






"""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				:





		'''simple docstring'''






		def __init__(   self							:	Union[str, Any]						,			a_							:	Dict						,			a_							:	Dict=13						,			a_							:	Dict=7						,			a_							:	Optional[Any]=True						,			a_							:	Optional[Any]=True						,			a_							:	List[str]=False						,			a_							:	int=True						,			a_							:	Optional[int]=99						,			a_							:	Optional[int]=32						,			a_							:	Tuple=5						,			a_							:	Optional[Any]=4						,			a_							:	int=37						,			a_							:	Union[str, Any]="gelu"						,			a_							:	Optional[int]=0.1						,			a_							:	List[str]=0.1						,			a_							:	Optional[Any]=5_12						,			a_							:	Optional[Any]=16						,			a_							:	Any=2						,			a_							:	Union[str, Any]=0.02						,			a_							:	Optional[Any]=3						,			a_							:	List[str]=4						,			a_							:	List[Any]=None						,			):
				lowerCAmelCase_   :							Optional[int]									=			parent
				lowerCAmelCase_   :							List[Any]									=			batch_size
				lowerCAmelCase_   :							Union[str, Any]									=			seq_length
				lowerCAmelCase_   :							int									=			is_training
				lowerCAmelCase_   :							str									=			use_input_mask
				lowerCAmelCase_   :							Union[str, Any]									=			use_token_type_ids
				lowerCAmelCase_   :							Optional[int]									=			use_labels
				lowerCAmelCase_   :							List[str]									=			vocab_size
				lowerCAmelCase_   :							Tuple									=			hidden_size
				lowerCAmelCase_   :							Tuple									=			num_hidden_layers
				lowerCAmelCase_   :							Tuple									=			num_attention_heads
				lowerCAmelCase_   :							Optional[Any]									=			intermediate_size
				lowerCAmelCase_   :							Dict									=			hidden_act
				lowerCAmelCase_   :							Union[str, Any]									=			hidden_dropout_prob
				lowerCAmelCase_   :							Optional[Any]									=			attention_probs_dropout_prob
				lowerCAmelCase_   :							str									=			max_position_embeddings
				lowerCAmelCase_   :							int									=			type_vocab_size
				lowerCAmelCase_   :							List[Any]									=			type_sequence_label_size
				lowerCAmelCase_   :							Optional[int]									=			initializer_range
				lowerCAmelCase_   :							Dict									=			num_labels
				lowerCAmelCase_   :							List[Any]									=			num_choices
				lowerCAmelCase_   :							Optional[int]									=			scope





		def    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							Dict									=			ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size	)

				lowerCAmelCase_   :							Optional[int]									=			None
				if self.use_input_mask:
						lowerCAmelCase_   :							Optional[int]									=			random_attention_mask([self.batch_size, self.seq_length]	)

				lowerCAmelCase_   :							Optional[int]									=			None
				if self.use_token_type_ids:
						lowerCAmelCase_   :							str									=			ids_tensor([self.batch_size, self.seq_length]						,			self.type_vocab_size	)

				lowerCAmelCase_   :							Tuple									=			None
				lowerCAmelCase_   :							Optional[int]									=			None
				lowerCAmelCase_   :							Optional[int]									=			None
				if self.use_labels:
						lowerCAmelCase_   :							Optional[int]									=			ids_tensor([self.batch_size]						,			self.type_sequence_label_size	)
						lowerCAmelCase_   :							Any									=			ids_tensor([self.batch_size, self.seq_length]						,			self.num_labels	)
						lowerCAmelCase_   :							Dict									=			ids_tensor([self.batch_size]						,			self.num_choices	)

				lowerCAmelCase_   :							Optional[Any]									=			self.get_config()

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





		def    lowerCamelCase       (   self							:	List[str]	):
				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=a_						,			initializer_range=self.initializer_range						,			)





		def    lowerCamelCase       (   self							:	str						,			a_							:	Optional[int]						,			a_							:	Dict						,			a_							:	Tuple						,			a_							:	Any						,			a_							:	Dict						,			a_							:	Tuple						,			a_							:	Optional[Any]	):
				lowerCAmelCase_   :							Optional[int]									=			BioGptModel(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Tuple									=			model(a_						,			attention_mask=a_	)
				lowerCAmelCase_   :							Dict									=			model(a_	)
				self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	Optional[int]						,			a_							:	Union[str, Any]						,			a_							:	Any						,			a_							:	int						,			a_							:	Union[str, Any]						,			a_							:	Dict						,			a_							:	List[str]						,			a_							:	Optional[Any]						,			a_							:	Optional[Any]						,			):
				lowerCAmelCase_   :							Union[str, Any]									=			BioGptForCausalLM(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							List[str]									=			model(a_						,			attention_mask=a_						,			token_type_ids=a_						,			labels=a_	)
				self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)	)





