



'''simple docstring'''

import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
			import numpy as np
			import torch

			from . import (
			    AlbertForPreTraining,
			    BartForConditionalGeneration,
			    BertForPreTraining,
			    BertForQuestionAnswering,
			    BertForSequenceClassification,
			    CamembertForMaskedLM,
			    CTRLLMHeadModel,
			    DistilBertForMaskedLM,
			    DistilBertForQuestionAnswering,
			    DPRContextEncoder,
			    DPRQuestionEncoder,
			    DPRReader,
			    ElectraForPreTraining,
			    FlaubertWithLMHeadModel,
			    GPTaLMHeadModel,
			    LayoutLMForMaskedLM,
			    LxmertForPreTraining,
			    LxmertVisualFeatureEncoder,
			    OpenAIGPTLMHeadModel,
			    RobertaForMaskedLM,
			    RobertaForSequenceClassification,
			    TaForConditionalGeneration,
			    TransfoXLLMHeadModel,
			    XLMRobertaForMaskedLM,
			    XLMWithLMHeadModel,
			    XLNetLMHeadModel,
			)


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE__     :						int	=  {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def 				a  (     UpperCamelCase_    :    Any      ,					UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Optional[int]=False      ,					UpperCamelCase_    :    Union[str, Any]=True			)       ->	List[str]:
		if model_type not in MODEL_CLASSES:
				raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

		snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =MODEL_CLASSES[model_type]

		# Initialise TF model
		if config_file in aws_config_map:
				snake_case__	     =cached_file(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					force_download=not use_cached_models			)
		snake_case__	     =config_class.from_json_file(SCREAMING_SNAKE_CASE_			)
		snake_case__	     =True
		snake_case__	     =True
		print(f"""Building TensorFlow model from configuration: {config}"""			)
		snake_case__	     =model_class(SCREAMING_SNAKE_CASE_			)

		# Load weights from tf checkpoint
		if pytorch_checkpoint_path in aws_config_map.keys():
				snake_case__	     =cached_file(
				    SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					force_download=not use_cached_models			)
		# Load PyTorch checkpoint in tf2 model:
		snake_case__	     =load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_			)

		if compare_with_pt_model:
				snake_case__	     =tf_model(tf_model.dummy_inputs      ,					training=SCREAMING_SNAKE_CASE_			)  # build the network

				snake_case__	     =torch.load(SCREAMING_SNAKE_CASE_      ,					map_location='cpu'			)
				snake_case__	     =pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_      ,					config=SCREAMING_SNAKE_CASE_      ,					state_dict=SCREAMING_SNAKE_CASE_			)

				with torch.no_grad():
						snake_case__	     =pt_model(**pt_model.dummy_inputs			)

				snake_case__	     =pto[0].numpy()
				snake_case__	     =tfo[0].numpy()
				snake_case__	     =np.amax(np.abs(np_pt - np_tf			)			)
				print(f"""Max absolute difference between models outputs {diff}"""			)
				assert diff <= 2e-2, f"""Error, model absolute difference is >2e-2: {diff}"""

		# Save pytorch-model
		print(f"""Save TensorFlow model to {tf_dump_path}"""			)
		tf_model.save_weights(SCREAMING_SNAKE_CASE_      ,					save_format='h5'			)






def 				a  (     UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    Union[str, Any]=None      ,					UpperCamelCase_    :    Tuple=None      ,					UpperCamelCase_    :    List[str]=False      ,					UpperCamelCase_    :    List[str]=False      ,					UpperCamelCase_    :    Dict=False      ,					UpperCamelCase_    :    List[Any]=False      ,					)       ->	Any:
		if args_model_type is None:
				snake_case__	     =list(MODEL_CLASSES.keys()			)
		else:
				snake_case__	     =[args_model_type]

		for j, model_type in enumerate(SCREAMING_SNAKE_CASE_      ,					start=1			):
				print('=' * 100			)
				print(f""" Converting model type {j}/{len(SCREAMING_SNAKE_CASE_			)}: {model_type}"""			)
				print('=' * 100			)
				if model_type not in MODEL_CLASSES:
						raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

				snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						snake_case__	     =list(aws_model_maps.keys()			)
				if config_shortcut_names_or_path is None:
						snake_case__	     =model_shortcut_names_or_path

