



'''simple docstring'''






import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCamelCase__:     List[Any]										=    get_logger(__name__)


UpperCamelCase__:     Any										=    Path(__file__).parent / "model_card_template.md"
UpperCamelCase__:     List[Any]										=    uuida().hex
UpperCamelCase__:     int										=    os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__:     Dict										=    os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__:     Optional[int]										=    HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"


def 				snake_case_  (      _lowerCAmelCase						:		Union[Dict, str, None] = None						)   ->     str:
							UpperCAmelCase				: Any											=	f"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
							if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
														return ua + "; telemetry/off"
							if is_torch_available():
														ua += f"""; torch/{_torch_version}"""
							if is_flax_available():
														ua += f"""; jax/{_jax_version}"""
														ua += f"""; flax/{_flax_version}"""
							if is_onnx_available():
														ua += f"""; onnxruntime/{_onnxruntime_version}"""
							# CI will set this value to True
							if os.environ.get('''DIFFUSERS_IS_CI'''     ,   ''''''						).upper() in ENV_VARS_TRUE_VALUES:
														ua += "; is_ci/true"
							if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()						)
							elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														ua += "; " + user_agent
							return ua


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[str] = None     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     Dict:
							if token is None:
														UpperCAmelCase				: Optional[int]											=	HfFolder.get_token()
							if organization is None:
														UpperCAmelCase				: str											=	whoami(_lowerCAmelCase						)['''name''']
														return f"""{username}/{model_id}"""
							else:
														return f"""{organization}/{model_id}"""


def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							if not is_jinja_available():
														raise ValueError(
														    '''Modelcard rendering is based on Jinja templates.'''
														    ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
														    ''' To install it, please run `pip install Jinja2`.'''						)

							if hasattr(_lowerCAmelCase     ,   '''local_rank'''						) and args.local_rank not in [-1, 0]:
														return

							UpperCAmelCase				: Optional[int]											=	args.hub_token if hasattr(_lowerCAmelCase     ,   '''hub_token'''						) else None
							UpperCAmelCase				: Tuple											=	get_full_repo_name(_lowerCAmelCase     ,   token=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	ModelCard.from_template(
							    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
							        language='''en'''     ,   license='''apache-2.0'''     ,   library_name='''diffusers'''     ,   tags=[]     ,   datasets=args.dataset_name     ,   metrics=[]     ,   )     ,   template_path=_lowerCAmelCase     ,   model_name=_lowerCAmelCase     ,   repo_name=_lowerCAmelCase     ,   dataset_name=args.dataset_name if hasattr(_lowerCAmelCase     ,   '''dataset_name'''						) else None     ,   learning_rate=args.learning_rate     ,   train_batch_size=args.train_batch_size     ,   eval_batch_size=args.eval_batch_size     ,   gradient_accumulation_steps=(
							        args.gradient_accumulation_steps if hasattr(_lowerCAmelCase     ,   '''gradient_accumulation_steps'''						) else None
							    )     ,   adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase     ,   '''adam_beta1'''						) else None     ,   adam_betaa=args.adam_betaa if hasattr(_lowerCAmelCase     ,   '''adam_beta2'''						) else None     ,   adam_weight_decay=args.adam_weight_decay if hasattr(_lowerCAmelCase     ,   '''adam_weight_decay'''						) else None     ,   adam_epsilon=args.adam_epsilon if hasattr(_lowerCAmelCase     ,   '''adam_epsilon'''						) else None     ,   lr_scheduler=args.lr_scheduler if hasattr(_lowerCAmelCase     ,   '''lr_scheduler'''						) else None     ,   lr_warmup_steps=args.lr_warmup_steps if hasattr(_lowerCAmelCase     ,   '''lr_warmup_steps'''						) else None     ,   ema_inv_gamma=args.ema_inv_gamma if hasattr(_lowerCAmelCase     ,   '''ema_inv_gamma'''						) else None     ,   ema_power=args.ema_power if hasattr(_lowerCAmelCase     ,   '''ema_power'''						) else None     ,   ema_max_decay=args.ema_max_decay if hasattr(_lowerCAmelCase     ,   '''ema_max_decay'''						) else None     ,   mixed_precision=args.mixed_precision     ,   )

