




'''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()

UpperCAmelCase_					=   {
    "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							(   a_						:				int					,       a_						:				Optional[int]					,       a_						:				Dict					,       a_						:				Dict					,       a_						:				Dict=False					,       a_						:				Tuple=True      ):






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

  __a						,							__a						,							__a						,							__a								=       MODEL_CLASSES[model_type]

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

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

  if compare_with_pt_model:
    __a								=       tf_model(tf_model.dummy_inputs					,       training=a_      )  # build the network

    __a								=       torch.load(a_					,       map_location='cpu'      )
    __a								=       pt_model_class.from_pretrained(
        pretrained_model_name_or_path=a_					,       config=a_					,       state_dict=a_      )

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

    __a								=       pto[0].numpy()
    __a								=       tfo[0].numpy()
    __a								=       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     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				Optional[int]					,       a_						:				Union[str, Any]=None					,       a_						:				int=None					,       a_						:				Dict=False					,       a_						:				Optional[int]=False					,       a_						:				Optional[int]=False					,       a_						:				Any=False					,       ):






  if args_model_type is None:
    __a								=       list(MODEL_CLASSES.keys()      )
  else:
    __a								=       [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()      )}."      )

    __a						,							__a						,							__a						,							__a						,							__a								=       MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      __a								=       list(aws_model_maps.keys()      )
    if config_shortcut_names_or_path is None:
      __a								=       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
        __a								=       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:
        __a								=       cached_file(a_					,       a_					,       force_download=not use_cached_models      )
      else:
        __a								=       config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        __a								=       cached_file(a_					,       a_					,       force_download=not use_cached_models      )
      else:
        __a								=       model_shortcut_name

      if os.path.isfile(a_      ):
        __a								=       '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__":
  UpperCAmelCase_					=   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_					=   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,
  )





import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)







class 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a=0.01							,				a=10_00					)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						p_stop
								_UpperCamelCase         =						max_length







						def __iter__(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						False
								while not stop and count < self.max_length:
										yield count
										count += 1
										_UpperCamelCase         =						random.random() < self.p_stop







class 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self							,				a							,				a							,				a=False							,				a=True					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						[
								    BatchSamplerShard(a							,				2							,				a							,				split_batches=a							,				even_batches=a					)
								    for i in range(2					)
								]
								_UpperCamelCase         =						[list(a					) for batch_sampler_shard in batch_sampler_shards]
								if not split_batches:
										self.assertListEqual([len(a					) for shard in batch_sampler_shards]							,				[len(a					) for e in expected]					)
								self.assertListEqual(a							,				a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a					)

								# Check the shards when the dataset is a round multiple of batch size but not total batch size.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(20					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(20					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a					)

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(a							,				a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a					)

						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1]], [[0, 1]]]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a					)

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=3							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								# Check the shards when the dataset is a round multiple of batch size but not total batch size.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								_UpperCamelCase         =						BatchSampler(range(20					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(20					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1]], []]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=3							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a							,				even_batches=a					)

						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(24					)							,				batch_size=4							,				drop_last=a					)
								# Expected shouldn't change
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size.
								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(22					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(21					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								# Check the shards when the dataset is very small.
								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[[0, 1]], []]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

								_UpperCamelCase         =						BatchSampler(range(2					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						[[], []]
								self.check_batch_sampler_shards(a							,				a							,				split_batches=a							,				even_batches=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								_UpperCamelCase         =						[BatchSamplerShard(a							,				2							,				a							,				even_batches=a					) for i in range(2					)]

								self.assertEqual(len(batch_sampler_shards[0]					)							,				3					)
								self.assertEqual(len(batch_sampler_shards[1]					)							,				2					)

								self.assertListEqual(list(batch_sampler_shards[0]					)							,				[[0, 1, 2], [5, 6, 7, 8], [12, 13]]					)
								self.assertListEqual(list(batch_sampler_shards[1]					)							,				[[3, 4], [9, 10, 11]]					)

						def 			A_					(      self							,				a							,				a							,				a							,				a=False							,				a=2							,				a=False					)       ->   Any:


								'''simple docstring'''

								random.seed(a					)
								_UpperCamelCase         =						list(a					)

								_UpperCamelCase         =						[
								    IterableDatasetShard(
								        a							,				batch_size=a							,				drop_last=a							,				num_processes=a							,				process_index=a							,				split_batches=a							,				)
								    for i in range(a					)
								]
								_UpperCamelCase         =						[]
								for iterable_dataset_shard in iterable_dataset_shards:
										# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
										random.seed(a					)
										iterable_dataset_lists.append(list(a					)					)

								_UpperCamelCase         =						batch_size // num_processes if split_batches else batch_size
								# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
								_UpperCamelCase         =						iterable_dataset_lists[0]
								for l in iterable_dataset_lists[1:]:
										self.assertEqual(len(a					)							,				len(a					)					)
										self.assertTrue(len(a					) % shard_batch_size == 0					)

								_UpperCamelCase         =						[]
								for idx in range(0							,				len(a					)							,				a					):
										for l in iterable_dataset_lists:
												observed += l[idx : idx + shard_batch_size]

								if not drop_last:
										while len(a					) < len(a					):
												reference += reference
								self.assertListEqual(a							,				reference[: len(a					)]					)

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						42
								_UpperCamelCase         =						RandomIterableDataset()

								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)
								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)
								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)
								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)

								# Edge case with a very small dataset
								_UpperCamelCase         =						RandomIterableDataset(max_length=2					)

								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)
								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)
								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)
								self.check_iterable_dataset_shards(a							,				a							,				batch_size=4							,				drop_last=a							,				split_batches=a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						BatchSampler(range(16					)							,				batch_size=4							,				drop_last=a					)
								_UpperCamelCase         =						SkipBatchSampler(a							,				2					)
								self.assertListEqual(list(a					)							,				[[8, 9, 10, 11], [12, 13, 14, 15]]					)

						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						SkipDataLoader(list(range(16					)					)							,				batch_size=4							,				skip_batches=2					)
								self.assertListEqual([t.tolist() for t in dataloader]							,				[[8, 9, 10, 11], [12, 13, 14, 15]]					)

						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						DataLoader(list(range(16					)					)							,				batch_size=4					)
								_UpperCamelCase         =						skip_first_batches(a							,				num_batches=2					)
								self.assertListEqual([t.tolist() for t in new_dataloader]							,				[[8, 9, 10, 11], [12, 13, 14, 15]]					)

						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								_UpperCamelCase         =						DataLoaderShard(list(range(16					)					)							,				batch_size=4					)
								for idx, _ in enumerate(a					):
										self.assertEqual(dataloader.end_of_dataloader							,				idx == 3					)

								# Test it also works on the second iteration
								for idx, _ in enumerate(a					):
										self.assertEqual(dataloader.end_of_dataloader							,				idx == 3					)







						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								Accelerator()
								_UpperCamelCase         =						DataLoaderDispatcher(range(16					)							,				batch_size=4					)
								for idx, _ in enumerate(a					):
										self.assertEqual(dataloader.end_of_dataloader							,				idx == 3					)

								# Test it also works on the second iteration
								for idx, _ in enumerate(a					):
										self.assertEqual(dataloader.end_of_dataloader							,				idx == 3					)



