


'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase							:   List[str]  						=	logging.get_logger(__name__)


if is_vision_available():
 import PIL
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							["""pixel_values"""]



      def __init__(self    :    Any   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Union[int, float] = 1 / 255   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    bool = True   ,						**UpperCamelCase    :    Any   ,						):

             '''simple docstring'''







             super().__init__(**UpperCamelCase )
             lowercase__      			= size if size is not None else {'''shortest_edge''': 224}
             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase )
             lowercase__      			= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase   ,						param_name='''crop_size''' )

             lowercase__      			= do_resize
             lowercase__      			= size
             lowercase__      			= resample
             lowercase__      			= do_center_crop
             lowercase__      			= crop_size
             lowercase__      			= do_rescale
             lowercase__      			= rescale_factor
             lowercase__      			= do_normalize
             lowercase__      			= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             lowercase__      			= image_std if image_std is not None else OPENAI_CLIP_STD
             lowercase__      			= do_convert_rgb



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Dict[str, int]   ,						UpperCamelCase    :    PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







             lowercase__      			= get_size_dict(UpperCamelCase   ,						default_to_square=UpperCamelCase )
             if "shortest_edge" not in size:
                    raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
             lowercase__      			= get_resize_output_image_size(UpperCamelCase   ,						size=size['''shortest_edge''']   ,						default_to_square=UpperCamelCase )
             return resize(UpperCamelCase   ,						size=UpperCamelCase   ,						resample=UpperCamelCase   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Dict[str, int]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= get_size_dict(UpperCamelCase )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
             return center_crop(UpperCamelCase   ,						size=(size['''height'''], size['''width'''])   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Union[int, float]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







             return rescale(UpperCamelCase   ,						scale=UpperCamelCase   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    np.ndarray   ,						UpperCamelCase    :    Union[float, List[float]]   ,						UpperCamelCase    :    Union[float, List[float]]   ,						UpperCamelCase    :    Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             return normalize(UpperCamelCase   ,						mean=UpperCamelCase   ,						std=UpperCamelCase   ,						data_format=UpperCamelCase   ,						**UpperCamelCase )






      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    ImageInput   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Dict[str, int] = None   ,						UpperCamelCase    :    PILImageResampling = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    int = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    float = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    Optional[Union[float, List[float]]] = None   ,						UpperCamelCase    :    bool = None   ,						UpperCamelCase    :    Optional[Union[str, TensorType]] = None   ,						UpperCamelCase    :    Optional[ChannelDimension] = ChannelDimension.FIRST   ,						**UpperCamelCase    :    Optional[int]   ,						):

             '''simple docstring'''







             lowercase__      			= do_resize if do_resize is not None else self.do_resize
             lowercase__      			= size if size is not None else self.size
             lowercase__      			= get_size_dict(UpperCamelCase   ,						param_name='''size'''   ,						default_to_square=UpperCamelCase )
             lowercase__      			= resample if resample is not None else self.resample
             lowercase__      			= do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__      			= crop_size if crop_size is not None else self.crop_size
             lowercase__      			= get_size_dict(UpperCamelCase   ,						param_name='''crop_size'''   ,						default_to_square=UpperCamelCase )
             lowercase__      			= do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      			= rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      			= do_normalize if do_normalize is not None else self.do_normalize
             lowercase__      			= image_mean if image_mean is not None else self.image_mean
             lowercase__      			= image_std if image_std is not None else self.image_std
             lowercase__      			= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             lowercase__      			= make_list_of_images(UpperCamelCase )

             if not valid_images(UpperCamelCase ):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

             if do_resize and size is None:
                    raise ValueError('''Size must be specified if do_resize is True.''' )

             if do_center_crop and crop_size is None:
                    raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    lowercase__      			= [convert_to_rgb(UpperCamelCase ) for image in images]

             # All transformations expect numpy arrays.
             lowercase__      			= [to_numpy_array(UpperCamelCase ) for image in images]

             if do_resize:
                    lowercase__      			= [self.resize(image=UpperCamelCase   ,						size=UpperCamelCase   ,						resample=UpperCamelCase ) for image in images]

             if do_center_crop:
                    lowercase__      			= [self.center_crop(image=UpperCamelCase   ,						size=UpperCamelCase ) for image in images]

             if do_rescale:
                    lowercase__      			= [self.rescale(image=UpperCamelCase   ,						scale=UpperCamelCase ) for image in images]

             if do_normalize:
                    lowercase__      			= [self.normalize(image=UpperCamelCase   ,						mean=UpperCamelCase   ,						std=UpperCamelCase ) for image in images]

             lowercase__      			= [to_channel_dimension_format(UpperCamelCase   ,						UpperCamelCase ) for image in images]

             lowercase__      			= {'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase   ,						tensor_type=UpperCamelCase )





