





from typing import List

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


if is_torch_available():
							import torch

class    __A  (    a     ):
				__A  =					["""image_processor""", """tokenizer"""]
				__A  =					"""OneFormerImageProcessor"""
				__A  =					("""CLIPTokenizer""", """CLIPTokenizerFast""")


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_ = 77 ,			UpperCAmelCase_ = 77 ,			**UpperCAmelCase_				):
								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`."""				)

								lowerCamelCase									=max_seq_length
								lowerCamelCase									=task_seq_length

								super().__init__(UpperCAmelCase_ ,			UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=77				):
								if text_list is None:
												raise ValueError("""tokens cannot be None."""				)

								lowerCamelCase									=self.tokenizer(UpperCAmelCase_ ,			padding="""max_length""" ,			max_length=UpperCAmelCase_ ,			truncation=UpperCAmelCase_				)

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

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

								lowerCamelCase									=torch.cat(UpperCAmelCase_ ,			dim=0				)
								return token_inputs


				def __call__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			**UpperCAmelCase_				):

								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."""				)

								lowerCamelCase									=self.image_processor(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			**UpperCAmelCase_				)

								if isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				):
												lowerCamelCase									=[task_inputs]

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

								if hasattr(UpperCAmelCase_ ,			"""text_inputs"""				):
												lowerCamelCase									=encoded_inputs.text_inputs

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

												lowerCamelCase									=torch.cat(UpperCAmelCase_ ,			dim=0				)

								return encoded_inputs


				def _snake_case							(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			**UpperCAmelCase_				):

								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."""				)

								lowerCamelCase									=self.image_processor.encode_inputs(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			**UpperCAmelCase_				)

								if isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				):
												lowerCamelCase									=[task_inputs]

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

								if hasattr(UpperCAmelCase_ ,			"""text_inputs"""				):
												lowerCamelCase									=encoded_inputs.text_inputs

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

												lowerCamelCase									=torch.cat(UpperCAmelCase_ ,			dim=0				)

								return encoded_inputs


				def _snake_case							(		self ,			*UpperCAmelCase_ ,			**UpperCAmelCase_				):
								return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase_ ,			**UpperCAmelCase_				)


				def _snake_case							(		self ,			*UpperCAmelCase_ ,			**UpperCAmelCase_				):
								return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase_ ,			**UpperCAmelCase_				)


				def _snake_case							(		self ,			*UpperCAmelCase_ ,			**UpperCAmelCase_				):
								return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase_ ,			**UpperCAmelCase_				)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Optional[Any]       					=[
							    '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RwkvForCausalLM''',
							    '''RwkvModel''',
							    '''RwkvPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rwkv import (
														    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RwkvForCausalLM,
														    RwkvModel,
														    RwkvPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase__    :      int       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





