
"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_a														=		logging.get_logger(__name__)



def 				lowerCamelCase__		(      __snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  """huggingface/label-files"""
					_UpperCamelCase										=  """imagenet-1k-id2label.json"""
					_UpperCamelCase										=  json.load(open(hf_hub_download(__lowerCAmelCase,					__lowerCAmelCase,					repo_type='''dataset'''     ),					'''r'''     )     )
					_UpperCamelCase										=  {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
					_UpperCamelCase										=  {v: k for k, v in idalabel.items()}

					_UpperCamelCase										=  """std_conv""" if """bit""" in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					_UpperCamelCase										=  BitConfig(
					    conv_layer=__lowerCAmelCase,					num_labels=10_00,					idalabel=__lowerCAmelCase,					labelaid=__lowerCAmelCase,					)

					return config



def 				lowerCamelCase__		(      __snake_case     )							->		Tuple:


					"""simple docstring"""





					if "stem.conv" in name:
										_UpperCamelCase										=  name.replace('''stem.conv''',					'''bit.embedder.convolution'''     )
					if "blocks" in name:
										_UpperCamelCase										=  name.replace('''blocks''',					'''layers'''     )
					if "head.fc" in name:
										_UpperCamelCase										=  name.replace('''head.fc''',					'''classifier.1'''     )
					if name.startswith('''norm'''     ):
										_UpperCamelCase										=  """bit.""" + name
					if "bit" not in name and "classifier" not in name:
										_UpperCamelCase										=  """bit.encoder.""" + name

					return name



def 				lowerCamelCase__		(      )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
					_UpperCamelCase										=  Image.open(requests.get(__lowerCAmelCase,					stream=__lowerCAmelCase     ).raw     )
					return im



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case=False     )							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  get_config(__lowerCAmelCase     )

					# load original model from timm
					_UpperCamelCase										=  create_model(__lowerCAmelCase,					pretrained=__lowerCAmelCase     )
					timm_model.eval()

					# load state_dict of original model
					_UpperCamelCase										=  timm_model.state_dict()
					for key in state_dict.copy().keys():
										_UpperCamelCase										=  state_dict.pop(__lowerCAmelCase     )
										_UpperCamelCase										=  val.squeeze() if """head""" in key else val

					# load HuggingFace model
					_UpperCamelCase										=  BitForImageClassification(__lowerCAmelCase     )
					model.eval()
					model.load_state_dict(__lowerCAmelCase     )

					# create image processor
					_UpperCamelCase										=  create_transform(**resolve_data_config({},					model=__lowerCAmelCase     )     )
					_UpperCamelCase										=  transform.transforms

					_UpperCamelCase										=  {
					    """bilinear""": PILImageResampling.BILINEAR,
					    """bicubic""": PILImageResampling.BICUBIC,
					    """nearest""": PILImageResampling.NEAREST,
					}

					_UpperCamelCase										=  BitImageProcessor(
					    do_resize=__lowerCAmelCase,					size={'''shortest_edge''': timm_transforms[0].size},					resample=pillow_resamplings[timm_transforms[0].interpolation.value],					do_center_crop=__lowerCAmelCase,					crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},					do_normalize=__lowerCAmelCase,					image_mean=timm_transforms[-1].mean.tolist(),					image_std=timm_transforms[-1].std.tolist(),					)

					_UpperCamelCase										=  prepare_img()
					_UpperCamelCase										=  transform(__lowerCAmelCase     ).unsqueeze(0     )
					_UpperCamelCase										=  processor(__lowerCAmelCase,					return_tensors='''pt'''     ).pixel_values

					# verify pixel values
					assert torch.allclose(__lowerCAmelCase,					__lowerCAmelCase     )

					# verify logits
					with torch.no_grad():
										_UpperCamelCase										=  model(__lowerCAmelCase     )
										_UpperCamelCase										=  outputs.logits

					print('''Logits:''',					logits[0, :3]     )
					print('''Predicted class:''',					model.config.idalabel[logits.argmax(-1     ).item()]     )
					_UpperCamelCase										=  timm_model(__lowerCAmelCase     )
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(__lowerCAmelCase,					outputs.logits,					atol=1e-3     )
					print('''Looks ok!'''     )

					if pytorch_dump_folder_path is not None:
										Path(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
										print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''     )
										model.save_pretrained(__lowerCAmelCase     )
										processor.save_pretrained(__lowerCAmelCase     )

					if push_to_hub:
										print(F'''Pushing model {model_name} and processor to the hub'''     )
										model.push_to_hub(F'''ybelkada/{model_name}'''     )
										processor.push_to_hub(F'''ybelkada/{model_name}'''     )


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--model_name""",
				    default="""resnetv2_50x1_bitm""",
				    type=str,
				    help="""Name of the BiT timm model you'd like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)
				parser.add_argument(
				    """--push_to_hub""",
				    action="""store_true""",
				    help="""Whether to push the model to the hub.""",
				)

