



import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
    'compression_format, is_archive'       ,     [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]       ,     )
def 		UpperCamelCase	(   snake_case__       :    Any       ,     snake_case__       :    Dict       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    Dict       ,     snake_case__       :    str       ,     snake_case__       :    str       ,     snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    str       ,     )	->     str:
   UpperCamelCase    :  Optional[Any]							=	{
       '7z': (seven_zip_file, SevenZipExtractor),
       'bz2': (bza_file, BzipaExtractor),
       'gzip': (gz_file, GzipExtractor),
       'lz4': (lza_file, LzaExtractor),
       'tar': (tar_file, TarExtractor),
       'xz': (xz_file, XzExtractor),
       'zip': (zip_file, ZipExtractor),
       'zstd': (zstd_file, ZstdExtractor),
   }
   UpperCamelCase	,     UpperCamelCase    :  Dict							=	input_paths_and_base_extractors[compression_format]
   if input_path is None:
      UpperCamelCase    :  List[str]							=	F"""for '{compression_format}' compression_format, """
      if compression_format == "7z":
         reason += require_pyazr.kwargs["reason"]
      elif compression_format == "lz4":
         reason += require_lza.kwargs["reason"]
      elif compression_format == "zstd":
         reason += require_zstandard.kwargs["reason"]
      pytest.skip(snake_case__	)
   assert base_extractor.is_extractable(snake_case__	)
   UpperCamelCase    :  int							=	tmp_path / ('extracted' if is_archive else 'extracted.txt')
   base_extractor.extract(snake_case__       ,     snake_case__	)
   if is_archive:
      assert output_path.is_dir()
      for file_path in output_path.iterdir():
         assert file_path.name == text_file.name
         UpperCamelCase    :  List[str]							=	file_path.read_text(encoding='utf-8'	)
   else:
      UpperCamelCase    :  Optional[Any]							=	output_path.read_text(encoding='utf-8'	)
   UpperCamelCase    :  List[Any]							=	text_file.read_text(encoding='utf-8'	)
   assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
    'compression_format, is_archive'       ,     [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]       ,     )
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Any       ,     snake_case__       :    List[Any]       ,     snake_case__       :    List[str]       ,     snake_case__       :    Any       ,     snake_case__       :    Any       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Optional[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    Optional[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    int       ,     )	->     List[Any]:
   UpperCamelCase    :  Optional[int]							=	{
       '7z': seven_zip_file,
       'bz2': bza_file,
       'gzip': gz_file,
       'lz4': lza_file,
       'tar': tar_file,
       'xz': xz_file,
       'zip': zip_file,
       'zstd': zstd_file,
   }
   UpperCamelCase    :  Optional[int]							=	input_paths[compression_format]
   if input_path is None:
      UpperCamelCase    :  Optional[Any]							=	F"""for '{compression_format}' compression_format, """
      if compression_format == "7z":
         reason += require_pyazr.kwargs["reason"]
      elif compression_format == "lz4":
         reason += require_lza.kwargs["reason"]
      elif compression_format == "zstd":
         reason += require_zstandard.kwargs["reason"]
      pytest.skip(snake_case__	)
   UpperCamelCase    :  Union[str, Any]							=	Extractor.infer_extractor_format(snake_case__	)
   assert extractor_format is not None
   UpperCamelCase    :  List[Any]							=	tmp_path / ('extracted' if is_archive else 'extracted.txt')
   Extractor.extract(snake_case__       ,     snake_case__       ,     snake_case__	)
   if is_archive:
      assert output_path.is_dir()
      for file_path in output_path.iterdir():
         assert file_path.name == text_file.name
         UpperCamelCase    :  List[str]							=	file_path.read_text(encoding='utf-8'	)
   else:
      UpperCamelCase    :  Union[str, Any]							=	output_path.read_text(encoding='utf-8'	)
   UpperCamelCase    :  int							=	text_file.read_text(encoding='utf-8'	)
   assert extracted_file_content == expected_file_content
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    Tuple       ,     snake_case__       :    str	)	->     List[str]:
   import tarfile

   UpperCamelCase    :  Optional[Any]							=	tmp_path / 'data_dot_dot'
   directory.mkdir()
   UpperCamelCase    :  Optional[int]							=	directory / 'tar_file_with_dot_dot.tar'
   with tarfile.TarFile(snake_case__       ,     'w'	) as f:
      f.add(snake_case__       ,     arcname=os.path.join('..'       ,     text_file.name	)	)
   return path
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    Any	)	->     Tuple:
   import tarfile

   UpperCamelCase    :  Dict							=	tmp_path / 'data_sym_link'
   directory.mkdir()
   UpperCamelCase    :  Tuple							=	directory / 'tar_file_with_sym_link.tar'
   os.symlink('..'       ,     directory / 'subdir'       ,     target_is_directory=snake_case__	)
   with tarfile.TarFile(snake_case__       ,     'w'	) as f:
      f.add(str(directory / 'subdir'	)       ,     arcname='subdir'	)  # str required by os.readlink on Windows and Python < 3.8
   return path
@pytest.mark.parametrize(
    'insecure_tar_file, error_log'       ,     [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]       ,     )
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    int       ,     snake_case__       :    Dict       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Optional[Any]       ,     snake_case__       :    Union[str, Any]	)	->     Tuple:
   UpperCamelCase    :  Optional[int]							=	{
       'tar_file_with_dot_dot': tar_file_with_dot_dot,
       'tar_file_with_sym_link': tar_file_with_sym_link,
   }
   UpperCamelCase    :  Optional[int]							=	insecure_tar_files[insecure_tar_file]
   UpperCamelCase    :  List[Any]							=	tmp_path / 'extracted'
   TarExtractor.extract(snake_case__       ,     snake_case__	)
   assert caplog.text
   for record in caplog.records:
      assert record.levelname == "ERROR"
      assert error_log in record.msg


def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     str:
   # We should have less false positives than zipfile.is_zipfile
   # We do that by checking only the magic number
   UpperCamelCase    :  Optional[Any]							=	tmpdir / 'not_a_zip_file'
   # From: https://github.com/python/cpython/pull/5053
   UpperCamelCase    :  int							=	(
       B'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
       B'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
       B'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
       B'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
   )
   with not_a_zip_file.open('wb'	) as f:
      f.write(snake_case__	)
   assert zipfile.is_zipfile(str(snake_case__	)	)  # is a false positive for `zipfile`
   assert not ZipExtractor.is_extractable(snake_case__	)  # but we're right








from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)


__UpperCAmelCase          =       {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

          super().__init__(
              bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			tie_word_embeddings=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Optional[int]							=	OrderedDict({'input_ids': common_inputs['input_ids']}       )

          # Need to add the past_keys
          if self.use_past:
             if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
             else:
                import torch

                UpperCamelCase	,     UpperCamelCase    :  List[str]							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  List[Any]							=	seqlen + 2
                UpperCamelCase    :  List[str]							=	(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                UpperCamelCase    :  str							=	[
                    (torch.zeros(SCREAMING_SNAKE_CASE_       ), torch.zeros(SCREAMING_SNAKE_CASE_       )) for _ in range(self.num_layers       )
                ]

          UpperCamelCase    :  List[Any]							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  Optional[Any]							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



