



'''simple docstring'''



from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


lowerCAmelCase_     :       Tuple								=						logging.get_logger(__name__)


lowerCAmelCase_     :       int								=						OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

lowerCAmelCase_     :       int								=						OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

lowerCAmelCase_     :       int								=						OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

lowerCAmelCase_     :       List[str]								=						OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

lowerCAmelCase_     :       int								=						OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

lowerCAmelCase_     :       List[Any]								=						OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

lowerCAmelCase_     :       Optional[int]								=						OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

lowerCAmelCase_     :       Dict								=						OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

lowerCAmelCase_     :       Optional[Any]								=						OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

lowerCAmelCase_     :       Union[str, Any]								=						OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

lowerCAmelCase_     :       int								=						OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

lowerCAmelCase_     :       List[str]								=						OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

lowerCAmelCase_     :       Tuple								=						OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

lowerCAmelCase_     :       List[str]								=						OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

lowerCAmelCase_     :       str								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCAmelCase_     :       Union[str, Any]								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCAmelCase_     :       Optional[Any]								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCAmelCase_     :       Optional[int]								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCAmelCase_     :       int								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase_     :       Optional[Any]								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCAmelCase_     :       Tuple								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCAmelCase_     :       Dict								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase_     :       List[str]								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCAmelCase_     :       Tuple								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase_     :       List[str]								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCAmelCase_     :       Union[str, Any]								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCAmelCase_     :       Tuple								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCAmelCase_     :       int								=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   str	     =				FLAX_MODEL_MAPPING


lowerCAmelCase_     :       Dict								=						auto_class_update(FlaxAutoModel)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   Any	     =				FLAX_MODEL_FOR_PRETRAINING_MAPPING


lowerCAmelCase_     :       List[Any]								=						auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   List[str]	     =				FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


lowerCAmelCase_     :       List[Any]								=						auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   int	     =				FLAX_MODEL_FOR_MASKED_LM_MAPPING


lowerCAmelCase_     :       str								=						auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   List[Any]	     =				FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


lowerCAmelCase_     :       Dict								=						auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   Tuple	     =				FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


lowerCAmelCase_     :       Dict								=						auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   str	     =				FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


lowerCAmelCase_     :       Dict								=						auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   Dict	     =				FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


lowerCAmelCase_     :       int								=						auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   Dict	     =				FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


lowerCAmelCase_     :       Optional[int]								=						auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   Dict	     =				FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


lowerCAmelCase_     :       Tuple								=						auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   int	     =				FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


lowerCAmelCase_     :       Optional[Any]								=						auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   Any	     =				FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


lowerCAmelCase_     :       str								=						auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class 	lowerCamelCase_							(					_BaseAutoModelClass			):
						_lowerCAmelCase     :   str	     =				FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


lowerCAmelCase_     :       Tuple								=						auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)











'''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   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      str=13				,    lowerCAmelCase__   :      List[Any]=7				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Union[str, Any]=False				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      Dict=99				,    lowerCAmelCase__   :      List[str]=32				,    lowerCAmelCase__   :      str=5				,    lowerCAmelCase__   :      str=4				,    lowerCAmelCase__   :      Any=37				,    lowerCAmelCase__   :      Tuple="gelu"				,    lowerCAmelCase__   :      int=0.1				,    lowerCAmelCase__   :      Dict=0.1				,    lowerCAmelCase__   :      str=5_12				,    lowerCAmelCase__   :      List[str]=16				,    lowerCAmelCase__   :      List[str]=2				,    lowerCAmelCase__   :      Union[str, Any]=0.02				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Tuple=4				,    lowerCAmelCase__   :      List[Any]=None				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      Any     					=			seq_length
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			is_training
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_input_mask
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      int     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      int     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      str     					=			type_vocab_size
													SCREAMING_SNAKE_CASE					:      int     					=			type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Tuple     					=			num_labels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_choices
													SCREAMING_SNAKE_CASE					:      Any     					=			scope







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      str     					=			random_attention_mask([self.batch_size, self.seq_length]				)

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			None
													SCREAMING_SNAKE_CASE					:      List[str]     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor([self.batch_size]				,    self.type_sequence_label_size				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.batch_size]				,    self.num_choices				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_config()

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







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													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=lowerCAmelCase__				,    initializer_range=self.initializer_range				,    )







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			BioGptModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptForCausalLM(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    token_type_ids=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()

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

													# first forward pass
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				).to_tuple()

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

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

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

													# get two different outputs
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']

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

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







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[Any]				,    *lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			BioGptModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.ones(input_ids.shape				,    dtype=torch.long				,    device=lowerCAmelCase__				)

													# first forward pass
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    use_cache=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			outputs.to_tuple()

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

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

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      str     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				)[
													    '''last_hidden_state'''
													]

													# select random slice
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      Union[str, 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(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-3				)				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptForCausalLM(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													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   :      Any				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModel(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      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 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				,    *lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Tuple     					=			BioGptForTokenClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    token_type_ids=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			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
													)					,
													)					:      Optional[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Any     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Dict	     =				(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase     :   Optional[Any]	     =				(BioGptForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase     :   List[str]	     =				(
						    {
						        'feature-extraction': BioGptModel,
						        'text-classification': BioGptForSequenceClassification,
						        'text-generation': BioGptForCausalLM,
						        'token-classification': BioGptForTokenClassification,
						        'zero-shot': BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   Dict	     =				False







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Any     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    hidden_size=37				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			type
																				self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__				,    gradient_checkpointing=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													model.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''left'''

													# Define PAD Token = EOS Token = 50256
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.eos_token
													SCREAMING_SNAKE_CASE					:      Dict     					=			model.config.eos_token_id

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

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				,    return_tensors='''pt'''				,    padding=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			inputs['''input_ids'''].to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(
													    input_ids=lowerCAmelCase__				,    attention_mask=inputs['''attention_mask'''].to(lowerCAmelCase__				)				,    )

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer(sentences[0]				,    return_tensors='''pt'''				).input_ids.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			model.generate(input_ids=lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				,    skip_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.decode(output_non_padded[0]				,    skip_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.decode(output_padded[0]				,    skip_special_tokens=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			[
													    '''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(lowerCAmelCase__				,    lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    [non_padded_sentence, padded_sentence]				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			3
													SCREAMING_SNAKE_CASE					:      Dict     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      str     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.model_tester.batch_size]				,    self.model_tester.type_sequence_label_size				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			BioGptForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			3
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''multi_label_classification'''
													SCREAMING_SNAKE_CASE					:      Any     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      Any     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]				,    self.model_tester.type_sequence_label_size				).to(torch.float				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													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   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([[2, 48_05, 9, 6_56, 21]]				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				)[0]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			4_23_84

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.Size((1, 5, vocab_size)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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]				,    lowerCAmelCase__				,    atol=1e-4				)				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													model.to(lowerCAmelCase__				)

													torch.manual_seed(0				)
													SCREAMING_SNAKE_CASE					:      str     					=			tokenizer('''COVID-19 is'''				,    return_tensors='''pt'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(
													    **lowerCAmelCase__				,    min_length=1_00				,    max_length=10_24				,    num_beams=5				,    early_stopping=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer.decode(output_ids[0]				,    skip_special_tokens=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      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(lowerCAmelCase__				,    lowerCAmelCase__				)




