





import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowercase :			Dict     		=    logging.get_logger(__name__)





class 					A__       (				enum.Enum      ):




   """simple docstring"""



   __A  :							List[Any]			    =    0
   __A  :							int			    =    1





@add_end_docstrings(UpperCAmelCase_      )
class 					A__       (				UpperCAmelCase_      ):




   """simple docstring"""



   __A  :							Tuple			    =    '''generated'''

   def __init__(   self   ,    *lowercase   ,    **lowercase)     ->		Optional[int]:







          '''simple docstring'''

          super().__init__(*__a   ,    **__a)

          self.check_model_type(
              TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)

   def 				__lowercase						(   self   ,    lowercase=None   ,    lowercase=None   ,    lowercase=None   ,    lowercase=None   ,    lowercase=None   ,    lowercase=None   ,    **lowercase   ,    )     ->		int:







          '''simple docstring'''

          a__		:      Tuple					=							{}
          if truncation is not None:
                 a__		:      List[Any]					=							truncation

          a__		:      Optional[Any]					=							generate_kwargs

          a__		:      Tuple					=							{}
          if return_tensors is not None and return_type is None:
                 a__		:      List[Any]					=							ReturnType.TENSORS if return_tensors else ReturnType.TEXT
          if return_type is not None:
                 a__		:      str					=							return_type

          if clean_up_tokenization_spaces is not None:
                 a__		:      List[Any]					=							clean_up_tokenization_spaces

          if stop_sequence is not None:
                 a__		:      str					=							self.tokenizer.encode(__a   ,    add_special_tokens=__a)
                 if len(__a) > 1:
                        warnings.warn(
                            'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                            ' the stop sequence will be used as the stop sequence string in the interim.')
                 a__		:      int					=							stop_sequence_ids[0]

          return preprocess_params, forward_params, postprocess_params

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		List[str]:







          '''simple docstring'''

          return True

   def 				__lowercase						(   self   ,    *lowercase   ,    lowercase)     ->		Optional[Any]:







          '''simple docstring'''

          a__		:      List[str]					=							self.model.config.prefix if self.model.config.prefix is not None else ''
          if isinstance(args[0]   ,    __a):
                 if self.tokenizer.pad_token_id is None:
                        raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input')
                 a__		:      List[str]					=							([prefix + arg for arg in args[0]],)
                 a__		:      List[Any]					=							True

          elif isinstance(args[0]   ,    __a):
                 a__		:      List[str]					=							(prefix + args[0],)
                 a__		:      Union[str, Any]					=							False
          else:
                 raise ValueError(
                     F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`')
          a__		:      Optional[int]					=							self.tokenizer(*__a   ,    padding=__a   ,    truncation=__a   ,    return_tensors=self.framework)
          # This is produced by tokenizers but is an invalid generate kwargs
          if "token_type_ids" in inputs:
                 del inputs["token_type_ids"]
          return inputs

   def __call__(   self   ,    *lowercase   ,    **lowercase)     ->		int:







          '''simple docstring'''

          a__		:      Any					=							super().__call__(*__a   ,    **__a)
          if (
              isinstance(args[0]   ,    __a)
              and all(isinstance(__a   ,    __a) for el in args[0])
              and all(len(__a) == 1 for res in result)
          ):
                 return [res[0] for res in result]
          return result

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase=TruncationStrategy.DO_NOT_TRUNCATE   ,    **lowercase)     ->		List[str]:







          '''simple docstring'''

          a__		:      str					=							self._parse_and_tokenize(__a   ,    truncation=__a   ,    **__a)
          return inputs

   def 				__lowercase						(   self   ,    lowercase   ,    **lowercase)     ->		Union[str, Any]:







