
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings




@maybe_allow_in_graph
class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=0.0    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = "geglu"    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = "layer_norm"    ,    __SCREAMING_SNAKE_CASE = False    ,    )	-> Optional[Any]:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : str											=					only_cross_attention

           UpperCamelCase__     : Tuple											=					(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
           UpperCamelCase__     : Dict											=					(num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''

           if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
                 raise ValueError(
                     F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                     F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''	)

           # Define 3 blocks. Each block has its own normalization layer.
           # 1. Self-Attn
           if self.use_ada_layer_norm:
                 UpperCamelCase__     : Union[str, Any]											=					AdaLayerNorm(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           elif self.use_ada_layer_norm_zero:
                 UpperCamelCase__     : int											=					AdaLayerNormZero(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           else:
                 UpperCamelCase__     : Optional[int]											=					nn.LayerNorm(__SCREAMING_SNAKE_CASE    ,    elementwise_affine=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					Attention(
               query_dim=__SCREAMING_SNAKE_CASE    ,    heads=__SCREAMING_SNAKE_CASE    ,    dim_head=__SCREAMING_SNAKE_CASE    ,    dropout=__SCREAMING_SNAKE_CASE    ,    bias=__SCREAMING_SNAKE_CASE    ,    cross_attention_dim=cross_attention_dim if only_cross_attention else None    ,    upcast_attention=__SCREAMING_SNAKE_CASE    ,    )

           # 2. Cross-Attn
           if cross_attention_dim is not None or double_self_attention:
                 # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
                 # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
                 # the second cross attention block.
                 UpperCamelCase__     : Any											=					(
                     AdaLayerNorm(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                     if self.use_ada_layer_norm
                     else nn.LayerNorm(__SCREAMING_SNAKE_CASE    ,    elementwise_affine=__SCREAMING_SNAKE_CASE	)
                 )
                 UpperCamelCase__     : List[Any]											=					Attention(
                     query_dim=__SCREAMING_SNAKE_CASE    ,    cross_attention_dim=cross_attention_dim if not double_self_attention else None    ,    heads=__SCREAMING_SNAKE_CASE    ,    dim_head=__SCREAMING_SNAKE_CASE    ,    dropout=__SCREAMING_SNAKE_CASE    ,    bias=__SCREAMING_SNAKE_CASE    ,    upcast_attention=__SCREAMING_SNAKE_CASE    ,    )  # is self-attn if encoder_hidden_states is none
           else:
                 UpperCamelCase__     : List[str]											=					None
                 UpperCamelCase__     : str											=					None

           # 3. Feed-forward
           UpperCamelCase__     : Union[str, Any]											=					nn.LayerNorm(__SCREAMING_SNAKE_CASE    ,    elementwise_affine=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					FeedForward(__SCREAMING_SNAKE_CASE    ,    dropout=__SCREAMING_SNAKE_CASE    ,    activation_fn=__SCREAMING_SNAKE_CASE    ,    final_dropout=__SCREAMING_SNAKE_CASE	)

           # let chunk size default to None
           UpperCamelCase__     : Optional[int]											=					None
           UpperCamelCase__     : Dict											=					0






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					chunk_size
           UpperCamelCase__     : Optional[int]											=					dim






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    )	-> int:




           """simple docstring"""







           if self.use_ada_layer_norm:
                 UpperCamelCase__     : List[str]											=					self.norma(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           elif self.use_ada_layer_norm_zero:
                 UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					self.norma(
                     __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    hidden_dtype=hidden_states.dtype	)
           else:
                 UpperCamelCase__     : Optional[int]											=					self.norma(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					cross_attention_kwargs if cross_attention_kwargs is not None else {}

           UpperCamelCase__     : Union[str, Any]											=					self.attna(
               __SCREAMING_SNAKE_CASE    ,    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    )
           if self.use_ada_layer_norm_zero:
                 UpperCamelCase__     : int											=					gate_msa.unsqueeze(1	) * attn_output
           UpperCamelCase__     : Optional[int]											=					attn_output + hidden_states

           # 2. Cross-Attention
           if self.attna is not None:
                 UpperCamelCase__     : Any											=					(
                     self.norma(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) if self.use_ada_layer_norm else self.norma(__SCREAMING_SNAKE_CASE	)
                 )