							UpperCAmelCase				: Tuple											=	os.path.join(args.output_dir     ,   '''README.md'''						)
							model_card.save(_lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		Optional[str]     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     Dict:
							if resolved_file is None or commit_hash is not None:
														return commit_hash
							UpperCAmelCase				: List[str]											=	str(Path(_lowerCAmelCase						).as_posix()						)
							UpperCAmelCase				: List[str]											=	re.search(R'''snapshots/([^/]+)/'''     ,   _lowerCAmelCase						)
							if search is None:
														return None
							UpperCAmelCase				: Optional[int]											=	search.groups()[0]
							return commit_hash if REGEX_COMMIT_HASH.match(_lowerCAmelCase						) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCamelCase__:     List[Any]										=    os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCamelCase__:     Optional[int]										=    os.path.join(hf_cache_home, "diffusers")


def 				snake_case_  (      _lowerCAmelCase						:		Optional[str] = None     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     None:
							if new_cache_dir is None:
														UpperCAmelCase				: Tuple											=	DIFFUSERS_CACHE
							if old_cache_dir is None:
														UpperCAmelCase				: Tuple											=	old_diffusers_cache

							UpperCAmelCase				: Optional[int]											=	Path(_lowerCAmelCase						).expanduser()
							UpperCAmelCase				: Dict											=	Path(_lowerCAmelCase						).expanduser()
							for old_blob_path in old_cache_dir.glob('''**/blobs/*'''						):
														if old_blob_path.is_file() and not old_blob_path.is_symlink():
																					UpperCAmelCase				: str											=	new_cache_dir / old_blob_path.relative_to(_lowerCAmelCase						)
																					new_blob_path.parent.mkdir(parents=_lowerCAmelCase     ,   exist_ok=_lowerCAmelCase						)
																					os.replace(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					try:
																												os.symlink(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					except OSError:
																												logger.warning(
																												    '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''						)
							# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCamelCase__:     Any										=    os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
						UpperCamelCase__:     int										=    0
else:
						with open(cache_version_file) as f:
												try:
																		UpperCamelCase__:     Any										=    int(f.read())
												except ValueError:
																		UpperCamelCase__:     Dict										=    0

if cache_version < 1:
						UpperCamelCase__:     Optional[Any]										=    os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
						if old_cache_is_not_empty:
												logger.warning(
												    "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
												    "existing cached models. This is a one-time operation, you can interrupt it or run it "
												    "later by calling `diffusers.utils.hub_utils.move_cache()`."
												)
												try:
																		move_cache()
												except Exception as e:
																		UpperCamelCase__:     List[str]										=    "\n".join(traceback.format_tb(e.__traceback__))
																		logger.error(
																		    F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
																		    "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
																		    "message and we will do our best to help."
																		)

if cache_version < 1:
						try:
												os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
												with open(cache_version_file, "w") as f:
																		f.write("1")
						except Exception:
												logger.warning(
												    F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
												    "the directory exists and can be written to."
												)


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[str] = None						)   ->     str:
							if variant is not None:
														UpperCAmelCase				: Union[str, Any]											=	weights_name.split('''.'''						)
														UpperCAmelCase				: List[str]											=	splits[:-1] + [variant] + splits[-1:]
														UpperCAmelCase				: str											=	'''.'''.join(_lowerCAmelCase						)

