


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class     UpperCamelCase:


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]=9_9     ,    SCREAMING_SNAKE_CASE	:		Any=1_3     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7     ,    SCREAMING_SNAKE_CASE	:		Dict=9     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=3_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=5     ,    SCREAMING_SNAKE_CASE	:		int=4     ,    SCREAMING_SNAKE_CASE	:		Dict=3_7     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		int=0.002     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0     ,    SCREAMING_SNAKE_CASE	:		List[str]=0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=None     ,    )			->	str:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		encoder_seq_length
											__snake_case				=		decoder_seq_length
											# For common tests
											__snake_case				=		self.decoder_seq_length
											__snake_case				=		is_training
											__snake_case				=		use_attention_mask
											__snake_case				=		use_labels
											__snake_case				=		vocab_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		d_ff
											__snake_case				=		relative_attention_num_buckets
											__snake_case				=		dropout_rate
											__snake_case				=		initializer_factor
											__snake_case				=		eos_token_id
											__snake_case				=		pad_token_id
											__snake_case				=		decoder_start_token_id
											__snake_case				=		None
											__snake_case				=		decoder_layers


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											return TaConfig.from_pretrained("google/umt5-base"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=None     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	int:




											'''simple docstring'''

											if attention_mask is None:
																	__snake_case				=		input_ids.ne(config.pad_token_id						)
											if decoder_attention_mask is None:
																	__snake_case				=		decoder_input_ids.ne(config.pad_token_id						)
											if head_mask is None:
																	__snake_case				=		torch.ones(config.num_hidden_layers     ,    config.num_attention_heads     ,    device=SCREAMING_SNAKE_CASE						)
											if decoder_head_mask is None:
																	__snake_case				=		torch.ones(config.num_decoder_layers     ,    config.num_attention_heads     ,    device=SCREAMING_SNAKE_CASE						)
											if cross_attn_head_mask is None:
																	__snake_case				=		torch.ones(
																	    config.num_decoder_layers     ,    config.num_attention_heads     ,    device=SCREAMING_SNAKE_CASE						)
											return {
											    "input_ids": input_ids,
											    "decoder_input_ids": decoder_input_ids,
											    "attention_mask": attention_mask,
											    "decoder_attention_mask": decoder_attention_mask,
											    "head_mask": head_mask,
											    "decoder_head_mask": decoder_head_mask,
											    "cross_attn_head_mask": cross_attn_head_mask,
											}


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




											'''simple docstring'''

											__snake_case				=		ids_tensor([self.batch_size, self.encoder_seq_length]     ,    self.vocab_size						)
											__snake_case				=		ids_tensor([self.batch_size, self.decoder_seq_length]     ,    self.vocab_size						)

											# we need to clamp the input ids here to avoid having pad token in between
											# this is because for NllbMoe the position_ids are prepared such that
											# all pad tokens have pos id = 2 and rest are between 2..seq_length
											# and the seq_length here is seq_length - num_pad_tokens
											# but when using past, there is no way of knowing if the past input ids had
											# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
											# position_ids being off by num_pad_tokens in past input
											__snake_case				=		input_ids.clamp(self.pad_token_id + 1						)
											__snake_case				=		decoder_input_ids.clamp(self.pad_token_id + 1						)

											__snake_case				=		self.get_config()
											__snake_case				=		config.num_attention_heads
											__snake_case				=		self.prepare_inputs_dict(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											return config, input_dict


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Tuple:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.prepare_config_and_inputs()
											return config, inputs_dict


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											return TaConfig(
											    vocab_size=1_6_6     ,    d_model=self.hidden_size     ,    d_ff=self.d_ff     ,    d_kv=self.hidden_size // self.num_attention_heads     ,    num_layers=self.num_hidden_layers     ,    num_decoder_layers=self.decoder_layers     ,    num_heads=self.num_attention_heads     ,    relative_attention_num_buckets=self.relative_attention_num_buckets     ,    dropout_rate=self.dropout_rate     ,    initializer_factor=self.initializer_factor     ,    eos_token_id=self.eos_token_id     ,    bos_token_id=self.pad_token_id     ,    pad_token_id=self.pad_token_id     ,    decoder_start_token_id=self.decoder_start_token_id     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




											'''simple docstring'''

											return TaConfig(
											    vocab_size=self.vocab_size     ,    d_model=self.hidden_size     ,    d_ff=self.d_ff     ,    d_kv=self.hidden_size // self.num_attention_heads     ,    num_layers=self.num_hidden_layers     ,    num_decoder_layers=self.decoder_layers     ,    num_heads=self.num_attention_heads     ,    relative_attention_num_buckets=self.relative_attention_num_buckets     ,    dropout_rate=self.dropout_rate     ,    initializer_factor=self.initializer_factor     ,    eos_token_id=self.eos_token_id     ,    bos_token_id=self.pad_token_id     ,    pad_token_id=self.pad_token_id     ,    decoder_start_token_id=self.decoder_start_token_id     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		UMTaModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(
											    input_ids=SCREAMING_SNAKE_CASE     ,    decoder_input_ids=SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    decoder_attention_mask=SCREAMING_SNAKE_CASE     ,    )
											__snake_case				=		model(input_ids=SCREAMING_SNAKE_CASE     ,    decoder_input_ids=SCREAMING_SNAKE_CASE						)
											__snake_case				=		result.last_hidden_state
											__snake_case				=		result.past_key_values
											__snake_case				=		result.encoder_last_hidden_state

											self.parent.assertEqual(encoder_output.size()     ,    (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
											self.parent.assertEqual(decoder_output.size()     ,    (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
											# There should be `num_layers` key value embeddings stored in decoder_past
											self.parent.assertEqual(len(SCREAMING_SNAKE_CASE						)     ,    config.num_layers						)
											# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
											self.parent.assertEqual(len(decoder_past[0]						)     ,    4						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    )			->	str:




											'''simple docstring'''

											__snake_case				=		UMTaModel(config=SCREAMING_SNAKE_CASE						).get_decoder().to(SCREAMING_SNAKE_CASE						).eval()
											# first forward pass
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    use_cache=SCREAMING_SNAKE_CASE						)
											__snake_case				=		model(SCREAMING_SNAKE_CASE						)
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    use_cache=SCREAMING_SNAKE_CASE						)

											self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						)						)
											self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						) + 1						)

											__snake_case		,					__snake_case				=		outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											__snake_case				=		ids_tensor((self.batch_size, 1)     ,    config.vocab_size						)

											# append to next input_ids and
											__snake_case				=		torch.cat([input_ids, next_tokens]     ,    dim=-1						)

											__snake_case				=		model(SCREAMING_SNAKE_CASE						)["last_hidden_state"]
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    past_key_values=SCREAMING_SNAKE_CASE						)["last_hidden_state"]

											# select random slice
											__snake_case				=		ids_tensor((1,)     ,    output_from_past.shape[-1]						).item()
											__snake_case				=		output_from_no_past[:, -1, random_slice_idx].detach()
											__snake_case				=		output_from_past[:, 0, random_slice_idx].detach()

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    atol=1e-3						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    )			->	Tuple:




											'''simple docstring'''

											__snake_case				=		UMTaModel(config=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						).half().eval()
											__snake_case				=		model(**SCREAMING_SNAKE_CASE						)["last_hidden_state"]
											self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE						).any().item()						)






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Optional[Any]										=	(
					    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
					)
					snake_case_		:				Optional[Any]										=	(UMTaForConditionalGeneration,) if is_torch_available() else ()
					snake_case_		:				Optional[Any]										=	(
					    {
					        """conversational""": UMTaForConditionalGeneration,
					        """feature-extraction""": UMTaModel,
					        """summarization""": UMTaForConditionalGeneration,
					        """text2text-generation""": UMTaForConditionalGeneration,
					        """translation""": UMTaForConditionalGeneration,
					        """question-answering""": UMTaForQuestionAnswering,
					    }
					    if is_torch_available()
					    else {}
					)
					snake_case_		:				List[str]										=	True
					snake_case_		:				str										=	False
					snake_case_		:				int										=	False
					snake_case_		:				Dict										=	True
					snake_case_		:				str										=	True
					# The small UMT5 model needs higher percentages for CPU/MP tests
					snake_case_		:				List[Any]										=	[0.8, 0.9]


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		UMTaModelTester(self						)


					@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											__snake_case				=		UMTaModel(config_and_inputs[0]						).to(SCREAMING_SNAKE_CASE						)
											with tempfile.TemporaryDirectory() as tmpdirname:
																	torch.onnx.export(
																	    SCREAMING_SNAKE_CASE     ,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,    f'''{tmpdirname}/t5_test.onnx'''     ,    export_params=SCREAMING_SNAKE_CASE     ,    opset_version=9     ,    input_names=["input_ids", "decoder_input_ids"]     ,    )


					@unittest.skipIf(torch_device == "cpu"     ,    "Cant do half precision"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		["encoder_attentions", "decoder_attentions", "cross_attentions"]
											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											__snake_case				=		config_and_inputs[0]
											__snake_case				=		UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE						).eval()
											model.to(SCREAMING_SNAKE_CASE						)

											__snake_case				=		{
											    "head_mask": torch.zeros(config.num_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						),
											    "decoder_head_mask": torch.zeros(config.num_decoder_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						),
											    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						),
											}

											for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE     ,    head_masking.items()						):
																	__snake_case				=		{name: mask}
																	# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																	if name == "head_mask":
																							__snake_case				=		torch.ones(
																							    config.num_decoder_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						)

																	__snake_case				=		model.generate(
																	    config_and_inputs[1]["input_ids"]     ,    num_beams=1     ,    max_length=3     ,    output_attentions=SCREAMING_SNAKE_CASE     ,    return_dict_in_generate=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )
																	# We check the state of decoder_attentions and cross_attentions just from the last step
																	__snake_case				=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																	self.assertEqual(sum([w.sum().item() for w in attn_weights]						)     ,    0.0						)






					@unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




											'''simple docstring'''

											pass






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


					@slow
					@unittest.skip(
					    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		UMTaForConditionalGeneration.from_pretrained("google/umt5-small"     ,    return_dict=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AutoTokenizer.from_pretrained("google/umt5-small"     ,    use_fast=SCREAMING_SNAKE_CASE     ,    legacy=SCREAMING_SNAKE_CASE						)
											__snake_case				=		[
											    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
											    "No se como puedo <extra_id_0>.",
											    "This is the reason why we <extra_id_0> them.",
											    "The <extra_id_0> walks in <extra_id_1>, seats",
											    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
											]
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE     ,    return_tensors="pt"     ,    padding=SCREAMING_SNAKE_CASE						).input_ids
											# fmt: off
											__snake_case				=		torch.tensor(
											    [
											        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
											    ]						)
											# fmt: on
											torch.testing.assert_allclose(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											__snake_case				=		model.generate(input_ids.to(SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		[
											    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
											    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
											    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
											    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
											    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
											]
											__snake_case				=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