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	Dict						,			a_							:	Union[str, Any]						,			a_							:	List[str]						,			a_							:	Tuple						,			a_							:	Dict						,			*a_							:	Optional[int]	):
				lowerCAmelCase_   :							Dict									=			BioGptModel(config=a_	)
				model.to(a_	)
				model.eval()

				# create attention mask
				lowerCAmelCase_   :							Union[str, Any]									=			torch.ones(input_ids.shape						,			dtype=torch.long						,			device=a_	)
				lowerCAmelCase_   :							List[Any]									=			self.seq_length // 2
				lowerCAmelCase_   :							Optional[Any]									=			0

				# first forward pass
				lowerCAmelCase_				,	lowerCAmelCase_   :							Any									=			model(a_						,			attention_mask=a_	).to_tuple()

				# create hypothetical next token and extent to next_input_ids
				lowerCAmelCase_   :							Optional[Any]									=			ids_tensor((self.batch_size, 1)						,			config.vocab_size	)

				# change a random masked slice from input_ids
				lowerCAmelCase_   :							Tuple									=			ids_tensor((1,)						,			a_	).item() + 1
				lowerCAmelCase_   :							Any									=			ids_tensor((self.batch_size, 1)						,			config.vocab_size	).squeeze(-1	)
				lowerCAmelCase_   :							str									=			random_other_next_tokens

				# append to next input_ids and attn_mask
				lowerCAmelCase_   :							Dict									=			torch.cat([input_ids, next_tokens]						,			dim=-1	)
				lowerCAmelCase_   :							str									=			torch.cat(
				    [attn_mask, torch.ones((attn_mask.shape[0], 1)						,			dtype=torch.long						,			device=a_	)]						,			dim=1						,			)

				# get two different outputs
				lowerCAmelCase_   :							int									=			model(a_						,			attention_mask=a_	)["last_hidden_state"]
				lowerCAmelCase_   :							Tuple									=			model(a_						,			past_key_values=a_						,			attention_mask=a_	)["last_hidden_state"]

				# select random slice
				lowerCAmelCase_   :							Optional[int]									=			ids_tensor((1,)						,			output_from_past.shape[-1]	).item()
				lowerCAmelCase_   :							List[str]									=			output_from_no_past[:, -1, random_slice_idx].detach()
				lowerCAmelCase_   :							List[str]									=			output_from_past[:, 0, random_slice_idx].detach()

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





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	str						,			a_							:	Union[str, Any]						,			a_							:	Optional[int]						,			a_							:	str						,			a_							:	Dict						,			*a_							:	List[str]	):
				lowerCAmelCase_   :							List[Any]									=			BioGptModel(config=a_	).to(a_	).eval()

				lowerCAmelCase_   :							int									=			torch.ones(input_ids.shape						,			dtype=torch.long						,			device=a_	)

				# first forward pass
				lowerCAmelCase_   :							List[Any]									=			model(a_						,			attention_mask=a_						,			use_cache=a_	)

				lowerCAmelCase_				,	lowerCAmelCase_   :							int									=			outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				lowerCAmelCase_   :							List[Any]									=			ids_tensor((self.batch_size, 3)						,			config.vocab_size	)
				lowerCAmelCase_   :							Any									=			ids_tensor((self.batch_size, 3)						,			2	)

				# append to next input_ids and
				lowerCAmelCase_   :							Optional[int]									=			torch.cat([input_ids, next_tokens]						,			dim=-1	)
				lowerCAmelCase_   :							Optional[Any]									=			torch.cat([attention_mask, next_attn_mask]						,			dim=-1	)

				lowerCAmelCase_   :							List[Any]									=			model(a_						,			attention_mask=a_	)["last_hidden_state"]
				lowerCAmelCase_   :							Any									=			model(a_						,			attention_mask=a_						,			past_key_values=a_	)[
				    "last_hidden_state"
				]

