


'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
    from fairscale.optim import OSS


lowerCAmelCase						=					logging.get_logger(__name__)

lowerCAmelCase						=					{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def __init__( self			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Union[str, Any]:
            super().__init__(*snake_case_			,     **snake_case_   )

            if config is None:
                 assert isinstance(self.model			,     snake_case_   ), (
                     "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                     F' {self.model.__class__}'
                 )
                 _A				=							self.model.config
            else:
                 _A				=							config

            _A				=							data_args
            _A				=							self.config.tgt_vocab_size if isinstance(self.config			,     snake_case_   ) else self.config.vocab_size

            if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                 assert self.config.pad_token_id is not None, (
                     "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                     " calculation or doing label smoothing."
                 )

            if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                 logger.warning(
                     F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
                     ' padding..'   )

            if self.args.label_smoothing == 0:
                 _A				=							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id   )
            else:
                 # dynamically import label_smoothed_nll_loss
                 from utils import label_smoothed_nll_loss

                 _A				=							label_smoothed_nll_loss






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Optional[int]:
            if self.optimizer is None:
                 _A				=							['bias', 'LayerNorm.weight']
                 _A				=							[
                     {
                         'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay   )],
                         'weight_decay': self.args.weight_decay,
                     },
                     {
                         'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay   )],
                         'weight_decay': 0.0,
                     },
                 ]
                 _A				=							Adafactor if self.args.adafactor else AdamW
                 if self.args.adafactor:
                      _A				=							Adafactor
                      _A				=							{'scale_parameter': False, 'relative_step': False}
                 else:
                      _A				=							AdamW
                      _A				=							{
                          'betas': (self.args.adam_betaa, self.args.adam_betaa),
                          'eps': self.args.adam_epsilon,
                      }
                 _A				=							self.args.learning_rate
                 if self.sharded_ddp:
                      _A				=							OSS(
                          params=snake_case_			,     optim=snake_case_			,     **snake_case_			,     )
                 else:
                      _A				=							optimizer_cls(snake_case_			,     **snake_case_   )

            if self.lr_scheduler is None:
                 _A				=							self._get_lr_scheduler(snake_case_   )
            else:  # ignoring --lr_scheduler
                 logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            _A				=							arg_to_scheduler[self.args.lr_scheduler]
            if self.args.lr_scheduler == "constant":
                 _A				=							schedule_func(self.optimizer   )
            elif self.args.lr_scheduler == "constant_w_warmup":
                 _A				=							schedule_func(self.optimizer			,     num_warmup_steps=self.args.warmup_steps   )
            else:
                 _A				=							schedule_func(
                     self.optimizer			,     num_warmup_steps=self.args.warmup_steps			,     num_training_steps=snake_case_   )
            return scheduler






       def   UpperCamelCase						( self   )->      Optional[torch.utils.data.Sampler]:
            if isinstance(self.train_dataset			,     torch.utils.data.IterableDataset   ):
                 return None
            elif is_torch_tpu_available():
                 return get_tpu_sampler(self.train_dataset   )
            else:
                 if self.args.sortish_sampler:
                      self.train_dataset.make_sortish_sampler(
                          self.args.per_device_train_batch_size			,     distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)			,     )

                 return (
                     RandomSampler(self.train_dataset   )
                     if self.args.local_rank == -1
                     else DistributedSampler(self.train_dataset   )
                 )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Any:
            if self.args.label_smoothing == 0:
                 if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                      # force training to ignore pad token
                      _A				=							model(**snake_case_			,     use_cache=snake_case_   )[0]
                      _A				=							self.loss_fn(logits.view(-1			,     logits.shape[-1]   )			,     labels.view(-1   )   )
                 else:
                      # compute usual loss via models
                      _A		,				_A				=							model(**snake_case_			,     labels=snake_case_			,     use_cache=snake_case_   )[:2]
            else:
                 # compute label smoothed loss
                 _A				=							model(**snake_case_			,     use_cache=snake_case_   )[0]
                 _A				=							torch.nn.functional.log_softmax(snake_case_			,     dim=-1   )
                 _A		,				_A				=							self.loss_fn(snake_case_			,     snake_case_			,     self.args.label_smoothing			,     ignore_index=self.config.pad_token_id   )
            return loss, logits






