





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()

snake_case_							=			{
    '''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		)   ->     str:
		if model_type not in MODEL_CLASSES:
				raise ValueError(F"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}."		)

		lowerCamelCase   : Optional[int]								=MODEL_CLASSES[model_type]

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

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

		if compare_with_pt_model:
				lowerCamelCase   : Optional[Any]								=tf_model(tf_model.dummy_inputs							,       training=SCREAMING_SNAKE_CASE_		)  # build the network

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

				lowerCamelCase   : List[str]								=pto[0].numpy()
				lowerCamelCase   : Tuple								=tfo[0].numpy()
				lowerCamelCase   : int								=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							,       )   ->     Tuple:
		if args_model_type is None:
				lowerCamelCase   : Optional[int]								=list(MODEL_CLASSES.keys()		)
		else:
				lowerCamelCase   : Union[str, Any]								=[args_model_type]

		for j, model_type in enumerate(SCREAMING_SNAKE_CASE_							,       start=1		):
				print('''=''' * 1_0_0		)
				print(F" Converting model type {j}/{len(SCREAMING_SNAKE_CASE_		)}: {model_type}"		)
				print('''=''' * 1_0_0		)
				if model_type not in MODEL_CLASSES:
						raise ValueError(F"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}."		)

				lowerCamelCase   : Any								=MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						lowerCamelCase   : str								=list(aws_model_maps.keys()		)
				if config_shortcut_names_or_path is None:
						lowerCamelCase   : 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('''-''' * 1_0_0		)
						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
								lowerCamelCase   : 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(SCREAMING_SNAKE_CASE_		)}: {model_shortcut_name} - model_type {model_type}"		)
						print('''-''' * 1_0_0		)

						if config_shortcut_name in aws_config_map:
								lowerCamelCase   : Any								=cached_file(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       force_download=not use_cached_models		)
						else:
								lowerCamelCase   : Tuple								=config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								lowerCamelCase   : List[str]								=cached_file(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       force_download=not use_cached_models		)
						else:
								lowerCamelCase   : Union[str, Any]								=model_shortcut_name

						if os.path.isfile(SCREAMING_SNAKE_CASE_		):
								lowerCamelCase   : Union[str, 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__":
				snake_case_							=			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.''')
				snake_case_							=			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 string


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Optional[Any]								=''''''
		for i in sequence:
				lowerCamelCase   : int								=ord(SCREAMING_SNAKE_CASE_		)
				if 6_5 <= extract <= 9_0:
						output += chr(1_5_5 - extract		)
				elif 9_7 <= extract <= 1_2_2:
						output += chr(2_1_9 - extract		)
				else:
						output += i
		return output


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Tuple								=string.ascii_letters
		lowerCamelCase   : int								=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(SCREAMING_SNAKE_CASE_		)] if c in letters else c for c in sequence		)


def 					A__	(       )   ->     None:
		from timeit import timeit

		print('''Running performance benchmarks...'''		)
		lowerCamelCase   : Tuple								='''from string import printable ; from __main__ import atbash, atbash_slow'''
		print(F"> atbash_slow(): {timeit('atbash_slow(printable)'							,       setup=SCREAMING_SNAKE_CASE_		)} seconds"		)
		print(F">      atbash(): {timeit('atbash(printable)'							,       setup=SCREAMING_SNAKE_CASE_		)} seconds"		)


if __name__ == "__main__":
				for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
								print(F"""{example} encrypted in atbash: {atbash(example)}""")
				benchmark()

