





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()

UpperCAmelCase_							=				{
    '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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase=False				,       lowercase=True				)				-> List[Any]:

							"""simple docstring"""



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

							lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Optional[int]  				=     MODEL_CLASSES[model_type]

							# Initialise TF model
							if config_file in aws_config_map:
														lowercase_	:   Optional[int]  				=     cached_file(lowercase				,       lowercase				,       force_download=not use_cached_models				)
							lowercase_	:   str  				=     config_class.from_json_file(lowercase				)
							lowercase_	:   Dict  				=     True
							lowercase_	:   Union[str, Any]  				=     True
							print(f"""Building TensorFlow model from configuration: {config}"""				)
							lowercase_	:   Any  				=     model_class(lowercase				)

							# Load weights from tf checkpoint
							if pytorch_checkpoint_path in aws_config_map.keys():
														lowercase_	:   int  				=     cached_file(
														    lowercase				,       lowercase				,       force_download=not use_cached_models				)
							# Load PyTorch checkpoint in tf2 model:
							lowercase_	:   List[str]  				=     load_pytorch_checkpoint_in_tfa_model(lowercase				,       lowercase				)

							if compare_with_pt_model:
														lowercase_	:   int  				=     tf_model(tf_model.dummy_inputs				,       training=lowercase				)  # build the network

														lowercase_	:   Optional[int]  				=     torch.load(lowercase				,       map_location="""cpu"""				)
														lowercase_	:   int  				=     pt_model_class.from_pretrained(
														    pretrained_model_name_or_path=lowercase				,       config=lowercase				,       state_dict=lowercase				)

														with torch.no_grad():
																					lowercase_	:   Dict  				=     pt_model(**pt_model.dummy_inputs				)

														lowercase_	:   Tuple  				=     pto[0].numpy()
														lowercase_	:   Tuple  				=     tfo[0].numpy()
														lowercase_	:   Tuple  				=     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(lowercase				,       save_format="""h5"""				)





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				,       lowercase=None				,       lowercase=False				,       lowercase=False				,       lowercase=False				,       lowercase=False				,       )				-> List[str]:

							"""simple docstring"""



							if args_model_type is None:
														lowercase_	:   List[str]  				=     list(MODEL_CLASSES.keys()				)
							else:
														lowercase_	:   str  				=     [args_model_type]

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

														lowercase_				,		lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Union[str, Any]  				=     MODEL_CLASSES[model_type]

														if model_shortcut_names_or_path is None:
																					lowercase_	:   Optional[Any]  				=     list(aws_model_maps.keys()				)
														if config_shortcut_names_or_path is None:
																					lowercase_	:   List[Any]  				=     model_shortcut_names_or_path

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(lowercase				,       lowercase				)				,       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
																												lowercase_	:   List[str]  				=     model_shortcut_name
																					elif only_convert_finetuned_models:
																												print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""				)
																												continue
																					print(
																					    f"""    Converting checkpoint {i}/{len(lowercase				)}: {model_shortcut_name} - model_type {model_type}"""				)
																					print("""-""" * 100				)

																					if config_shortcut_name in aws_config_map:
																												lowercase_	:   Any  				=     cached_file(lowercase				,       lowercase				,       force_download=not use_cached_models				)
																					else:
																												lowercase_	:   str  				=     config_shortcut_name

																					if model_shortcut_name in aws_model_maps:
																												lowercase_	:   List[str]  				=     cached_file(lowercase				,       lowercase				,       force_download=not use_cached_models				)
																					else:
																												lowercase_	:   str  				=     model_shortcut_name

																					if os.path.isfile(lowercase				):
																												lowercase_	:   int  				=     """converted_model"""

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


if __name__ == "__main__":
							UpperCAmelCase_							=				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.""")
							UpperCAmelCase_							=				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'''

from math import sqrt

def _SCREAMING_SNAKE_CASE (A = 1_000_000			)			->			int:






       """simple docstring"""







       lowercase__      =					0
       lowercase__      =					0
       lowercase__      =					42

       while num_cuboids <= limit:
              max_cuboid_size += 1
              for sum_shortest_sides in range(2    ,					2 * max_cuboid_size + 1			):
                     if sqrt(sum_shortest_sides**2 + max_cuboid_size**2			).is_integer():
                            num_cuboids += (
                                min(A    ,					sum_shortest_sides // 2			)
                                - max(1    ,					sum_shortest_sides - max_cuboid_size			)
                                + 1
                            )

       return max_cuboid_size


if __name__ == "__main__":
 print(f"""{solution() = }""")




