




"""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()

_lowerCamelCase  :			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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=False  ,      _UpperCAmelCase=True	):




	"""simple docstring"""






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

	A_   , A_   , A_   , A_  :				Optional[int]	   =		MODEL_CLASSES[model_type]

	# Initialise TF model
	if config_file in aws_config_map:
		A_  :				List[str]	   =		cached_file(_UpperCAmelCase  ,      _UpperCAmelCase  ,      force_download=not use_cached_models	)
	A_  :				Union[str, Any]	   =		config_class.from_json_file(_UpperCAmelCase	)
	A_  :				Any	   =		True
	A_  :				Tuple	   =		True
	print(f"""Building TensorFlow model from configuration: {config}"""	)
	A_  :				Any	   =		model_class(_UpperCAmelCase	)

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

	if compare_with_pt_model:
		A_  :				Any	   =		tf_model(tf_model.dummy_inputs  ,      training=_UpperCAmelCase	)  # build the network

		A_  :				Optional[int]	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
		A_  :				Dict	   =		pt_model_class.from_pretrained(
		    pretrained_model_name_or_path=_UpperCAmelCase  ,      config=_UpperCAmelCase  ,      state_dict=_UpperCAmelCase	)

		with torch.no_grad():
			A_  :				int	   =		pt_model(**pt_model.dummy_inputs	)

		A_  :				Dict	   =		pto[0].numpy()
		A_  :				Optional[int]	   =		tfo[0].numpy()
		A_  :				List[str]	   =		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(_UpperCAmelCase  ,      save_format='''h5'''	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=None  ,      _UpperCAmelCase=None  ,      _UpperCAmelCase=False  ,      _UpperCAmelCase=False  ,      _UpperCAmelCase=False  ,      _UpperCAmelCase=False  ,      ):




	"""simple docstring"""






	if args_model_type is None:
		A_  :				int	   =		list(MODEL_CLASSES.keys()	)
	else:
		A_  :				Dict	   =		[args_model_type]

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

		A_   , A_   , A_   , A_   , A_  :				Any	   =		MODEL_CLASSES[model_type]

		if model_shortcut_names_or_path is None:
			A_  :				int	   =		list(aws_model_maps.keys()	)
		if config_shortcut_names_or_path is None:
			A_  :				List[str]	   =		model_shortcut_names_or_path

		for i, (model_shortcut_name, config_shortcut_name) in enumerate(
		    zip(_UpperCAmelCase  ,      _UpperCAmelCase	)  ,      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
				A_  :				List[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(_UpperCAmelCase	)}: {model_shortcut_name} - model_type {model_type}"""	)
			print('''-''' * 100	)

			if config_shortcut_name in aws_config_map:
				A_  :				str	   =		cached_file(_UpperCAmelCase  ,      _UpperCAmelCase  ,      force_download=not use_cached_models	)
			else:
				A_  :				Optional[Any]	   =		config_shortcut_name

			if model_shortcut_name in aws_model_maps:
				A_  :				Optional[Any]	   =		cached_file(_UpperCAmelCase  ,      _UpperCAmelCase  ,      force_download=not use_cached_models	)
			else:
				A_  :				Tuple	   =		model_shortcut_name

			if os.path.isfile(_UpperCAmelCase	):
				A_  :				List[Any]	   =		'''converted_model'''

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


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       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  :			List[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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase  :			Optional[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Any        =       {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Any         =   """luke"""







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]=5_02_67      ,     _lowerCamelCase   :					Tuple=50_00_00      ,     _lowerCamelCase   :					int=7_68      ,     _lowerCamelCase   :					Union[str, Any]=2_56      ,     _lowerCamelCase   :					Dict=12      ,     _lowerCamelCase   :					str=12      ,     _lowerCamelCase   :					Dict=30_72      ,     _lowerCamelCase   :					Union[str, Any]="gelu"      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					int=2      ,     _lowerCamelCase   :					Optional[int]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=None      ,     _lowerCamelCase   :					Union[str, Any]=1      ,     _lowerCamelCase   :					str=0      ,     _lowerCamelCase   :					Dict=2      ,     **_lowerCamelCase   :					Optional[int]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				str	   =		vocab_size
					A_  :				Optional[Any]	   =		entity_vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				List[str]	   =		entity_emb_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				Dict	   =		num_attention_heads
					A_  :				Union[str, Any]	   =		hidden_act
					A_  :				Dict	   =		intermediate_size
					A_  :				int	   =		hidden_dropout_prob
					A_  :				str	   =		attention_probs_dropout_prob
					A_  :				List[Any]	   =		max_position_embeddings
					A_  :				Optional[Any]	   =		type_vocab_size
					A_  :				Optional[int]	   =		initializer_range
					A_  :				str	   =		layer_norm_eps
					A_  :				List[str]	   =		use_entity_aware_attention
					A_  :				Optional[int]	   =		classifier_dropout





