
'''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()

_lowercase			:  Optional[Any]   							=	{
    "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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			Tuple=False				,				__SCREAMING_SNAKE_CASE :			int=True							):



						"""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_     :					List[Any]													=	cached_file(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				force_download=not use_cached_models							)
						lowercase_     :					str													=	config_class.from_json_file(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					int													=	True
						lowercase_     :					Tuple													=	True
						print(F'''Building TensorFlow model from configuration: {config}'''							)
						lowercase_     :					Any													=	model_class(__SCREAMING_SNAKE_CASE							)

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

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

												lowercase_     :					int													=	torch.load(__SCREAMING_SNAKE_CASE				,				map_location='''cpu'''							)
												lowercase_     :					List[Any]													=	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():
																		lowercase_     :					Dict													=	pt_model(**pt_model.dummy_inputs							)

												lowercase_     :					Optional[Any]													=	pto[0].numpy()
												lowercase_     :					Optional[int]													=	tfo[0].numpy()
												lowercase_     :					Any													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Dict=None				,				__SCREAMING_SNAKE_CASE :			int=None				,				__SCREAMING_SNAKE_CASE :			Optional[int]=False				,				__SCREAMING_SNAKE_CASE :			Dict=False				,				__SCREAMING_SNAKE_CASE :			Optional[Any]=False				,				__SCREAMING_SNAKE_CASE :			Dict=False				,				):



						"""simple docstring"""





						if args_model_type is None:
												lowercase_     :					List[Any]													=	list(MODEL_CLASSES.keys()							)
						else:
												lowercase_     :					Tuple													=	[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()							)}.'''							)

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

												if model_shortcut_names_or_path is None:
																		lowercase_     :					Optional[int]													=	list(aws_model_maps.keys()							)
												if config_shortcut_names_or_path is None:
																		lowercase_     :					Union[str, Any]													=	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
																								lowercase_     :					Tuple													=	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:
																								lowercase_     :					List[Any]													=	cached_file(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				force_download=not use_cached_models							)
																		else:
																								lowercase_     :					List[str]													=	config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								lowercase_     :					Optional[int]													=	cached_file(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				force_download=not use_cached_models							)
																		else:
																								lowercase_     :					Union[str, Any]													=	model_shortcut_name

																		if os.path.isfile(__SCREAMING_SNAKE_CASE							):
																								lowercase_     :					Any													=	'''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__":
							_lowercase			:  Optional[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.")
							_lowercase			:  Optional[int]   							=	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 __future__ import annotations

from typing import Any






class 			lowerCAmelCase__       :






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	num_of_nodes
											lowercase_     :					list[list[int]]													=	[]
											lowercase_     :					dict[int, int]													=	{}






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											self.m_edges.append([u_node, v_node, weight]		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if self.m_component[u_node] == u_node:
																	return u_node
											return self.find_component(self.m_component[u_node]		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if self.m_component[u_node] != u_node:
																	for k in self.m_component:
																							lowercase_     :					Optional[int]													=	self.find_component(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if component_size[u_node] <= component_size[v_node]:
																	lowercase_     :					Any													=	v_node
																	component_size[v_node] += component_size[u_node]
																	self.set_component(__SCREAMING_SNAKE_CASE		)

											elif component_size[u_node] >= component_size[v_node]:
																	lowercase_     :					int													=	self.find_component(__SCREAMING_SNAKE_CASE		)
																	component_size[u_node] += component_size[v_node]
																	self.set_component(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	[]
											lowercase_     :					Optional[Any]													=	0

											lowercase_     :					list[Any]													=	[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes		):
																	self.m_component.update({node: node}		)
																	component_size.append(1		)

											lowercase_     :					Union[str, Any]													=	self.m_num_of_nodes

											while num_of_components > 1:
																	for edge in self.m_edges:
																							lowercase_						,			lowercase_						,			lowercase_     :					List[Any]													=	edge

																							lowercase_     :					Dict													=	self.m_component[u]
																							lowercase_     :					Any													=	self.m_component[v]

																							if u_component != v_component:

																													for component in (u_component, v_component):
																																			if (
																																			    minimum_weight_edge[component] == -1
																																			    or minimum_weight_edge[component][2] > w
																																			):
																																									lowercase_     :					Union[str, Any]													=	[u, v, w]

																	for edge in minimum_weight_edge:
																							if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																													lowercase_						,			lowercase_						,			lowercase_     :					str													=	edge

																													lowercase_     :					Tuple													=	self.m_component[u]
																													lowercase_     :					Union[str, Any]													=	self.m_component[v]

																													if u_component != v_component:
																																			mst_weight += w
																																			self.union(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																																			print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''		)
																																			num_of_components -= 1

																	lowercase_     :					str													=	[-1] * self.m_num_of_nodes
											print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''		)







def  snake_case_		(    ):



						"""simple docstring"""







if __name__ == "__main__":
							import doctest

							doctest.testmod()



