
"""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()

_A						=			{
    '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 (		a_,						a_,						a_,						a_,						a_=False,						a_=True      ):





		'''simple docstring'''


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

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

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

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

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

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

				with torch.no_grad():
						lowerCamelCase					:      Optional[Any]          =   pt_model(**pt_model.dummy_inputs      )

				lowerCamelCase					:      Union[str, Any]          =   pto[0].numpy()
				lowerCamelCase					:      Union[str, Any]          =   tfo[0].numpy()
				lowerCamelCase					:      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(a_,						save_format='h5'      )
def 		UpperCAmelCase (		a_,						a_,						a_=None,						a_=None,						a_=False,						a_=False,						a_=False,						a_=False,						):





		'''simple docstring'''


		if args_model_type is None:
				lowerCamelCase					:      Tuple          =   list(MODEL_CLASSES.keys()      )
		else:
				lowerCamelCase					:      Optional[int]          =   [args_model_type]

		for j, model_type in enumerate(a_,						start=1      ):
				print('=' * 100      )
				print(F""" Converting model type {j}/{len(a_      )}: {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     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      str          =   MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						lowerCamelCase					:      Optional[Any]          =   list(aws_model_maps.keys()      )
				if config_shortcut_names_or_path is None:
						lowerCamelCase					:      Optional[Any]          =   model_shortcut_names_or_path

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

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

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

						if os.path.isfile(a_      ):
								lowerCamelCase					:      List[Any]          =   'converted_model'

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


if __name__ == "__main__":
					_A						=			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.')
					_A						=			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 os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
					import ctypes
					import msvcrt  # noqa






					class 	_lowercase       (    ctypes.Structure		):
						# _fields is a specific attr expected by ctypes
						lowercase_				=     [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		if os.name == "nt":
				lowerCamelCase					:      Optional[int]          =   CursorInfo()
				lowerCamelCase					:      Union[str, Any]          =   ctypes.windll.kernelaa.GetStdHandle(-11      )
				ctypes.windll.kernelaa.GetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
				lowerCamelCase					:      Dict          =   False
				ctypes.windll.kernelaa.SetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
		elif os.name == "posix":
				sys.stdout.write('\033[?25l'      )
				sys.stdout.flush()
def 		UpperCAmelCase (		):





		'''simple docstring'''


		if os.name == "nt":
				lowerCamelCase					:      List[str]          =   CursorInfo()
				lowerCamelCase					:      List[Any]          =   ctypes.windll.kernelaa.GetStdHandle(-11      )
				ctypes.windll.kernelaa.GetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
				lowerCamelCase					:      Optional[Any]          =   True
				ctypes.windll.kernelaa.SetConsoleCursorInfo(a_,						ctypes.byref(a_      )      )
		elif os.name == "posix":
				sys.stdout.write('\033[?25h'      )
				sys.stdout.flush()





@contextmanager
def 		UpperCAmelCase (		):





		'''simple docstring'''


		try:
				hide_cursor()
				yield
		finally:
				show_cursor()






