import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    FunnelBaseModel,
				    FunnelForMaskedLM,
				    FunnelForMultipleChoice,
				    FunnelForPreTraining,
				    FunnelForQuestionAnswering,
				    FunnelForSequenceClassification,
				    FunnelForTokenClassification,
				    FunnelModel,
				)



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_=13      ,				lowercase_=7      ,				lowercase_=True      ,				lowercase_=True      ,				lowercase_=True      ,				lowercase_=True      ,				lowercase_=99      ,				lowercase_=[1, 1, 2]      ,				lowercase_=1      ,				lowercase_=32      ,				lowercase_=4      ,				lowercase_=8      ,				lowercase_=37      ,				lowercase_="gelu_new"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=0.0      ,				lowercase_=512      ,				lowercase_=3      ,				lowercase_=0.02      ,				lowercase_=3      ,				lowercase_=4      ,				lowercase_=None      ,				lowercase_=False      ,				):
													_snake_case						:     str				=    parent
													_snake_case						:     Any				=    batch_size
													_snake_case						:     List[str]				=    seq_length
													_snake_case						:     Optional[int]				=    is_training
													_snake_case						:     List[Any]				=    use_input_mask
													_snake_case						:     Tuple				=    use_token_type_ids
													_snake_case						:     Optional[Any]				=    use_labels
													_snake_case						:     Dict				=    vocab_size
													_snake_case						:     Union[str, Any]				=    block_sizes
													_snake_case						:     List[str]				=    num_decoder_layers
													_snake_case						:     Optional[Any]				=    d_model
													_snake_case						:     Optional[Any]				=    n_head
													_snake_case						:     Any				=    d_head
													_snake_case						:     int				=    d_inner
													_snake_case						:     str				=    hidden_act
													_snake_case						:     Dict				=    hidden_dropout
													_snake_case						:     Dict				=    attention_dropout
													_snake_case						:     List[Any]				=    activation_dropout
													_snake_case						:     Dict				=    max_position_embeddings
													_snake_case						:     List[Any]				=    type_vocab_size
													_snake_case						:     List[Any]				=    2
													_snake_case						:     Optional[Any]				=    num_labels
													_snake_case						:     Any				=    num_choices
													_snake_case						:     int				=    scope
													_snake_case						:     str				=    initializer_std

													# Used in the tests to check the size of the first attention layer
													_snake_case						:     Tuple				=    n_head
													# Used in the tests to check the size of the first hidden state
													_snake_case						:     Tuple				=    self.d_model
													# Used in the tests to check the number of output hidden states/attentions
													_snake_case						:     Optional[Any]				=    sum(self.block_sizes				) + (0 if base else self.num_decoder_layers)
													# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
													# the last hidden state of the first block (which is the first hidden state of the decoder).
													if not base:
																			_snake_case						:     int				=    self.num_hidden_layers + 2



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size				)

													_snake_case						:     int				=    None
													if self.use_input_mask:
																			_snake_case						:     List[str]				=    ids_tensor([self.batch_size, self.seq_length]      ,				vocab_size=2				)

													_snake_case						:     Any				=    None
													if self.use_token_type_ids:
																			_snake_case						:     Union[str, Any]				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.type_vocab_size				)

													_snake_case						:     str				=    None
													_snake_case						:     Tuple				=    None
													_snake_case						:     List[Any]				=    None
													if self.use_labels:
																			_snake_case						:     int				=    ids_tensor([self.batch_size]      ,				self.type_sequence_label_size				)
																			_snake_case						:     Dict				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.num_labels				)
																			_snake_case						:     int				=    ids_tensor([self.batch_size]      ,				self.num_choices				)
																			_snake_case						:     List[str]				=    ids_tensor([self.batch_size, self.seq_length]      ,				1				)

													_snake_case						:     Tuple				=    self.get_config()

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    input_mask,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													    fake_token_labels,
													)



							def        UpperCamelCase       (						self				):
													return FunnelConfig(
													    vocab_size=self.vocab_size      ,				block_sizes=self.block_sizes      ,				num_decoder_layers=self.num_decoder_layers      ,				d_model=self.d_model      ,				n_head=self.n_head      ,				d_head=self.d_head      ,				d_inner=self.d_inner      ,				hidden_act=self.hidden_act      ,				hidden_dropout=self.hidden_dropout      ,				attention_dropout=self.attention_dropout      ,				activation_dropout=self.activation_dropout      ,				max_position_embeddings=self.max_position_embeddings      ,				type_vocab_size=self.type_vocab_size      ,				initializer_std=self.initializer_std      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     int				=    FunnelModel(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Optional[Any]				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_				)
													_snake_case						:     int				=    model(lowercase_      ,				token_type_ids=lowercase_				)
													_snake_case						:     int				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.d_model)				)

