
"""simple docstring"""
import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

SCREAMING_SNAKE_CASE_					     =	yaml.safe_load(
    '''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
  - name: "Dataset Card for X" # First-level markdown heading
    allow_empty: false
    allow_empty_text: true
    subsections:
      - name: "Table of Contents"
        allow_empty: false
        allow_empty_text: false
        subsections: null
      - name: "Dataset Description"
        allow_empty: false
        allow_empty_text: false
        subsections:
          - name: "Dataset Summary"
            allow_empty: false
            allow_empty_text: false
            subsections: null
          - name: "Supported Tasks and Leaderboards"
            allow_empty: true
            allow_empty_text: true
            subsections: null
          - name: Languages
            allow_empty: false
            allow_empty_text: true
            subsections: null
'''
)


SCREAMING_SNAKE_CASE_					     =	{
    '''name''': '''root''',
    '''text''': '''''',
    '''is_empty_text''': True,
    '''subsections''': [
        {
            '''name''': '''Dataset Card for My Dataset''',
            '''text''': '''''',
            '''is_empty_text''': True,
            '''subsections''': [
                {'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
                {
                    '''name''': '''Dataset Description''',
                    '''text''': '''Some text here.''',
                    '''is_empty_text''': False,
                    '''subsections''': [
                        {
                            '''name''': '''Dataset Summary''',
                            '''text''': '''Some text here.''',
                            '''is_empty_text''': False,
                            '''subsections''': [],
                        },
                        {
                            '''name''': '''Supported Tasks and Leaderboards''',
                            '''text''': '''''',
                            '''is_empty_text''': True,
                            '''subsections''': [],
                        },
                        {'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
                    ],
                },
            ],
        }
    ],
}


SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''


SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	{
    '''name''': '''root''',
    '''text''': '''''',
    '''is_empty_text''': True,
    '''subsections''': [
        {
            '''name''': '''Dataset Card for My Dataset''',
            '''text''': '''''',
            '''is_empty_text''': True,
            '''subsections''': [
                {'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
                {
                    '''name''': '''Dataset Description''',
                    '''text''': '''Some text here.''',
                    '''is_empty_text''': False,
                    '''subsections''': [
                        {
                            '''name''': '''Dataset Summary''',
                            '''text''': '''Some text here.''',
                            '''is_empty_text''': False,
                            '''subsections''': [
                                {
                                    '''name''': '''Extra Ignored Subsection''',
                                    '''text''': '''''',
                                    '''is_empty_text''': True,
                                    '''subsections''': [],
                                }
                            ],
                        },
                        {
                            '''name''': '''Supported Tasks and Leaderboards''',
                            '''text''': '''''',
                            '''is_empty_text''': True,
                            '''subsections''': [],
                        },
                        {'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
                    ],
                },
            ],
        }
    ],
}

SCREAMING_SNAKE_CASE_					     =	'''\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	(
    '''The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'''
)

SCREAMING_SNAKE_CASE_					     =	'''\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	(
    '''The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'''
)

SCREAMING_SNAKE_CASE_					     =	'''\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'''

SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'''


SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
'''
SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'''

SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'''


SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
'''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'''

SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'''

SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
'''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'''

SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'''

SCREAMING_SNAKE_CASE_					     =	''''''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'''

SCREAMING_SNAKE_CASE_					     =	'''\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''

SCREAMING_SNAKE_CASE_					     =	'''The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'''

@pytest.mark.parametrize(
    """readme_md, expected_dict""" ,    [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ] ,    )
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						assert ReadMe.from_string(_lowerCAmelCase ,    _lowerCAmelCase						).to_dict() == expected_dict

@pytest.mark.parametrize(
    """readme_md, expected_error""" ,    [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ] ,    )
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						with pytest.raises(_lowerCAmelCase ,    match=re.escape(expected_error.format(path="""root"""						)						)						):
												__lowerCAmelCase     =  ReadMe.from_string(_lowerCAmelCase ,    _lowerCAmelCase						)
												readme.validate()

@pytest.mark.parametrize(
    """readme_md, expected_error""" ,    [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ] ,    )
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						with pytest.raises(_lowerCAmelCase ,    match=re.escape(expected_error.format(path="""root"""						)						)						):
												ReadMe.from_string(_lowerCAmelCase ,    _lowerCAmelCase						)

