSoup(__lowerCamelCase     , "html.parser"			).find_all("script"			)
       try:
           return extract_user_profile(scripts[4]			)
       except (json.decoder.JSONDecodeError, KeyError):
           return extract_user_profile(scripts[3]			)





   def __repr__(       self					:			Union[str, Any]			):
       return f"{self.__class__.__name__}('{self.username}')"





   def __str__(       self					:			str			):
       return f"{self.fullname} ({self.username}) is {self.biography}"





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.user_data["username"]





   @property
   def      _snake_case		(       self					:			List[Any]			):
       return self.user_data["full_name"]





   @property
   def      _snake_case		(       self					:			List[str]			):
       return self.user_data["biography"]





   @property
   def      _snake_case		(       self					:			Tuple			):
       return self.user_data["business_email"]





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return self.user_data["external_url"]





   @property
   def      _snake_case		(       self					:			int			):
       return self.user_data["edge_followed_by"]["count"]





   @property
   def      _snake_case		(       self					:			List[str]			):
       return self.user_data["edge_follow"]["count"]





   @property
   def      _snake_case		(       self					:			List[Any]			):
       return self.user_data["edge_owner_to_timeline_media"]["count"]





   @property
   def      _snake_case		(       self					:			Any			):
       return self.user_data["profile_pic_url_hd"]





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.user_data["is_verified"]





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.user_data["is_private"]





def 	__a     (      A__			:  str = "github"       ):
    import os

    if os.environ.get("CI"       ):
        return  # test failing on GitHub Actions
    SCREAMING_SNAKE_CASE										=   InstagramUser(A__       )
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data			,  A__       )
    assert instagram_user.username == username
    if username != "github":
        return
    assert instagram_user.fullname == "GitHub"
    assert instagram_user.biography == "Built for developers."
    assert instagram_user.number_of_posts > 150
    assert instagram_user.number_of_followers > 120000
    assert instagram_user.number_of_followings > 15
    assert instagram_user.email == "support@github.com"
    assert instagram_user.website == "https://github.com/readme"
    assert instagram_user.profile_picture_url.startswith("https://instagram."       )
    assert instagram_user.is_verified is True
    assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    __A			:					Dict						=			InstagramUser('github')
    print(instagram_user)
    print(f'{instagram_user.number_of_posts = }')
    print(f'{instagram_user.number_of_followers = }')
    print(f'{instagram_user.number_of_followings = }')
    print(f'{instagram_user.email = }')
    print(f'{instagram_user.website = }')
    print(f'{instagram_user.profile_picture_url = }')
    print(f'{instagram_user.is_verified = }')
    print(f'{instagram_user.is_private = }')