'''simple docstring'''




import math
import unittest

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

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


if is_torch_available():
  import torch

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

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[Any]						,					A						:				Dict						,					A						:				Optional[Any]=13						,					A						:				Optional[Any]=7						,					A						:				Union[str, Any]=True						,					A						:				Optional[Any]=True						,					A						:				int=False						,					A						:				str=True						,					A						:				Optional[Any]=99						,					A						:				Union[str, Any]=32						,					A						:				int=5						,					A						:				Tuple=4						,					A						:				Union[str, Any]=37						,					A						:				Dict="gelu"						,					A						:				Union[str, Any]=0.1						,					A						:				str=0.1						,					A						:				Union[str, Any]=512						,					A						:				int=16						,					A						:				List[str]=2						,					A						:				Tuple=0.02						,					A						:				int=3						,					A						:				List[str]=4						,					A						:				str=None						,					):
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  int							      =    seq_length
             _UpperCAmelCase     :  Union[str, Any]							      =    is_training
             _UpperCAmelCase     :  Any							      =    use_input_mask
             _UpperCAmelCase     :  Optional[Any]							      =    use_token_type_ids
             _UpperCAmelCase     :  str							      =    use_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    vocab_size
             _UpperCAmelCase     :  Tuple							      =    hidden_size
             _UpperCAmelCase     :  Union[str, Any]							      =    num_hidden_layers
             _UpperCAmelCase     :  Optional[Any]							      =    num_attention_heads
             _UpperCAmelCase     :  Union[str, Any]							      =    intermediate_size
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  List[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  List[Any]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Optional[int]							      =    max_position_embeddings
             _UpperCAmelCase     :  str							      =    type_vocab_size
             _UpperCAmelCase     :  str							      =    type_sequence_label_size
             _UpperCAmelCase     :  int							      =    initializer_range
             _UpperCAmelCase     :  Optional[Any]							      =    num_labels
             _UpperCAmelCase     :  List[str]							      =    num_choices
             _UpperCAmelCase     :  List[str]							      =    scope




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size       )

             _UpperCAmelCase     :  Union[str, Any]							      =    None
             if self.use_input_mask:
                    _UpperCAmelCase     :  List[Any]							      =    random_attention_mask([self.batch_size, self.seq_length]       )

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

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

             _UpperCAmelCase     :  List[Any]							      =    self.get_config()

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




      def      _A	(       self						:				Dict       ):
             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      _A	(       self						:				int						,					A						:				List[Any]						,					A						:				Any						,					A						:				int						,					A						:				Union[str, Any]						,					A						:				Dict						,					A						:				List[Any]						,					A						:				Dict       ):
             _UpperCAmelCase     :  List[str]							      =    BioGptModel(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    model(A						,					attention_mask=A       )
             _UpperCAmelCase     :  int							      =    model(A       )
             self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)       )




      def      _A	(       self						:				List[Any]						,					A						:				str						,					A						:				List[Any]						,					A						:				Dict						,					A						:				List[Any]						,					A						:				List[str]						,					A						:				Union[str, Any]						,					A						:				int						,					A						:				List[str]						,					A						:				Dict						,					):
             _UpperCAmelCase     :  Optional[int]							      =    BioGptForCausalLM(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				List[Any]						,					A						:				str						,					A						:				str						,					A						:				str						,					A						:				Any						,					A						:				List[str]						,					*A						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    BioGptModel(config=A       )
             model.to(A       )
             model.eval()

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

             # first forward pass
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    model(A						,					attention_mask=A       ).to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _UpperCAmelCase     :  List[str]							      =    ids_tensor((self.batch_size, 1)						,					config.vocab_size       )

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

             # append to next input_ids and attn_mask
             _UpperCAmelCase     :  List[Any]							      =    torch.cat([input_ids, next_tokens]						,					dim=-1       )
             _UpperCAmelCase     :  Optional[int]							      =    torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1)						,					dtype=torch.long						,					device=A       )]						,					dim=1						,					)

             # get two different outputs
             _UpperCAmelCase     :  List[Any]							      =    model(A						,					attention_mask=A       )["last_hidden_state"]
             _UpperCAmelCase     :  Optional[Any]							      =    model(A						,					past_key_values=A						,					attention_mask=A       )["last_hidden_state"]

             # select random slice
             _UpperCAmelCase     :  Any							      =    ids_tensor((1,)						,					output_from_past.shape[-1]       ).item()
             _UpperCAmelCase     :  Optional[Any]							      =    output_from_no_past[:, -1, random_slice_idx].detach()
             _UpperCAmelCase     :  Any							      =    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      _A	(       self						:				int						,					A						:				Dict						,					A						:				str						,					A						:				Dict						,					A						:				Union[str, Any]						,					A						:				Any						,					*A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptModel(config=A       ).to(A       ).eval()

             _UpperCAmelCase     :  List[Any]							      =    torch.ones(input_ids.shape						,					dtype=torch.long						,					device=A       )

             # first forward pass
             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					attention_mask=A						,					use_cache=A       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    outputs.to_tuple()

