



from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """  ,							UpperCAmelCase__  ,							)
class       a							(			UpperCAmelCase__				):
		__lowerCAmelCase	:    Tuple											=					RobertaConfig
		__lowerCAmelCase	:    Union[str, Any]											=					"""roberta"""



		def __init__(     self     :Any   ,__lowercase     :List[str]  ):
						super().__init__(UpperCAmelCase__  )

						snake_case__	:      Optional[int]     		=	RobertaEmbeddings(UpperCAmelCase__  )
						self.init_weights()


@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """  ,							UpperCAmelCase__  ,							)
class       a							(			UpperCAmelCase__				):
		__lowerCAmelCase	:    Union[str, Any]											=					RobertaConfig
		__lowerCAmelCase	:    List[Any]											=					"""roberta"""



		def __init__(     self     :Optional[Any]   ,__lowercase     :Tuple  ):
						super().__init__(UpperCAmelCase__  )
						snake_case__	:      Optional[Any]     		=	config.num_labels
						snake_case__	:      List[str]     		=	config.num_hidden_layers

						snake_case__	:      List[Any]     		=	DeeRobertaModel(UpperCAmelCase__  )
						snake_case__	:      List[Any]     		=	nn.Dropout(config.hidden_dropout_prob  )
						snake_case__	:      int     		=	nn.Linear(config.hidden_size   ,self.config.num_labels  )






		@add_start_docstrings_to_model_forward(UpperCAmelCase__  )
		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :Tuple=None   ,__lowercase     :Union[str, Any]=None   ,__lowercase     :Optional[Any]=None   ,__lowercase     :Dict=None   ,__lowercase     :List[Any]=None   ,__lowercase     :Optional[Any]=None   ,__lowercase     :int=None   ,__lowercase     :Dict=-1   ,__lowercase     :Optional[int]=False   ,):
						snake_case__	:      Any     		=	self.num_layers
						try:
										snake_case__	:      Optional[int]     		=	self.roberta(
										    UpperCAmelCase__   ,attention_mask=UpperCAmelCase__   ,token_type_ids=UpperCAmelCase__   ,position_ids=UpperCAmelCase__   ,head_mask=UpperCAmelCase__   ,inputs_embeds=UpperCAmelCase__   ,)

										snake_case__	:      Any     		=	outputs[1]

										snake_case__	:      Dict     		=	self.dropout(UpperCAmelCase__  )
										snake_case__	:      Tuple     		=	self.classifier(UpperCAmelCase__  )
										snake_case__	:      int     		=	(logits,) + outputs[2:]  # add hidden states and attention if they are here
						except HighwayException as e:
										snake_case__	:      Tuple     		=	e.message
										snake_case__	:      List[str]     		=	e.exit_layer
										snake_case__	:      Optional[int]     		=	outputs[0]

						if not self.training:
										snake_case__	:      int     		=	entropy(UpperCAmelCase__  )
										snake_case__	:      List[Any]     		=	[]
										snake_case__	:      str     		=	[]
						if labels is not None:
										if self.num_labels == 1:
														#  We are doing regression
														snake_case__	:      str     		=	MSELoss()
														snake_case__	:      Any     		=	loss_fct(logits.view(-1  )   ,labels.view(-1  )  )
										else:
														snake_case__	:      Tuple     		=	CrossEntropyLoss()
														snake_case__	:      Any     		=	loss_fct(logits.view(-1   ,self.num_labels  )   ,labels.view(-1  )  )

										# work with highway exits
										snake_case__	:      List[str]     		=	[]
										for highway_exit in outputs[-1]:
														snake_case__	:      List[Any]     		=	highway_exit[0]
														if not self.training:
																		highway_logits_all.append(UpperCAmelCase__  )
																		highway_entropy.append(highway_exit[2]  )
														if self.num_labels == 1:
																		#  We are doing regression
																		snake_case__	:      Union[str, Any]     		=	MSELoss()
																		snake_case__	:      int     		=	loss_fct(highway_logits.view(-1  )   ,labels.view(-1  )  )
														else:
																		snake_case__	:      Union[str, Any]     		=	CrossEntropyLoss()
																		snake_case__	:      Tuple     		=	loss_fct(highway_logits.view(-1   ,self.num_labels  )   ,labels.view(-1  )  )
														highway_losses.append(UpperCAmelCase__  )

										if train_highway:
														snake_case__	:      str     		=	(sum(highway_losses[:-1]  ),) + outputs
														# exclude the final highway, of course
										else:
														snake_case__	:      Union[str, Any]     		=	(loss,) + outputs
						if not self.training:
										snake_case__	:      Optional[Any]     		=	outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
														snake_case__	:      List[Any]     		=	(
														    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
														)  # use the highway of the last layer

						return outputs  # (loss), logits, (hidden_states), (attentions), entropy







import os

import pytest

from transformers.dynamic_module_utils import get_imports


_lowerCamelCase	:		Any							       =   """
import os
"""

_lowerCamelCase	:		Optional[int]							       =   """
def foo():
    import os
    return False
"""

_lowerCamelCase	:		List[Any]							       =   """
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except ImportError:
    raise ValueError()
"""

_lowerCamelCase	:		Union[str, Any]							       =   """
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
"""

_lowerCamelCase	:		str							       =   """
import os

try:
    import bar
except:
    raise ValueError()
"""

_lowerCamelCase	:		Optional[Any]							       =   """
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
"""

_lowerCamelCase	:		Any							       =   """
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
"""

_lowerCamelCase	:		Dict							       =   [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]







@pytest.mark.parametrize('''case''' ,				lowercase_						)
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    os.path.join(lowercase_ ,				'''test_file.py'''						)
       with open(lowercase_ ,				'''w'''						) as _tmp_file:
              _tmp_file.write(lowercase_						)

       A__       =    get_imports(lowercase_						)
       assert parsed_imports == ["os"]


