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   lowercase      :




							def __init__(  self				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple=13		,							_UpperCamelCase				:   Any=7		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Optional[Any]=False		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   Optional[Any]=99		,							_UpperCamelCase				:   List[Any]=32		,							_UpperCamelCase				:   str=5		,							_UpperCamelCase				:   str=4		,							_UpperCamelCase				:   Tuple=37		,							_UpperCamelCase				:   Optional[int]="gelu"		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   Optional[int]=0.1		,							_UpperCamelCase				:   Any=512		,							_UpperCamelCase				:   Union[str, Any]=16		,							_UpperCamelCase				:   str=2		,							_UpperCamelCase				:   str=0.0_2		,							_UpperCamelCase				:   Any=3		,							_UpperCamelCase				:   Optional[int]=4		,							_UpperCamelCase				:   int=None		,							)   ->			List[Any]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''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=_UpperCamelCase		,							initializer_range=self.initializer_range		,							)




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Union[str, Any] )   ->			int:



												'''simple docstring'''

												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 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Union[str, Any]		,							)   ->			Union[str, Any]:



												'''simple docstring'''

												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 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   int		,							*_UpperCamelCase				:   Dict )   ->			int:



												'''simple docstring'''

												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 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Tuple		,							*_UpperCamelCase				:   List[Any] )   ->			int:



												'''simple docstring'''

												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 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Union[str, Any]		,							*_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Optional[Any]=False )   ->			List[str]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Dict		,							*_UpperCamelCase				:   List[Any] )   ->			Tuple:



												'''simple docstring'''

												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_0_1 )
																						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )		,							0.0_1 )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int		,							*_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Any )   ->			Dict:



												'''simple docstring'''

												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   lowercase      (       a			,    a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Union[str, Any]									=							(
							    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
							    if is_torch_available()
							    else ()
							)
							lowercase__  :    List[Any]									=							(BioGptForCausalLM,) if is_torch_available() else ()
							lowercase__  :    Dict									=							(
							    {
							        """feature-extraction""": BioGptModel,
							        """text-classification""": BioGptForSequenceClassification,
							        """text-generation""": BioGptForCausalLM,
							        """token-classification""": BioGptForTokenClassification,
							        """zero-shot""": BioGptForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase__  :    Dict									=							False




							def 		__snake_case(  self				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BioGptModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase		,							hidden_size=37 )




							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''simple docstring'''

												self.config_tester.run_common_tests()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )




							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												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 		__snake_case(  self				:   List[str] )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_UpperCamelCase )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_forward_and_backwards(*_UpperCamelCase		,							gradient_checkpointing=_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_weight_initialization(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_for_token_classification(*_UpperCamelCase )




							@slow
							def 		__snake_case(  self				:   Any )   ->			Union[str, Any]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Optional[int] )   ->			Optional[int]:



												'''simple docstring'''

												for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE           =    BioGptModel.from_pretrained(_UpperCamelCase )
																	self.assertIsNotNone(_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

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



												'''simple docstring'''

												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   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[str]:



												'''simple docstring'''

												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(
												    [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]] )

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





							@slow
							def 		__snake_case(  self				:   str )   ->			List[Any]:



												'''simple docstring'''

												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 )





import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
							import jax.numpy as jnp

							from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )["last_hidden_state"]
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# compare the actual values for a slice of last dim
												self.assertTrue(jnp.allclose(output[:, :, -1]		,							_UpperCamelCase		,							atol=1e-3 ) )