													_snake_case						:     Optional[Any]				=    False
													_snake_case						:     Any				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.d_model)				)

													_snake_case						:     Dict				=    False
													_snake_case						:     str				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.d_model)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Optional[Any]				=    FunnelBaseModel(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Union[str, Any]				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_				)
													_snake_case						:     Tuple				=    model(lowercase_      ,				token_type_ids=lowercase_				)
													_snake_case						:     List[Any]				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, 2, self.d_model)				)

													_snake_case						:     List[Any]				=    False
													_snake_case						:     List[Any]				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, 3, self.d_model)				)

													_snake_case						:     Dict				=    False
													_snake_case						:     List[Any]				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, 2, self.d_model)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     List[Any]				=    self.num_labels
													_snake_case						:     Optional[int]				=    FunnelForPreTraining(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Union[str, Any]				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_      ,				labels=lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     List[Any]				=    FunnelForMaskedLM(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     int				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_      ,				labels=lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Optional[Any]				=    self.num_labels
													_snake_case						:     str				=    FunnelForSequenceClassification(lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Dict				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_      ,				labels=lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     List[str]				=    self.num_choices
													_snake_case						:     str				=    FunnelForMultipleChoice(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Union[str, Any]				=    input_ids.unsqueeze(1				).expand(-1      ,				self.num_choices      ,				-1				).contiguous()
													_snake_case						:     Optional[Any]				=    token_type_ids.unsqueeze(1				).expand(-1      ,				self.num_choices      ,				-1				).contiguous()
													_snake_case						:     List[Any]				=    input_mask.unsqueeze(1				).expand(-1      ,				self.num_choices      ,				-1				).contiguous()
													_snake_case						:     Optional[Any]				=    model(
													    lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_      ,				labels=lowercase_      ,				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Dict				=    self.num_labels
													_snake_case						:     Optional[Any]				=    FunnelForTokenClassification(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Dict				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_      ,				labels=lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.num_labels)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     int				=    FunnelForQuestionAnswering(config=lowercase_				)
													model.to(lowercase_				)
													model.eval()
													_snake_case						:     Optional[Any]				=    model(
													    lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_      ,				start_positions=lowercase_      ,				end_positions=lowercase_      ,				)
													self.parent.assertEqual(result.start_logits.shape      ,				(self.batch_size, self.seq_length)				)
													self.parent.assertEqual(result.end_logits.shape      ,				(self.batch_size, self.seq_length)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.prepare_config_and_inputs()
													(
													    (
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,
													)						:     str				=    config_and_inputs
													_snake_case						:     Optional[int]				=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
													return config, inputs_dict




@require_torch
class    lowercase_    (     __snake_case						, __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		(
							    (
							        FunnelModel,
							        FunnelForMaskedLM,
							        FunnelForPreTraining,
							        FunnelForQuestionAnswering,
							        FunnelForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_lowerCamelCase						=		(
							    {
							        'feature-extraction': (FunnelBaseModel, FunnelModel),
							        'fill-mask': FunnelForMaskedLM,
							        'question-answering': FunnelForQuestionAnswering,
							        'text-classification': FunnelForSequenceClassification,
							        'token-classification': FunnelForTokenClassification,
							        'zero-shot': FunnelForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_=False				):
													_snake_case						:     Optional[int]				=    super()._prepare_for_class(lowercase_      ,				lowercase_      ,				return_labels=lowercase_				)

													if return_labels:
																			if model_class in get_values(lowercase_				):
																									_snake_case						:     Tuple				=    torch.zeros(
																									    (self.model_tester.batch_size, self.model_tester.seq_length)      ,				dtype=torch.long      ,				device=lowercase_				)
													return inputs_dict



							def        UpperCamelCase       (						self				):
													_snake_case						:     str				=    FunnelModelTester(self				)
													_snake_case						:     int				=    ConfigTester(self      ,				config_class=lowercase_				)



							def        UpperCamelCase       (						self				):
													self.config_tester.run_common_tests()



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[int]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													if hasattr(lowercase_      ,				"weight"				) and module.weight is not None:
																			module.weight.data.fill_(3				)
													if hasattr(lowercase_      ,				"bias"				) and module.bias is not None:
																			module.bias.data.fill_(3				)

													for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
																			if hasattr(lowercase_      ,				lowercase_				) and getattr(lowercase_      ,				lowercase_				) is not None:
																									_snake_case						:     List[str]				=    getattr(lowercase_      ,				lowercase_				)
																									weight.data.fill_(3				)