							return weights_name




def 				snake_case_  (      _lowerCAmelCase						:		int     ,   *,
    _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[Any]=None     ,   )   ->     Optional[Any]:
							UpperCAmelCase				: Any											=	str(_lowerCAmelCase						)
							if os.path.isfile(_lowerCAmelCase						):
														return pretrained_model_name_or_path
							elif os.path.isdir(_lowerCAmelCase						):
														if os.path.isfile(os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase						)						):
																					# Load from a PyTorch checkpoint
																					UpperCAmelCase				: Optional[Any]											=	os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					return model_file
														elif subfolder is not None and os.path.isfile(
														    os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)						):
																					UpperCAmelCase				: List[str]											=	os.path.join(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
																					return model_file
														else:
																					raise EnvironmentError(
																					    f"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""						)
							else:
														# 1. First check if deprecated way of loading from branches is used
														if (
														    revision in DEPRECATED_REVISION_ARGS
														    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
														    and version.parse(version.parse(_lowerCAmelCase						).base_version						) >= version.parse('''0.20.0'''						)
														):
																					try:
																												UpperCAmelCase				: Dict											=	hf_hub_download(
																												    _lowerCAmelCase     ,   filename=_add_variant(_lowerCAmelCase     ,   _lowerCAmelCase						)     ,   cache_dir=_lowerCAmelCase     ,   force_download=_lowerCAmelCase     ,   proxies=_lowerCAmelCase     ,   resume_download=_lowerCAmelCase     ,   local_files_only=_lowerCAmelCase     ,   use_auth_token=_lowerCAmelCase     ,   user_agent=_lowerCAmelCase     ,   subfolder=_lowerCAmelCase     ,   revision=revision or commit_hash     ,   )
																												warnings.warn(
																												    f"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""     ,   _lowerCAmelCase     ,   )
																												return model_file
																					except:  # noqa: E722
																												warnings.warn(
																												    f"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(_lowerCAmelCase     ,   _lowerCAmelCase						)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(_lowerCAmelCase     ,   _lowerCAmelCase						)}' so that the correct variant file can be added."""     ,   _lowerCAmelCase     ,   )
														try:
																					# 2. Load model file as usual
																					UpperCAmelCase				: Optional[Any]											=	hf_hub_download(
																					    _lowerCAmelCase     ,   filename=_lowerCAmelCase     ,   cache_dir=_lowerCAmelCase     ,   force_download=_lowerCAmelCase     ,   proxies=_lowerCAmelCase     ,   resume_download=_lowerCAmelCase     ,   local_files_only=_lowerCAmelCase     ,   use_auth_token=_lowerCAmelCase     ,   user_agent=_lowerCAmelCase     ,   subfolder=_lowerCAmelCase     ,   revision=revision or commit_hash     ,   )
																					return model_file

														except RepositoryNotFoundError:
																					raise EnvironmentError(
																					    f"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
																					    '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
																					    '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
																					    '''login`.'''						)
														except RevisionNotFoundError:
																					raise EnvironmentError(
																					    f"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
																					    '''this model name. Check the model page at '''
																					    f"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""						)
														except EntryNotFoundError:
																					raise EnvironmentError(
																					    f"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""						)
														except HTTPError as err:
																					raise EnvironmentError(
																					    f"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""						)
														except ValueError:
																					raise EnvironmentError(
																					    f"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
																					    f""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
																					    f""" directory containing a file named {weights_name} or"""
																					    ''' \nCheckout your internet connection or see how to run the library in'''
																					    ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''						)
														except EnvironmentError:
																					raise EnvironmentError(
																					    f"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
																					    '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
																					    f"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
																					    f"""containing a file named {weights_name}"""						)










'''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__:     List[str]										=    logging.getLogger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def   A			(		self    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  int	,    __snake_case    :  List[Any]=None	,    __snake_case    :  List[Any]=None	)    ->	Optional[int]:
								UpperCAmelCase				: Union[str, Any]											=	self.layer[current_layer](__snake_case	,    __snake_case	,    head_mask[current_layer]	)

								UpperCAmelCase				: Optional[Any]											=	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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  int	,    __snake_case    :  Any	)    ->	Any:
								super().__init__(__snake_case	)

								UpperCAmelCase				: Any											=	BertEncoderWithPabee(__snake_case	)

								self.init_weights()
								UpperCAmelCase				: Optional[Any]											=	0
								UpperCAmelCase				: List[str]											=	0
								UpperCAmelCase				: Tuple											=	0

								UpperCAmelCase				: List[str]											=	0







	def   A			(		self    :  List[str]	,    __snake_case    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase				: Union[str, Any]											=	threshold







	def   A			(		self    :  str	,    __snake_case    :  List[str]	)    ->	Optional[Any]:
								UpperCAmelCase				: List[str]											=	patience







	def   A			(		self    :  Dict	)    ->	str:
								UpperCAmelCase				: List[Any]											=	0
								UpperCAmelCase				: Dict											=	0







	def   A			(		self    :  Optional[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: Dict											=	self.inference_layers_num / self.inference_instances_num
								UpperCAmelCase				: int											=	(
								    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(__snake_case	)







	@add_start_docstrings_to_model_forward(__snake_case	)
	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  str=None	,    __snake_case    :  int=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  Optional[int]=None	,    __snake_case    :  Optional[int]=False	,    )    ->	List[str]:

								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:
															UpperCAmelCase				: Union[str, Any]											=	input_ids.size()
								elif inputs_embeds is not None:
															UpperCAmelCase				: List[Any]											=	inputs_embeds.size()[:-1]
								else:
															raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)

