


from typing import List

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


if is_torch_available():
			import torch



class      snake_case__	(   lowerCamelCase__	):
				__lowerCAmelCase		=   ['''image_processor''', '''tokenizer''']
				__lowerCAmelCase		=   '''OneFormerImageProcessor'''
				__lowerCAmelCase		=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
				def __init__(	self  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase = 77  ,   _UpperCAmelCase = 77  ,   **_UpperCAmelCase   ):




										"""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`.'   )

										__magic_name__					:				Optional[int]				=   max_seq_length
										__magic_name__					:				List[str]				=   task_seq_length

										super().__init__(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=77   ):




										"""simple docstring"""




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

										__magic_name__					:				int				=   self.tokenizer(_UpperCAmelCase  ,   padding='max_length'  ,   max_length=_UpperCAmelCase  ,   truncation=_UpperCAmelCase   )

										__magic_name__					:				Union[str, Any]				=   tokens['''attention_mask'''], tokens['''input_ids''']

										__magic_name__					:				int				=   []
										for attn_mask, input_id in zip(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				Optional[Any]				=   torch.tensor(_UpperCAmelCase   ) * torch.tensor(_UpperCAmelCase   )
																token_inputs.append(token.unsqueeze(0   )   )

										__magic_name__					:				List[str]				=   torch.cat(_UpperCAmelCase  ,   dim=0   )
										return token_inputs
				def __call__(	self  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   **_UpperCAmelCase   ):




										"""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.'   )

										__magic_name__					:				Any				=   self.image_processor(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   **_UpperCAmelCase   )

										if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				str				=   [task_inputs]

										if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ) and all(isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ) for task_input in task_inputs   ):
																__magic_name__					:				Optional[int]				=   []
																for task in task_inputs:
																						__magic_name__					:				Optional[int]				=   F"the task is {task}"
																						task_token_inputs.append(_UpperCAmelCase   )
																__magic_name__					:				str				=   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'   ):
																__magic_name__					:				List[str]				=   encoded_inputs.text_inputs

																__magic_name__					:				List[Any]				=   []
																for texts in texts_list:
																						__magic_name__					:				str				=   self._preprocess_text(_UpperCAmelCase  ,   max_length=self.max_seq_length   )
																						text_inputs.append(text_input_list.unsqueeze(0   )   )

																__magic_name__					:				int				=   torch.cat(_UpperCAmelCase  ,   dim=0   )

										return encoded_inputs
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   **_UpperCAmelCase   ):




										"""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.'   )

										__magic_name__					:				List[str]				=   self.image_processor.encode_inputs(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   **_UpperCAmelCase   )

										if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				Union[str, Any]				=   [task_inputs]

										if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ) and all(isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ) for task_input in task_inputs   ):
																__magic_name__					:				List[str]				=   []
																for task in task_inputs:
																						__magic_name__					:				Tuple				=   F"the task is {task}"
																						task_token_inputs.append(_UpperCAmelCase   )
																__magic_name__					:				int				=   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'   ):
																__magic_name__					:				Tuple				=   encoded_inputs.text_inputs

																__magic_name__					:				str				=   []
																for texts in texts_list:
																						__magic_name__					:				List[str]				=   self._preprocess_text(_UpperCAmelCase  ,   max_length=self.max_seq_length   )
																						text_inputs.append(text_input_list.unsqueeze(0   )   )

																__magic_name__					:				Tuple				=   torch.cat(_UpperCAmelCase  ,   dim=0   )

										return encoded_inputs
				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase  ,   **_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




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





				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




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





"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