				# select random slice
				lowerCAmelCase_   :							List[Any]									=			ids_tensor((1,)						,			output_from_past.shape[-1]	).item()
				lowerCAmelCase_   :							Dict									=			output_from_no_past[:, -3:, random_slice_idx].detach()
				lowerCAmelCase_   :							List[Any]									=			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(a_						,			a_						,			atol=1e-3	)	)





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	List[Any]						,			a_							:	int						,			a_							:	str						,			a_							:	Tuple						,			a_							:	Union[str, Any]						,			*a_							:	Dict						,			a_							:	Dict=False	):
				lowerCAmelCase_   :							str									=			BioGptForCausalLM(a_	)
				model.to(a_	)
				if gradient_checkpointing:
						model.gradient_checkpointing_enable()

				lowerCAmelCase_   :							Optional[int]									=			model(a_						,			labels=a_	)
				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    lowerCamelCase       (   self							:	str						,			a_							:	List[Any]						,			*a_							:	Tuple	):
				lowerCAmelCase_   :							Optional[int]									=			BioGptModel(a_	)
				lowerCAmelCase_   :							int									=			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	)						,			0.001	)
								self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]	) - 0.0	)						,			0.01	)





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Union[str, Any]						,			a_							:	Any						,			a_							:	Any						,			a_							:	Optional[Any]						,			a_							:	List[str]						,			*a_							:	List[str]	):
				lowerCAmelCase_   :							Union[str, Any]									=			self.num_labels
				lowerCAmelCase_   :							Optional[int]									=			BioGptForTokenClassification(a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							int									=			model(a_						,			attention_mask=a_						,			token_type_ids=a_	)
				self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.num_labels)	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Union[str, Any]									=			self.prepare_config_and_inputs()
				(
				    (
				    lowerCAmelCase_		
				)				,	(
				    lowerCAmelCase_		
				)				,	(
				    lowerCAmelCase_		
				)				,	(
				    lowerCAmelCase_		
				)				,	(
				    lowerCAmelCase_		
				)				,	(
				    lowerCAmelCase_		
				)				,	(
				    lowerCAmelCase_		
				)				,			
				)   :							Optional[Any]									=			config_and_inputs
				lowerCAmelCase_   :							Dict									=			{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict







@require_torch
class 		__lowerCamelCase				( snake_case_ ,							snake_case_ ,							snake_case_ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				Union[str, Any]            =							(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		a_	:				Dict            =							(BioGptForCausalLM,) if is_torch_available() else ()
		a_	:				List[str]            =							(
		    {
		        """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    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Union[str, Any]									=			BioGptModelTester(self	)
				lowerCAmelCase_   :							Tuple									=			ConfigTester(self						,			config_class=a_						,			hidden_size=37	)





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				self.config_tester.run_common_tests()





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Any									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a_	)





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							int									=			self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						lowerCAmelCase_   :							Tuple									=			type
						self.model_tester.create_and_check_model(*a_	)





		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Optional[Any]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_	)





		def    lowerCamelCase       (   self							:	int	):
				lowerCAmelCase_   :							Optional[Any]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*a_						,			gradient_checkpointing=a_	)





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Optional[Any]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_	)





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*a_	)





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							str									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*a_	)





		@slow
		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
				model.to(a_	)
				lowerCAmelCase_   :							int									=			BioGptTokenizer.from_pretrained("microsoft/biogpt"	)

				lowerCAmelCase_   :							List[str]									=			"left"

				# Define PAD Token = EOS Token = 50256
				lowerCAmelCase_   :							Any									=			tokenizer.eos_token
				lowerCAmelCase_   :							List[str]									=			model.config.eos_token_id

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

				lowerCAmelCase_   :							Tuple									=			tokenizer(a_						,			return_tensors="pt"						,			padding=a_	)
				lowerCAmelCase_   :							Any									=			inputs["input_ids"].to(a_	)

				lowerCAmelCase_   :							List[Any]									=			model.generate(
				    input_ids=a_						,			attention_mask=inputs["attention_mask"].to(a_	)						,			)

				lowerCAmelCase_   :							Optional[int]									=			tokenizer(sentences[0]						,			return_tensors="pt"	).input_ids.to(a_	)
				lowerCAmelCase_   :							List[str]									=			model.generate(input_ids=a_	)