@pytest.mark.parametrize(
    """readme_md,""" ,    [
        (README_MULTIPLE_SAME_HEADING_1),
    ] ,    )
def  lowercase     (_lowerCAmelCase						):
						ReadMe.from_string(_lowerCAmelCase ,    _lowerCAmelCase ,    suppress_parsing_errors=_lowerCAmelCase						)

@pytest.mark.parametrize(
    """readme_md, expected_dict""" ,    [
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ] ,    )
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase     =  Path(_lowerCAmelCase						) / """README.md"""
												with open(_lowerCAmelCase ,    """w+"""						) as readme_file:
																		readme_file.write(_lowerCAmelCase						)
												__lowerCAmelCase     =  ReadMe.from_readme(_lowerCAmelCase ,    _lowerCAmelCase						).to_dict()
												assert out["name"] == path
												assert out["text"] == ""
												assert out["is_empty_text"]
												assert out["subsections"] == expected_dict["subsections"]

@pytest.mark.parametrize(
    """readme_md, expected_error""" ,    [
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ] ,    )
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase     =  Path(_lowerCAmelCase						) / """README.md"""
												with open(_lowerCAmelCase ,    """w+"""						) as readme_file:
																		readme_file.write(_lowerCAmelCase						)
												__lowerCAmelCase     =  expected_error.format(path=_lowerCAmelCase						)
												with pytest.raises(_lowerCAmelCase ,    match=re.escape(_lowerCAmelCase						)						):
																		__lowerCAmelCase     =  ReadMe.from_readme(_lowerCAmelCase ,    _lowerCAmelCase						)
																		readme.validate()

@pytest.mark.parametrize(
    """readme_md, expected_error""" ,    [
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ] ,    )
def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase     =  Path(_lowerCAmelCase						) / """README.md"""
												with open(_lowerCAmelCase ,    """w+"""						) as readme_file:
																		readme_file.write(_lowerCAmelCase						)
												__lowerCAmelCase     =  expected_error.format(path=_lowerCAmelCase						)
												with pytest.raises(_lowerCAmelCase ,    match=re.escape(_lowerCAmelCase						)						):
																		ReadMe.from_readme(_lowerCAmelCase ,    _lowerCAmelCase						)



@pytest.mark.parametrize(
    """readme_md,""" ,    [
        (README_MULTIPLE_SAME_HEADING_1),
    ] ,    )
def  lowercase     (_lowerCAmelCase						):
						with tempfile.TemporaryDirectory() as tmp_dir:
												__lowerCAmelCase     =  Path(_lowerCAmelCase						) / """README.md"""
												with open(_lowerCAmelCase ,    """w+"""						) as readme_file:
																		readme_file.write(_lowerCAmelCase						)
												ReadMe.from_readme(_lowerCAmelCase ,    _lowerCAmelCase ,    suppress_parsing_errors=_lowerCAmelCase						)


"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  torch.exp(_lowerCAmelCase						)
						__lowerCAmelCase     =  torch.sum(_lowerCAmelCase ,    dim=1						)  # sum of exp(x_i)
						__lowerCAmelCase     =  torch.sum(x * exp_x ,    dim=1						)  # sum of x_i * exp(x_i)
						return torch.log(_lowerCAmelCase						) - B / A
class       lowerCAmelCase_		(				nn.Module ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Optional[Any]:
													super().__init__()
													__lowerCAmelCase     =  config.output_attentions
													__lowerCAmelCase     =  config.output_hidden_states
													__lowerCAmelCase     =  nn.ModuleList([BertLayer(snake_case_				) for _ in range(config.num_hidden_layers				)]				)
													__lowerCAmelCase     =  nn.ModuleList([BertHighway(snake_case_				) for _ in range(config.num_hidden_layers				)]				)

													__lowerCAmelCase     =  [-1 for _ in range(config.num_hidden_layers				)]




							def    A__						( self    ,				snake_case_				)       ->  List[Any]:
													if (type(snake_case_				) is float) or (type(snake_case_				) is int):
																			for i in range(len(self.early_exit_entropy				)				):
																									__lowerCAmelCase     =  x
													else:
																			__lowerCAmelCase     =  x




							def    A__						( self    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  pooler.state_dict()
													for highway in self.highway:
																			for name, param in highway.pooler.state_dict().items():
																									param.copy_(loaded_model[name]				)




							def    A__						( self    ,				snake_case_    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				)       ->  List[str]:
													__lowerCAmelCase     =  ()
													__lowerCAmelCase     =  ()
													__lowerCAmelCase     =  ()
													for i, layer_module in enumerate(self.layer				):
																			if self.output_hidden_states:
																									__lowerCAmelCase     =  all_hidden_states + (hidden_states,)

