import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   __lowerCamelCase      :			Optional[int]							       =		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      :			List[str]							       =		parser.parse_args()

   if args.model_type == "roberta":
      __lowerCamelCase      :			Union[str, Any]							       =		RobertaForMaskedLM.from_pretrained(args.model_name)
      __lowerCamelCase      :			List[str]							       =		'''roberta'''
   elif args.model_type == "gpt2":
      __lowerCamelCase      :			str							       =		GPTaLMHeadModel.from_pretrained(args.model_name)
      __lowerCamelCase      :			int							       =		'''transformer'''

   __lowerCamelCase      :			int							       =		model.state_dict()
   __lowerCamelCase      :			Tuple							       =		{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         __lowerCamelCase      :			Tuple							       =		state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         __lowerCamelCase      :			Tuple							       =		F"""{prefix}.embeddings.{w}.weight"""
         __lowerCamelCase      :			Union[str, Any]							       =		state_dict[param_name]
      for w in ["weight", "bias"]:
         __lowerCamelCase      :			List[Any]							       =		F"""{prefix}.embeddings.LayerNorm.{w}"""
         __lowerCamelCase      :			Optional[int]							       =		state_dict[param_name]

    # Transformer Blocks #
   __lowerCamelCase      :			Optional[int]							       =		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      :			List[str]							       =		state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         __lowerCamelCase      :			List[Any]							       =		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      :			List[Any]							       =		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      :			Union[str, Any]							       =		state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            __lowerCamelCase      :			Optional[int]							       =		state_dict[F"""lm_head.dense.{w}"""]
            __lowerCamelCase      :			Optional[int]							       =		state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         __lowerCamelCase      :			Optional[int]							       =		state_dict[F"""{prefix}.ln_f.{w}"""]
      __lowerCamelCase      :			Tuple							       =		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

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
