






from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


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


_UpperCamelCase			:	Union[str, Any]								=OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

_UpperCamelCase			:	List[Any]								=OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

_UpperCamelCase			:	str								=OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

_UpperCamelCase			:	List[Any]								=OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

_UpperCamelCase			:	List[str]								=OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

_UpperCamelCase			:	str								=OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

_UpperCamelCase			:	List[str]								=OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

_UpperCamelCase			:	str								=OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

_UpperCamelCase			:	Union[str, Any]								=OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

_UpperCamelCase			:	str								=OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

_UpperCamelCase			:	Tuple								=OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

_UpperCamelCase			:	str								=OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

_UpperCamelCase			:	Optional[int]								=OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

_UpperCamelCase			:	Optional[Any]								=OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

_UpperCamelCase			:	List[str]								=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_UpperCamelCase			:	Optional[Any]								=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_UpperCamelCase			:	str								=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_UpperCamelCase			:	Tuple								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_UpperCamelCase			:	Optional[Any]								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase			:	Optional[Any]								=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_UpperCamelCase			:	Union[str, Any]								=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_UpperCamelCase			:	Any								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase			:	Tuple								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_UpperCamelCase			:	List[Any]								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_UpperCamelCase			:	Any								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_UpperCamelCase			:	Dict								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_UpperCamelCase			:	str								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_UpperCamelCase			:	Dict								=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       List[Any]     =   FLAX_MODEL_MAPPING



_UpperCamelCase			:	str								=auto_class_update(FlaxAutoModel)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Any     =   FLAX_MODEL_FOR_PRETRAINING_MAPPING



_UpperCamelCase			:	str								=auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Tuple     =   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



_UpperCamelCase			:	str								=auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Dict     =   FLAX_MODEL_FOR_MASKED_LM_MAPPING



_UpperCamelCase			:	List[str]								=auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Dict     =   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



_UpperCamelCase			:	Tuple								=auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Any     =   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



_UpperCamelCase			:	List[Any]								=auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       int     =   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



_UpperCamelCase			:	Tuple								=auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       int     =   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



_UpperCamelCase			:	int								=auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Any     =   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



_UpperCamelCase			:	Union[str, Any]								=auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       List[str]     =   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



_UpperCamelCase			:	Optional[int]								=auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       List[Any]     =   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



_UpperCamelCase			:	List[Any]								=auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       Any     =   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



_UpperCamelCase			:	Any								=auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')






class 						UpperCAmelCase__ (							_BaseAutoModelClass							):
		__snake_case      :       str     =   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



_UpperCamelCase			:	str								=auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)











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 = }''')