								UpperCAmelCase				: Optional[Any]											=	input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
															UpperCAmelCase				: Optional[Any]											=	torch.ones(__snake_case	,    device=__snake_case	)
								if token_type_ids is None:
															UpperCAmelCase				: Tuple											=	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.
								UpperCAmelCase				: torch.Tensor											=	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 self.config.is_decoder and encoder_hidden_states is not None:
															UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	encoder_hidden_states.size()
															UpperCAmelCase				: List[Any]											=	(encoder_batch_size, encoder_sequence_length)
															if encoder_attention_mask is None:
																						UpperCAmelCase				: str											=	torch.ones(__snake_case	,    device=__snake_case	)
															UpperCAmelCase				: Tuple											=	self.invert_attention_mask(__snake_case	)
								else:
															UpperCAmelCase				: List[str]											=	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]
								UpperCAmelCase				: List[Any]											=	self.get_head_mask(__snake_case	,    self.config.num_hidden_layers	)

								UpperCAmelCase				: Any											=	self.embeddings(
								    input_ids=__snake_case	,    position_ids=__snake_case	,    token_type_ids=__snake_case	,    inputs_embeds=__snake_case	)
								UpperCAmelCase				: Optional[int]											=	embedding_output

								if self.training:
															UpperCAmelCase				: str											=	[]
															for i in range(self.config.num_hidden_layers	):
																						UpperCAmelCase				: Optional[int]											=	self.encoder.adaptive_forward(
																						    __snake_case	,    current_layer=__snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	)

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

																						UpperCAmelCase				: List[str]											=	self.pooler(__snake_case	)
																						UpperCAmelCase				: Optional[int]											=	output_layers[i](__snake_case	)
																						if regression:
																													UpperCAmelCase				: Union[str, Any]											=	logits.detach()
																													if patient_result is not None:
																																				UpperCAmelCase				: List[str]											=	patient_result.detach()
																													if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
																																				patient_counter += 1
																													else:
																																				UpperCAmelCase				: Dict											=	0
																						else:
																													UpperCAmelCase				: Union[str, Any]											=	logits.detach().argmax(dim=1	)
																													if patient_result is not None:
																																				UpperCAmelCase				: str											=	patient_result.detach().argmax(dim=1	)
																													if (patient_result is not None) and torch.all(labels.eq(__snake_case	)	):
																																				patient_counter += 1
																													else:
																																				UpperCAmelCase				: Union[str, Any]											=	0

																						UpperCAmelCase				: List[str]											=	logits
																						if patient_counter == self.patience:
																													break
															UpperCAmelCase				: List[Any]											=	[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
    the pooled output) e.g. for GLUE tasks. """							,	A__							,	)
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  str	)    ->	Optional[Any]:
								super().__init__(__snake_case	)
								UpperCAmelCase				: Optional[int]											=	config.num_labels

								UpperCAmelCase				: Optional[Any]											=	BertModelWithPabee(__snake_case	)
								UpperCAmelCase				: Tuple											=	nn.Dropout(config.hidden_dropout_prob	)
								UpperCAmelCase				: Dict											=	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(__snake_case	)
	def   A			(		self    :  Any	,    __snake_case    :  Dict=None	,    __snake_case    :  str=None	,    __snake_case    :  List[Any]=None	,    __snake_case    :  List[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  int=None	,    __snake_case    :  str=None	,    )    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[int]											=	self.bert(
								    input_ids=__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    position_ids=__snake_case	,    head_mask=__snake_case	,    inputs_embeds=__snake_case	,    output_dropout=self.dropout	,    output_layers=self.classifiers	,    regression=self.num_labels == 1	,    )

								UpperCAmelCase				: List[str]											=	(logits[-1],)

								if labels is not None:
															UpperCAmelCase				: Tuple											=	None
															UpperCAmelCase				: Any											=	0
															for ix, logits_item in enumerate(__snake_case	):
																						if self.num_labels == 1:
																													#  We are doing regression
																													UpperCAmelCase				: Optional[Any]											=	MSELoss()
																													UpperCAmelCase				: Optional[Any]											=	loss_fct(logits_item.view(-1	)	,    labels.view(-1	)	)
																						else:
																													UpperCAmelCase				: Dict											=	CrossEntropyLoss()
																													UpperCAmelCase				: Dict											=	loss_fct(logits_item.view(-1	,    self.num_labels	)	,    labels.view(-1	)	)
																						if total_loss is None:
																													UpperCAmelCase				: Dict											=	loss
																						else:
																													total_loss += loss * (ix + 1)
																						total_weights += ix + 1
															UpperCAmelCase				: Any											=	(total_loss / total_weights,) + outputs

								return outputs





