


"""simple docstring"""


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
		raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
		raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
a_       							=    logging.get_logger(__name__)

a_       							=    "Hello, World!"
a_       							=    "en_XX"






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			Path("data_bin"   )
				_A					  =			FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(__lowercase   ).parent   )     ,			checkpoint_file=Path(__lowercase   ).name     ,			_name="xmod_base"     ,			arch="xmod_base"     ,			task="multilingual_masked_lm"     ,			data_name_or_path=str(__lowercase   )     ,			bpe="sentencepiece"     ,			sentencepiece_model=str(Path(__lowercase   ).parent / "sentencepiece.bpe.model"   )     ,			src_dict=str(data_dir / "dict.txt"   )     ,			)
				xmod.eval()  # disable dropout
				print(__lowercase   )

				_A					  =			xmod.model.encoder.sentence_encoder
				_A					  =			XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings     ,			hidden_size=xmod.cfg.model.encoder_embed_dim     ,			num_hidden_layers=xmod.cfg.model.encoder_layers     ,			num_attention_heads=xmod.cfg.model.encoder_attention_heads     ,			intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim     ,			max_position_embeddings=514     ,			type_vocab_size=1     ,			layer_norm_eps=1E-5     ,			pre_norm=xmod.cfg.model.encoder_normalize_before     ,			adapter_reduction_factor=getattr(xmod.cfg.model     ,			"bottleneck"     ,			2   )     ,			adapter_layer_norm=xmod.cfg.model.adapter_layer_norm     ,			adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm     ,			ln_before_adapter=xmod.cfg.model.ln_before_adapter     ,			languages=xmod.cfg.model.languages     ,			)
				if classification_head:
								_A					  =			xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

				print("Our X-MOD config:"     ,			__lowercase   )

				_A					  =			XmodForSequenceClassification(__lowercase   ) if classification_head else XmodForMaskedLM(__lowercase   )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				_A					  =			xmod_sent_encoder.embed_tokens.weight
				_A					  =			xmod_sent_encoder.embed_positions.weight
				_A					  =			torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c xmod doesn't use them.

				_A					  =			xmod_sent_encoder.layernorm_embedding.weight
				_A					  =			xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers   ):
								# Encoder: start of layer
								_A					  =			model.roberta.encoder.layer[i]
								_A					  =			xmod_sent_encoder.layers[i]

								# self attention
								_A					  =			layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)   )
								):
												raise AssertionError("Dimensions of self-attention weights do not match."   )

								_A					  =			xmod_layer.self_attn.q_proj.weight
								_A					  =			xmod_layer.self_attn.q_proj.bias
								_A					  =			xmod_layer.self_attn.k_proj.weight
								_A					  =			xmod_layer.self_attn.k_proj.bias
								_A					  =			xmod_layer.self_attn.v_proj.weight
								_A					  =			xmod_layer.self_attn.v_proj.bias

								# self-attention output
								_A					  =			layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError("Dimensions of self-attention output weights do not match."   )
								_A					  =			xmod_layer.self_attn.out_proj.weight
								_A					  =			xmod_layer.self_attn.out_proj.bias
								_A					  =			xmod_layer.self_attn_layer_norm.weight
								_A					  =			xmod_layer.self_attn_layer_norm.bias

								# intermediate
								_A					  =			layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError("Dimensions of intermediate weights do not match."   )
								_A					  =			xmod_layer.fca.weight
								_A					  =			xmod_layer.fca.bias

								# output
								_A					  =			layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError("Dimensions of feed-forward weights do not match."   )
								_A					  =			xmod_layer.fca.weight
								_A					  =			xmod_layer.fca.bias
								_A					  =			xmod_layer.final_layer_norm.weight
								_A					  =			xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												_A					  =			xmod_layer.adapter_layer_norm.weight
												_A					  =			xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()   ) != sorted(xmod_layer.adapter_modules.keys()   ):
												raise AssertionError("Lists of language adapters do not match."   )
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												_A					  =			bert_output.adapter_modules[lang_code]
												_A					  =			xmod_layer.adapter_modules[lang_code]
												_A					  =			from_adapter.fca.weight
												_A					  =			from_adapter.fca.bias
												_A					  =			from_adapter.fca.weight
												_A					  =			from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								_A					  =			xmod_sent_encoder.layer_norm.weight
								_A					  =			xmod_sent_encoder.layer_norm.bias

				if classification_head:
								_A					  =			xmod.model.classification_heads["mnli"].dense.weight
								_A					  =			xmod.model.classification_heads["mnli"].dense.bias
								_A					  =			xmod.model.classification_heads["mnli"].out_proj.weight
								_A					  =			xmod.model.classification_heads["mnli"].out_proj.bias
				else:
								# LM Head
								_A					  =			xmod.model.encoder.lm_head.dense.weight
								_A					  =			xmod.model.encoder.lm_head.dense.bias
								_A					  =			xmod.model.encoder.lm_head.layer_norm.weight
								_A					  =			xmod.model.encoder.lm_head.layer_norm.bias
								_A					  =			xmod.model.encoder.lm_head.weight
								_A					  =			xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				_A					  =			xmod.encode(__lowercase   ).unsqueeze(0   )  # batch of size 1
				model.roberta.set_default_language(__lowercase   )

