
'''simple docstring'''







import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
						import torch

						from transformers import (
						    BioGptForCausalLM,
						    BioGptForSequenceClassification,
						    BioGptForTokenClassification,
						    BioGptModel,
						    BioGptTokenizer,
						)
						from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST


class 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=13					,						UpperCamelCase=7					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=False					,						UpperCamelCase=True					,						UpperCamelCase=99					,						UpperCamelCase=32					,						UpperCamelCase=5					,						UpperCamelCase=4					,						UpperCamelCase=37					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=512					,						UpperCamelCase=16					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=3					,						UpperCamelCase=4					,						UpperCamelCase=None					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= seq_length
								_SCREAMING_SNAKE_CASE				= is_training
								_SCREAMING_SNAKE_CASE				= use_input_mask
								_SCREAMING_SNAKE_CASE				= use_token_type_ids
								_SCREAMING_SNAKE_CASE				= use_labels
								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= type_sequence_label_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= num_labels
								_SCREAMING_SNAKE_CASE				= num_choices
								_SCREAMING_SNAKE_CASE				= scope




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size      )

								_SCREAMING_SNAKE_CASE				= None
								if self.use_input_mask:
														_SCREAMING_SNAKE_CASE				= random_attention_mask([self.batch_size, self.seq_length]      )

								_SCREAMING_SNAKE_CASE				= None
								if self.use_token_type_ids:
														_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size, self.seq_length]					,						self.type_vocab_size      )

								_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= None
								if self.use_labels:
														_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size]					,						self.type_sequence_label_size      )
														_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels      )
														_SCREAMING_SNAKE_CASE				= ids_tensor([self.batch_size]					,						self.num_choices      )

								_SCREAMING_SNAKE_CASE				= self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




		def     lowercase       (				self      ):
								return BioGptConfig(
								    vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						type_vocab_size=self.type_vocab_size					,						is_decoder=UpperCamelCase					,						initializer_range=self.initializer_range					,						)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= BioGptModel(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )
								self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= BioGptForCausalLM(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						token_type_ids=UpperCamelCase					,						labels=UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						*UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= BioGptModel(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()

								# create attention mask
								_SCREAMING_SNAKE_CASE				= torch.ones(input_ids.shape					,						dtype=torch.long					,						device=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.seq_length // 2
								_SCREAMING_SNAKE_CASE				= 0

								# first forward pass
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase      ).to_tuple()

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

								# change a random masked slice from input_ids
								_SCREAMING_SNAKE_CASE				= ids_tensor((1,)					,						UpperCamelCase      ).item() + 1
								_SCREAMING_SNAKE_CASE				= ids_tensor((self.batch_size, 1)					,						config.vocab_size      ).squeeze(-1      )
								_SCREAMING_SNAKE_CASE				= random_other_next_tokens

								# append to next input_ids and attn_mask
								_SCREAMING_SNAKE_CASE				= torch.cat([input_ids, next_tokens]					,						dim=-1      )
								_SCREAMING_SNAKE_CASE				= torch.cat(
								    [attn_mask, torch.ones((attn_mask.shape[0], 1)					,						dtype=torch.long					,						device=UpperCamelCase      )]					,						dim=1					,						)

								# get two different outputs
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase      )["last_hidden_state"]
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						past_key_values=UpperCamelCase					,						attention_mask=UpperCamelCase      )["last_hidden_state"]

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

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						*UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= BioGptModel(config=UpperCamelCase      ).to(UpperCamelCase      ).eval()

								_SCREAMING_SNAKE_CASE				= torch.ones(input_ids.shape					,						dtype=torch.long					,						device=UpperCamelCase      )

								# first forward pass
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						use_cache=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= outputs.to_tuple()

								# create hypothetical multiple next token and extent to next_input_ids
								_SCREAMING_SNAKE_CASE				= ids_tensor((self.batch_size, 3)					,						config.vocab_size      )
								_SCREAMING_SNAKE_CASE				= ids_tensor((self.batch_size, 3)					,						2      )

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

								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase      )["last_hidden_state"]
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						past_key_values=UpperCamelCase      )[
								    "last_hidden_state"
								]

								# select random slice
								_SCREAMING_SNAKE_CASE				= ids_tensor((1,)					,						output_from_past.shape[-1]      ).item()
								_SCREAMING_SNAKE_CASE				= output_from_no_past[:, -3:, random_slice_idx].detach()
								_SCREAMING_SNAKE_CASE				= output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						*UpperCamelCase					,						UpperCamelCase=False      ):
								_SCREAMING_SNAKE_CASE				= BioGptForCausalLM(UpperCamelCase      )
								model.to(UpperCamelCase      )
								if gradient_checkpointing:
														model.gradient_checkpointing_enable()

								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						labels=UpperCamelCase      )
								self.parent.assertEqual(result.loss.shape					,						()      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)      )
								result.loss.backward()




		def     lowercase       (				self					,						UpperCamelCase					,						*UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= BioGptModel(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers      )
								for key in model.state_dict().keys():
														if "c_proj" in key and "weight" in key:
																				self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]      ) - model_std      )					,						0.0_01      )
																				self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]      ) - 0.0      )					,						0.01      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						*UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.num_labels
								_SCREAMING_SNAKE_CASE				= BioGptForTokenClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						token_type_ids=UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.prepare_config_and_inputs()
								(
								    (
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							(
								    _SCREAMING_SNAKE_CASE
								),							
								)				= config_and_inputs
								_SCREAMING_SNAKE_CASE				= {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					Union[str, Any]       =					(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		a							:					int       =					(BioGptForCausalLM,) if is_torch_available() else ()
		a							:					Any       =					(
		    {
		        """feature-extraction""": BioGptModel,
		        """text-classification""": BioGptForSequenceClassification,
		        """text-generation""": BioGptForCausalLM,
		        """token-classification""": BioGptForTokenClassification,
		        """zero-shot""": BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		a							:					List[Any]       =					False