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Union[str, Any]:
            _A				=							inputs.pop('labels'   )
            _A		,				_A				=							self._compute_loss(snake_case_			,     snake_case_			,     snake_case_   )
            return loss






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     )->      Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
            _A				=							self._prepare_inputs(snake_case_   )

            _A				=							{
                'max_length': self.data_args.val_max_target_length
                if self.data_args is not None
                else self.config.max_length,
                'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
            }

            if self.args.predict_with_generate and not self.args.prediction_loss_only:
                 _A				=							self.model.generate(
                     inputs['input_ids']			,     attention_mask=inputs['attention_mask']			,     **snake_case_			,     )
                 # in case the batch is shorter than max length, the output should be padded
                 if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                      _A				=							self._pad_tensors_to_max_len(snake_case_			,     gen_kwargs['max_length']   )

            _A				=							inputs.pop('labels'   )
            with torch.no_grad():
                 # compute loss on predict data
                 _A		,				_A				=							self._compute_loss(snake_case_			,     snake_case_			,     snake_case_   )

            _A				=							loss.mean().detach()
            if self.args.prediction_loss_only:
                 return (loss, None, None)

            _A				=							generated_tokens if self.args.predict_with_generate else logits

            if labels.shape[-1] < gen_kwargs["max_length"]:
                 _A				=							self._pad_tensors_to_max_len(snake_case_			,     gen_kwargs['max_length']   )

            return (loss, logits, labels)






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Any:
            # If PAD token is not defined at least EOS token has to be defined
            _A				=							self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

            if pad_token_id is None:
                 raise ValueError(
                     'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
                     F' padded to `max_length`={max_length}'   )

            _A				=							pad_token_id * torch.ones(
                (tensor.shape[0], max_length)			,     dtype=tensor.dtype			,     device=tensor.device   )
            _A				=							tensor
            return padded_tensor










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_													=      {
    '''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		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Dict=False					, __lowerCamelCase						:     int=True						)	->    Dict:
					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						 =MODEL_CLASSES[model_type]

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

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

					if compare_with_pt_model:
										__lowerCAmelCase						 =tf_model(tf_model.dummy_inputs					, training=__lowerCamelCase						)  # build the network

										__lowerCAmelCase						 =torch.load(__lowerCamelCase					, map_location="""cpu"""						)
										__lowerCAmelCase						 =pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=__lowerCamelCase					, config=__lowerCamelCase					, state_dict=__lowerCamelCase						)

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

										__lowerCAmelCase						 =pto[0].numpy()
										__lowerCAmelCase						 =tfo[0].numpy()
										__lowerCAmelCase						 =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(__lowerCamelCase					, save_format="""h5"""						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Tuple=None					, __lowerCamelCase						:     Any=None					, __lowerCamelCase						:     Any=False					, __lowerCamelCase						:     str=False					, __lowerCamelCase						:     List[str]=False					, __lowerCamelCase						:     Tuple=False					, )	->    Dict:
					if args_model_type is None:
										__lowerCAmelCase						 =list(MODEL_CLASSES.keys()						)
					else:
										__lowerCAmelCase						 =[args_model_type]

					for j, model_type in enumerate(__lowerCamelCase					, start=1						):
										print("""=""" * 100						)
										print(f""" Converting model type {j}/{len(__lowerCamelCase						)}: {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						 =MODEL_CLASSES[model_type]

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

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

															if config_shortcut_name in aws_config_map:
																				__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
															else:
																				__lowerCAmelCase						 =config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
															else:
																				__lowerCAmelCase						 =model_shortcut_name

															if os.path.isfile(__lowerCamelCase						):
																				__lowerCAmelCase						 ="""converted_model"""

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


if __name__ == "__main__":
				lowercase_													=      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_													=      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,
				)