          '''simple docstring'''

          if self.framework == "pt":
                 a__					,				a__		:      str					=							model_inputs['input_ids'].shape
          elif self.framework == "tf":
                 a__					,				a__		:      List[Any]					=							tf.shape(model_inputs['input_ids']).numpy()

          a__		:      Optional[int]					=							generate_kwargs.get('min_length'   ,    self.model.config.min_length)
          a__		:      List[Any]					=							generate_kwargs.get('max_length'   ,    self.model.config.max_length)
          self.check_inputs(__a   ,    generate_kwargs['min_length']   ,    generate_kwargs['max_length'])
          a__		:      Optional[int]					=							self.model.generate(**__a   ,    **__a)
          a__		:      List[Any]					=							output_ids.shape[0]
          if self.framework == "pt":
                 a__		:      Tuple					=							output_ids.reshape(__a   ,    out_b // in_b   ,    *output_ids.shape[1:])
          elif self.framework == "tf":
                 a__		:      Dict					=							tf.reshape(__a   ,    (in_b, out_b // in_b, *output_ids.shape[1:]))
          return {"output_ids": output_ids}

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase=ReturnType.TEXT   ,    lowercase=False)     ->		List[str]:







          '''simple docstring'''

          a__		:      List[Any]					=							[]
          for output_ids in model_outputs["output_ids"][0]:
                 if return_type == ReturnType.TENSORS:
                        a__		:      Union[str, Any]					=							{F'{self.return_name}_token_ids': output_ids}
                 elif return_type == ReturnType.TEXT:
                        a__		:      str					=							{
                            F'{self.return_name}_text': self.tokenizer.decode(
                                __a   ,    skip_special_tokens=__a   ,    clean_up_tokenization_spaces=__a   ,    )
                        }
                 records.append(__a)
          return records





@add_end_docstrings(UpperCAmelCase_      )
class 					A__       (				UpperCAmelCase_      ):




   """simple docstring"""



   __A  :							Optional[int]			    =    '''summary'''

   def __call__(   self   ,    *lowercase   ,    **lowercase)     ->		int:







          '''simple docstring'''

          return super().__call__(*__a   ,    **__a)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		Tuple:







          '''simple docstring'''

          if max_length < min_length:
                 logger.warning(F'Your min_length={min_length} must be inferior than your max_length={max_length}.')

          if input_length < max_length:
                 logger.warning(
                     F'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
                     'a summarization task, where outputs shorter than the input are typically wanted, you might '
                     F'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})')





@add_end_docstrings(UpperCAmelCase_      )
class 					A__       (				UpperCAmelCase_      ):




   """simple docstring"""



   __A  :							Optional[int]			    =    '''translation'''

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		int:







          '''simple docstring'''

          if input_length > 0.9 * max_length:
                 logger.warning(
                     F'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
                     'increasing your max_length manually, e.g. translator(\'...\', max_length=400)')
          return True

   def 				__lowercase						(   self   ,    *lowercase   ,    lowercase=TruncationStrategy.DO_NOT_TRUNCATE   ,    lowercase=None   ,    lowercase=None)     ->		int:







          '''simple docstring'''

          if getattr(self.tokenizer   ,    '_build_translation_inputs'   ,    __a):
                 return self.tokenizer._build_translation_inputs(
                     *__a   ,    return_tensors=self.framework   ,    truncation=__a   ,    src_lang=__a   ,    tgt_lang=__a)
          else:
                 return super()._parse_and_tokenize(*__a   ,    truncation=__a)

   def 				__lowercase						(   self   ,    lowercase=None   ,    lowercase=None   ,    **lowercase)     ->		Any:







          '''simple docstring'''

          a__					,				a__					,				a__		:      Optional[int]					=							super()._sanitize_parameters(**__a)
          if src_lang is not None:
                 a__		:      str					=							src_lang
          if tgt_lang is not None:
                 a__		:      Dict					=							tgt_lang
          if src_lang is None and tgt_lang is None:
                 # Backward compatibility, direct arguments use is preferred.
                 a__		:      Dict					=							kwargs.get('task'   ,    self.task)
                 a__		:      List[Any]					=							task.split('_')
                 if task and len(__a) == 4:
                        # translation, XX, to YY
                        a__		:      List[Any]					=							items[1]
                        a__		:      str					=							items[3]
          return preprocess_params, forward_params, postprocess_params

   def __call__(   self   ,    *lowercase   ,    **lowercase)     ->		Any:







          '''simple docstring'''

          return super().__call__(*__a   ,    **__a)







import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		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(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)