
"""simple docstring"""



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

UpperCAmelCase__          = logging.get_logger(__name__)

class       lowerCAmelCase__				(						enum.Enum	):
     __a						=					0
     __a						=					1






@add_end_docstrings(A_	)
class       lowerCAmelCase__				(						A_	):
     __a						=					"""generated"""







     def __init__(							self			:			Any    ,      *_lowerCamelCase			:			Dict    ,      **_lowerCamelCase			:			Union[str, Any]			):
       super().__init__(*_lowerCamelCase    ,      **_lowerCamelCase			)

       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			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[Any]=None    ,      _lowerCamelCase			:			Union[str, Any]=None    ,      _lowerCamelCase			:			List[str]=None    ,      _lowerCamelCase			:			Union[str, Any]=None    ,      _lowerCamelCase			:			Optional[int]=None    ,      _lowerCamelCase			:			Any=None    ,      **_lowerCamelCase			:			List[Any]    ,      ):
       _snake_case					     =       {}
       if truncation is not None:
         _snake_case					     =       truncation

       _snake_case					     =       generate_kwargs

       _snake_case					     =       {}
       if return_tensors is not None and return_type is None:
         _snake_case					     =       ReturnType.TENSORS if return_tensors else ReturnType.TEXT
       if return_type is not None:
         _snake_case					     =       return_type

       if clean_up_tokenization_spaces is not None:
         _snake_case					     =       clean_up_tokenization_spaces

       if stop_sequence is not None:
         _snake_case					     =       self.tokenizer.encode(_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
         if len(_lowerCamelCase			) > 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.'''			)
         _snake_case					     =       stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       return True







     def      lowercase	(							self			:			str    ,      *_lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       self.model.config.prefix if self.model.config.prefix is not None else ''''''
       if isinstance(args[0]    ,      _lowerCamelCase			):
         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'''			)
         _snake_case					     =       ([prefix + arg for arg in args[0]],)
         _snake_case					     =       True

       elif isinstance(args[0]    ,      _lowerCamelCase			):
         _snake_case					     =       (prefix + args[0],)
         _snake_case					     =       False
       else:
         raise ValueError(
             f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''			)
       _snake_case					     =       self.tokenizer(*_lowerCamelCase    ,      padding=_lowerCamelCase    ,      truncation=_lowerCamelCase    ,      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			:			List[Any]    ,      *_lowerCamelCase			:			str    ,      **_lowerCamelCase			:			Any			):
       _snake_case					     =       super().__call__(*_lowerCamelCase    ,      **_lowerCamelCase			)
       if (
           isinstance(args[0]    ,      _lowerCamelCase			)
           and all(isinstance(_lowerCamelCase    ,      _lowerCamelCase			) for el in args[0]			)
           and all(len(_lowerCamelCase			) == 1 for res in result			)
       ):
         return [res[0] for res in result]
       return result







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			Tuple=TruncationStrategy.DO_NOT_TRUNCATE    ,      **_lowerCamelCase			:			str			):
       _snake_case					     =       self._parse_and_tokenize(_lowerCamelCase    ,      truncation=_lowerCamelCase    ,      **_lowerCamelCase			)
       return inputs







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[Any]    ,      **_lowerCamelCase			:			List[Any]			):
       if self.framework == "pt":
         _snake_case     ,					_snake_case					     =       model_inputs['''input_ids'''].shape
       elif self.framework == "tf":
         _snake_case     ,					_snake_case					     =       tf.shape(model_inputs['''input_ids''']			).numpy()

       _snake_case					     =       generate_kwargs.get('''min_length'''    ,      self.model.config.min_length			)
       _snake_case					     =       generate_kwargs.get('''max_length'''    ,      self.model.config.max_length			)
       self.check_inputs(_lowerCamelCase    ,      generate_kwargs['''min_length''']    ,      generate_kwargs['''max_length''']			)
       _snake_case					     =       self.model.generate(**_lowerCamelCase    ,      **_lowerCamelCase			)
       _snake_case					     =       output_ids.shape[0]
       if self.framework == "pt":
         _snake_case					     =       output_ids.reshape(_lowerCamelCase    ,      out_b // in_b    ,      *output_ids.shape[1:]			)
       elif self.framework == "tf":
         _snake_case					     =       tf.reshape(_lowerCamelCase    ,      (in_b, out_b // in_b, *output_ids.shape[1:])			)
       return {"output_ids": output_ids}







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Dict=ReturnType.TEXT    ,      _lowerCamelCase			:			List[str]=False			):
       _snake_case					     =       []
       for output_ids in model_outputs["output_ids"][0]:
         if return_type == ReturnType.TENSORS:
           _snake_case					     =       {f'''{self.return_name}_token_ids''': output_ids}
         elif return_type == ReturnType.TEXT:
           _snake_case					     =       {
               f'''{self.return_name}_text''': self.tokenizer.decode(
                   _lowerCamelCase    ,      skip_special_tokens=_lowerCamelCase    ,      clean_up_tokenization_spaces=_lowerCamelCase    ,      )
           }
         records.append(_lowerCamelCase			)
       return records






@add_end_docstrings(A_	)
class       lowerCAmelCase__				(						A_	):
     __a						=					"""summary"""







     def __call__(							self			:			List[Any]    ,      *_lowerCamelCase			:			Any    ,      **_lowerCamelCase			:			Tuple			):
       return super().__call__(*_lowerCamelCase    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       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(A_	)
class       lowerCAmelCase__				(						A_	):
     __a						=					"""translation"""







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       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			:			int    ,      *_lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			List[str]=TruncationStrategy.DO_NOT_TRUNCATE    ,      _lowerCamelCase			:			Optional[Any]=None    ,      _lowerCamelCase			:			Any=None			):
       if getattr(self.tokenizer    ,      '''_build_translation_inputs'''    ,      _lowerCamelCase			):
         return self.tokenizer._build_translation_inputs(
             *_lowerCamelCase    ,      return_tensors=self.framework    ,      truncation=_lowerCamelCase    ,      src_lang=_lowerCamelCase    ,      tgt_lang=_lowerCamelCase			)
       else:
         return super()._parse_and_tokenize(*_lowerCamelCase    ,      truncation=_lowerCamelCase			)







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







     def __call__(							self			:			List[Any]    ,      *_lowerCamelCase			:			Tuple    ,      **_lowerCamelCase			:			Union[str, Any]			):
       return super().__call__(*_lowerCamelCase    ,      **_lowerCamelCase			)





"""simple docstring"""



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser(
     description=(
         'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
         ' Distillation'
     )
 )
 parser.add_argument('--model_type', default='bert', choices=['bert'])
 parser.add_argument('--model_name', default='bert-base-uncased', type=str)
 parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
 parser.add_argument('--vocab_transform', action='store_true')
 UpperCAmelCase__          = parser.parse_args()

 if args.model_type == "bert":
  UpperCAmelCase__          = BertForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase__          = 'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 UpperCAmelCase__          = model.state_dict()
 UpperCAmelCase__          = {}

 for w in ["word_embeddings", "position_embeddings"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.{w}.weight"]
 for w in ["weight", "bias"]:
  UpperCAmelCase__          = state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

 UpperCAmelCase__          = 0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
   ]
  std_idx += 1

 UpperCAmelCase__          = state_dict['cls.predictions.decoder.weight']
 UpperCAmelCase__          = state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.dense.{w}"]
   UpperCAmelCase__          = state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

 print(F"N layers selected for distillation: {std_idx}")
 print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

 print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
 torch.save(compressed_sd, args.dump_checkpoint)



