



'''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()

lowercase_      :				Optional[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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      Dict ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      str ,  lowercase_    :      List[str]=False ,  lowercase_    :      str=True						):
						if model_type not in MODEL_CLASSES:
												raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()						)}."""						)

						lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							MODEL_CLASSES[model_type]

						# Initialise TF model
						if config_file in aws_config_map:
												lowercase        =							cached_file(lowercase_ ,  lowercase_ ,  force_download=not use_cached_models						)
						lowercase        =							config_class.from_json_file(lowercase_						)
						lowercase        =							True
						lowercase        =							True
						print(F"""Building TensorFlow model from configuration: {config}"""						)
						lowercase        =							model_class(lowercase_						)

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

						if compare_with_pt_model:
												lowercase        =							tf_model(tf_model.dummy_inputs ,  training=lowercase_						)  # build the network

												lowercase        =							torch.load(lowercase_ ,  map_location="""cpu"""						)
												lowercase        =							pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=lowercase_ ,  config=lowercase_ ,  state_dict=lowercase_						)

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

												lowercase        =							pto[0].numpy()
												lowercase        =							tfo[0].numpy()
												lowercase        =							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(lowercase_ ,  save_format="""h5"""						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      int ,  lowercase_    :      Tuple=None ,  lowercase_    :      Optional[int]=None ,  lowercase_    :      int=False ,  lowercase_    :      List[str]=False ,  lowercase_    :      Dict=False ,  lowercase_    :      List[Any]=False ,  ):
						if args_model_type is None:
												lowercase        =							list(MODEL_CLASSES.keys()						)
						else:
												lowercase        =							[args_model_type]

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

												lowercase     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							MODEL_CLASSES[model_type]

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

												for i, (model_shortcut_name, config_shortcut_name) in enumerate(
												    zip(lowercase_ ,  lowercase_						) ,  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
																								lowercase        =							model_shortcut_name
																		elif only_convert_finetuned_models:
																								print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""						)
																								continue
																		print(
																		    F"""    Converting checkpoint {i}/{len(lowercase_						)}: {model_shortcut_name} - model_type {model_type}"""						)
																		print("""-""" * 100						)

																		if config_shortcut_name in aws_config_map:
																								lowercase        =							cached_file(lowercase_ ,  lowercase_ ,  force_download=not use_cached_models						)
																		else:
																								lowercase        =							config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								lowercase        =							cached_file(lowercase_ ,  lowercase_ ,  force_download=not use_cached_models						)
																		else:
																								lowercase        =							model_shortcut_name

																		if os.path.isfile(lowercase_						):
																								lowercase        =							"""converted_model"""

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


if __name__ == "__main__":
						lowercase_      :				Optional[Any]   					=    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.''')
						lowercase_      :				Union[str, 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'''


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowercase_      :				Tuple   					=    logging.getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							git.Repo(search_parent_directories=lowercase_						)
						lowercase        =							{
						    """repo_id""": str(lowercase_						),
						    """repo_sha""": str(repo.head.object.hexsha						),
						    """repo_branch""": str(repo.active_branch						),
						}

						with open(os.path.join(lowercase_ ,  """git_log.json"""						) ,  """w"""						) as f:
												json.dump(lowercase_ ,  lowercase_ ,  indent=4						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if params.n_gpu <= 0:
												lowercase        =							0
												lowercase        =							-1
												lowercase        =							True
												lowercase        =							False
												return

						assert torch.cuda.is_available()

						logger.info("""Initializing GPUs"""						)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase        =							int(os.environ["""WORLD_SIZE"""]						)
												lowercase        =							int(os.environ["""N_GPU_NODE"""]						)
												lowercase        =							int(os.environ["""RANK"""]						)

												# number of nodes / node ID
												lowercase        =							params.world_size // params.n_gpu_per_node
												lowercase        =							params.global_rank // params.n_gpu_per_node
												lowercase        =							True

												assert params.n_nodes == int(os.environ["""N_NODES"""]						)
												assert params.node_id == int(os.environ["""NODE_RANK"""]						)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase        =							1
												lowercase        =							0
												lowercase        =							0
												lowercase        =							0
												lowercase        =							1
												lowercase        =							1
												lowercase        =							False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase        =							params.node_id == 0 and params.local_rank == 0
						lowercase        =							params.n_nodes > 1

						# summary
						lowercase        =							F"""--- Global rank: {params.global_rank} - """
						logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes						)
						logger.info(PREFIX + """Node ID        : %i""" % params.node_id						)
						logger.info(PREFIX + """Local rank     : %i""" % params.local_rank						)
						logger.info(PREFIX + """World size     : %i""" % params.world_size						)
						logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node						)
						logger.info(PREFIX + """Master         : %s""" % str(params.is_master						)						)
						logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node						)						)
						logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu						)						)
						logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()						)

						# set GPU device
						torch.cuda.set_device(params.local_rank						)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info("""Initializing PyTorch distributed"""						)
												torch.distributed.init_process_group(
												    init_method="""env://""" ,  backend="""nccl""" ,  )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed						)


