

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[tf.Tensor, np.ndarray]						)		->				List[int]:
 '''simple docstring'''


 if isinstance(__lowercase		,				np.ndarray						):
  return list(tensor.shape						)

 SCREAMING_SNAKE_CASE		    =      tf.shape(__lowercase						)

 if tensor.shape == tf.TensorShape(__lowercase						):
  return dynamic

 SCREAMING_SNAKE_CASE		    =      tensor.shape.as_list()

 return [dynamic[i] if s is None else s for i, s in enumerate(__lowercase						)]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	tf.Tensor		,				_UpperCamelCase		:	Optional[int] = None		,				_UpperCamelCase		:	Optional[str] = None						)		->				tf.Tensor:
 '''simple docstring'''


 return tf.nn.softmax(logits=logits + 1e-9		,				axis=__lowercase		,				name=__lowercase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Tuple=1e-5		,				_UpperCamelCase		:	Dict=-1						)		->				str:
 '''simple docstring'''


 if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowercase		,				__lowercase						):
  raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'						)

 # Get mean and variance on the axis to be normalized
 SCREAMING_SNAKE_CASE		    =      tf.nn.moments(__lowercase		,				axes=[axis]		,				keepdims=__lowercase						)

 if axis != -1:
  # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
  # on every dimension except axis
  SCREAMING_SNAKE_CASE		    =      [1] * inputs.shape.rank
  SCREAMING_SNAKE_CASE		    =      shape_list(__lowercase						)[axis]
  SCREAMING_SNAKE_CASE		    =      tf.reshape(__lowercase		,				__lowercase						)
  SCREAMING_SNAKE_CASE		    =      tf.reshape(__lowercase		,				__lowercase						)

 # Compute layer normalization using the batch_normalization
 # function.
 SCREAMING_SNAKE_CASE		    =      tf.nn.batch_normalization(
     __lowercase		,				__lowercase		,				__lowercase		,				offset=__lowercase		,				scale=__lowercase		,				variance_epsilon=__lowercase		,				)
 return outputs


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any		,				_UpperCamelCase		:	str=0		,				_UpperCamelCase		:	List[str]=-1						)		->				Optional[Any]:
 '''simple docstring'''


 if end_dim < 0:
  end_dim += input.shape.rank
 if start_dim < 0:
  start_dim += input.shape.rank

 if start_dim == end_dim:
  return input

 SCREAMING_SNAKE_CASE		    =      tf.shape(__lowercase						)
 SCREAMING_SNAKE_CASE		    =      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
 SCREAMING_SNAKE_CASE		    =      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,				axis=0						)
 return tf.reshape(__lowercase		,				__lowercase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	tf.Tensor						)		->				tf.Tensor:
 '''simple docstring'''


 if not isinstance(__lowercase		,				tf.Tensor						):
  SCREAMING_SNAKE_CASE		    =      tf.convert_to_tensor(__lowercase						)  # Catches stray NumPy inputs
 if encoder_attention_mask.shape.rank == 3:
  SCREAMING_SNAKE_CASE		    =      encoder_attention_mask[:, None, :, :]
 if encoder_attention_mask.shape.rank == 2:
  SCREAMING_SNAKE_CASE		    =      encoder_attention_mask[:, None, None, :]
 # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
 # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
 # /transformer/transformer_layers.py#L270
 # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
 # encoder_extended_attention_mask.transpose(-1, -2))
 SCREAMING_SNAKE_CASE		    =      (
     tf.cast(1		,				encoder_attention_mask.dtype						) - encoder_extended_attention_mask
 ) * encoder_extended_attention_mask.dtype.min

 return encoder_extended_attention_mask


def 							__lowerCAmelCase     (							_UpperCamelCase		:	tf.Tensor		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	str = "input_ids"						)		->				None:
 '''simple docstring'''


 tf.debugging.assert_less(
     __lowercase		,				tf.cast(__lowercase		,				dtype=tensor.dtype						)		,				message=(
         f"""The maximum value of {tensor_name} ({tf.math.reduce_max(__lowercase						)}) must be smaller than the embedding """
         f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
     )		,				)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Dict						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      6_45_12
 # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
 # because in that case even chunking the array would not make the saving
 # possible.
 SCREAMING_SNAKE_CASE		    =      [x for x in data if len(__lowercase						) > HDF5_OBJECT_HEADER_LIMIT]