				_a														=		parser.parse_args()
				convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""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    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=False    ,		__a=True    ,		__a=99    ,		__a=32    ,		__a=5    ,		__a=4    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=16    ,		__a=2    ,		__a=0.02    ,		__a=3    ,		__a=4    ,		__a=None    ,		)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  seq_length
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_input_mask
							_UpperCamelCase										=  use_token_type_ids
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  type_sequence_label_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  num_labels
							_UpperCamelCase										=  num_choices
							_UpperCamelCase										=  scope



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size)

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

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

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

							_UpperCamelCase										=  self.get_config()

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



		def 						UpperCAmelCase							( self)	->	str:






							'''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=__a    ,		initializer_range=self.initializer_range    ,		)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a)
							_UpperCamelCase										=  model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(config=__a)
							model.to(__a)
							model.eval()

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

							# first forward pass
							_UpperCamelCase			,  _UpperCamelCase										=  model(__a    ,		attention_mask=__a).to_tuple()

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

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

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

							# get two different outputs
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a)['''last_hidden_state''']
							_UpperCamelCase										=  model(__a    ,		past_key_values=__a    ,		attention_mask=__a)['''last_hidden_state''']

							# select random slice
							_UpperCamelCase										=  ids_tensor((1,)    ,		output_from_past.shape[-1]).item()
							_UpperCamelCase										=  output_from_no_past[:, -1, random_slice_idx].detach()
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(config=__a).to(__a).eval()

							_UpperCamelCase										=  torch.ones(input_ids.shape    ,		dtype=torch.long    ,		device=__a)

							# first forward pass
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		use_cache=__a)

							_UpperCamelCase			,  _UpperCamelCase										=  outputs.to_tuple()

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

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

							_UpperCamelCase										=  model(__a    ,		attention_mask=__a)['''last_hidden_state''']
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		past_key_values=__a)[
							    '''last_hidden_state'''
							]

							# select random slice
							_UpperCamelCase										=  ids_tensor((1,)    ,		output_from_past.shape[-1]).item()
							_UpperCamelCase										=  output_from_no_past[:, -3:, random_slice_idx].detach()
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a    ,		__a=False)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM(__a)
							model.to(__a)
							if gradient_checkpointing:
												model.gradient_checkpointing_enable()

							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		*__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(__a)
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  BioGptForTokenClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  
							)										=  config_and_inputs
							_UpperCamelCase										=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		lowercase__      =				(BioGptForCausalLM,) if is_torch_available() else ()
		lowercase__      =				(
		    {
		        'feature-extraction': BioGptModel,
		        'text-classification': BioGptForSequenceClassification,
		        'text-generation': BioGptForCausalLM,
		        'token-classification': BioGptForTokenClassification,
		        'zero-shot': BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__      =				False



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



		def 						UpperCAmelCase							( self)	->	str:






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



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
												_UpperCamelCase										=  type
												self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*__a    ,		gradient_checkpointing=__a)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*__a)



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*__a)



		@slow
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(__a)
							_UpperCamelCase										=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

							_UpperCamelCase										=  '''left'''

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

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

							_UpperCamelCase										=  tokenizer(__a    ,		return_tensors='''pt'''    ,		padding=__a)
							_UpperCamelCase										=  inputs['''input_ids'''].to(__a)

							_UpperCamelCase										=  model.generate(
							    input_ids=__a    ,		attention_mask=inputs['''attention_mask'''].to(__a)    ,		)

							_UpperCamelCase										=  tokenizer(sentences[0]    ,		return_tensors='''pt''').input_ids.to(__a)
							_UpperCamelCase										=  model.generate(input_ids=__a)

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

							_UpperCamelCase										=  tokenizer.batch_decode(__a    ,		skip_special_tokens=__a)
							_UpperCamelCase										=  tokenizer.decode(output_non_padded[0]    ,		skip_special_tokens=__a)
							_UpperCamelCase										=  tokenizer.decode(output_padded[0]    ,		skip_special_tokens=__a)

							_UpperCamelCase										=  [
							    '''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 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  BioGptModel.from_pretrained(__a)
												self.assertIsNotNone(__a)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase										=  3
							_UpperCamelCase										=  input_dict['''input_ids''']
							_UpperCamelCase										=  input_ids.ne(1).to(__a)
							_UpperCamelCase										=  ids_tensor([self.model_tester.batch_size]    ,		self.model_tester.type_sequence_label_size)
							_UpperCamelCase										=  BioGptForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		labels=__a)
							self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels))




		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase										=  3
							_UpperCamelCase										=  '''multi_label_classification'''
							_UpperCamelCase										=  input_dict['''input_ids''']
							_UpperCamelCase										=  input_ids.ne(1).to(__a)
							_UpperCamelCase										=  ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]    ,		self.model_tester.type_sequence_label_size).to(torch.float)
							_UpperCamelCase										=  BioGptForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							_UpperCamelCase										=  torch.tensor([[2, 48_05, 9, 6_56, 21]])
							_UpperCamelCase										=  model(__a)[0]

							_UpperCamelCase										=  4_23_84

							_UpperCamelCase										=  torch.Size((1, 5, vocab_size))
							self.assertEqual(output.shape    ,		__a)

							_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')
							_UpperCamelCase										=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(__a)

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

							_UpperCamelCase										=  (
							    '''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)