                 UpperCamelCase__     : str											=					self.attna(
                     __SCREAMING_SNAKE_CASE    ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    )
                 UpperCamelCase__     : Dict											=					attn_output + hidden_states

           # 3. Feed-forward
           UpperCamelCase__     : List[str]											=					self.norma(__SCREAMING_SNAKE_CASE	)

           if self.use_ada_layer_norm_zero:
                 UpperCamelCase__     : Optional[Any]											=					norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

           if self._chunk_size is not None:
                 # "feed_forward_chunk_size" can be used to save memory
                 if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                       raise ValueError(
                           F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''	)

                 UpperCamelCase__     : Optional[Any]											=					norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
                 UpperCamelCase__     : Tuple											=					torch.cat(
                     [self.ff(__SCREAMING_SNAKE_CASE	) for hid_slice in norm_hidden_states.chunk(__SCREAMING_SNAKE_CASE    ,    dim=self._chunk_dim	)]    ,    dim=self._chunk_dim    ,    )
           else:
                 UpperCamelCase__     : Tuple											=					self.ff(__SCREAMING_SNAKE_CASE	)

           if self.use_ada_layer_norm_zero:
                 UpperCamelCase__     : List[str]											=					gate_mlp.unsqueeze(1	) * ff_output

           UpperCamelCase__     : str											=					ff_output + hidden_states

           return hidden_states




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = 4    ,    __SCREAMING_SNAKE_CASE = 0.0    ,    __SCREAMING_SNAKE_CASE = "geglu"    ,    __SCREAMING_SNAKE_CASE = False    ,    )	-> Dict:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : int											=					int(dim * mult	)
           UpperCamelCase__     : int											=					dim_out if dim_out is not None else dim

           if activation_fn == "gelu":
                 UpperCamelCase__     : Tuple											=					GELU(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           if activation_fn == "gelu-approximate":
                 UpperCamelCase__     : Optional[Any]											=					GELU(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    approximate='''tanh'''	)
           elif activation_fn == "geglu":
                 UpperCamelCase__     : Any											=					GEGLU(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           elif activation_fn == "geglu-approximate":
                 UpperCamelCase__     : List[str]											=					ApproximateGELU(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					nn.ModuleList([]	)
           # project in
           self.net.append(__SCREAMING_SNAKE_CASE	)
           # project dropout
           self.net.append(nn.Dropout(__SCREAMING_SNAKE_CASE	)	)
           # project out
           self.net.append(nn.Linear(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	)
           # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
           if final_dropout:
                 self.net.append(nn.Dropout(__SCREAMING_SNAKE_CASE	)	)






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




           """simple docstring"""







           for module in self.net:
                 UpperCamelCase__     : str											=					module(__SCREAMING_SNAKE_CASE	)
           return hidden_states




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = "none"	)	-> Union[str, Any]:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : Any											=					nn.Linear(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					approximate






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           if gate.device.type != "mps":
                 return F.gelu(__SCREAMING_SNAKE_CASE    ,    approximate=self.approximate	)
           # mps: gelu is not implemented for float16
           return F.gelu(gate.to(dtype=torch.floataa	)    ,    approximate=self.approximate	).to(dtype=gate.dtype	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.proj(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					self.gelu(__SCREAMING_SNAKE_CASE	)
           return hidden_states




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : Dict											=					nn.Linear(__SCREAMING_SNAKE_CASE    ,    dim_out * 2	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           if gate.device.type != "mps":
                 return F.gelu(__SCREAMING_SNAKE_CASE	)
           # mps: gelu is not implemented for float16
           return F.gelu(gate.to(dtype=torch.floataa	)	).to(dtype=gate.dtype	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : int											=					self.proj(__SCREAMING_SNAKE_CASE	).chunk(2    ,    dim=-1	)
           return hidden_states * self.gelu(__SCREAMING_SNAKE_CASE	)




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : List[str]											=					nn.Linear(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.proj(__SCREAMING_SNAKE_CASE	)
           return x * torch.sigmoid(1.702 * x	)




