



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()

_lowerCAmelCase	:   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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=False    ,			snake_case__=True					)	->     int:



				"""simple docstring"""





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

				lowerCAmelCase__		  =  MODEL_CLASSES[model_type]

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

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

				if compare_with_pt_model:
								lowerCAmelCase__		  =  tf_model(tf_model.dummy_inputs    ,			training=__snake_case					)  # build the network

								lowerCAmelCase__		  =  torch.load(__snake_case    ,			map_location='cpu'					)
								lowerCAmelCase__		  =  pt_model_class.from_pretrained(
								    pretrained_model_name_or_path=__snake_case    ,			config=__snake_case    ,			state_dict=__snake_case					)

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

								lowerCAmelCase__		  =  pto[0].numpy()
								lowerCAmelCase__		  =  tfo[0].numpy()
								lowerCAmelCase__		  =  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(__snake_case    ,			save_format='h5'					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__=None    ,			snake_case__=None    ,			snake_case__=False    ,			snake_case__=False    ,			snake_case__=False    ,			snake_case__=False    ,			)	->     str:



				"""simple docstring"""





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

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

								lowerCAmelCase__		  =  MODEL_CLASSES[model_type]

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

								for i, (model_shortcut_name, config_shortcut_name) in enumerate(
								    zip(__snake_case    ,			__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
																lowerCAmelCase__		  =  model_shortcut_name
												elif only_convert_finetuned_models:
																print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'					)
																continue
												print(
												    f'    Converting checkpoint {i}/{len(__snake_case					)}: {model_shortcut_name} - model_type {model_type}'					)
												print('-' * 100					)

												if config_shortcut_name in aws_config_map:
																lowerCAmelCase__		  =  cached_file(__snake_case    ,			__snake_case    ,			force_download=not use_cached_models					)
												else:
																lowerCAmelCase__		  =  config_shortcut_name

												if model_shortcut_name in aws_model_maps:
																lowerCAmelCase__		  =  cached_file(__snake_case    ,			__snake_case    ,			force_download=not use_cached_models					)
												else:
																lowerCAmelCase__		  =  model_shortcut_name

												if os.path.isfile(__snake_case					):
																lowerCAmelCase__		  =  """converted_model"""

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


if __name__ == "__main__":
	_lowerCAmelCase	:   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.")
	_lowerCAmelCase	:   Any    =   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 collections import defaultdict

def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		first_str.lower().strip()
   _lowerCamelCase	:					int   =		second_str.lower().strip()

   # Remove whitespace
   _lowerCamelCase	:					Any   =		first_str.replace(""" """						, """"""   )
   _lowerCamelCase	:					List[str]   =		second_str.replace(""" """						, """"""   )

   # Strings of different lengths are not anagrams
   if len(__snake_case   ) != len(__snake_case   ):
      return False

   # Default values for count should be 0
   _lowerCamelCase	:					defaultdict[str, int]   =		defaultdict(__snake_case   )

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(__snake_case   )   ):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase				=						input("""Enter the first string """).strip()
 UpperCAmelCase				=						input("""Enter the second string """).strip()

 UpperCAmelCase				=						check_anagrams(input_a, input_b)
 print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')

