




'''simple docstring'''



from typing import List

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


if is_torch_available():
     import torch





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Union[str, Any]        =			['''image_processor''', '''tokenizer''']
       __lowercase  :      Any        =			'''OneFormerImageProcessor'''
       __lowercase  :      int        =			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE = 77    ,  __SCREAMING_SNAKE_CASE = 77    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''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`.'''  )

            __snake_case    					=  max_seq_length
            __snake_case    					=  task_seq_length

            super().__init__(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=77  )      ->  Union[str, Any]:



            '''simple docstring'''




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

            __snake_case    					=  self.tokenizer(__SCREAMING_SNAKE_CASE    ,  padding='''max_length'''    ,  max_length=__SCREAMING_SNAKE_CASE    ,  truncation=__SCREAMING_SNAKE_CASE  )

            __snake_case					,    __snake_case    					=  tokens['''attention_mask'''], tokens['''input_ids''']

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

            __snake_case    					=  torch.cat(__SCREAMING_SNAKE_CASE    ,  dim=0  )
            return token_inputs


       def __call__(   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  **__SCREAMING_SNAKE_CASE  )      ->  List[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.'''  )

            __snake_case    					=  self.image_processor(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )

            if isinstance(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  ):
                 __snake_case    					=  [task_inputs]

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

            if hasattr(__SCREAMING_SNAKE_CASE    ,  '''text_inputs'''  ):
                 __snake_case    					=  encoded_inputs.text_inputs

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

                 __snake_case    					=  torch.cat(__SCREAMING_SNAKE_CASE    ,  dim=0  )

            return encoded_inputs


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None    ,  **__SCREAMING_SNAKE_CASE  )      ->  str:



            '''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.'''  )

            __snake_case    					=  self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )

            if isinstance(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  ):
                 __snake_case    					=  [task_inputs]

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

            if hasattr(__SCREAMING_SNAKE_CASE    ,  '''text_inputs'''  ):
                 __snake_case    					=  encoded_inputs.text_inputs

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

                 __snake_case    					=  torch.cat(__SCREAMING_SNAKE_CASE    ,  dim=0  )

            return encoded_inputs


       def       lowerCAmelCase      (   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




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


       def       lowerCAmelCase      (   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




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




       def       lowerCAmelCase      (   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  int:



            '''simple docstring'''




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








'''simple docstring'''



# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List







def 			_UpperCamelCase (_lowerCamelCase							:     List[str]   ,	_lowerCamelCase							:     str	)->   str:




     '''simple docstring'''





     if name != "training_function" and name != "main":
          raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''	)
     __snake_case					,    __snake_case    					=  [], False
     for line in lines:
          if not found_start and f'''def {name}''' in line:
               __snake_case    					=  True
               good_lines.append(_lowerCamelCase	)
               continue
          if found_start:
               if name == "training_function" and "def main" in line:
                    return good_lines
               if name == "main" and "if __name__" in line:
                    return good_lines
               good_lines.append(_lowerCamelCase	)







def 			_UpperCamelCase (_lowerCamelCase							:     List[str]	)->   int:




     '''simple docstring'''





     return [line for line in lines if not line.lstrip().startswith('''#'''	) and line != "\n"]







def 			_UpperCamelCase (_lowerCamelCase							:     str   ,	_lowerCamelCase							:     str   ,	_lowerCamelCase							:     bool   ,	_lowerCamelCase							:     str = None	)->   Optional[Any]:




     '''simple docstring'''





     with open(_lowerCamelCase   ,	'''r'''	) as f:
          __snake_case    					=  f.readlines()
     with open(os.path.abspath(os.path.join('''examples'''   ,	'''nlp_example.py'''	)	)   ,	'''r'''	) as f:
          __snake_case    					=  f.readlines()
     with open(_lowerCamelCase   ,	'''r'''	) as f:
          __snake_case    					=  f.readlines()
     if secondary_filename is not None:
          with open(_lowerCamelCase   ,	'''r'''	) as f:
               __snake_case    					=  f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
     if parser_only:
          __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''main'''	)	)
          __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''main'''	)	)
          __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''main'''	)	)
          if secondary_filename is not None:
               __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''main'''	)	)
     else:
          __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''training_function'''	)	)
          __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''training_function'''	)	)
          __snake_case    					=  clean_lines(get_function_contents_by_name(_lowerCamelCase   ,	'''training_function'''	)	)
          if secondary_filename is not None:
               __snake_case    					=  clean_lines(
                   get_function_contents_by_name(_lowerCamelCase   ,	'''training_function'''	)	)

     __snake_case    					=  '''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

     # Specific code in our script that differs from the full version, aka what is new
     __snake_case    					=  []
     __snake_case    					=  []  # We keep track of the idxs just in case it's a repeated statement
     __snake_case    					=  iter(_lowerCamelCase	)
     for i in range(len(_lowerCamelCase	) - 1	):
          if i not in passed_idxs:
               __snake_case    					=  next(_lowerCamelCase	)
               if (line not in full_file_func) and (line.lstrip() != _dl_line):
                    if "TESTING_MOCKED_DATALOADERS" not in line:
                         new_feature_code.append(_lowerCamelCase	)
                         passed_idxs.append(_lowerCamelCase	)
                    else:
                         # Skip over the `config['num_epochs'] = 2` statement
                         __snake_case    					=  next(_lowerCamelCase	)

    # Extract out just the new parts from the full_file_training_func
     __snake_case    					=  []
     __snake_case    					=  []  # We keep track of the idxs just in case it's a repeated statement
     for i, line in enumerate(_lowerCamelCase	):
          if i not in passed_idxs:
               if (line not in full_file_func) and (line.lstrip() != _dl_line):
                    if "TESTING_MOCKED_DATALOADERS" not in line:
                         new_full_example_parts.append(_lowerCamelCase	)
                         passed_idxs.append(_lowerCamelCase	)

    # Finally, get the overall diff
     __snake_case    					=  [line for line in new_feature_code if line not in new_full_example_parts]
     if secondary_filename is not None:
          __snake_case    					=  [line for line in full_file_contents if line not in secondary_file_func]
          __snake_case    					=  [line for line in diff_from_example if line not in diff_from_two]

     return diff_from_example