@require_torch
class    lowercase_    (     __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		(
							    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
							)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[int]				=    FunnelModelTester(self      ,				base=lowercase_				)
													_snake_case						:     List[str]				=    ConfigTester(self      ,				config_class=lowercase_				)



							def        UpperCamelCase       (						self				):
													self.config_tester.run_common_tests()



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_base_model(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Union[str, Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case				,_snake_case						:     Any				=    self.model_tester.prepare_config_and_inputs_for_common()
													_snake_case						:     Union[str, Any]				=    True

													for model_class in self.all_model_classes:
																			if model_class.__name__ == "FunnelBaseModel":
																									continue
																			_snake_case						:     Any				=    model_class(lowercase_				)
																			model.to(lowercase_				)
																			model.train()
																			_snake_case						:     Optional[Any]				=    self._prepare_for_class(lowercase_      ,				lowercase_      ,				return_labels=lowercase_				)
																			_snake_case						:     str				=    model(**lowercase_				).loss
																			loss.backward()



							def        UpperCamelCase       (						self      ,				lowercase_				):
													if hasattr(lowercase_      ,				"weight"				) and module.weight is not None:
																			module.weight.data.fill_(3				)
													if hasattr(lowercase_      ,				"bias"				) and module.bias is not None:
																			module.bias.data.fill_(3				)

													for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
																			if hasattr(lowercase_      ,				lowercase_				) and getattr(lowercase_      ,				lowercase_				) is not None:
																									_snake_case						:     Optional[int]				=    getattr(lowercase_      ,				lowercase_				)
																									weight.data.fill_(3				)




@require_torch
@require_sentencepiece
@require_tokenizers
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[Any]				=    13
													_snake_case						:     int				=    7
													_snake_case						:     Any				=    torch.arange(0      ,				batch_size * sequence_length				).long().reshape(lowercase_      ,				lowercase_				)
													_snake_case						:     int				=    [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
													_snake_case						:     Tuple				=    torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]				)

													_snake_case						:     Dict				=    FunnelModel.from_pretrained("sgugger/funnel-random-tiny"				)
													_snake_case						:     Optional[int]				=    model(lowercase_      ,				token_type_ids=lowercase_				)[0].abs()

													_snake_case						:     List[Any]				=    torch.tensor(2_344.8_352				)
													_snake_case						:     Optional[int]				=    torch.tensor(0.8_052				)
													self.assertTrue(torch.allclose(output.sum()      ,				lowercase_      ,				atol=1e-4				)				)
													self.assertTrue(torch.allclose(output.mean()      ,				lowercase_      ,				atol=1e-4				)				)

													_snake_case						:     Optional[int]				=    torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]				)
													_snake_case						:     Optional[int]				=    model(lowercase_      ,				attention_mask=lowercase_      ,				token_type_ids=lowercase_				)[0].abs()

													_snake_case						:     Dict				=    torch.tensor(2_343.8_425				)
													_snake_case						:     Tuple				=    torch.tensor(0.8_049				)
													self.assertTrue(torch.allclose(output.sum()      ,				lowercase_      ,				atol=1e-4				)				)
													self.assertTrue(torch.allclose(output.mean()      ,				lowercase_      ,				atol=1e-4				)				)



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    FunnelTokenizer.from_pretrained("huggingface/funnel-small"				)
													_snake_case						:     Optional[Any]				=    FunnelModel.from_pretrained("huggingface/funnel-small"				)
													_snake_case						:     List[str]				=    tokenizer("Hello! I am the Funnel Transformer model."      ,				return_tensors="pt"				)
													_snake_case						:     List[Any]				=    model(**lowercase_				)[0]

													_snake_case						:     List[str]				=    torch.tensor(235.7_246				)
													_snake_case						:     Any				=    torch.tensor(0.0_256				)
													self.assertTrue(torch.allclose(output.sum()      ,				lowercase_      ,				atol=1e-4				)				)
													self.assertTrue(torch.allclose(output.mean()      ,				lowercase_      ,				atol=1e-4				)				)
import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(__lowercase						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize("shards_per_node"					,			[1, 2, 3]						)
def 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						def gen(__lowercase						):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17						))

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       Dict:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle(seed=42						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle()

						_snake_case						:     Optional[int]				=    split_dataset_by_node(full_ds.shuffle(seed=42						)					,			rank=0					,			world_size=__lowercase						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(full_ds.shuffle()					,			rank=0					,			world_size=__lowercase						)
@pytest.mark.parametrize("streaming"					,			[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)

@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,			)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     Dict				=    True
						_snake_case						:     Optional[int]				=    get_torch_dist_unique_port()
						_snake_case						:     List[str]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     List[str]				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     Tuple				=    F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
						_snake_case						:     Any				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)