
import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					int(UpperCamelCase__		)
      UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
      return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=3_0_0		):
      # docstyle-ignore
      return f'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Any											=					'''<table border="1" class="dataframe">\n'''
      html_code += """  <thead>\n <tr style="text-align: left;">\n"""
      for i in items[0]:
            html_code += f'''      <th>{i}</th>\n'''
      html_code += "    </tr>\n  </thead>\n  <tbody>\n"
      for line in items[1:]:
            html_code += "    <tr>\n"
            for elt in line:
                  UpperCamelCase__     : Union[str, Any]											=					f'''{elt:.6f}''' if isinstance(UpperCamelCase__			,		UpperCamelCase__		) else str(UpperCamelCase__		)
                  html_code += f'''      <td>{elt}</td>\n'''
            html_code += "    </tr>\n"
      html_code += "  </tbody>\n</table><p>"
      return html_code




class 					_lowerCamelCase			:





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	5
     SCREAMING_SNAKE_CASE_		 =	0.2






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = 3_0_0    ,    )	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					total
           UpperCamelCase__     : str											=					'''''' if prefix is None else prefix
           UpperCamelCase__     : Optional[int]											=					leave
           UpperCamelCase__     : Tuple											=					parent
           UpperCamelCase__     : Dict											=					width
           UpperCamelCase__     : Dict											=					None
           UpperCamelCase__     : Union[str, Any]											=					None
           UpperCamelCase__     : Any											=					None






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = None	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					value
           if comment is not None:
                 UpperCamelCase__     : List[Any]											=					comment
           if self.last_value is None:
                 UpperCamelCase__     : List[Any]											=					time.time()
                 UpperCamelCase__     : Any											=					value
                 UpperCamelCase__     : Optional[int]											=					None
                 UpperCamelCase__     : int											=					self.warmup
                 UpperCamelCase__     : Union[str, Any]											=					1
                 self.update_bar(__SCREAMING_SNAKE_CASE	)
           elif value <= self.last_value and not force_update:
                 return
           elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for    ,    self.total	):
                 if self.first_calls > 0:
                       self.first_calls -= 1
                 UpperCamelCase__     : Dict											=					time.time()
                 UpperCamelCase__     : Optional[int]											=					current_time - self.start_time
                 # We could have value = self.start_value if the update is called twixe with the same start value.
                 if value > self.start_value:
                       UpperCamelCase__     : List[Any]											=					self.elapsed_time / (value - self.start_value)
                 else:
                       UpperCamelCase__     : Optional[int]											=					None
                 if value >= self.total:
                       UpperCamelCase__     : str											=					self.total
                       UpperCamelCase__     : Union[str, Any]											=					None
                       if not self.leave:
                             self.close()
                 elif self.average_time_per_item is not None:
                       UpperCamelCase__     : str											=					self.average_time_per_item * (self.total - value)
                 self.update_bar(__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : str											=					value
                 UpperCamelCase__     : Tuple											=					current_time
                 if self.average_time_per_item is None:
                       UpperCamelCase__     : List[Any]											=					1
                 else:
                       UpperCamelCase__     : Tuple											=					max(int(self.update_every / self.average_time_per_item	)    ,    1	)






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					''' ''' * (len(str(self.total	)	) - len(str(__SCREAMING_SNAKE_CASE	)	)) + str(__SCREAMING_SNAKE_CASE	)
           if self.elapsed_time is None:
                 UpperCamelCase__     : str											=					F'''[{spaced_value}/{self.total} : < :'''
           elif self.predicted_remaining is None:
                 UpperCamelCase__     : Union[str, Any]											=					F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time	)}'''
           else:
                 UpperCamelCase__     : str											=					(
                     F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time	)} <'''
                     F''' {format_time(self.predicted_remaining	)}'''
                 )
                 self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
           self.label += "]" if self.comment is None or len(self.comment	) == 0 else F''', {self.comment}]'''
           self.display()






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					html_progress_bar(self.value    ,    self.total    ,    self.prefix    ,    self.label    ,    self.width	)
           if self.parent is not None:
                 # If this is a child bar, the parent will take care of the display.
                 self.parent.display()
                 return
           if self.output is None:
                 UpperCamelCase__     : List[Any]											=					disp.display(disp.HTML(self.html_code	)    ,    display_id=__SCREAMING_SNAKE_CASE	)
           else:
                 self.output.update(disp.HTML(self.html_code	)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           if self.parent is None and self.output is not None:
                 self.output.update(disp.HTML(''''''	)	)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None	)	-> Optional[Any]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					None if column_names is None else [column_names]
           UpperCamelCase__     : List[Any]											=					None






