

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   __UpperCAmelCase								=			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""")
   __UpperCAmelCase								=			parser.parse_args()

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

   __UpperCAmelCase								=			model.state_dict()
   __UpperCAmelCase								=			{}

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

    # Transformer Blocks #
   __UpperCAmelCase								=			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"]:
               __UpperCAmelCase								=			state_dict[
                   F'{prefix}.h.{teacher_idx}.{layer}.{w}'
               ]
         __UpperCAmelCase								=			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"]:
               __UpperCAmelCase								=			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"]:
         __UpperCAmelCase								=			state_dict[F'{layer}']
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            __UpperCAmelCase								=			state_dict[F'lm_head.dense.{w}']
            __UpperCAmelCase								=			state_dict[F'lm_head.layer_norm.{w}']
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         __UpperCAmelCase								=			state_dict[F'{prefix}.ln_f.{w}']
      __UpperCAmelCase								=			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)





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=13	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=99	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Optional[Any]=37	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   Any=5_12	,							lowerCAmelCase			:   Optional[Any]=16	,							lowerCAmelCase			:   Dict=2	,							lowerCAmelCase			:   Tuple=0.02	,							lowerCAmelCase			:   Optional[int]=4	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      Dict    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      Union[str, Any]    =						is_training
     __lowerCAmelCase  :      List[Any]    =						use_attention_mask
     __lowerCAmelCase  :      List[Any]    =						use_token_type_ids
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      Any    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[int]    =						num_attention_heads
     __lowerCAmelCase  :      Dict    =						intermediate_size
     __lowerCAmelCase  :      Tuple    =						hidden_act
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      Any    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Union[str, Any]    =						max_position_embeddings
     __lowerCAmelCase  :      int    =						type_vocab_size
     __lowerCAmelCase  :      Tuple    =						type_sequence_label_size
     __lowerCAmelCase  :      int    =						initializer_range
     __lowerCAmelCase  :      Optional[int]    =						num_choices







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[int]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Union[str, Any]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[str]    =						BertConfig(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						config_and_inputs
     __lowerCAmelCase  :      Any    =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						config_and_inputs

     __lowerCAmelCase  :      Any    =						True
     __lowerCAmelCase  :      str    =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
     __lowerCAmelCase  :      int    =						ids_tensor([self.batch_size, self.seq_length]	,							vocab_size=2   )

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      int										=True

  lowerCamelCase   :      Any										=(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						FlaxBertModelTester(self   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						FlaxBertModel.from_pretrained("""bert-base-cased"""   )
     __lowerCAmelCase  :      str    =						model(np.ones((1, 1)   )   )
     self.assertIsNotNone(lowerCAmelCase   )


