


'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase:		List[Any]							=     logging.get_logger(__name__)

__lowercase:		Optional[Any]							=     {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}







class  UpperCAmelCase (       UpperCamelCase_):
	_lowerCamelCase :					Optional[Any]    					=					"""hubert"""

	def __init__( self :	int,					a_ :	Any=32,					a_ :	Optional[Any]=768,					a_ :	Optional[Any]=12,					a_ :	Any=12,					a_ :	Optional[Any]=3072,					a_ :	Tuple="gelu",					a_ :	Tuple=0.1,					a_ :	Optional[int]=0.1,					a_ :	List[Any]=0.1,					a_ :	str=True,					a_ :	Optional[Any]=0.0,					a_ :	Optional[Any]=0.1,					a_ :	Dict=0.1,					a_ :	Dict=0.02,					a_ :	Optional[Any]=1e-5,					a_ :	Any="group",					a_ :	Optional[Any]="gelu",					a_ :	int=(512, 512, 512, 512, 512, 512, 512),					a_ :	Tuple=(5, 2, 2, 2, 2, 2, 2),					a_ :	Tuple=(10, 3, 3, 3, 3, 2, 2),					a_ :	Tuple=False,					a_ :	List[Any]=128,					a_ :	Optional[int]=16,					a_ :	Any=False,					a_ :	Optional[int]=True,					a_ :	Any=0.05,					a_ :	Union[str, Any]=10,					a_ :	Optional[Any]=2,					a_ :	Dict=0.0,					a_ :	Optional[int]=10,					a_ :	Tuple=0,					a_ :	Optional[int]="sum",					a_ :	Dict=False,					a_ :	str=False,					a_ :	str=256,					a_ :	List[Any]=0,					a_ :	Union[str, Any]=1,					a_ :	Any=2,					**a_ :	Optional[Any],					):





				"""simple docstring"""



				super().__init__(**__SCREAMING_SNAKE_CASE,					pad_token_id=__SCREAMING_SNAKE_CASE,					bos_token_id=__SCREAMING_SNAKE_CASE,					eos_token_id=__SCREAMING_SNAKE_CASE					)
				UpperCamelCase__ 						=						hidden_size
				UpperCamelCase__ 						=						feat_extract_norm
				UpperCamelCase__ 						=						feat_extract_activation
				UpperCamelCase__ 						=						list(__SCREAMING_SNAKE_CASE					)
				UpperCamelCase__ 						=						list(__SCREAMING_SNAKE_CASE					)
				UpperCamelCase__ 						=						list(__SCREAMING_SNAKE_CASE					)
				UpperCamelCase__ 						=						conv_bias
				UpperCamelCase__ 						=						num_conv_pos_embeddings
				UpperCamelCase__ 						=						num_conv_pos_embedding_groups
				UpperCamelCase__ 						=						len(self.conv_dim					)
				UpperCamelCase__ 						=						num_hidden_layers
				UpperCamelCase__ 						=						intermediate_size
				UpperCamelCase__ 						=						hidden_act
				UpperCamelCase__ 						=						num_attention_heads
				UpperCamelCase__ 						=						hidden_dropout
				UpperCamelCase__ 						=						attention_dropout
				UpperCamelCase__ 						=						activation_dropout
				UpperCamelCase__ 						=						feat_proj_layer_norm
				UpperCamelCase__ 						=						feat_proj_dropout
				UpperCamelCase__ 						=						final_dropout
				UpperCamelCase__ 						=						layerdrop
				UpperCamelCase__ 						=						layer_norm_eps
				UpperCamelCase__ 						=						initializer_range
				UpperCamelCase__ 						=						vocab_size
				UpperCamelCase__ 						=						do_stable_layer_norm
				UpperCamelCase__ 						=						use_weighted_layer_sum
				UpperCamelCase__ 						=						classifier_proj_size

				if (
				    (len(self.conv_stride					) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel					) != self.num_feat_extract_layers)
				    or (len(self.conv_dim					) != self.num_feat_extract_layers)
				):
							raise ValueError(
							    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
							    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
							    f' {len(self.conv_dim					)}`, `len(config.conv_stride) = {len(self.conv_stride					)}`,'
							    f' `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'					)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				UpperCamelCase__ 						=						apply_spec_augment
				UpperCamelCase__ 						=						mask_time_prob
				UpperCamelCase__ 						=						mask_time_length
				UpperCamelCase__ 						=						mask_time_min_masks
				UpperCamelCase__ 						=						mask_feature_prob
				UpperCamelCase__ 						=						mask_feature_length
				UpperCamelCase__ 						=						mask_feature_min_masks

				# ctc loss
				UpperCamelCase__ 						=						ctc_loss_reduction
				UpperCamelCase__ 						=						ctc_zero_infinity

	@property
	def 		lowercase_						( self :	Dict					):





				"""simple docstring"""



				return functools.reduce(operator.mul,					self.conv_stride,					1					)



'''simple docstring'''

from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch







class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
 _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
 _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):





    """simple docstring"""



    if image_processor is None:
       raise ValueError("You need to specify an `image_processor`."					)
    if tokenizer is None:
       raise ValueError("You need to specify a `tokenizer`."					)

    UpperCamelCase__ 						=						max_seq_length
    UpperCamelCase__ 						=						task_seq_length

    super().__init__(a_,					a_					)

 def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):





    """simple docstring"""



    if text_list is None:
       raise ValueError("tokens cannot be None."					)

    UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)

    UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]

    UpperCamelCase__ 						=						[]
    for attn_mask, input_id in zip(a_,					a_					):
       UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
       token_inputs.append(token.unsqueeze(0					)					)

    UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
    return token_inputs

 def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):





    """simple docstring"""



    if task_inputs is None:
       raise ValueError("You have to specify the task_input. Found None."					)
    elif images is None:
       raise ValueError("You have to specify the image. Found None."					)

    if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
       raise ValueError("task_inputs must be semantic, instance, or panoptic."					)

    UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
    else:
       raise TypeError("Task Inputs should be a string or a list of strings."					)

    if hasattr(a_,					"text_inputs"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

       UpperCamelCase__ 						=						[]
       for texts in texts_list:
          UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
          text_inputs.append(text_input_list.unsqueeze(0					)					)

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):





    """simple docstring"""



    if task_inputs is None:
       raise ValueError("You have to specify the task_input. Found None."					)
    elif images is None:
       raise ValueError("You have to specify the image. Found None."					)

    if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
       raise ValueError("task_inputs must be semantic, instance, or panoptic."					)

    UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
    else:
       raise TypeError("Task Inputs should be a string or a list of strings."					)

    if hasattr(a_,					"text_inputs"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

       UpperCamelCase__ 						=						[]
       for texts in texts_list:
          UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
          text_inputs.append(text_input_list.unsqueeze(0					)					)

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):





    """simple docstring"""



    return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)

 def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):





    """simple docstring"""



    return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)

 def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):





    """simple docstring"""



    return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)