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					html_progress_bar(self.value    ,    self.total    ,    self.prefix    ,    self.label    ,    self.width	)
           if self.inner_table is not None:
                 self.html_code += text_to_html_table(self.inner_table	)
           if self.child_bar is not None:
                 self.html_code += self.child_bar.html_code
           if self.output is None:
                 UpperCamelCase__     : Dict											=					disp.display(disp.HTML(self.html_code	)    ,    display_id=__SCREAMING_SNAKE_CASE	)
           else:
                 self.output.update(disp.HTML(self.html_code	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           if self.inner_table is None:
                 UpperCamelCase__     : str											=					[list(values.keys()	), list(values.values()	)]
           else:
                 UpperCamelCase__     : str											=					self.inner_table[0]
                 if len(self.inner_table	) == 1:
                       # We give a chance to update the column names at the first iteration
                       for key in values.keys():
                             if key not in columns:
                                   columns.append(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : Any											=					columns
                 self.inner_table.append([values[c] for c in columns]	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=3_0_0	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : int											=					NotebookProgressBar(__SCREAMING_SNAKE_CASE    ,    prefix=__SCREAMING_SNAKE_CASE    ,    parent=self    ,    width=__SCREAMING_SNAKE_CASE	)
           return self.child_bar






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					None
           self.display()




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     def __init__(				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Any											=					None
           UpperCamelCase__     : Dict											=					None
           UpperCamelCase__     : str											=					False






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




           """simple docstring"""







           UpperCamelCase__     : str											=					'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
           UpperCamelCase__     : List[str]											=					0
           UpperCamelCase__     : str											=					0
           UpperCamelCase__     : List[str]											=					[self.first_column] + ['''Training Loss''']
           if args.evaluation_strategy != IntervalStrategy.NO:
                 column_names.append('''Validation Loss'''	)
           UpperCamelCase__     : Tuple											=					NotebookTrainingTracker(state.max_steps    ,    __SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					int(state.epoch	) if int(state.epoch	) == state.epoch else F'''{state.epoch:.2f}'''
           self.training_tracker.update(
               state.global_step + 1    ,    comment=F'''Epoch {epoch}/{state.num_train_epochs}'''    ,    force_update=self._force_next_update    ,    )
           UpperCamelCase__     : List[Any]											=					False






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           if not has_length(__SCREAMING_SNAKE_CASE	):
                 return
           if self.prediction_bar is None:
                 if self.training_tracker is not None:
                       UpperCamelCase__     : Optional[Any]											=					self.training_tracker.add_child(len(__SCREAMING_SNAKE_CASE	)	)
                 else:
                       UpperCamelCase__     : str											=					NotebookProgressBar(len(__SCREAMING_SNAKE_CASE	)	)
                 self.prediction_bar.update(1	)
           else:
                 self.prediction_bar.update(self.prediction_bar.value + 1	)






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




           """simple docstring"""







           if self.prediction_bar is not None:
                 self.prediction_bar.close()
           UpperCamelCase__     : List[Any]											=					None






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> Union[str, Any]:




           """simple docstring"""







           if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
                 UpperCamelCase__     : Tuple											=					{'''Training Loss''': logs['''loss''']}
                 # First column is necessarily Step sine we're not in epoch eval strategy
                 UpperCamelCase__     : Optional[Any]											=					state.global_step
                 self.training_tracker.write_line(__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           if self.training_tracker is not None:
                 UpperCamelCase__     : List[Any]											=					{'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
                 for log in reversed(state.log_history	):
                       if "loss" in log:
                             UpperCamelCase__     : Tuple											=					log['''loss''']
                             break

                 if self.first_column == "Epoch":
                       UpperCamelCase__     : Dict											=					int(state.epoch	)
                 else:
                       UpperCamelCase__     : List[Any]											=					state.global_step
                 UpperCamelCase__     : Union[str, Any]											=					'''eval'''
                 for k in metrics:
                       if k.endswith('''_loss'''	):
                             UpperCamelCase__     : Any											=					re.sub(r'''\_loss$'''    ,    ''''''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : List[Any]											=					metrics.pop('''total_flos'''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : int											=					metrics.pop('''epoch'''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : int											=					metrics.pop(F'''{metric_key_prefix}_runtime'''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Optional[int]											=					metrics.pop(F'''{metric_key_prefix}_samples_per_second'''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Union[str, Any]											=					metrics.pop(F'''{metric_key_prefix}_steps_per_second'''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Dict											=					metrics.pop(F'''{metric_key_prefix}_jit_compilation_time'''    ,    __SCREAMING_SNAKE_CASE	)
                 for k, v in metrics.items():
                       if k == F'''{metric_key_prefix}_loss''':
                             UpperCamelCase__     : List[Any]											=					v
                       else:
                             UpperCamelCase__     : str											=					k.split('''_'''	)
                             UpperCamelCase__     : int											=					''' '''.join([part.capitalize() for part in splits[1:]]	)
                             UpperCamelCase__     : Optional[int]											=					v
                 self.training_tracker.write_line(__SCREAMING_SNAKE_CASE	)
                 self.training_tracker.remove_child()
                 UpperCamelCase__     : Optional[Any]											=					None
                 # Evaluation takes a long time so we should force the next update.
                 UpperCamelCase__     : Dict											=					True






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




           """simple docstring"""







           self.training_tracker.update(
               state.global_step    ,    comment=F'''Epoch {int(state.epoch	)}/{state.num_train_epochs}'''    ,    force_update=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					None




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	)