				lowerCAmelCase_   :							Union[str, Any]									=			inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
				lowerCAmelCase_   :							Any									=			tokenizer(sentences[1]						,			return_tensors="pt"	).input_ids.to(a_	)
				lowerCAmelCase_   :							str									=			model.generate(input_ids=a_						,			max_length=model.config.max_length - num_paddings	)

				lowerCAmelCase_   :							Tuple									=			tokenizer.batch_decode(a_						,			skip_special_tokens=a_	)
				lowerCAmelCase_   :							int									=			tokenizer.decode(output_non_padded[0]						,			skip_special_tokens=a_	)
				lowerCAmelCase_   :							Union[str, Any]									=			tokenizer.decode(output_padded[0]						,			skip_special_tokens=a_	)

				lowerCAmelCase_   :							Union[str, Any]									=			[
				    "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(a_						,			a_	)
				self.assertListEqual(a_						,			[non_padded_sentence, padded_sentence]	)





		@slow
		def    lowerCamelCase       (   self							:	Dict	):
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCAmelCase_   :							int									=			BioGptModel.from_pretrained(a_	)
						self.assertIsNotNone(a_	)





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Tuple									=			self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase_   :							Optional[Any]									=			3
				lowerCAmelCase_   :							List[str]									=			input_dict["input_ids"]
				lowerCAmelCase_   :							List[str]									=			input_ids.ne(1	).to(a_	)
				lowerCAmelCase_   :							List[str]									=			ids_tensor([self.model_tester.batch_size]						,			self.model_tester.type_sequence_label_size	)
				lowerCAmelCase_   :							List[str]									=			BioGptForSequenceClassification(a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Dict									=			model(a_						,			attention_mask=a_						,			labels=a_	)
				self.assertEqual(result.logits.shape						,			(self.model_tester.batch_size, self.model_tester.num_labels)	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Optional[int]									=			self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase_   :							str									=			3
				lowerCAmelCase_   :							Union[str, Any]									=			"multi_label_classification"
				lowerCAmelCase_   :							str									=			input_dict["input_ids"]
				lowerCAmelCase_   :							List[Any]									=			input_ids.ne(1	).to(a_	)
				lowerCAmelCase_   :							List[Any]									=			ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]						,			self.model_tester.type_sequence_label_size	).to(torch.float	)
				lowerCAmelCase_   :							Dict									=			BioGptForSequenceClassification(a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Optional[Any]									=			model(a_						,			attention_mask=a_						,			labels=a_	)
				self.assertEqual(result.logits.shape						,			(self.model_tester.batch_size, self.model_tester.num_labels)	)







@require_torch
class 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






		@slow
		def    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							Optional[int]									=			BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
				lowerCAmelCase_   :							int									=			torch.tensor([[2, 48_05, 9, 6_56, 21]]	)
				lowerCAmelCase_   :							List[str]									=			model(a_	)[0]

				lowerCAmelCase_   :							Any									=			4_23_84

				lowerCAmelCase_   :							str									=			torch.Size((1, 5, vocab_size)	)
				self.assertEqual(output.shape						,			a_	)

				lowerCAmelCase_   :							str									=			torch.tensor(
				    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]	)

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





		@slow
		def    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							Optional[Any]									=			BioGptTokenizer.from_pretrained("microsoft/biogpt"	)
				lowerCAmelCase_   :							str									=			BioGptForCausalLM.from_pretrained("microsoft/biogpt"	)
				model.to(a_	)

				torch.manual_seed(0	)
				lowerCAmelCase_   :							Any									=			tokenizer("COVID-19 is"						,			return_tensors="pt"	).to(a_	)
				lowerCAmelCase_   :							List[Any]									=			model.generate(
				    **a_						,			min_length=1_00						,			max_length=10_24						,			num_beams=5						,			early_stopping=a_						,			)
				lowerCAmelCase_   :							List[Any]									=			tokenizer.decode(output_ids[0]						,			skip_special_tokens=a_	)

				lowerCAmelCase_   :							Union[str, Any]									=			(
				    "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(a_						,			a_	)








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowercase  							=					{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					["GPTNeoXTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTNeoXForCausalLM",
	    "GPTNeoXForQuestionAnswering",
	    "GPTNeoXForSequenceClassification",
	    "GPTNeoXForTokenClassification",
	    "GPTNeoXLayer",
	    "GPTNeoXModel",
	    "GPTNeoXPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