																			__lowerCAmelCase     =  layer_module(
																			    snake_case_    ,				snake_case_    ,				head_mask[i]    ,				snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  layer_outputs[0]

																			if self.output_attentions:
																									__lowerCAmelCase     =  all_attentions + (layer_outputs[1],)

																			__lowerCAmelCase     =  (hidden_states,)
																			if self.output_hidden_states:
																									__lowerCAmelCase     =  current_outputs + (all_hidden_states,)
																			if self.output_attentions:
																									__lowerCAmelCase     =  current_outputs + (all_attentions,)

																			__lowerCAmelCase     =  self.highway[i](snake_case_				)
																			# logits, pooled_output

																			if not self.training:
																									__lowerCAmelCase     =  highway_exit[0]
																									__lowerCAmelCase     =  entropy(snake_case_				)
																									__lowerCAmelCase     =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																									__lowerCAmelCase     =  all_highway_exits + (highway_exit,)

																									if highway_entropy < self.early_exit_entropy[i]:
																															__lowerCAmelCase     =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																															raise HighwayException(snake_case_    ,				i + 1				)
																			else:
																									__lowerCAmelCase     =  all_highway_exits + (highway_exit,)

        # Add last layer
													if self.output_hidden_states:
																			__lowerCAmelCase     =  all_hidden_states + (hidden_states,)

													__lowerCAmelCase     =  (hidden_states,)
													if self.output_hidden_states:
																			__lowerCAmelCase     =  outputs + (all_hidden_states,)
													if self.output_attentions:
																			__lowerCAmelCase     =  outputs + (all_attentions,)

													__lowerCAmelCase     =  outputs + (all_highway_exits,)
													return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''				,				A__				,				)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Tuple:
													super().__init__(snake_case_				)
													__lowerCAmelCase     =  config

													__lowerCAmelCase     =  BertEmbeddings(snake_case_				)
													__lowerCAmelCase     =  DeeBertEncoder(snake_case_				)
													__lowerCAmelCase     =  BertPooler(snake_case_				)

													self.init_weights()




							def    A__						( self				)       ->  Dict:
													self.encoder.init_highway_pooler(self.pooler				)




							def    A__						( self				)       ->  List[Any]:
													return self.embeddings.word_embeddings




							def    A__						( self    ,				snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  value




							def    A__						( self    ,				snake_case_				)       ->  List[str]:
													for layer, heads in heads_to_prune.items():
																			self.encoder.layer[layer].attention.prune_heads(snake_case_				)




							@add_start_docstrings_to_model_forward(snake_case_				)
							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				)       ->  Optional[Any]:
													if input_ids is not None and inputs_embeds is not None:
																			raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""				)
													elif input_ids is not None:
																			__lowerCAmelCase     =  input_ids.size()
													elif inputs_embeds is not None:
																			__lowerCAmelCase     =  inputs_embeds.size()[:-1]
													else:
																			raise ValueError("""You have to specify either input_ids or inputs_embeds"""				)

													__lowerCAmelCase     =  input_ids.device if input_ids is not None else inputs_embeds.device

													if attention_mask is None:
																			__lowerCAmelCase     =  torch.ones(snake_case_    ,				device=snake_case_				)
													if encoder_attention_mask is None:
																			__lowerCAmelCase     =  torch.ones(snake_case_    ,				device=snake_case_				)
													if token_type_ids is None:
																			__lowerCAmelCase     =  torch.zeros(snake_case_    ,				dtype=torch.long    ,				device=snake_case_				)

													# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
													# ourselves in which case we just need to make it broadcastable to all heads.
													__lowerCAmelCase     =  self.get_extended_attention_mask(snake_case_    ,				snake_case_    ,				snake_case_				)

													# If a 2D ou 3D attention mask is provided for the cross-attention
													# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
													if encoder_attention_mask.dim() == 3:
																			__lowerCAmelCase     =  encoder_attention_mask[:, None, :, :]
													if encoder_attention_mask.dim() == 2:
																			__lowerCAmelCase     =  encoder_attention_mask[:, None, None, :]

													__lowerCAmelCase     =  encoder_extended_attention_mask.to(
													    dtype=next(self.parameters()				).dtype				)  # fp16 compatibility
													__lowerCAmelCase     =  (1.0 - encoder_extended_attention_mask) * -10_000.0