				_A					  =			model(__lowercase   )[0]
				if classification_head:
								_A					  =			xmod.model.classification_heads["mnli"](xmod.extract_features(__lowercase   )   )
				else:
								_A					  =			xmod.model(__lowercase     ,			lang_id=[SAMPLE_LANGUAGE]   )[0]
				print(our_output.shape     ,			their_output.shape   )
				_A					  =			torch.max(torch.abs(our_output - their_output   )   ).item()
				print(f"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
				_A					  =			torch.allclose(__lowercase     ,			__lowercase     ,			atol=1E-3   )
				print("Do both models output the same tensors?"     ,			"🔥" if success else "💩"   )
				if not success:
								raise Exception("Something went wRoNg"   )

				Path(__lowercase   ).mkdir(parents=__lowercase     ,			exist_ok=__lowercase   )
				print(f"""Saving model to {pytorch_dump_folder_path}"""   )
				model.save_pretrained(__lowercase   )


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--classification_head", action="store_true", help="Whether to convert a final classification head."
		)
		a_       							=    parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)



"""simple docstring"""


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 snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Optional[int]    ,   a__      :       str=0.0_1    ,   a__      :       str=10_00		)       ->						int:





						'''simple docstring'''







						_A					  =			p_stop
						_A					  =			max_length







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





						'''simple docstring'''







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





class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       List[Any]    ,   a__      :       Union[str, Any]    ,   a__      :       Union[str, Any]    ,   a__      :       List[str]=False    ,   a__      :       str=True		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			[
						    BatchSamplerShard(a__    ,   2    ,   a__    ,   split_batches=a__    ,   even_batches=a__		)
						    for i in range(2		)
						]
						_A					  =			[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      :       List[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			BatchSampler(range(24		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			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.
						_A					  =			BatchSampler(range(21		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(21		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(22		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(22		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(20		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(20		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(2		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[[[0, 1, 0]], [[1, 0, 1]]]
						self.check_batch_sampler_shards(a__    ,   a__		)

						_A					  =			BatchSampler(range(2		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[[], []]
						self.check_batch_sampler_shards(a__    ,   a__		)







		def  a_ (					self      :       int		)       ->						int:





						'''simple docstring'''







						_A					  =			BatchSampler(range(24		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			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.
						_A					  =			BatchSampler(range(22		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(22		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(21		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(21		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(2		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[[[0, 1]], [[0, 1]]]
						self.check_batch_sampler_shards(a__    ,   a__    ,   split_batches=a__		)

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







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





						'''simple docstring'''







						_A					  =			BatchSampler(range(24		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			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.
						_A					  =			BatchSampler(range(21		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(21		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(22		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(22		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(20		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(20		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(2		)    ,   batch_size=3    ,   drop_last=a__		)
						_A					  =			[[[0, 1]], []]
						self.check_batch_sampler_shards(a__    ,   a__    ,   even_batches=a__		)

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







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





						'''simple docstring'''







						_A					  =			BatchSampler(range(24		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			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.
						_A					  =			BatchSampler(range(22		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(22		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(21		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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__		)

						_A					  =			BatchSampler(range(21		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[
						    [[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.
						_A					  =			BatchSampler(range(2		)    ,   batch_size=4    ,   drop_last=a__		)
						_A					  =			[[[0, 1]], []]
						self.check_batch_sampler_shards(a__    ,   a__    ,   split_batches=a__    ,   even_batches=a__		)

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







		def  a_ (					self      :       Union[str, Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
						_A					  =			[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      :       Optional[int]    ,   a__      :       Optional[int]    ,   a__      :       Tuple    ,   a__      :       Optional[int]    ,   a__      :       Union[str, Any]=False    ,   a__      :       int=2    ,   a__      :       List[Any]=False		)       ->						str:





						'''simple docstring'''







						random.seed(a__		)
						_A					  =			list(a__		)

						_A					  =			[
						    IterableDatasetShard(
						        a__    ,   batch_size=a__    ,   drop_last=a__    ,   num_processes=a__    ,   process_index=a__    ,   split_batches=a__    ,   )
						    for i in range(a__		)
						]
						_A					  =			[]
						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__		)		)

						_A					  =			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
						_A					  =			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		)

						_A					  =			[]
						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      :       List[str]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			42
						_A					  =			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
						_A					  =			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      :       List[str]		)       ->						Dict:





						'''simple docstring'''







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







		def  a_ (					self      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			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      :       int		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			DataLoader(list(range(16		)		)    ,   batch_size=4		)
						_A					  =			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      :       Optional[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			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      :       int		)       ->						int:





						'''simple docstring'''







						Accelerator()
						_A					  =			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		)