


'''simple docstring'''


import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_UpperCamelCase 							=  """http://www.mocksite.com/file1.txt"""
_UpperCamelCase 							=  """\"text\": [\"foo\", \"foo\"]"""
_UpperCamelCase 							=  """6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"""







class 							__magic_name__	:

     """simple docstring"""






     lowerCamelCase__       						=				200
     lowerCamelCase__       						=				{'Content-Length': '100'}
     lowerCamelCase__       						=				{}


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          return [bytes(lowerCamelCase			,     "utf-8"		)]


def 		_lowercase (*SCREAMING_SNAKE_CASE       ,					**SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     return MockResponse()


@pytest.mark.parametrize("urls_type"       ,					[str, list, dict]							)
def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     import requests

     monkeypatch.setattr(SCREAMING_SNAKE_CASE       ,					"request"       ,					SCREAMING_SNAKE_CASE							)

     __A	:       str      =							URL
     if issubclass(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
          __A	:       Dict      =							url
     elif issubclass(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
          __A	:       int      =							[url]
     elif issubclass(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
          __A	:       List[str]      =							{"train": url}
     __A	:       Optional[Any]      =							"dummy"
     __A	:       Dict      =							"downloads"
     __A	:       int      =							tmp_path
     __A	:       Optional[int]      =							DownloadConfig(
         cache_dir=os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)       ,					use_etag=SCREAMING_SNAKE_CASE       ,					)
     __A	:       Optional[Any]      =							DownloadManager(dataset_name=SCREAMING_SNAKE_CASE       ,					download_config=SCREAMING_SNAKE_CASE							)
     __A	:       Union[str, Any]      =							dl_manager.download(SCREAMING_SNAKE_CASE							)
     __A	:       str      =							urls
     for downloaded_paths in [downloaded_paths]:
          if isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               __A	:       Dict      =							[downloaded_paths]
               __A	:       int      =							[urls]
          elif isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               assert "train" in downloaded_paths.keys()
               __A	:       Dict      =							downloaded_paths.values()
               __A	:       Optional[Any]      =							urls.values()
          assert downloaded_paths
          for downloaded_path, input_url in zip(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               assert downloaded_path == dl_manager.downloaded_paths[input_url]
               __A	:       List[str]      =							Path(SCREAMING_SNAKE_CASE							)
               __A	:       Optional[Any]      =							downloaded_path.parts
               assert parts[-1] == HASH
               assert parts[-2] == cache_subdir
               assert downloaded_path.exists()
               __A	:       Any      =							downloaded_path.read_text()
               assert content == CONTENT
               __A	:       Optional[Any]      =							downloaded_path.with_suffix(".json"							)
               assert metadata_downloaded_path.exists()
               __A	:       List[str]      =							json.loads(metadata_downloaded_path.read_text()							)
               assert metadata_content == {"url": URL, "etag": None}


@pytest.mark.parametrize("paths_type"       ,					[str, list, dict]							)
def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Optional[Any]      =							str(SCREAMING_SNAKE_CASE							)
     if issubclass(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
          __A	:       Union[str, Any]      =							filename
     elif issubclass(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
          __A	:       Union[str, Any]      =							[filename]
     elif issubclass(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
          __A	:       Any      =							{"train": filename}
     __A	:       int      =							"dummy"
     __A	:       List[Any]      =							xz_file.parent
     __A	:       Optional[int]      =							"extracted"
     __A	:       Optional[Any]      =							DownloadConfig(
         cache_dir=SCREAMING_SNAKE_CASE       ,					use_etag=SCREAMING_SNAKE_CASE       ,					)
     __A	:       Optional[Any]      =							DownloadManager(dataset_name=SCREAMING_SNAKE_CASE       ,					download_config=SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							dl_manager.extract(SCREAMING_SNAKE_CASE							)
     __A	:       Union[str, Any]      =							paths
     for extracted_paths in [extracted_paths]:
          if isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               __A	:       str      =							[extracted_paths]
               __A	:       str      =							[paths]
          elif isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               assert "train" in extracted_paths.keys()
               __A	:       Tuple      =							extracted_paths.values()
               __A	:       Dict      =							paths.values()
          assert extracted_paths
          for extracted_path, input_path in zip(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               assert extracted_path == dl_manager.extracted_paths[input_path]
               __A	:       Optional[Any]      =							Path(SCREAMING_SNAKE_CASE							)
               __A	:       str      =							extracted_path.parts
               assert parts[-1] == hash_url_to_filename(SCREAMING_SNAKE_CASE       ,					etag=SCREAMING_SNAKE_CASE							)
               assert parts[-2] == extracted_subdir
               assert extracted_path.exists()
               __A	:       str      =							extracted_path.read_text()
               __A	:       List[str]      =							text_file.read_text()
               assert extracted_file_content == expected_file_content


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     assert path.endswith(".jsonl"							)
     for num_items, line in enumerate(SCREAMING_SNAKE_CASE       ,					start=1							):
          __A	:       Any      =							json.loads(line.decode("utf-8"							)							)
          assert item.keys() == {"col_1", "col_2", "col_3"}
     assert num_items == 4


@pytest.mark.parametrize("archive_jsonl"       ,					["tar_jsonl_path", "zip_jsonl_path"]							)
def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Optional[Any]      =							request.getfixturevalue(SCREAMING_SNAKE_CASE							)
     __A	:       Optional[int]      =							DownloadManager()
     for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(SCREAMING_SNAKE_CASE							)       ,					start=1							):
          _test_jsonl(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     assert num_jsonl == 2


@pytest.mark.parametrize("archive_nested_jsonl"       ,					["tar_nested_jsonl_path", "zip_nested_jsonl_path"]							)
def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[Any]      =							request.getfixturevalue(SCREAMING_SNAKE_CASE							)
     __A	:       Any      =							DownloadManager()
     for num_tar, (path, file) in enumerate(dl_manager.iter_archive(SCREAMING_SNAKE_CASE							)       ,					start=1							):
          for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(SCREAMING_SNAKE_CASE							)       ,					start=1							):
               _test_jsonl(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     assert num_tar == 1
     assert num_jsonl == 2






def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[Any]      =							DownloadManager()
     for num_file, file in enumerate(dl_manager.iter_files(SCREAMING_SNAKE_CASE							)       ,					start=1							):
          assert os.path.basename(SCREAMING_SNAKE_CASE							) == ("test.txt" if num_file == 1 else "train.txt")
     assert num_file == 2





'''simple docstring'''


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)

