


"""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							:    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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE=True				)    ->				List[Any]:
							if model_type not in MODEL_CLASSES:
														raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()				)}."""				)

							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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE=False				, )    ->				Optional[int]:
							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_           =		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							:    List[str]		=    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,
						)







"""simple docstring"""
from math import factorial







def 						_a				(      _SCREAMING_SNAKE_CASE = 20				)    ->				int:
							snake_case_           =		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
							# 2, 3,...
							snake_case_           =		n // 2

							return int(factorial(_SCREAMING_SNAKE_CASE				) / (factorial(_SCREAMING_SNAKE_CASE				) * factorial(n - k				))				)


if __name__ == "__main__":
				import sys

				if len(sys.argv) == 1:
								print(solution(20))
				else:
								try:
												__SCREAMING_SNAKE_CASE							:    Optional[int]		=    int(sys.argv[1])
												print(solution(n))
								except ValueError:
												print('Invalid entry - please enter a number.')



