



import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE           =  {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "mask2former"
						a__   =  ["swin"]
						a__   =  {"hidden_size": "hidden_dim"}







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Dict] = None	,	__lowerCamelCase      :			int = 2_56	,	__lowerCamelCase      :			int = 2_56	,	__lowerCamelCase      :			int = 2_56	,	__lowerCamelCase      :			int = 10_24	,	__lowerCamelCase      :			str = "relu"	,	__lowerCamelCase      :			int = 6	,	__lowerCamelCase      :			int = 10	,	__lowerCamelCase      :			int = 8	,	__lowerCamelCase      :			float = 0.0	,	__lowerCamelCase      :			int = 20_48	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			int = 4	,	__lowerCamelCase      :			int = 2_55	,	__lowerCamelCase      :			int = 1_00	,	__lowerCamelCase      :			float = 0.1	,	__lowerCamelCase      :			float = 2.0	,	__lowerCamelCase      :			float = 5.0	,	__lowerCamelCase      :			float = 5.0	,	__lowerCamelCase      :			int = 1_25_44	,	__lowerCamelCase      :			float = 3.0	,	__lowerCamelCase      :			float = 0.75	,	__lowerCamelCase      :			float = 0.02	,	__lowerCamelCase      :			float = 1.0	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			List[int] = [4, 8, 16, 32]	,	__lowerCamelCase      :			bool = None	,	**__lowerCamelCase      :			int	,	)      ->				List[str]:
									if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
												A							:					List[Any]              =						CONFIG_MAPPING["swin"](
												    image_size=2_24	,	in_channels=3	,	patch_size=4	,	embed_dim=96	,	depths=[2, 2, 18, 2]	,	num_heads=[3, 6, 12, 24]	,	window_size=7	,	drop_path_rate=0.3	,	use_absolute_embeddings=__lowerCamelCase	,	out_features=["stage1", "stage2", "stage3", "stage4"]	,	)

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					List[str]              =						backbone_config.pop("model_type" )
												A							:					str              =						CONFIG_MAPPING[backbone_model_type]
												A							:					List[str]              =						config_class.from_dict(__lowerCamelCase )

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
												logger.warning_once(
												    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
												    F"""Supported model types: {",".join(self.backbones_supported )}""" )

									A							:					int              =						backbone_config
									A							:					Optional[Any]              =						feature_size
									A							:					int              =						mask_feature_size
									A							:					str              =						hidden_dim
									A							:					List[Any]              =						encoder_feedforward_dim
									A							:					int              =						activation_function
									A							:					Optional[int]              =						encoder_layers
									A							:					Tuple              =						decoder_layers
									A							:					Dict              =						num_attention_heads
									A							:					int              =						dropout
									A							:					List[str]              =						dim_feedforward
									A							:					Optional[int]              =						pre_norm
									A							:					List[str]              =						enforce_input_projection
									A							:					int              =						common_stride
									A							:					Dict              =						ignore_value
									A							:					Tuple              =						num_queries
									A							:					Union[str, Any]              =						no_object_weight
									A							:					Union[str, Any]              =						class_weight
									A							:					int              =						mask_weight
									A							:					int              =						dice_weight
									A							:					Optional[int]              =						train_num_points
									A							:					int              =						oversample_ratio
									A							:					Dict              =						importance_sample_ratio
									A							:					Tuple              =						init_std
									A							:					List[str]              =						init_xavier_std
									A							:					int              =						use_auxiliary_loss
									A							:					Union[str, Any]              =						feature_strides
									A							:					int              =						output_auxiliary_logits
									A							:					Union[str, Any]              =						decoder_layers

									super().__init__(**__lowerCamelCase )







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Optional[int]	,	__lowerCamelCase      :			PretrainedConfig	,	**__lowerCamelCase      :			Any )      ->				Optional[int]:
									return cls(
									    backbone_config=__lowerCamelCase	,	**__lowerCamelCase	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict[str, any]:
									A							:					Optional[Any]              =						copy.deepcopy(self.__dict__ )
									A							:					List[Any]              =						self.backbone_config.to_dict()
									A							:					List[Any]              =						self.__class__.model_type
									return output










import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Optional[int]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Optional[Any] )      ->				bool:
									raise NotImplementedError("StoppingCriteria needs to be subclassed" )







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[int] = None )      ->				List[Any]:
									A							:					str              =						max_length
									A							:					Optional[int]              =						max_position_embeddings







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			List[str]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Any )      ->				bool:
									A							:					List[Any]              =						input_ids.shape[-1]
									A							:					Any              =						cur_len >= self.max_length
									if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
												logger.warning_once(
												    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
												    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
												    "exceptions, performance degradation, or nothing at all." )
									return is_done







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			int )      ->				List[Any]:
									warnings.warn(
									    "The class `MaxNewTokensCriteria` is deprecated. "
									    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
									    "with `max_length = start_length + max_new_tokens` instead."	,	__lowerCamelCase	,	)
									A							:					str              =						start_length
									A							:					Optional[Any]              =						max_new_tokens
									A							:					Dict              =						start_length + max_new_tokens







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			int	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Tuple )      ->				bool:
									return input_ids.shape[-1] >= self.max_length







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			float	,	__lowerCamelCase      :			Optional[float] = None )      ->				List[Any]:
									A							:					str              =						max_time
									A							:					Dict              =						time.time() if initial_timestamp is None else initial_timestamp







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Any	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			Tuple )      ->				bool:
									return time.time() - self.initial_timestamp > self.max_time







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@add_start_docstrings(__lowerCamelCase )
						def __call__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			torch.LongTensor	,	__lowerCamelCase      :			torch.FloatTensor	,	**__lowerCamelCase      :			int )      ->				bool:
									return any(criteria(__lowerCamelCase	,	__lowerCamelCase ) for criteria in self )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									for stopping_criterium in self:
												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															return stopping_criterium.max_length
												elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															return stopping_criterium.max_length
									return None







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[int]              =						stopping_criteria.max_length
			A							:					Any              =						deepcopy(_lowerCamelCase    )
			if stopping_max_length is not None and stopping_max_length != max_length:
						warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter" ,    _lowerCamelCase    )
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCamelCase    )    )
			return new_stopping_criteria