
"""simple docstring"""


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    _snake_case					(		snake_case__ :  List[Any]						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  int						,   snake_case__ :  Tuple						,   snake_case__ :  List[str]						,   snake_case__ :  List[str]						,   snake_case__ :  Optional[Any]						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  Any						,   snake_case__ :  Dict						,   snake_case__ :  Tuple						,   snake_case__ :  Optional[int]						,   ):
	A     = {
	    '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),
	}
	A ,   A     = input_paths_and_base_extractors[compression_format]
	if input_path is None:
		A     = 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__  )
	A     = 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
			A     = file_path.read_text(encoding='utf-8'  )
	else:
		A     = output_path.read_text(encoding='utf-8'  )
	A     = 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    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[Any]						,   snake_case__ :  Optional[int]						,   snake_case__ :  int						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  int						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  Optional[int]						,   snake_case__ :  Optional[Any]						,   snake_case__ :  str						,   snake_case__ :  List[Any]						,   ):
	A     = {
	    '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,
	}
	A     = input_paths[compression_format]
	if input_path is None:
		A     = 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__  )
	A     = Extractor.infer_extractor_format(snake_case__  )
	assert extractor_format is not None
	A     = 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
			A     = file_path.read_text(encoding='utf-8'  )
	else:
		A     = output_path.read_text(encoding='utf-8'  )
	A     = text_file.read_text(encoding='utf-8'  )
	assert extracted_file_content == expected_file_content






@pytest.fixture
def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Dict  ):
	import tarfile

	A     = tmp_path / 'data_dot_dot'
	directory.mkdir()
	A     = 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    _snake_case					(		snake_case__ :  List[str]  ):
	import tarfile

	A     = tmp_path / 'data_sym_link'
	directory.mkdir()
	A     = 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    _snake_case					(		snake_case__ :  str						,   snake_case__ :  int						,   snake_case__ :  Tuple						,   snake_case__ :  List[str]						,   snake_case__ :  Any						,   snake_case__ :  str  ):
	A     = {
	    'tar_file_with_dot_dot': tar_file_with_dot_dot,
	    'tar_file_with_sym_link': tar_file_with_sym_link,
	}
	A     = insecure_tar_files[insecure_tar_file]
	A     = 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    _snake_case					(		snake_case__ :  Dict  ):
	# We should have less false positives than zipfile.is_zipfile
	# We do that by checking only the magic number
	A     = tmpdir / 'not_a_zip_file'
	# From: https://github.com/python/cpython/pull/5053
	A     = (
	    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 __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDebertaVaForMaskedLM,
       TFDebertaVaForQuestionAnswering,
       TFDebertaVaForSequenceClassification,
       TFDebertaVaForTokenClassification,
       TFDebertaVaModel,
   )



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=1_3			,    a__=7			,    a__=True			,    a__=True			,    a__=True			,    a__=True			,    a__=9_9			,    a__=3_2			,    a__=2			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=1_6			,    a__=2			,    a__=0.0_2			,    a__=False			,    a__=True			,    a__="None"			,    a__=3			,    a__=4			,    a__=None			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       seq_length
         A__					=       is_training
         A__					=       use_input_mask
         A__					=       use_token_type_ids
         A__					=       use_labels
         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       intermediate_size
         A__					=       hidden_act
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       type_sequence_label_size
         A__					=       initializer_range
         A__					=       num_labels
         A__					=       num_choices
         A__					=       relative_attention
         A__					=       position_biased_input
         A__					=       pos_att_type
         A__					=       scope




     def 		snake_case_		(					self):
         A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size)

         A__					=       None
         if self.use_input_mask:
             A__					=       random_attention_mask([self.batch_size, self.seq_length])

         A__					=       None
         if self.use_token_type_ids:
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size)

         A__					=       None
         A__					=       None
         A__					=       None
         if self.use_labels:
             A__					=       ids_tensor([self.batch_size]			,    self.type_sequence_label_size)
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels)

         A__					=       DebertaVaConfig(
             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			,    relative_attention=self.relative_attention			,    position_biased_input=self.position_biased_input			,    initializer_range=self.initializer_range			,    return_dict=a__			,    )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaModel(config=a__)
         A__					=       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A__					=       [input_ids, input_mask]
         A__					=       model(a__)

         A__					=       model(a__)

         self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaForMaskedLM(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFDebertaVaForSequenceClassification(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFDebertaVaForTokenClassification(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaForQuestionAnswering(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A__					=       model(a__)
         self.parent.assertEqual(result.start_logits.shape			,    (self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape			,    (self.batch_size, self.seq_length))




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       config_and_inputs
         A__					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict







@require_tf
class      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(
         (
             TFDebertaVaModel,
             TFDebertaVaForMaskedLM,
             TFDebertaVaForQuestionAnswering,
             TFDebertaVaForSequenceClassification,
             TFDebertaVaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		     =					(
         {
             '''feature-extraction''': TFDebertaVaModel,
             '''fill-mask''': TFDebertaVaForMaskedLM,
             '''question-answering''': TFDebertaVaForQuestionAnswering,
             '''text-classification''': TFDebertaVaForSequenceClassification,
             '''token-classification''': TFDebertaVaForTokenClassification,
             '''zero-shot''': TFDebertaVaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModelTester(self)
         A__					=       ConfigTester(self			,    config_class=a__			,    hidden_size=3_7)




     def 		snake_case_		(					self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__)




     @slow
     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
         self.assertIsNotNone(a__)







@require_tf
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @unittest.skip(reason='''Model not available yet''')
     def 		snake_case_		(					self):
         pass




     @slow
     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
         A__					=       tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
         A__					=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
         A__					=       model(a__			,    attention_mask=a__)[0]

         A__					=       tf.constant(
             [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,    a__			,    atol=1e-4)
