

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

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

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__			):
						A__							:    Tuple        =		data






					def __iter__( self			):
						for element in self.data:
							yield element



def 						UpperCamelCase					(lowercase_: List[str]=True					)     ->      List[Any]:
	A__							:    int        =		Accelerator(even_batches=lowercase_					)
	assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
	return accelerator

def 						UpperCamelCase					(lowercase_: Accelerator    ,      lowercase_: int    ,      lowercase_: int    ,      lowercase_: bool = False					)     ->      Optional[int]:
	if iterable:
		A__							:    str        =		DummyIterableDataset(torch.as_tensor(range(lowercase_					)					)					)
	else:
		A__							:    Tuple        =		TensorDataset(torch.as_tensor(range(lowercase_					)					)					)

	A__							:    Union[str, Any]        =		DataLoader(lowercase_    ,      batch_size=lowercase_					)
	A__							:    Dict        =		accelerator.prepare(lowercase_					)

	return dl

def 						UpperCamelCase					(lowercase_: Accelerator    ,      lowercase_: int    ,      lowercase_: int    ,      lowercase_: List[int]    ,      lowercase_: List[int]    ,      )     ->      Tuple:
	A__							:    Tuple        =		create_dataloader(accelerator=lowercase_    ,      dataset_size=lowercase_    ,      batch_size=lowercase_					)

	A__							:    int        =		[len(batch[0]					) for batch in dl]

	if accelerator.process_index == 0:
		assert batch_sizes == process_0_expected_batch_sizes
	elif accelerator.process_index == 1:
		assert batch_sizes == process_1_expected_batch_sizes

def 						UpperCamelCase					()     ->      Any:
	A__							:    Union[str, Any]        =		create_accelerator()

	# without padding, we would expect a different number of batches
	verify_dataloader_batch_sizes(
	    lowercase_    ,      dataset_size=3    ,      batch_size=1    ,      process_0_expected_batch_sizes=[1, 1]    ,      process_1_expected_batch_sizes=[1, 1]    ,      )

	# without padding, we would expect the same number of batches, but different sizes
	verify_dataloader_batch_sizes(
	    lowercase_    ,      dataset_size=7    ,      batch_size=2    ,      process_0_expected_batch_sizes=[2, 2]    ,      process_1_expected_batch_sizes=[2, 2]    ,      )

def 						UpperCamelCase					()     ->      Optional[Any]:
	A__							:    Optional[Any]        =		create_accelerator(even_batches=lowercase_					)

	verify_dataloader_batch_sizes(
	    lowercase_    ,      dataset_size=3    ,      batch_size=1    ,      process_0_expected_batch_sizes=[1, 1]    ,      process_1_expected_batch_sizes=[1]    ,      )

	verify_dataloader_batch_sizes(
	    lowercase_    ,      dataset_size=7    ,      batch_size=2    ,      process_0_expected_batch_sizes=[2, 2]    ,      process_1_expected_batch_sizes=[2, 1]    ,      )

def 						UpperCamelCase					()     ->      Tuple:
	A__							:    int        =		create_accelerator(even_batches=lowercase_					)

	A__							:    Union[str, Any]        =		torch.nn.Linear(1    ,      1					)
	A__							:    int        =		accelerator.prepare(lowercase_					)

	A__							:    List[str]        =		create_dataloader(lowercase_    ,      dataset_size=3    ,      batch_size=1					)

	A__							:    str        =		[]
	with accelerator.join_uneven_inputs([ddp_model]					):
		for batch_idx, batch in enumerate(lowercase_					):
			A__							:    List[str]        =		ddp_model(batch[0].float()					)
			A__							:    Union[str, Any]        =		output.sum()
			loss.backward()
			batch_idxs.append(lowercase_					)

	accelerator.wait_for_everyone()

	if accelerator.process_index == 0:
		assert batch_idxs == [0, 1]
	elif accelerator.process_index == 1:
		assert batch_idxs == [0]

def 						UpperCamelCase					(lowercase_: Tuple					)     ->      List[str]:
	with warnings.catch_warnings(record=lowercase_					) as w:
		with accelerator.join_uneven_inputs([Mock()]					):
			pass

