
from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel





@require_tf
class    __A:
					SCREAMING_SNAKE_CASE           =     BlenderbotConfig
					SCREAMING_SNAKE_CASE           =     {}
					SCREAMING_SNAKE_CASE           =     '''gelu'''

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Dict=1_3 ,       __UpperCamelCase       :  str=7 ,       __UpperCamelCase       :  Union[str, Any]=True ,       __UpperCamelCase       :  Union[str, Any]=False ,       __UpperCamelCase       :  Tuple=9_9 ,       __UpperCamelCase       :  Any=3_2 ,       __UpperCamelCase       :  Optional[int]=2 ,       __UpperCamelCase       :  Optional[int]=4 ,       __UpperCamelCase       :  Optional[int]=3_7 ,       __UpperCamelCase       :  Union[str, Any]=0.1 ,       __UpperCamelCase       :  Dict=0.1 ,       __UpperCamelCase       :  List[Any]=2_0 ,       __UpperCamelCase       :  Optional[Any]=2 ,       __UpperCamelCase       :  List[Any]=1 ,       __UpperCamelCase       :  List[str]=0 ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size

												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       eos_token_id
												lowerCamelCase_					=       pad_token_id
												lowerCamelCase_					=       bos_token_id

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size )
												lowerCamelCase_					=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,       1 )
												lowerCamelCase_					=       tf.concat([input_ids, eos_tensor] ,       axis=1 )

												lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size )

												lowerCamelCase_					=       self.config_cls(
												    vocab_size=self.vocab_size ,       d_model=self.hidden_size ,       encoder_layers=self.num_hidden_layers ,       decoder_layers=self.num_hidden_layers ,       encoder_attention_heads=self.num_attention_heads ,       decoder_attention_heads=self.num_attention_heads ,       encoder_ffn_dim=self.intermediate_size ,       decoder_ffn_dim=self.intermediate_size ,       dropout=self.hidden_dropout_prob ,       attention_dropout=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       eos_token_ids=[2] ,       bos_token_id=self.bos_token_id ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.pad_token_id ,       **self.config_updates ,       )
												lowerCamelCase_					=       prepare_blenderbot_inputs_dict(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												return config, inputs_dict



					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       TFBlenderbotModel(config=__UpperCamelCase ).get_decoder()
												lowerCamelCase_					=       inputs_dict["""input_ids"""]

												lowerCamelCase_					=       input_ids[:1, :]
												lowerCamelCase_					=       inputs_dict["""attention_mask"""][:1, :]
												lowerCamelCase_					=       inputs_dict["""head_mask"""]
												lowerCamelCase_					=       1

												# first forward pass
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       head_mask=__UpperCamelCase ,       use_cache=__UpperCamelCase )

												lowerCamelCase_	, lowerCamelCase_					=       outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												lowerCamelCase_					=       ids_tensor((self.batch_size, 3) ,       config.vocab_size )
												lowerCamelCase_					=       tf.cast(ids_tensor((self.batch_size, 3) ,       2 ) ,       tf.inta )

												# append to next input_ids and
												lowerCamelCase_					=       tf.concat([input_ids, next_tokens] ,       axis=-1 )
												lowerCamelCase_					=       tf.concat([attention_mask, next_attn_mask] ,       axis=-1 )

												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase )[0]
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       past_key_values=__UpperCamelCase )[0]

												self.parent.assertEqual(next_tokens.shape[1] ,       output_from_past.shape[1] )

												# select random slice
												lowerCamelCase_					=       int(ids_tensor((1,) ,       output_from_past.shape[-1] ) )
												lowerCamelCase_					=       output_from_no_past[:, -3:, random_slice_idx]
												lowerCamelCase_					=       output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(__UpperCamelCase ,       __UpperCamelCase ,       rtol=1E-3 )



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Union[str, Any]=None      ,					UpperCAmelCase__					:   int=None      ,					UpperCAmelCase__					:   int=None      ,					UpperCAmelCase__					:   Dict=None      ,					)	->     Tuple:
							if attention_mask is None:
														lowerCamelCase_					=       tf.cast(tf.math.not_equal(UpperCAmelCase__      ,					config.pad_token_id							)      ,					tf.inta							)
							if decoder_attention_mask is None:
														lowerCamelCase_					=       tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape      ,					dtype=tf.inta							),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,					config.pad_token_id							)      ,					tf.inta							),
														    ]      ,					axis=-1      ,					)
							if head_mask is None:
														lowerCamelCase_					=       tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCamelCase_					=       tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCamelCase_					=       tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}





@require_tf
class    __A(					UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE           =     (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE           =     (
					    {
					        '''conversational''': TFBlenderbotForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotModel,
					        '''summarization''': TFBlenderbotForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
					        '''translation''': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       TFBlenderbotModelTester(self )
												lowerCamelCase_					=       ConfigTester(self ,       config_class=__UpperCamelCase )

					def  lowercase__   (						self       :  int ):
												self.config_tester.run_common_tests()



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )





@require_tokenizers
@require_tf
class    __A(					unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     ['''My friends are cool but they eat too many carbs.''']
					SCREAMING_SNAKE_CASE           =     '''facebook/blenderbot-400M-distill'''

					@cached_property
					def  lowercase__   (						self       :  List[Any] ):
												return BlenderbotTokenizer.from_pretrained(self.model_name )

					@cached_property
					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
												return model



					@slow
					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       self.tokenizer(self.src_text ,       return_tensors="""tf""" )
												lowerCamelCase_					=       self.model.generate(
												    model_inputs.input_ids ,       )
												lowerCamelCase_					=       self.tokenizer.batch_decode(generated_ids.numpy() ,       skip_special_tokens=__UpperCamelCase )[0]
												assert (
												    generated_words
												    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
												)







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 = }""")