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BioGptModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=UpperCamelCase					,						hidden_size=37      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
														_SCREAMING_SNAKE_CASE				= type
														self.model_tester.create_and_check_model(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_forward_and_backwards(*UpperCamelCase					,						gradient_checkpointing=UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCamelCase      )




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BioGptForCausalLM.from_pretrained("microsoft/biogpt"      )
								model.to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= BioGptTokenizer.from_pretrained("microsoft/biogpt"      )

								_SCREAMING_SNAKE_CASE				= "left"

								# Define PAD Token = EOS Token = 50256
								_SCREAMING_SNAKE_CASE				= tokenizer.eos_token
								_SCREAMING_SNAKE_CASE				= model.config.eos_token_id

								# use different length sentences to test batching
								_SCREAMING_SNAKE_CASE				= [
								    "Hello, my dog is a little",
								    "Today, I",
								]

								_SCREAMING_SNAKE_CASE				= tokenizer(UpperCamelCase					,						return_tensors="pt"					,						padding=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= inputs["input_ids"].to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= model.generate(
								    input_ids=UpperCamelCase					,						attention_mask=inputs["attention_mask"].to(UpperCamelCase      )					,						)

								_SCREAMING_SNAKE_CASE				= tokenizer(sentences[0]					,						return_tensors="pt"      ).input_ids.to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model.generate(input_ids=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
								_SCREAMING_SNAKE_CASE				= tokenizer(sentences[1]					,						return_tensors="pt"      ).input_ids.to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model.generate(input_ids=UpperCamelCase					,						max_length=model.config.max_length - num_paddings      )

								_SCREAMING_SNAKE_CASE				= tokenizer.batch_decode(UpperCamelCase					,						skip_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.decode(output_non_padded[0]					,						skip_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.decode(output_padded[0]					,						skip_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= [
								    "Hello, my dog is a little bit bigger than a little bit.",
								    "Today, I have a good idea of how to use the information",
								]
								self.assertListEqual(UpperCamelCase					,						UpperCamelCase      )
								self.assertListEqual(UpperCamelCase					,						[non_padded_sentence, padded_sentence]      )




		@slow
		def     lowercase       (				self      ):
								for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE				= BioGptModel.from_pretrained(UpperCamelCase      )
														self.assertIsNotNone(UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()
								_SCREAMING_SNAKE_CASE				= 3
								_SCREAMING_SNAKE_CASE				= input_dict["input_ids"]
								_SCREAMING_SNAKE_CASE				= input_ids.ne(1      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= ids_tensor([self.model_tester.batch_size]					,						self.model_tester.type_sequence_label_size      )
								_SCREAMING_SNAKE_CASE				= BioGptForSequenceClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						labels=UpperCamelCase      )
								self.assertEqual(result.logits.shape					,						(self.model_tester.batch_size, self.model_tester.num_labels)      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()
								_SCREAMING_SNAKE_CASE				= 3
								_SCREAMING_SNAKE_CASE				= "multi_label_classification"
								_SCREAMING_SNAKE_CASE				= input_dict["input_ids"]
								_SCREAMING_SNAKE_CASE				= input_ids.ne(1      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]					,						self.model_tester.type_sequence_label_size      ).to(torch.float      )
								_SCREAMING_SNAKE_CASE				= BioGptForSequenceClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						attention_mask=UpperCamelCase					,						labels=UpperCamelCase      )
								self.assertEqual(result.logits.shape					,						(self.model_tester.batch_size, self.model_tester.num_labels)      )


@require_torch
class 			lowerCAmelCase (				unittest.TestCase			):




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BioGptForCausalLM.from_pretrained("microsoft/biogpt"      )
								_SCREAMING_SNAKE_CASE				= torch.tensor([[2, 4_805, 9, 656, 21]]      )
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= 42_384

								_SCREAMING_SNAKE_CASE				= torch.Size((1, 5, vocab_size)      )
								self.assertEqual(output.shape					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.tensor(
								    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]      )

								self.assertTrue(torch.allclose(output[:, :3, :3]					,						UpperCamelCase					,						atol=1e-4      )      )




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BioGptTokenizer.from_pretrained("microsoft/biogpt"      )
								_SCREAMING_SNAKE_CASE				= BioGptForCausalLM.from_pretrained("microsoft/biogpt"      )
								model.to(UpperCamelCase      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= tokenizer("COVID-19 is"					,						return_tensors="pt"      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model.generate(
								    **UpperCamelCase					,						min_length=100					,						max_length=1_024					,						num_beams=5					,						early_stopping=UpperCamelCase					,						)
								_SCREAMING_SNAKE_CASE				= tokenizer.decode(output_ids[0]					,						skip_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= (
								    "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
								    " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
								    " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
								    " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
								    " more than 800,000 deaths."
								)
								self.assertEqual(UpperCamelCase					,						UpperCamelCase      )

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:   int     =				{
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Optional[int]     =				["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)