
"""simple docstring"""




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  A__   :					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')
  A__   :					str							=   parser.parse_args()

  if args.model_type == "roberta":
    A__   :					Optional[Any]							=   RobertaForMaskedLM.from_pretrained(args.model_name)
    A__   :					int							=   "roberta"
  elif args.model_type == "gpt2":
    A__   :					List[str]							=   GPTaLMHeadModel.from_pretrained(args.model_name)
    A__   :					Optional[int]							=   "transformer"

  A__   :					Tuple							=   model.state_dict()
  A__   :					Any							=   {}

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

    # Transformer Blocks #
  A__   :					List[str]							=   0
  for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
    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"]:
          A__   :					str							=   state_dict[
              f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
          ]
      A__   :					List[str]							=   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"]:
          A__   :					Optional[int]							=   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"]:
      A__   :					Any							=   state_dict[f"""{layer}"""]
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        A__   :					Dict							=   state_dict[f"""lm_head.dense.{w}"""]
        A__   :					str							=   state_dict[f"""lm_head.layer_norm.{w}"""]
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      A__   :					str							=   state_dict[f"""{prefix}.ln_f.{w}"""]
    A__   :					List[Any]							=   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)


"""simple docstring"""




import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


A__   :					Any							=   'hf-internal-testing/tiny-random-bert'
A__   :					int							=   os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
A__   :					Dict							=   '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'


class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     cached_file(A_ ,				A_       )
         # Should have downloaded the file in here
         self.assertTrue(os.path.isdir(A_       )       )
         # Cache should contain at least those three subfolders:
         for subfolder in ["blobs", "refs", "snapshots"]:
           self.assertTrue(os.path.isdir(os.path.join(A_ ,				A_       )       )       )
         with open(os.path.join(A_ ,				'''refs''' ,				'''main'''       )       ) as f:
           _lowercase:			Tuple       =     f.read()
         self.assertEqual(A_ ,				os.path.join(A_ ,				'''snapshots''' ,				A_ ,				A_       )       )
         self.assertTrue(os.path.isfile(A_       )       )

         # File is cached at the same place the second time.
         _lowercase:			List[Any]       =     cached_file(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )

         # Using a specific revision to test the full commit hash.
         _lowercase:			Union[str, Any]       =     cached_file(A_ ,				A_ ,				revision='''9b8c223'''       )
         self.assertEqual(A_ ,				os.path.join(A_ ,				'''snapshots''' ,				A_ ,				A_       )       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         with self.assertRaisesRegex(A_ ,				'''is not a valid model identifier'''       ):
           _lowercase:			Union[str, Any]       =     cached_file('''tiny-random-bert''' ,				A_       )

         with self.assertRaisesRegex(A_ ,				'''is not a valid git identifier'''       ):
           _lowercase:			List[Any]       =     cached_file(A_ ,				A_ ,				revision='''aaaa'''       )

         with self.assertRaisesRegex(A_ ,				'''does not appear to have a file named'''       ):
           _lowercase:			int       =     cached_file(A_ ,				'''conf'''       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         with self.assertRaisesRegex(A_ ,				'''does not appear to have a file named'''       ):
           _lowercase:			Dict       =     cached_file(A_ ,				'''conf'''       )

         with open(os.path.join(A_ ,				'''refs''' ,				'''main'''       )       ) as f:
           _lowercase:			int       =     f.read()
         self.assertTrue(os.path.isfile(os.path.join(A_ ,				'''.no_exist''' ,				A_ ,				'''conf'''       )       )       )

         _lowercase:			Optional[int]       =     cached_file(A_ ,				'''conf''' ,				_raise_exceptions_for_missing_entries=A_       )
         self.assertIsNone(A_       )

         _lowercase:			Optional[int]       =     cached_file(A_ ,				'''conf''' ,				local_files_only=A_ ,				_raise_exceptions_for_missing_entries=A_       )
         self.assertIsNone(A_       )

         _lowercase:			Optional[Any]       =     mock.Mock()
         _lowercase:			Optional[Any]       =     500
         _lowercase:			Optional[Any]       =     {}
         _lowercase:			Dict       =     HTTPError
         _lowercase:			Optional[Any]       =     {}

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch('''requests.Session.request''' ,				return_value=A_       ) as mock_head:
           _lowercase:			Tuple       =     cached_file(A_ ,				'''conf''' ,				_raise_exceptions_for_connection_errors=A_       )
           self.assertIsNone(A_       )
           # This check we did call the fake head request
           mock_head.assert_called()
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,				A_       )       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         self.assertIsNone(get_file_from_repo('''bert-base-cased''' ,				'''ahah.txt'''       )       )

         # The function raises if the repository does not exist.
         with self.assertRaisesRegex(A_ ,				'''is not a valid model identifier'''       ):
           get_file_from_repo('''bert-base-case''' ,				A_       )

         # The function raises if the revision does not exist.
         with self.assertRaisesRegex(A_ ,				'''is not a valid git identifier'''       ):
           get_file_from_repo('''bert-base-cased''' ,				A_ ,				revision='''ahaha'''       )

         _lowercase:			Optional[int]       =     get_file_from_repo('''bert-base-cased''' ,				A_       )
         # The name is the cached name which is not very easy to test, so instead we load the content.
         _lowercase:			Union[str, Any]       =     json.loads(open(A_ ,				'''r'''       ).read()       )
         self.assertEqual(config['''hidden_size'''] ,				768       )



       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         with tempfile.TemporaryDirectory() as tmp_dir:
           _lowercase:			List[Any]       =     Path(A_       ) / '''a.txt'''
           filename.touch()
           self.assertEqual(get_file_from_repo(A_ ,				'''a.txt'''       ) ,				str(A_       )       )

           self.assertIsNone(get_file_from_repo(A_ ,				'''b.txt'''       )       )
