

import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Tuple=13     , __lowerCamelCase					:			Tuple=30     , __lowerCamelCase					:			Any=2     , __lowerCamelCase					:			Union[str, Any]=3     , __lowerCamelCase					:			int=True     , __lowerCamelCase					:			Optional[int]=True     , __lowerCamelCase					:			List[Any]=32     , __lowerCamelCase					:			Dict=5     , __lowerCamelCase					:			Union[str, Any]=4     , __lowerCamelCase					:			Optional[Any]=37     , __lowerCamelCase					:			Union[str, Any]="gelu"     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			Any=0.1     , __lowerCamelCase					:			Union[str, Any]=10     , __lowerCamelCase					:			int=0.02     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   patch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
       SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE										=   type_sequence_label_size
       SCREAMING_SNAKE_CASE										=   initializer_range

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE										=   (image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE										=   num_patches + 1





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       SCREAMING_SNAKE_CASE										=   ViTConfig(
           image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , 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     , is_decoder=__lowerCamelCase     , initializer_range=self.initializer_range     , )

       return config, pixel_values





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   FlaxViTModel(config=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE										=   (self.image_size, self.image_size)
       SCREAMING_SNAKE_CASE										=   (self.patch_size, self.patch_size)
       SCREAMING_SNAKE_CASE										=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, num_patches + 1, self.hidden_size)			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.type_sequence_label_size
       SCREAMING_SNAKE_CASE										=   FlaxViTForImageClassification(config=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)			)

       # test greyscale images
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   FlaxViTForImageClassification(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	
       )										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_flax
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   FlaxViTModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase     , hidden_size=37			)





   def      _snake_case		(       self					:			Dict			):
       self.config_tester.run_common_tests()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   inspect.signature(model.__call__			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           SCREAMING_SNAKE_CASE										=   [*signature.parameters.keys()]

           SCREAMING_SNAKE_CASE										=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__			):
               SCREAMING_SNAKE_CASE										=   self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

               @jax.jit
               def model_jitted(__lowerCamelCase					:			str     , **__lowerCamelCase					:			Union[str, Any]			):
                   return model(pixel_values=__lowerCamelCase     , **__lowerCamelCase			)

               with self.subTest("JIT Enabled"			):
                   SCREAMING_SNAKE_CASE										=   model_jitted(**__lowerCamelCase			).to_tuple()

               with self.subTest("JIT Disabled"			):
                   with jax.disable_jit():
                       SCREAMING_SNAKE_CASE										=   model_jitted(**__lowerCamelCase			).to_tuple()

               self.assertEqual(len(__lowerCamelCase			)     , len(__lowerCamelCase			)			)
               for jitted_output, output in zip(__lowerCamelCase     , __lowerCamelCase			):
                   self.assertEqual(jitted_output.shape     , output.shape			)





   @slow
   def      _snake_case		(       self					:			Dict			):
       for model_class_name in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class_name.from_pretrained("google/vit-base-patch16-224"			)
           SCREAMING_SNAKE_CASE										=   model(np.ones((1, 3, 224, 224)			)			)
           self.assertIsNotNone(__lowerCamelCase			)


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 = }')