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : Tuple											=					nn.Embedding(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					nn.SiLU()
           UpperCamelCase__     : str											=					nn.Linear(__SCREAMING_SNAKE_CASE    ,    embedding_dim * 2	)
           UpperCamelCase__     : Optional[Any]											=					nn.LayerNorm(__SCREAMING_SNAKE_CASE    ,    elementwise_affine=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.linear(self.silu(self.emb(__SCREAMING_SNAKE_CASE	)	)	)
           UpperCamelCase__					,UpperCamelCase__     : Dict											=					torch.chunk(__SCREAMING_SNAKE_CASE    ,    2	)
           UpperCamelCase__     : Optional[Any]											=					self.norm(__SCREAMING_SNAKE_CASE	) * (1 + scale) + shift
           return x




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[Any]:




           """simple docstring"""







           super().__init__()

           UpperCamelCase__     : Optional[int]											=					CombinedTimestepLabelEmbeddings(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					nn.SiLU()
           UpperCamelCase__     : Optional[Any]											=					nn.Linear(__SCREAMING_SNAKE_CASE    ,    6 * embedding_dim    ,    bias=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					nn.LayerNorm(__SCREAMING_SNAKE_CASE    ,    elementwise_affine=__SCREAMING_SNAKE_CASE    ,    eps=1e-6	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.linear(self.silu(self.emb(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    hidden_dtype=__SCREAMING_SNAKE_CASE	)	)	)
           UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Tuple											=					emb.chunk(6    ,    dim=1	)
           UpperCamelCase__     : Tuple											=					self.norm(__SCREAMING_SNAKE_CASE	) * (1 + scale_msa[:, None]) + shift_msa[:, None]
           return x, gate_msa, shift_mlp, scale_mlp, gate_mlp




class 					_lowerCamelCase			(   nn.Module			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = 1e-5	)	-> Any:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__     : Tuple											=					num_groups
           UpperCamelCase__     : int											=					eps

           if act_fn is None:
                 UpperCamelCase__     : Any											=					None
           else:
                 UpperCamelCase__     : int											=					get_activation(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					nn.Linear(__SCREAMING_SNAKE_CASE    ,    out_dim * 2	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           if self.act:
                 UpperCamelCase__     : List[str]											=					self.act(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					self.linear(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					emb[:, :, None, None]
           UpperCamelCase__					,UpperCamelCase__     : str											=					emb.chunk(2    ,    dim=1	)

           UpperCamelCase__     : Optional[Any]											=					F.group_norm(__SCREAMING_SNAKE_CASE    ,    self.num_groups    ,    eps=self.eps	)
           UpperCamelCase__     : int											=					x * (1 + scale) + shift
           return x




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    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=1_3    ,    __SCREAMING_SNAKE_CASE=7    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=9_9    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=5    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=3_7    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=5_1_2    ,    __SCREAMING_SNAKE_CASE=1_6    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=None    ,    )	-> List[str]:




           """simple docstring"""







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






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size	)

           UpperCamelCase__     : Tuple											=					None
           if self.use_input_mask:
                 UpperCamelCase__     : List[str]											=					random_attention_mask([self.batch_size, self.seq_length]	)

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

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

           UpperCamelCase__     : Dict											=					self.get_config()

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[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=__SCREAMING_SNAKE_CASE    ,    initializer_range=self.initializer_range    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					model(__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    )	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptForCausalLM(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Union[str, Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    token_type_ids=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()

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

           # first forward pass
           UpperCamelCase__					,UpperCamelCase__     : Optional[int]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	).to_tuple()

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

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

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

           # get two different outputs
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']

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

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	).to(__SCREAMING_SNAKE_CASE	).eval()

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

           # first forward pass
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    use_cache=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__					,UpperCamelCase__     : List[Any]											=					outputs.to_tuple()

           # create hypothetical multiple next token and extent to next_input_ids
           UpperCamelCase__     : List[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__     : Tuple											=					torch.cat([attention_mask, next_attn_mask]    ,    dim=-1	)

           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']
           UpperCamelCase__     : int											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE	)[
               '''last_hidden_state'''
           ]

           # select random slice
           UpperCamelCase__     : Union[str, Any]											=					ids_tensor((1,)    ,    output_from_past.shape[-1]	).item()
           UpperCamelCase__     : Union[str, Any]											=					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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					BioGptForCausalLM(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

