


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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True )->  List[str]:




						"""simple docstring"""

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

						snake_case_	,   snake_case_	,   snake_case_	,   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 	__lowerCAmelCase   (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 , )->  str:




						"""simple docstring"""

						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_	,   snake_case_	,   snake_case_	,   snake_case_	,   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__":
			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,
			)



# Function to print upper half of diamond (pyramid)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(0 , SCREAMING_SNAKE_CASE ):
            for _ in range(0 , n - i - 1 ):  # printing spaces
                  print(''' ''' , end='''''' )
            for _ in range(0 , i + 1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(SCREAMING_SNAKE_CASE , 0 , -1 ):
            for _ in range(SCREAMING_SNAKE_CASE , 0 , -1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()
            for _ in range(n - i + 1 , 0 , -1 ):  # printing spaces
                  print(''' ''' , end='''''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      if n <= 0:
            print('''       ...       ....        nothing printing :(''' )
            return
      floyd(SCREAMING_SNAKE_CASE )  # upper half
      reverse_floyd(SCREAMING_SNAKE_CASE )  # lower half


if __name__ == "__main__":
   print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
   print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
   UpperCAmelCase      =   1
   while K:
      UpperCAmelCase      =   int(input("""enter the number and , and see the magic : """))
      print()
      pretty_print(user_number)
      UpperCAmelCase      =   int(input("""press 0 to exit... and 1 to continue..."""))

   print("""Good Bye...""")