




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()

UpperCamelCase__ :   str						=							{
    '''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						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     int=False  ,							lowerCamelCase_ :     Optional[Any]=True  )       ->  List[str]:







			"""simple docstring"""





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

			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[Any]					  =      MODEL_CLASSES[model_type]

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

			# Load weights from tf checkpoint
			if pytorch_checkpoint_path in aws_config_map.keys():
						SCREAMING_SNAKE_CASE_   : str					  =      cached_file(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							force_download=not use_cached_models  )
			# Load PyTorch checkpoint in tf2 model:
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      load_pytorch_checkpoint_in_tfa_model(lowerCamelCase_  ,							lowerCamelCase_  )

			if compare_with_pt_model:
						SCREAMING_SNAKE_CASE_   : Dict					  =      tf_model(tf_model.dummy_inputs  ,							training=lowerCamelCase_  )  # build the network

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.load(lowerCamelCase_  ,							map_location='cpu'  )
						SCREAMING_SNAKE_CASE_   : str					  =      pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=lowerCamelCase_  ,							config=lowerCamelCase_  ,							state_dict=lowerCamelCase_  )

						with torch.no_grad():
									SCREAMING_SNAKE_CASE_   : Dict					  =      pt_model(**pt_model.dummy_inputs  )

						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      pto[0].numpy()
						SCREAMING_SNAKE_CASE_   : List[str]					  =      tfo[0].numpy()
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     List[str]=None  ,							lowerCamelCase_ :     Union[str, Any]=None  ,							lowerCamelCase_ :     Optional[Any]=False  ,							lowerCamelCase_ :     str=False  ,							lowerCamelCase_ :     Union[str, Any]=False  ,							lowerCamelCase_ :     List[Any]=False  ,							)       ->  Dict:







			"""simple docstring"""





			if args_model_type is None:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      list(MODEL_CLASSES.keys()  )
			else:
						SCREAMING_SNAKE_CASE_   : Dict					  =      [args_model_type]

			for j, model_type in enumerate(lowerCamelCase_  ,							start=1  ):
						print('=' * 1_00  )
						print(F' Converting model type {j}/{len(lowerCamelCase_  )}: {model_type}'  )
						print('=' * 1_00  )
						if model_type not in MODEL_CLASSES:
									raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()  )}.'  )

						SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      MODEL_CLASSES[model_type]

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

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(lowerCamelCase_  ,							lowerCamelCase_  )  ,							start=1  ):
									print('-' * 1_00  )
									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
												SCREAMING_SNAKE_CASE_   : 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(lowerCamelCase_  )}: {model_shortcut_name} - model_type {model_type}'  )
									print('-' * 1_00  )

									if config_shortcut_name in aws_config_map:
												SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      cached_file(lowerCamelCase_  ,							lowerCamelCase_  ,							force_download=not use_cached_models  )
									else:
												SCREAMING_SNAKE_CASE_   : List[str]					  =      config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												SCREAMING_SNAKE_CASE_   : Dict					  =      cached_file(lowerCamelCase_  ,							lowerCamelCase_  ,							force_download=not use_cached_models  )
									else:
												SCREAMING_SNAKE_CASE_   : Any					  =      model_shortcut_name

									if os.path.isfile(lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE_   : Tuple					  =      '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__":
						UpperCamelCase__ :   Tuple						=							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.''')
						UpperCamelCase__ :   str						=							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,
						)









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


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


UpperCamelCase__ :   int						=							TypeVar('''DatasetType''', Dataset, IterableDataset)





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[DatasetType]  ,							lowerCamelCase_ :     Optional[List[float]] = None  ,							lowerCamelCase_ :     Optional[int] = None  ,							lowerCamelCase_ :     Optional[DatasetInfo] = None  ,							lowerCamelCase_ :     Optional[NamedSplit] = None  ,							lowerCamelCase_ :     Literal["first_exhausted", "all_exhausted"] = "first_exhausted"  ,							)       ->  DatasetType:







			"""simple docstring"""





			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError('Unable to interleave an empty list of datasets.'  )
			for i, dataset in enumerate(lowerCamelCase_  ):
						if not isinstance(lowerCamelCase_  ,							(Dataset, IterableDataset)  ):
									if isinstance(lowerCamelCase_  ,							(DatasetDict, IterableDatasetDict)  ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    'is an empty dataset dictionary.'  )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(lowerCamelCase_  )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_  )  )}\']'  )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_  ).__name__}.'  )
						if i == 0:
									SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      (
									    (Dataset, IterableDataset) if isinstance(lowerCamelCase_  ,							lowerCamelCase_  ) else (IterableDataset, Dataset)
									)
						elif not isinstance(lowerCamelCase_  ,							lowerCamelCase_  ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'  )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'  )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							stopping_strategy=lowerCamelCase_  )
			else:
						return _interleave_iterable_datasets(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							stopping_strategy=lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[DatasetType]  ,							lowerCamelCase_ :     Optional[DatasetInfo] = None  ,							lowerCamelCase_ :     Optional[NamedSplit] = None  ,							lowerCamelCase_ :     int = 0  ,							)       ->  DatasetType:







			"""simple docstring"""





			if not dsets:
						raise ValueError('Unable to concatenate an empty list of datasets.'  )
			for i, dataset in enumerate(lowerCamelCase_  ):
						if not isinstance(lowerCamelCase_  ,							(Dataset, IterableDataset)  ):
									if isinstance(lowerCamelCase_  ,							(DatasetDict, IterableDatasetDict)  ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    'is an empty dataset dictionary.'  )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(lowerCamelCase_  )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_  )  )}\']'  )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_  ).__name__}.'  )
						if i == 0:
									SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Any					  =      (
									    (Dataset, IterableDataset) if isinstance(lowerCamelCase_  ,							lowerCamelCase_  ) else (IterableDataset, Dataset)
									)
						elif not isinstance(lowerCamelCase_  ,							lowerCamelCase_  ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'  )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							axis=lowerCamelCase_  )
			else:
						return _concatenate_iterable_datasets(lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							axis=lowerCamelCase_  )



