





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_A					:				Dict				=			logging.get_logger(__name__)


_A					:				List[str]				=			{
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[str]				=	"""codegen"""
    lowerCamelCase__    :		int				=	{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }



    def __init__(       self   ,				A_=5_04_00   ,				A_=20_48   ,				A_=20_48   ,				A_=40_96   ,				A_=28   ,				A_=16   ,				A_=64   ,				A_=None   ,				A_="gelu_new"   ,				A_=0.0   ,				A_=0.0   ,				A_=0.0   ,				A_=1E-5   ,				A_=0.02   ,				A_=True   ,				A_=5_02_56   ,				A_=5_02_56   ,				A_=False   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			n_ctx
        SCREAMING_SNAKE_CASE__											=			n_positions
        SCREAMING_SNAKE_CASE__											=			n_embd
        SCREAMING_SNAKE_CASE__											=			n_layer
        SCREAMING_SNAKE_CASE__											=			n_head
        SCREAMING_SNAKE_CASE__											=			n_inner
        SCREAMING_SNAKE_CASE__											=			rotary_dim
        SCREAMING_SNAKE_CASE__											=			activation_function
        SCREAMING_SNAKE_CASE__											=			resid_pdrop
        SCREAMING_SNAKE_CASE__											=			embd_pdrop
        SCREAMING_SNAKE_CASE__											=			attn_pdrop
        SCREAMING_SNAKE_CASE__											=			layer_norm_epsilon
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			use_cache

        SCREAMING_SNAKE_CASE__											=			bos_token_id
        SCREAMING_SNAKE_CASE__											=			eos_token_id

        super().__init__(
            bos_token_id=A_   ,				eos_token_id=A_   ,				tie_word_embeddings=A_   ,				**A_     )





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_ = "default"   ,				A_ = None   ,				A_ = False   ,				):





        '''simple docstring'''

        super().__init__(A_   ,				task=A_   ,				patching_specs=A_   ,				use_past=A_     )
        if not getattr(self._config   ,				'''pad_token_id'''   ,				A_     ):
            # TODO: how to do that better?
            SCREAMING_SNAKE_CASE__											=			0



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}     )
        if self.use_past:
            self.fill_with_past_key_values_(A_   ,				direction='''inputs'''     )
            SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
            SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''sequence'''}

        return common_inputs



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._config.n_layer



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._config.n_head



    def  lowercase_					(       self   ,				A_   ,				A_ = -1   ,				A_ = -1   ,				A_ = False   ,				A_ = None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			super(A_   ,				self     ).generate_dummy_inputs(
            A_   ,				batch_size=A_   ,				seq_length=A_   ,				is_pair=A_   ,				framework=A_     )

        # We need to order the input in the way they appears in the forward()
        SCREAMING_SNAKE_CASE__											=			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}     )

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
            else:
                import torch

                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                SCREAMING_SNAKE_CASE__											=			seqlen + 2
                SCREAMING_SNAKE_CASE__											=			(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                SCREAMING_SNAKE_CASE__											=			[
                    (torch.zeros(A_     ), torch.zeros(A_     )) for _ in range(self.num_layers     )
                ]

        SCREAMING_SNAKE_CASE__											=			common_inputs['''attention_mask''']
        if self.use_past:
            SCREAMING_SNAKE_CASE__											=			ordered_inputs['''attention_mask'''].dtype
            SCREAMING_SNAKE_CASE__											=			torch.cat(
                [ordered_inputs['''attention_mask'''], torch.ones(A_   ,				A_   ,				dtype=A_     )]   ,				dim=1     )

        return ordered_inputs




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 13


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase   =	{'''UserAgent''': UserAgent().random}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     dict:
							lowerCamelCase_					=       script.contents[0]
							lowerCamelCase_					=       json.loads(data[data.find("""{\"config\""""							) : -1]							)
							return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class    __A:

					def __init__(						self       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       F'''https://www.instagram.com/{username}/'''
												lowerCamelCase_					=       self.get_json()

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       requests.get(self.url ,       headers=__UpperCamelCase ).text
												lowerCamelCase_					=       BeautifulSoup(__UpperCamelCase ,       """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       :  Dict ):
												return F'''{self.__class__.__name__}(\'{self.username}\')'''

					def __str__(						self       :  Optional[Any] ):
												return F'''{self.fullname} ({self.username}) is {self.biography}'''

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

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

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

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

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["external_url"]

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

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

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

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["profile_pic_url_hd"]

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return self.user_data["is_verified"]



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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "github"							)	->     None:
							import os

							if os.environ.get("""CI"""							):
														return  # test failing on GitHub Actions
							lowerCamelCase_					=       InstagramUser(UpperCAmelCase__							)
							assert instagram_user.user_data
							assert isinstance(instagram_user.user_data      ,					UpperCAmelCase__							)
							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_5_0
							assert instagram_user.number_of_followers > 1_2_0_0_0_0
							assert instagram_user.number_of_followings > 1_5
							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   =	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 = }""")