													# Prepare head mask if needed
													# 1.0 in head_mask indicate we keep the head
													# attention_probs has shape bsz x n_heads x N x N
													# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
													# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
													__lowerCAmelCase     =  self.get_head_mask(snake_case_    ,				self.config.num_hidden_layers				)

													__lowerCAmelCase     =  self.embeddings(
													    input_ids=snake_case_    ,				position_ids=snake_case_    ,				token_type_ids=snake_case_    ,				inputs_embeds=snake_case_				)
													__lowerCAmelCase     =  self.encoder(
													    snake_case_    ,				attention_mask=snake_case_    ,				head_mask=snake_case_    ,				encoder_hidden_states=snake_case_    ,				encoder_attention_mask=snake_case_    ,				)
													__lowerCAmelCase     =  encoder_outputs[0]
													__lowerCAmelCase     =  self.pooler(snake_case_				)

													__lowerCAmelCase     =  (
													    sequence_output,
													    pooled_output,
													) + encoder_outputs[
													    1:
													]  # add hidden_states and attentions if they are here
													return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_				)       ->  Optional[int]:
													__lowerCAmelCase     =  message
													__lowerCAmelCase     =  exit_layer  # start from 1!
class       lowerCAmelCase_		(				nn.Module ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  List[Any]:
													super().__init__()
													__lowerCAmelCase     =  BertPooler(snake_case_				)
													__lowerCAmelCase     =  nn.Dropout(config.hidden_dropout_prob				)
													__lowerCAmelCase     =  nn.Linear(config.hidden_size    ,				config.num_labels				)




							def    A__						( self    ,				snake_case_				)       ->  Optional[int]:
													# Pooler
													__lowerCAmelCase     =  encoder_outputs[0]
													__lowerCAmelCase     =  self.pooler(snake_case_				)
													# "return" pooler_output

													# BertModel
													__lowerCAmelCase     =  (pooler_input, pooler_output) + encoder_outputs[1:]
													# "return" bmodel_output

													# Dropout and classification
													__lowerCAmelCase     =  bmodel_output[1]

													__lowerCAmelCase     =  self.dropout(snake_case_				)
													__lowerCAmelCase     =  self.classifier(snake_case_				)

													return logits, pooled_output
@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''				,				A__				,				)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_				)       ->  Optional[Any]:
													super().__init__(snake_case_				)
													__lowerCAmelCase     =  config.num_labels
													__lowerCAmelCase     =  config.num_hidden_layers

													__lowerCAmelCase     =  DeeBertModel(snake_case_				)
													__lowerCAmelCase     =  nn.Dropout(config.hidden_dropout_prob				)
													__lowerCAmelCase     =  nn.Linear(config.hidden_size    ,				self.config.num_labels				)

													self.init_weights()




							@add_start_docstrings_to_model_forward(snake_case_				)
							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=-1    ,				snake_case_=False    ,				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.num_layers
													try:
																			__lowerCAmelCase     =  self.bert(
																			    snake_case_    ,				attention_mask=snake_case_    ,				token_type_ids=snake_case_    ,				position_ids=snake_case_    ,				head_mask=snake_case_    ,				inputs_embeds=snake_case_    ,				)
																			# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

																			__lowerCAmelCase     =  outputs[1]

																			__lowerCAmelCase     =  self.dropout(snake_case_				)
																			__lowerCAmelCase     =  self.classifier(snake_case_				)
																			__lowerCAmelCase     =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
													except HighwayException as e:
																			__lowerCAmelCase     =  e.message
																			__lowerCAmelCase     =  e.exit_layer
																			__lowerCAmelCase     =  outputs[0]

													if not self.training:
																			__lowerCAmelCase     =  entropy(snake_case_				)
																			__lowerCAmelCase     =  []
																			__lowerCAmelCase     =  []
													if labels is not None:
																			if self.num_labels == 1:
																									#  We are doing regression
																									__lowerCAmelCase     =  MSELoss()
																									__lowerCAmelCase     =  loss_fct(logits.view(-1				)    ,				labels.view(-1				)				)
																			else:
																									__lowerCAmelCase     =  CrossEntropyLoss()
																									__lowerCAmelCase     =  loss_fct(logits.view(-1    ,				self.num_labels				)    ,				labels.view(-1				)				)

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

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

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