


'''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():

_UpperCAmelCase				: List[str]							    =				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
"""
)


_UpperCAmelCase				: Tuple							    =				{
    """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""": []},
                    ],
                },
            ],
        }
    ],
}


_UpperCAmelCase				: Dict							    =				"""\
---
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
"""


_UpperCAmelCase				: List[str]							    =				"""\
---
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
"""

_UpperCAmelCase				: str							    =				{
    """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""": []},
                    ],
                },
            ],
        }
    ],
}

_UpperCAmelCase				: List[Any]							    =				"""\
---
---
# 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
"""

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

_UpperCAmelCase				: Dict							    =				"""\
# 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
"""

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

_UpperCAmelCase				: List[str]							    =				"""\
---
# 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
"""

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

_UpperCAmelCase				: Union[str, Any]							    =				"""\
---
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
"""
_UpperCAmelCase				: Any							    =				"""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)."""


_UpperCAmelCase				: List[str]							    =				"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
"""
_UpperCAmelCase				: Dict							    =				"""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'."""

_UpperCAmelCase				: int							    =				"""\
---
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
"""

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


_UpperCAmelCase				: Union[str, Any]							    =				"""\
---
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
"""

_UpperCAmelCase				: List[Any]							    =				"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""

_UpperCAmelCase				: Optional[Any]							    =				"""\
---
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
"""
_UpperCAmelCase				: List[Any]							    =				"""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."""

_UpperCAmelCase				: str							    =				"""\
---
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
"""

_UpperCAmelCase				: List[str]							    =				"""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."""

_UpperCAmelCase				: str							    =				"""\
---
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
"""

_UpperCAmelCase				: int							    =				"""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."""

_UpperCAmelCase				: List[str]							    =				""""""

_UpperCAmelCase				: Dict							    =				"""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."""

_UpperCAmelCase				: Tuple							    =				"""\
---
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
"""

_UpperCAmelCase				: str							    =				"""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 							__magic_name__(						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 							__magic_name__(						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 							__magic_name__(						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 							__magic_name__(						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 							__magic_name__(						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 							__magic_name__(						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 							__magic_name__(						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 							__magic_name__(						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 logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


_UpperCAmelCase				: Optional[Any]							    =				logging.getLogger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.layer[current_layer](__lowercase       ,			__lowercase       ,			head_mask[current_layer]		)

										__lowerCAmelCase							       =				layer_outputs[0]

										return hidden_states


@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase							       =				BertEncoderWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0

										__lowerCAmelCase							       =				0




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				threshold




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				patience




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase							       =				(
										    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
										    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
										)
										print(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=False       ,			):

										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(__lowercase       ,			device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase							       =				torch.zeros(__lowercase       ,			dtype=torch.long       ,			device=__lowercase		)

										# 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(__lowercase       ,			__lowercase       ,			__lowercase		)

										# 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 self.config.is_decoder and encoder_hidden_states is not None:
																__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				encoder_hidden_states.size()
																__lowerCAmelCase							       =				(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
																__lowerCAmelCase							       =				self.invert_attention_mask(__lowercase		)
										else:
																__lowerCAmelCase							       =				None

										# 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(__lowercase       ,			self.config.num_hidden_layers		)

										__lowerCAmelCase							       =				self.embeddings(
										    input_ids=__lowercase       ,			position_ids=__lowercase       ,			token_type_ids=__lowercase       ,			inputs_embeds=__lowercase		)
										__lowerCAmelCase							       =				embedding_output

										if self.training:
																__lowerCAmelCase							       =				[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase							       =				self.encoder.adaptive_forward(
																						    __lowercase       ,			current_layer=__lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase		)

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase							       =				self.encoder(
																    __lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase       ,			encoder_hidden_states=__lowercase       ,			encoder_attention_mask=__lowercase       ,			)
																__lowerCAmelCase							       =				self.pooler(encoder_outputs[0]		)
																__lowerCAmelCase							       =				[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										else:
																__lowerCAmelCase							       =				0
																__lowerCAmelCase							       =				None
																__lowerCAmelCase							       =				0
																for i in range(self.config.num_hidden_layers		):
																						calculated_layer_num += 1
																						__lowerCAmelCase							       =				self.encoder.adaptive_forward(
																						    __lowercase       ,			current_layer=__lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase		)

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase							       =				logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase							       =				patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase							       =				0
																						else:
																												__lowerCAmelCase							       =				logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase							       =				patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase							       =				0

																						__lowerCAmelCase							       =				logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase							       =				[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res


@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase							       =				config.num_labels

										__lowerCAmelCase							       =				BertModelWithPabee(__lowercase		)
										__lowerCAmelCase							       =				nn.Dropout(config.hidden_dropout_prob		)
										__lowerCAmelCase							       =				nn.ModuleList(
										    [nn.Linear(config.hidden_size       ,			self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

										self.init_weights()




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				self.bert(
										    input_ids=__lowercase       ,			attention_mask=__lowercase       ,			token_type_ids=__lowercase       ,			position_ids=__lowercase       ,			head_mask=__lowercase       ,			inputs_embeds=__lowercase       ,			output_dropout=self.dropout       ,			output_layers=self.classifiers       ,			regression=self.num_labels == 1       ,			)

										__lowerCAmelCase							       =				(logits[-1],)

										if labels is not None:
																__lowerCAmelCase							       =				None
																__lowerCAmelCase							       =				0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase							       =				MSELoss()
																												__lowerCAmelCase							       =				loss_fct(logits_item.view(-1		)       ,			labels.view(-1		)		)
																						else:
																												__lowerCAmelCase							       =				CrossEntropyLoss()
																												__lowerCAmelCase							       =				loss_fct(logits_item.view(-1       ,			self.num_labels		)       ,			labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase							       =				loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase							       =				(total_loss / total_weights,) + outputs

										return outputs

