


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__a	 =      logging.get_logger(__name__)


__a	 =      {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}






class 	__lowercase					(	UpperCamelCase_	):
      UpperCamelCase										=							'''codegen'''
      UpperCamelCase										=							{
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Dict=5_0_4_0_0       ,							__lowerCamelCase    :       List[Any]=2_0_4_8       ,							__lowerCamelCase    :       str=2_0_4_8       ,							__lowerCamelCase    :       Dict=4_0_9_6       ,							__lowerCamelCase    :       str=2_8       ,							__lowerCamelCase    :       List[str]=1_6       ,							__lowerCamelCase    :       Tuple=6_4       ,							__lowerCamelCase    :       List[Any]=None       ,							__lowerCamelCase    :       Tuple="gelu_new"       ,							__lowerCamelCase    :       Dict=0.0       ,							__lowerCamelCase    :       Any=0.0       ,							__lowerCamelCase    :       Tuple=0.0       ,							__lowerCamelCase    :       Dict=1e-5       ,							__lowerCamelCase    :       Union[str, Any]=0.02       ,							__lowerCamelCase    :       Tuple=True       ,							__lowerCamelCase    :       Dict=5_0_2_5_6       ,							__lowerCamelCase    :       Optional[int]=5_0_2_5_6       ,							__lowerCamelCase    :       Optional[int]=False       ,							**__lowerCamelCase    :       Any       ,							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  vocab_size
         UpperCAmelCase      					=  n_ctx
         UpperCAmelCase      					=  n_positions
         UpperCAmelCase      					=  n_embd
         UpperCAmelCase      					=  n_layer
         UpperCAmelCase      					=  n_head
         UpperCAmelCase      					=  n_inner
         UpperCAmelCase      					=  rotary_dim
         UpperCAmelCase      					=  activation_function
         UpperCAmelCase      					=  resid_pdrop
         UpperCAmelCase      					=  embd_pdrop
         UpperCAmelCase      					=  attn_pdrop
         UpperCAmelCase      					=  layer_norm_epsilon
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  use_cache

         UpperCAmelCase      					=  bos_token_id
         UpperCAmelCase      					=  eos_token_id

         super().__init__(
             bos_token_id=__SCREAMING_SNAKE_CASE       ,							eos_token_id=__SCREAMING_SNAKE_CASE       ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE							)






class 	__lowercase					(	UpperCamelCase_	):





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       List[str] = "default"       ,							__lowerCamelCase    :       Any = None       ,							__lowerCamelCase    :       int = False       ,							)     -> str:






         """simple docstring"""







         super().__init__(__SCREAMING_SNAKE_CASE       ,							task=__SCREAMING_SNAKE_CASE       ,							patching_specs=__SCREAMING_SNAKE_CASE       ,							use_past=__SCREAMING_SNAKE_CASE							)
         if not getattr(self._config       ,							"""pad_token_id"""       ,							__SCREAMING_SNAKE_CASE							):
            # TODO: how to do that better?
            UpperCAmelCase      					=  0





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         UpperCAmelCase      					=  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
         if self.use_past:
            self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE       ,							direction="""inputs"""							)
            UpperCAmelCase      					=  {0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
            UpperCAmelCase      					=  {0: '''batch''', 1: '''sequence'''}

         return common_inputs





      @property
      def 							_lowercase					(		self    :       List[str]							)     -> int:






         """simple docstring"""







         return self._config.n_layer





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         return self._config.n_head





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       Union[str, Any] = -1       ,							__lowerCamelCase    :       str = -1       ,							__lowerCamelCase    :       Optional[Any] = False       ,							__lowerCamelCase    :       int = None       ,							)     -> Mapping[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  super(__SCREAMING_SNAKE_CASE       ,							self							).generate_dummy_inputs(
             __SCREAMING_SNAKE_CASE       ,							batch_size=__SCREAMING_SNAKE_CASE       ,							seq_length=__SCREAMING_SNAKE_CASE       ,							is_pair=__SCREAMING_SNAKE_CASE       ,							framework=__SCREAMING_SNAKE_CASE							)

         # We need to order the input in the way they appears in the forward()
         UpperCAmelCase      					=  OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

         # Need to add the past_keys
         if self.use_past:
            if not is_torch_available():
               raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
            else:
               import torch

               UpperCAmelCase      					=  common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               UpperCAmelCase      					=  seqlen + 2
               UpperCAmelCase      					=  (
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               UpperCAmelCase      					=  [
                   (torch.zeros(__SCREAMING_SNAKE_CASE							), torch.zeros(__SCREAMING_SNAKE_CASE							)) for _ in range(self.num_layers							)
               ]

         UpperCAmelCase      					=  common_inputs['''attention_mask''']
         if self.use_past:
            UpperCAmelCase      					=  ordered_inputs['''attention_mask'''].dtype
            UpperCAmelCase      					=  torch.cat(
                [ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							dtype=__SCREAMING_SNAKE_CASE							)]       ,							dim=1							)

         return ordered_inputs





      @property
      def 							_lowercase					(		self    :       Any							)     -> int:






         """simple docstring"""







         return 1_3





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	)


