





'''simple docstring'''

import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
    from ... import PreTrainedTokenizerBase, TensorType

a   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[int]						   =						'vision-encoder-decoder'
   __UpperCamelCase :			Optional[int]						   =						True





   def __init__(       self					:      List[Any]					,**lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      if "encoder" not in kwargs or "decoder" not in kwargs:
         raise ValueError(
             f"""A configuraton of type {self.model_type} cannot be instantiated because """
             f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""     )

      __SCREAMING_SNAKE_CASE   				=							kwargs.pop("""encoder"""     )
      __SCREAMING_SNAKE_CASE   				=							encoder_config.pop("""model_type"""     )
      __SCREAMING_SNAKE_CASE   				=							kwargs.pop("""decoder"""     )
      __SCREAMING_SNAKE_CASE   				=							decoder_config.pop("""model_type"""     )

      __SCREAMING_SNAKE_CASE   				=							AutoConfig.for_model(lowerCamelCase					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							AutoConfig.for_model(lowerCamelCase					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							True





   @classmethod
   def   UpperCAmelCase__  (       cls					:      Tuple					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      PretrainedConfig					,**lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""     )
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							True

      return cls(encoder=encoder_config.to_dict()					,decoder=decoder_config.to_dict()					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							self.encoder.to_dict()
      __SCREAMING_SNAKE_CASE   				=							self.decoder.to_dict()
      __SCREAMING_SNAKE_CASE   				=							self.__class__.model_type
      return output







class __a			( _snake_case      ):
   __UpperCamelCase :			Dict						   =						version.parse('1.11'      )





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]     )





   @property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return 1E-4





   @property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}     )







class __a			( _snake_case      ):





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							OrderedDict()
      __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """encoder_sequence"""}

      return common_inputs





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      "PreTrainedTokenizerBase"					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      Optional["TensorType"] = None					,):

      '''simple docstring'''

      import torch

      __SCREAMING_SNAKE_CASE   				=							OrderedDict()

      __SCREAMING_SNAKE_CASE   				=							super().generate_dummy_inputs(
          lowerCamelCase					,batch_size=lowerCamelCase					,seq_length=lowerCamelCase					,is_pair=lowerCamelCase					,framework=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							dummy_input["""input_ids"""].shape
      __SCREAMING_SNAKE_CASE   				=							(batch, encoder_sequence, self._config.encoder_hidden_size)
      __SCREAMING_SNAKE_CASE   				=							dummy_input.pop("""input_ids"""     )
      __SCREAMING_SNAKE_CASE   				=							dummy_input.pop("""attention_mask"""     )
      __SCREAMING_SNAKE_CASE   				=							torch.zeros(lowerCamelCase     )

      return common_inputs







class __a			( _snake_case      ):





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      PretrainedConfig     ):

      '''simple docstring'''

      return VisionEncoderDecoderEncoderOnnxConfig(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      str = "default"     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(lowerCamelCase					,lowerCamelCase     )












import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
       parser.add_argument('--model_name', default='roberta-large', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       _lowerCamelCase      							=   parser.parse_args()

       if args.model_type == "roberta":
              _lowerCamelCase      							=   RobertaForMaskedLM.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'roberta'
       elif args.model_type == "gpt2":
              _lowerCamelCase      							=   GPTaLMHeadModel.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'transformer'

       _lowerCamelCase      							=   model.state_dict()
       _lowerCamelCase      							=   {}

       # Embeddings #
       if args.model_type == "gpt2":
              for param_name in ["wte.weight", "wpe.weight"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.{param_name}''']
       else:
              for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                     _lowerCamelCase      							=   F'''{prefix}.embeddings.{w}.weight'''
                     _lowerCamelCase      							=   state_dict[param_name]
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   F'''{prefix}.embeddings.LayerNorm.{w}'''
                     _lowerCamelCase      							=   state_dict[param_name]

    # Transformer Blocks #
       _lowerCamelCase      							=   0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              if args.model_type == "gpt2":
                     for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                            for w in ["weight", "bias"]:
                                   _lowerCamelCase      							=   state_dict[
                                       F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                                   ]
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
              else:
                     for layer in [
                         "attention.self.query",
                         "attention.self.key",
                         "attention.self.value",
                         "attention.output.dense",
                         "attention.output.LayerNorm",
                         "intermediate.dense",
                         "output.dense",
                         "output.LayerNorm",
                     ]:
                            for w in ["weight", "bias"]:
                                   _lowerCamelCase      							=   state_dict[
                                       F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
                                   ]
              std_idx += 1

       # Language Modeling Head ###s
       if args.model_type == "roberta":
              for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{layer}''']
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            _lowerCamelCase      							=   state_dict[F'''lm_head.dense.{w}''']
                            _lowerCamelCase      							=   state_dict[F'''lm_head.layer_norm.{w}''']
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.ln_f.{w}''']
              _lowerCamelCase      							=   state_dict['lm_head.weight']

       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)

