






"""simple docstring"""



from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__lowercase		:		Dict														=							{"UserAgent": UserAgent().random}





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							script.contents[0]
     __snake_case				=							json.loads(data[data.find('''{"config"''') : -1])
     return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     Tuple    )    ->       int:
         __snake_case				=							f"https://www.instagram.com/{username}/"
         __snake_case				=							self.get_json()
    def  lowercase					(		self							:     List[str]    )    ->       dict:
         __snake_case				=							requests.get(self.url	,      headers=A_    ).text
         __snake_case				=							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							:     List[Any]    )    ->       str:
         return f"{self.__class__.__name__}('{self.username}')"
    def __str__(		self							:     Union[str, Any]    )    ->       str:
         return f"{self.fullname} ({self.username}) is {self.biography}"
    @property
    def  lowercase					(		self							:     int    )    ->       str:
         return self.user_data["username"]
    @property
    def  lowercase					(		self							:     str    )    ->       str:
         return self.user_data["full_name"]
    @property
    def  lowercase					(		self							:     Optional[int]    )    ->       str:
         return self.user_data["biography"]
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       str:
         return self.user_data["business_email"]
    @property
    def  lowercase					(		self							:     List[str]    )    ->       str:
         return self.user_data["external_url"]
    @property
    def  lowercase					(		self							:     str    )    ->       int:
         return self.user_data["edge_followed_by"]["count"]
    @property
    def  lowercase					(		self							:     Dict    )    ->       int:
         return self.user_data["edge_follow"]["count"]
    @property
    def  lowercase					(		self							:     str    )    ->       int:
         return self.user_data["edge_owner_to_timeline_media"]["count"]
    @property
    def  lowercase					(		self							:     Any    )    ->       str:
         return self.user_data["profile_pic_url_hd"]
    @property
    def  lowercase					(		self							:     List[str]    )    ->       bool:
         return self.user_data["is_verified"]







    @property
    def  lowercase					(		self							:     int    )    ->       bool:
         return self.user_data["is_private"]






def    SCREAMING_SNAKE_CASE       (     snake_case = "github"):
     import os

     if os.environ.get('''CI'''):
          return  # test failing on GitHub Actions
     __snake_case				=							InstagramUser(snake_case)
     assert instagram_user.user_data
     assert isinstance(instagram_user.user_data,      snake_case)
     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 > 1_50
     assert instagram_user.number_of_followers > 12_00_00
     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()
       __lowercase		:		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 = }""")
"""simple docstring"""



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     List[Any]=13	,      A_							:     Dict=7	,      A_							:     Optional[int]=True	,      A_							:     Optional[int]=True	,      A_							:     List[Any]=True	,      A_							:     Union[str, Any]=True	,      A_							:     str=99	,      A_							:     Union[str, Any]=32	,      A_							:     Optional[int]=5	,      A_							:     Union[str, Any]=4	,      A_							:     Dict=37	,      A_							:     Any="gelu"	,      A_							:     Tuple=0.1	,      A_							:     str=0.1	,      A_							:     List[str]=512	,      A_							:     List[str]=16	,      A_							:     Optional[int]=2	,      A_							:     Optional[Any]=0.02	,      A_							:     str=4	,      )    ->       Any:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_attention_mask
         __snake_case				=							use_token_type_ids
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							type_vocab_size
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range
         __snake_case				=							num_choices
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							None
         if self.use_attention_mask:
              __snake_case				=							random_attention_mask([self.batch_size, self.seq_length]    )

         __snake_case				=							None
         if self.use_token_type_ids:
              __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size    )

         __snake_case				=							RoFormerConfig(
             vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      is_decoder=A_	,      initializer_range=self.initializer_range	,      )

         return config, input_ids, token_type_ids, attention_mask







    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							self.prepare_config_and_inputs()
         __snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							config_and_inputs
         __snake_case				=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					True

    UpperCamelCase_	:					List[str]							=					(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerModelTester(self    )







    @slow
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''	,      from_pt=A_    )
              __snake_case				=							model(np.ones((1, 1)    )    )
              self.assertIsNotNone(A_    )






@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @slow
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
         __snake_case				=							jnp.array([[0, 1, 2, 3, 4, 5]]    )
         __snake_case				=							model(A_    )[0]

         __snake_case				=							50_000

         __snake_case				=							(1, 6, vocab_size)
         self.assertEqual(output.shape	,      A_    )

         __snake_case				=							jnp.array(
             [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]    )

         self.assertTrue(jnp.allclose(output[:, :3, :3]	,      A_	,      atol=1E-4    )    )