





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		test_file.split(os.path.sep   )
       if components[0:2] != ["tests", "models"]:
              raise ValueError(
                  '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
                  F"""{test_file} instead."""   )
       _lowerCamelCase				:  List[str]						=		components[-1]
       if not test_fn.endswith('''py'''   ):
              raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
       if not test_fn.startswith('''test_modeling_'''   ):
              raise ValueError(
                  F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

       _lowerCamelCase				:  Union[str, Any]						=		components[:-1] + [test_fn.replace('''.py'''			,     ''''''   )]
       _lowerCamelCase				:  Tuple						=		'''.'''.join(lowercase_   )

       return test_module_path




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Tuple						=		get_module_path(lowercase_   )
       _lowerCamelCase				:  Any						=		importlib.import_module(lowercase_   )

       return test_module




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Union[str, Any]						=		[]
       _lowerCamelCase				:  Any						=		get_test_module(lowercase_   )
       for attr in dir(lowercase_   ):
              if attr.endswith('''ModelTester'''   ):
                     tester_classes.append(getattr(lowercase_			,     lowercase_   )   )

    # sort with class names
       return sorted(lowercase_			,     key=lambda lowercase_   : x.__name__   )




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		[]
       _lowerCamelCase				:  List[str]						=		get_test_module(lowercase_   )
       for attr in dir(lowercase_   ):
              _lowerCamelCase				:  Tuple						=		getattr(lowercase_			,     lowercase_   )
              # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
              # `all_model_classes` is not empty (which also excludes other special classes).
              _lowerCamelCase				:  Optional[int]						=		getattr(lowercase_			,     '''all_model_classes'''			,     []   )
              if len(lowercase_   ) > 0:
                     test_classes.append(lowercase_   )

    # sort with class names
       return sorted(lowercase_			,     key=lambda lowercase_   : x.__name__   )




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Optional[Any]						=		get_test_classes(lowercase_   )
       _lowerCamelCase				:  List[str]						=		set()
       for test_class in test_classes:
              model_classes.update(test_class.all_model_classes   )

       # sort with class names
       return sorted(lowercase_			,     key=lambda lowercase_   : x.__name__   )




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		test_class()
       if hasattr(lowercase_			,     '''setUp'''   ):
              test.setUp()

       _lowerCamelCase				:  Tuple						=		None
       if hasattr(lowercase_			,     '''model_tester'''   ):
              # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
              if test.model_tester is not None:
                     _lowerCamelCase				:  List[Any]						=		test.model_tester.__class__

       return model_tester




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		get_test_classes(lowercase_   )

       _lowerCamelCase				:  Optional[int]						=		[]
       for test_class in test_classes:
              if model_class in test_class.all_model_classes:
                     target_test_classes.append(lowercase_   )

    # sort with class names
       return sorted(lowercase_			,     key=lambda lowercase_   : x.__name__   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Optional[int]						=		get_test_classes_for_model(lowercase_			,     lowercase_   )

       _lowerCamelCase				:  Optional[Any]						=		[]
       for test_class in test_classes:
              _lowerCamelCase				:  Tuple						=		get_model_tester_from_test_class(lowercase_   )
              if tester_class is not None:
                     tester_classes.append(lowercase_   )

    # sort with class names
       return sorted(lowercase_			,     key=lambda lowercase_   : x.__name__   )




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Optional[int]						=		get_test_classes(lowercase_   )
       _lowerCamelCase				:  Any						=		{test_class: get_model_tester_from_test_class(lowercase_   ) for test_class in test_classes}
       return test_tester_mapping




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[str]						=		get_model_classes(lowercase_   )
       _lowerCamelCase				:  Optional[int]						=		{
           model_class: get_test_classes_for_model(lowercase_			,     lowercase_   ) for model_class in model_classes
       }
       return model_test_mapping




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Dict						=		get_model_classes(lowercase_   )
       _lowerCamelCase				:  Optional[int]						=		{
           model_class: get_tester_classes_for_model(lowercase_			,     lowercase_   ) for model_class in model_classes
       }
       return model_to_tester_mapping




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     lowercase_   ):
              return o
       elif isinstance(lowercase_			,     lowercase_   ):
              return o.__name__
       elif isinstance(lowercase_			,     (list, tuple)   ):
              return [to_json(lowercase_   ) for x in o]
       elif isinstance(lowercase_			,     lowercase_   ):
              return {to_json(lowercase_   ): to_json(lowercase_   ) for k, v in o.items()}
       else:
              return o








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)

