

"""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()

a__       :							Optional[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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=False						,		lowerCAmelCase_=True     ):




					'''simple docstring'''


					if model_type not in MODEL_CLASSES:
										raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()     )}."""     )

					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       MODEL_CLASSES[model_type]

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

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

					if compare_with_pt_model:
										__SCREAMING_SNAKE_CASE		     =       tf_model(tf_model.dummy_inputs						,		training=lowerCamelCase__     )  # build the network

										__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCamelCase__						,		map_location="cpu"     )
										__SCREAMING_SNAKE_CASE		     =       pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=lowerCamelCase__						,		config=lowerCamelCase__						,		state_dict=lowerCamelCase__     )

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

										__SCREAMING_SNAKE_CASE		     =       pto[0].numpy()
										__SCREAMING_SNAKE_CASE		     =       tfo[0].numpy()
										__SCREAMING_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(lowerCamelCase__						,		save_format="h5"     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		lowerCAmelCase_=False						,		lowerCAmelCase_=False						,		lowerCAmelCase_=False						,		lowerCAmelCase_=False						,		):




					'''simple docstring'''


					if args_model_type is None:
										__SCREAMING_SNAKE_CASE		     =       list(MODEL_CLASSES.keys()     )
					else:
										__SCREAMING_SNAKE_CASE		     =       [args_model_type]

					for j, model_type in enumerate(lowerCamelCase__						,		start=1     ):
										print("=" * 100     )
										print(f""" Converting model type {j}/{len(lowerCamelCase__     )}: {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()     )}."""     )

										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       MODEL_CLASSES[model_type]

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

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(lowerCamelCase__						,		lowerCamelCase__     )						,		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
																				__SCREAMING_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(lowerCamelCase__     )}: {model_shortcut_name} - model_type {model_type}"""     )
															print("-" * 100     )

															if config_shortcut_name in aws_config_map:
																				__SCREAMING_SNAKE_CASE		     =       cached_file(lowerCamelCase__						,		lowerCamelCase__						,		force_download=not use_cached_models     )
															else:
																				__SCREAMING_SNAKE_CASE		     =       config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				__SCREAMING_SNAKE_CASE		     =       cached_file(lowerCamelCase__						,		lowerCamelCase__						,		force_download=not use_cached_models     )
															else:
																				__SCREAMING_SNAKE_CASE		     =       model_shortcut_name

															if os.path.isfile(lowerCamelCase__     ):
																				__SCREAMING_SNAKE_CASE		     =       "converted_model"

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


if __name__ == "__main__":
		a__       :							Any        =  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.''')
		a__       :							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,
		)




"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return (data["data"], data["target"])




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		display_labels=lowerCAmelCase_						,		cmap="Blues"						,		normalize="true"						,		)
					plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
					plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