		assert issubclass(w[-1].category    ,      lowercase_					)
		assert "only supported for multi-GPU" in str(w[-1].message					)

def 						UpperCamelCase					()     ->      Optional[Any]:
	A__							:    Union[str, Any]        =		True
	A__							:    List[Any]        =		False
	A__							:    List[Any]        =		create_accelerator(even_batches=lowercase_					)
	A__							:    Optional[int]        =		torch.nn.Linear(1    ,      1					)
	A__							:    Any        =		accelerator.prepare(lowercase_					)
	A__							:    Any        =		create_dataloader(lowercase_    ,      dataset_size=3    ,      batch_size=1					)
	A__							:    List[str]        =		create_dataloader(lowercase_    ,      dataset_size=3    ,      batch_size=1					)

	with accelerator.join_uneven_inputs([ddp_model]    ,      even_batches=lowercase_					):
		A__							:    List[str]        =		train_dl.batch_sampler.even_batches
		A__							:    Tuple        =		valid_dl.batch_sampler.even_batches

	assert train_dl_overridden_value == overridden_even_batches
	assert valid_dl_overridden_value == overridden_even_batches
	assert train_dl.batch_sampler.even_batches == default_even_batches
	assert valid_dl.batch_sampler.even_batches == default_even_batches

def 						UpperCamelCase					()     ->      Tuple:
	A__							:    str        =		True
	A__							:    int        =		False
	A__							:    int        =		create_accelerator(even_batches=lowercase_					)
	A__							:    Union[str, Any]        =		torch.nn.Linear(1    ,      1					)
	A__							:    Optional[Any]        =		accelerator.prepare(lowercase_					)
	create_dataloader(lowercase_    ,      dataset_size=3    ,      batch_size=1    ,      iterable=lowercase_					)
	A__							:    Dict        =		create_dataloader(lowercase_    ,      dataset_size=3    ,      batch_size=1					)

	with warnings.catch_warnings():
		warnings.filterwarnings("""ignore"""					)
		try:
			with accelerator.join_uneven_inputs([ddp_model]    ,      even_batches=lowercase_					):
				A__							:    List[Any]        =		batch_dl.batch_sampler.even_batches
		except AttributeError:
			# ensure attribute error is not raised when processing iterable dl
			raise AssertionError

	assert batch_dl_overridden_value == overridden_even_batches
	assert batch_dl.batch_sampler.even_batches == default_even_batches

def 						UpperCamelCase					()     ->      Tuple:
	A__							:    Optional[Any]        =		create_accelerator()
	A__							:    Optional[Any]        =		torch.nn.Linear(1    ,      1					)
	A__							:    str        =		accelerator.prepare(lowercase_					)
	create_dataloader(lowercase_    ,      dataset_size=3    ,      batch_size=1    ,      iterable=lowercase_					)

	with warnings.catch_warnings(record=lowercase_					) as w:
		with accelerator.join_uneven_inputs([ddp_model]    ,      even_batches=lowercase_					):
			pass

		assert issubclass(w[-1].category    ,      lowercase_					)
		assert "only supported for map-style datasets" in str(w[-1].message					)

def 						UpperCamelCase					()     ->      Optional[int]:
	A__							:    Tuple        =		create_accelerator()

	accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""					)
	test_default_ensures_even_batch_sizes()

	accelerator.print("""Run tests with even_batches disabled"""					)
	test_can_disable_even_batches()

	accelerator.print("""Test joining uneven inputs"""					)
	test_can_join_uneven_inputs()

	accelerator.print("""Test overriding even_batches when joining uneven inputs"""					)
	test_join_can_override_even_batches()

	accelerator.print("""Test overriding even_batches for mixed dataloader types"""					)
	test_join_can_override_for_mixed_type_dataloaders()

	accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""					)
	test_join_raises_warning_for_iterable_when_overriding_even_batches()

	accelerator.print("""Test join with non DDP distributed raises warning"""					)
	A__							:    Tuple        =		accelerator.state.distributed_type
	A__							:    Optional[int]        =		DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(lowercase_					)
	A__							:    Any        =		original_state


if __name__ == "__main__":
	main()






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep			) * [0]
						return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