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

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

             _UpperCAmelCase     :  Any							      =    model(A						,					attention_mask=A       )["last_hidden_state"]
             _UpperCAmelCase     :  Dict							      =    model(A						,					attention_mask=A						,					past_key_values=A       )[
                 "last_hidden_state"
             ]

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

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

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




      def      _A	(       self						:				Optional[Any]						,					A						:				Tuple						,					A						:				List[str]						,					A						:				Tuple						,					A						:				Dict						,					A						:				List[Any]						,					*A						:				Tuple						,					A						:				List[str]=False       ):
             _UpperCAmelCase     :  Optional[int]							      =    BioGptForCausalLM(A       )
             model.to(A       )
             if gradient_checkpointing:
                    model.gradient_checkpointing_enable()

             _UpperCAmelCase     :  Union[str, Any]							      =    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      _A	(       self						:				Optional[Any]						,					A						:				Any						,					*A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Tuple							      =    BioGptModel(A       )
             _UpperCAmelCase     :  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      _A	(       self						:				Optional[int]						,					A						:				Dict						,					A						:				Tuple						,					A						:				Optional[int]						,					A						:				int						,					A						:				List[str]						,					*A						:				Dict       ):
             _UpperCAmelCase     :  Any							      =    self.num_labels
             _UpperCAmelCase     :  Any							      =    BioGptForTokenClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					attention_mask=A						,					token_type_ids=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Dict							      =    self.prepare_config_and_inputs()
             (
                 (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  
             )     :  List[str]							      =    config_and_inputs
             _UpperCAmelCase     :  Optional[int]							      =    {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase:  List[str]						=						(BioGptForCausalLM,) if is_torch_available() else ()
      __UpperCamelCase:  str						=						(
          {
              "feature-extraction": BioGptModel,
              "text-classification": BioGptForSequenceClassification,
              "text-generation": BioGptForCausalLM,
              "token-classification": BioGptForTokenClassification,
              "zero-shot": BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase:  Union[str, Any]						=						False




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    BioGptModelTester(self       )
             _UpperCAmelCase     :  str							      =    ConfigTester(self						,					config_class=A						,					hidden_size=37       )




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




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Any							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _UpperCAmelCase     :  Tuple							      =    type
                    self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*A						,					gradient_checkpointing=A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*A       )




      @slow
      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             model.to(A       )
             _UpperCAmelCase     :  Tuple							      =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )

             _UpperCAmelCase     :  str							      =    "left"

             # Define PAD Token = EOS Token = 50256
             _UpperCAmelCase     :  Any							      =    tokenizer.eos_token
             _UpperCAmelCase     :  int							      =    model.config.eos_token_id

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

             _UpperCAmelCase     :  Tuple							      =    tokenizer(A						,					return_tensors="pt"						,					padding=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    inputs["input_ids"].to(A       )

             _UpperCAmelCase     :  Any							      =    model.generate(
                 input_ids=A						,					attention_mask=inputs["attention_mask"].to(A       )						,					)

             _UpperCAmelCase     :  int							      =    tokenizer(sentences[0]						,					return_tensors="pt"       ).input_ids.to(A       )
             _UpperCAmelCase     :  List[Any]							      =    model.generate(input_ids=A       )

             _UpperCAmelCase     :  List[Any]							      =    inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
             _UpperCAmelCase     :  int							      =    tokenizer(sentences[1]						,					return_tensors="pt"       ).input_ids.to(A       )
             _UpperCAmelCase     :  int							      =    model.generate(input_ids=A						,					max_length=model.config.max_length - num_paddings       )

             _UpperCAmelCase     :  Dict							      =    tokenizer.batch_decode(A						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Any							      =    tokenizer.decode(output_non_padded[0]						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.decode(output_padded[0]						,					skip_special_tokens=A       )

             _UpperCAmelCase     :  str							      =    [
                 "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      _A	(       self						:				str       ):
             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _UpperCAmelCase     :  Optional[Any]							      =    BioGptModel.from_pretrained(A       )
                    self.assertIsNotNone(A       )




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





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

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @slow
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             _UpperCAmelCase     :  List[str]							      =    torch.tensor([[2, 4805, 9, 656, 21]]       )
             _UpperCAmelCase     :  List[Any]							      =    model(A       )[0]

             _UpperCAmelCase     :  int							      =    42384

             _UpperCAmelCase     :  int							      =    torch.Size((1, 5, vocab_size)       )
             self.assertEqual(output.shape						,					A       )

             _UpperCAmelCase     :  Any							      =    torch.tensor(
                 [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]       )

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





      @slow
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  str							      =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )
             _UpperCAmelCase     :  Tuple							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             model.to(A       )

             torch.manual_seed(0       )
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer("COVID-19 is"						,					return_tensors="pt"       ).to(A       )
             _UpperCAmelCase     :  Dict							      =    model.generate(
                 **A						,					min_length=100						,					max_length=1024						,					num_beams=5						,					early_stopping=A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.decode(output_ids[0]						,					skip_special_tokens=A       )

             _UpperCAmelCase     :  List[str]							      =    (
                 "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       )