           UpperCamelCase__     : Tuple											=					model(__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					BioGptModel(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.num_labels
           UpperCamelCase__     : Any											=					BioGptForTokenClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    token_type_ids=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.num_labels)	)






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.prepare_config_and_inputs()
           (
               (
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,
           )     : List[str]											=					config_and_inputs
           UpperCamelCase__     : Optional[Any]											=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_torch
class 					_lowerCamelCase			(   UpperCamelCase_						,				UpperCamelCase_						,				UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		 =	(BioGptForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		 =	(
         {
             '''feature-extraction''': BioGptModel,
             '''text-classification''': BioGptForSequenceClassification,
             '''text-generation''': BioGptForCausalLM,
             '''token-classification''': BioGptForTokenClassification,
             '''zero-shot''': BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_		 =	False






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptModelTester(self	)
           UpperCamelCase__     : Dict											=					ConfigTester(self    ,    config_class=__SCREAMING_SNAKE_CASE    ,    hidden_size=3_7	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           self.config_tester.run_common_tests()






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




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 UpperCamelCase__     : Optional[Any]											=					type
                 self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*__SCREAMING_SNAKE_CASE    ,    gradient_checkpointing=__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : str											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*__SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           model.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''	)

           UpperCamelCase__     : Optional[Any]											=					'''left'''

           # Define PAD Token = EOS Token = 50256
           UpperCamelCase__     : Optional[int]											=					tokenizer.eos_token
           UpperCamelCase__     : List[str]											=					model.config.eos_token_id

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

           UpperCamelCase__     : Dict											=					tokenizer(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''    ,    padding=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					inputs['''input_ids'''].to(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					model.generate(
               input_ids=__SCREAMING_SNAKE_CASE    ,    attention_mask=inputs['''attention_mask'''].to(__SCREAMING_SNAKE_CASE	)    ,    )

           UpperCamelCase__     : int											=					tokenizer(sentences[0]    ,    return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					model.generate(input_ids=__SCREAMING_SNAKE_CASE	)

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

           UpperCamelCase__     : Any											=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.decode(output_non_padded[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					tokenizer.decode(output_padded[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Any											=					[
               '''Hello, my dog is a little bit bigger than a little bit.''',
               '''Today, I have a good idea of how to use the information''',
           ]
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    [non_padded_sentence, padded_sentence]	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__     : Optional[int]											=					BioGptModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Dict											=					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__     : int											=					3
           UpperCamelCase__     : Tuple											=					input_dict['''input_ids''']
           UpperCamelCase__     : List[Any]											=					input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					ids_tensor([self.model_tester.batch_size]    ,    self.model_tester.type_sequence_label_size	)
           UpperCamelCase__     : Optional[int]											=					BioGptForSequenceClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : int											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(result.logits.shape    ,    (self.model_tester.batch_size, self.model_tester.num_labels)	)






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




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : List[str]											=					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__     : List[str]											=					3
           UpperCamelCase__     : List[Any]											=					'''multi_label_classification'''
           UpperCamelCase__     : List[str]											=					input_dict['''input_ids''']
           UpperCamelCase__     : Tuple											=					input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					ids_tensor(
               [self.model_tester.batch_size, config.num_labels]    ,    self.model_tester.type_sequence_label_size	).to(torch.float	)
           UpperCamelCase__     : List[str]											=					BioGptForSequenceClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           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  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           UpperCamelCase__     : Union[str, Any]											=					torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]	)
           UpperCamelCase__     : Tuple											=					model(__SCREAMING_SNAKE_CASE	)[0]

           UpperCamelCase__     : Optional[Any]											=					4_2_3_8_4

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

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

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






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''	)
           UpperCamelCase__     : List[Any]											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           model.to(__SCREAMING_SNAKE_CASE	)

           torch.manual_seed(0	)
           UpperCamelCase__     : Dict											=					tokenizer('''COVID-19 is'''    ,    return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					model.generate(
               **__SCREAMING_SNAKE_CASE    ,    min_length=1_0_0    ,    max_length=1_0_2_4    ,    num_beams=5    ,    early_stopping=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : int											=					tokenizer.decode(output_ids[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, Any]											=					(
               '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
               ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
               ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
               ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
               ''' more than 800,000 deaths.'''
           )
           self.assertEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)


