



'''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()

snake_case_ :	int         =  {
    '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__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=True							):
						if model_type not in MODEL_CLASSES:
												raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()							)}.'							)

						_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Tuple 				=   MODEL_CLASSES[model_type]

						# Initialise TF model
						if config_file in aws_config_map:
												_UpperCamelCase					:		List[str] 				=   cached_file(UpperCAmelCase_  ,  UpperCAmelCase_  ,  force_download=not use_cached_models							)
						_UpperCamelCase					:		Optional[Any] 				=   config_class.from_json_file(UpperCAmelCase_							)
						_UpperCamelCase					:		List[Any] 				=   True
						_UpperCamelCase					:		List[str] 				=   True
						print(f'Building TensorFlow model from configuration: {config}'							)
						_UpperCamelCase					:		Optional[int] 				=   model_class(UpperCAmelCase_							)

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

						if compare_with_pt_model:
												_UpperCamelCase					:		Optional[int] 				=   tf_model(tf_model.dummy_inputs  ,  training=UpperCAmelCase_							)  # build the network

												_UpperCamelCase					:		List[Any] 				=   torch.load(UpperCAmelCase_  ,  map_location='cpu'							)
												_UpperCamelCase					:		List[str] 				=   pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=UpperCAmelCase_  ,  config=UpperCAmelCase_  ,  state_dict=UpperCAmelCase_							)

												with torch.no_grad():
																		_UpperCamelCase					:		Tuple 				=   pt_model(**pt_model.dummy_inputs							)

												_UpperCamelCase					:		int 				=   pto[0].numpy()
												_UpperCamelCase					:		Optional[Any] 				=   tfo[0].numpy()
												_UpperCamelCase					:		Dict 				=   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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=False  ,  ):
						if args_model_type is None:
												_UpperCamelCase					:		Optional[Any] 				=   list(MODEL_CLASSES.keys()							)
						else:
												_UpperCamelCase					:		Optional[int] 				=   [args_model_type]

						for j, model_type in enumerate(UpperCAmelCase_  ,  start=1							):
												print('=' * 1_0_0							)
												print(f' Converting model type {j}/{len(UpperCAmelCase_							)}: {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()							)}.'							)

												_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		int 				=   MODEL_CLASSES[model_type]

												if model_shortcut_names_or_path is None:
																		_UpperCamelCase					:		Union[str, Any] 				=   list(aws_model_maps.keys()							)
												if config_shortcut_names_or_path is None:
																		_UpperCamelCase					:		str 				=   model_shortcut_names_or_path

												for i, (model_shortcut_name, config_shortcut_name) in enumerate(
												    zip(UpperCAmelCase_  ,  UpperCAmelCase_							)  ,  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
																								_UpperCamelCase					:		Union[str, Any] 				=   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('-' * 1_0_0							)

																		if config_shortcut_name in aws_config_map:
																								_UpperCamelCase					:		Tuple 				=   cached_file(UpperCAmelCase_  ,  UpperCAmelCase_  ,  force_download=not use_cached_models							)
																		else:
																								_UpperCamelCase					:		List[Any] 				=   config_shortcut_name

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

																		if os.path.isfile(UpperCAmelCase_							):
																								_UpperCamelCase					:		Dict 				=   '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__":
					snake_case_ :	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.')
					snake_case_ :	Tuple         =  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"""





import torch

from transformers import AutoModel







class 				SCREAMING_SNAKE_CASE					(		torch.nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict="sayef/fsner-bert-base-uncased"		):
						super(lowercase_   ,self		).__init__()

						lowerCAmelCase__	:						int  				=	AutoModel.from_pretrained(lowercase_   ,return_dict=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.nn.CosineSimilarity(3   ,1E-08		)
						lowerCAmelCase__	:						List[str]  				=	torch.nn.Softmax(dim=1		)







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				int		):
						return self.bert(**lowercase_		).last_hidden_state







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]		):
						return token_embeddings.sum(2   ,keepdim=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				Tuple=1		):
						return self.softmax(T * self.cos(lowercase_   ,lowercase_		)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[Any]  				=	W_supports['''sizes'''].tolist()
						lowerCAmelCase__	:						Dict  				=	W_supports['''start_token_id'''].item()
						lowerCAmelCase__	:						Union[str, Any]  				=	W_supports['''end_token_id'''].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						lowerCAmelCase__	:						Optional[Any]  				=	self.BERT(**lowercase_		)
						lowerCAmelCase__	:						int  				=	self.BERT(**lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	None
						lowerCAmelCase__	:						Union[str, Any]  				=	None

						lowerCAmelCase__	:						int  				=	W_supports['''input_ids'''] == start_token_id
						lowerCAmelCase__	:						Optional[Any]  				=	W_supports['''input_ids'''] == end_token_id

						for i, size in enumerate(lowercase_		):
							if i == 0:
								lowerCAmelCase__	:						str  				=	0
							else:
								lowerCAmelCase__	:						List[Any]  				=	support_sizes[i - 1]

							lowerCAmelCase__	:						Optional[Any]  				=	S[s : s + size][start_token_masks[s : s + size]]
							lowerCAmelCase__	:						List[Any]  				=	S[s : s + size][end_token_masks[s : s + size]]

							lowerCAmelCase__	:						Union[str, Any]  				=	torch.matmul(q[i]   ,s_start.T		).sum(1		).softmax(0		)
							lowerCAmelCase__	:						Any  				=	torch.matmul(q[i]   ,s_end.T		).sum(1		).softmax(0		)

							if p_starts is not None:
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_starts, p_start)		)
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_ends, p_end)		)
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	p_start
								lowerCAmelCase__	:						str  				=	p_end

						return p_starts, p_ends