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_			)      ,					start=1			):
						print('-' * 100			)
						if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
								if not only_convert_finetuned_models:
										print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""			)
										continue
								snake_case__	     =model_shortcut_name
						elif only_convert_finetuned_models:
								print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""			)
								continue
						print(
						    f"""    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE_			)}: {model_shortcut_name} - model_type {model_type}"""			)
						print('-' * 100			)

						if config_shortcut_name in aws_config_map:
								snake_case__	     =cached_file(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					force_download=not use_cached_models			)
						else:
								snake_case__	     =config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								snake_case__	     =cached_file(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					force_download=not use_cached_models			)
						else:
								snake_case__	     =model_shortcut_name

						if os.path.isfile(SCREAMING_SNAKE_CASE_			):
								snake_case__	     ='converted_model'

						convert_pt_checkpoint_to_tf(
						    model_type=SCREAMING_SNAKE_CASE_      ,					pytorch_checkpoint_path=SCREAMING_SNAKE_CASE_      ,					config_file=SCREAMING_SNAKE_CASE_      ,					tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE_      ,					model_shortcut_name + '-tf_model.h5'			)      ,					compare_with_pt_model=SCREAMING_SNAKE_CASE_      ,					)
						if remove_cached_files:
								os.remove(SCREAMING_SNAKE_CASE_			)
								os.remove(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Optional[int]	=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
			)
			parser.add_argument(
			    '''--model_type''',
			    default=None,
			    type=str,
			    help=(
			        f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
			        '''convert all the models from AWS.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_checkpoint_path''',
			    default=None,
			    type=str,
			    help=(
			        '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
			        '''If not given, will download and convert all the checkpoints from AWS.'''
			    ),
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    help=(
			        '''The config json file corresponding to the pre-trained model. \n'''
			        '''This specifies the model architecture. If not given and '''
			        '''--pytorch_checkpoint_path is not given or is a shortcut name '''
			        '''use the configuration associated to the shortcut name on the AWS'''
			    ),
			)
			parser.add_argument(
			    '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
			)
			parser.add_argument(
			    '''--use_cached_models''',
			    action='''store_true''',
			    help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
			)
			parser.add_argument(
			    '''--remove_cached_files''',
			    action='''store_true''',
			    help='''Remove pytorch models after conversion (save memory when converting in batches).''',
			)
			parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
			SCREAMING_SNAKE_CASE__     :						Dict	=  parser.parse_args()

			# if args.pytorch_checkpoint_path is not None:
			#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
			#                                 args.pytorch_checkpoint_path,
			#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
			#                                 args.tf_dump_path,
			#                                 compare_with_pt_model=args.compare_with_pt_model,
			#                                 use_cached_models=args.use_cached_models)
			# else:
			convert_all_pt_checkpoints_to_tf(
			    args.model_type.lower() if args.model_type is not None else None,
			    args.tf_dump_path,
			    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
			    if args.pytorch_checkpoint_path is not None
			    else None,
			    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
			    compare_with_pt_model=args.compare_with_pt_model,
			    use_cached_models=args.use_cached_models,
			    remove_cached_files=args.remove_cached_files,
			    only_convert_finetuned_models=args.only_convert_finetuned_models,
			)






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


A		: Union[str, Any]								=     PolynomialFeatures(degree=4)
A		: str								=     poly_reg.fit_transform(X)
A		: Union[str, Any]								=     LinearRegression()
pol_reg.fit(X_poly, y)




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_       )       )		,   color="""blue"""       )
						plt.title("""Truth or Bluff (Linear Regression)"""       )
						plt.xlabel("""Position level"""       )
						plt.ylabel("""Salary"""       )
						plt.show()


if __name__ == "__main__":
		viz_polymonial()

		# Predicting a new result with Polymonial Regression
		pol_reg.predict(poly_reg.fit_transform([[5.5]]))
		# output should be 132148.43750003