 # Expecting this to never be true.
 if bad_attributes:
  raise RuntimeError(
      'The following attributes cannot be saved to HDF5 file because '
      f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
      f"""bytes: {bad_attributes}"""						)

 SCREAMING_SNAKE_CASE		    =      np.asarray(__lowercase						)

 SCREAMING_SNAKE_CASE		    =      1
 SCREAMING_SNAKE_CASE		    =      np.array_split(__lowercase		,				__lowercase						)

 # This will never loop forever thanks to the test above.
 while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data						):
  num_chunks += 1
  SCREAMING_SNAKE_CASE		    =      np.array_split(__lowercase		,				__lowercase						)

 if num_chunks > 1:
  for chunk_id, chunk_data in enumerate(__lowercase						):
   SCREAMING_SNAKE_CASE		    =      chunk_data
 else:
  SCREAMING_SNAKE_CASE		    =      data


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	List[str]						)		->				Optional[Any]:
 '''simple docstring'''


 if name in group.attrs:
  SCREAMING_SNAKE_CASE		    =      [n.decode('utf8'						) if hasattr(__lowercase		,				'decode'						) else n for n in group.attrs[name]]
 else:
  SCREAMING_SNAKE_CASE		    =      []
  SCREAMING_SNAKE_CASE		    =      0
  while "%s%d" % (name, chunk_id) in group.attrs:
   data.extend(
       [n.decode('utf8'						) if hasattr(__lowercase		,				'decode'						) else n for n in group.attrs['%s%d' % (name, chunk_id)]]						)
   chunk_id += 1
 return data




def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 def _expand_single_ad_tensor(_UpperCamelCase		:	Union[str, Any]						):
  if isinstance(__lowercase		,				tf.Tensor						) and t.shape.rank == 1:
   return tf.expand_dims(__lowercase		,				axis=-1						)
  return t

 return tf.nest.map_structure(_expand_single_ad_tensor		,				__lowercase						)









from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_UpperCamelCase			:	Union[str, Any]								={'UserAgent': UserAgent().random}



def  a__ (__lowercase							:Optional[Any]   )		-> dict:
				_A	:				str   =      script.contents[0]
				_A	:				Dict   =      json.loads(data[data.find('''{"config"'''   ) : -1]   )
				return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 						UpperCAmelCase__ :







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







		def  A__      (					self		):
						_A	:				str   =      requests.get(self.url       ,headers=A__		).text
						_A	:				Dict   =      BeautifulSoup(A__       ,'''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		):
						return f"""{self.__class__.__name__}('{self.username}')"""







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







		@property
		def  A__      (					self		):
						return self.user_data["username"]







		@property
		def  A__      (					self		):
						return self.user_data["full_name"]







		@property
		def  A__      (					self		):
						return self.user_data["biography"]







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







		@property
		def  A__      (					self		):
						return self.user_data["external_url"]







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







		@property
		def  A__      (					self		):
						return self.user_data["edge_follow"]["count"]







		@property
		def  A__      (					self		):
						return self.user_data["edge_owner_to_timeline_media"]["count"]







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







		@property
		def  A__      (					self		):
						return self.user_data["is_verified"]







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



def  a__ (__lowercase							:str = "github"   )		-> None:
				import os

				if os.environ.get('''CI'''   ):
								return  # test failing on GitHub Actions
				_A	:				Optional[int]   =      InstagramUser(__lowercase   )
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data					,  __lowercase   )
				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 > 12_0000
				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()
	_UpperCamelCase			:	List[Any]								=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 = }''')



