
'''simple docstring'''



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  '''sew'''




						def __init__(   self       ,  _lowercase=32       ,  _lowercase=768       ,  _lowercase=12       ,  _lowercase=12       ,  _lowercase=3_072       ,  _lowercase=2       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.0       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.02       ,  _lowercase=1e-5       ,  _lowercase="group"       ,  _lowercase="gelu"       ,  _lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,  _lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,  _lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,  _lowercase=False       ,  _lowercase=128       ,  _lowercase=16       ,  _lowercase=True       ,  _lowercase=0.05       ,  _lowercase=10       ,  _lowercase=2       ,  _lowercase=0.0       ,  _lowercase=10       ,  _lowercase=0       ,  _lowercase="mean"       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase=256       ,  _lowercase=0       ,  _lowercase=1       ,  _lowercase=2       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(**_lowercase       ,  pad_token_id=_lowercase       ,  bos_token_id=_lowercase       ,  eos_token_id=_lowercase )
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					feat_extract_norm
												_lowerCAmelCase    		=					feat_extract_activation
												_lowerCAmelCase    		=					list(_lowercase )
												_lowerCAmelCase    		=					list(_lowercase )
												_lowerCAmelCase    		=					list(_lowercase )
												_lowerCAmelCase    		=					conv_bias
												_lowerCAmelCase    		=					num_conv_pos_embeddings
												_lowerCAmelCase    		=					num_conv_pos_embedding_groups
												_lowerCAmelCase    		=					len(self.conv_dim )
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					squeeze_factor
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					hidden_dropout
												_lowerCAmelCase    		=					attention_dropout
												_lowerCAmelCase    		=					activation_dropout
												_lowerCAmelCase    		=					feat_proj_dropout
												_lowerCAmelCase    		=					final_dropout
												_lowerCAmelCase    		=					layerdrop
												_lowerCAmelCase    		=					layer_norm_eps
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					vocab_size

												if (
												    (len(self.conv_stride ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim ) != self.num_feat_extract_layers)
												):
																		raise ValueError(
																		    """Configuration for convolutional layers is incorrect."""
																		    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																		    F'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
																		    F'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												_lowerCAmelCase    		=					apply_spec_augment
												_lowerCAmelCase    		=					mask_time_prob
												_lowerCAmelCase    		=					mask_time_length
												_lowerCAmelCase    		=					mask_time_min_masks
												_lowerCAmelCase    		=					mask_feature_prob
												_lowerCAmelCase    		=					mask_feature_length
												_lowerCAmelCase    		=					mask_feature_min_masks

												# ctc loss
												_lowerCAmelCase    		=					ctc_loss_reduction
												_lowerCAmelCase    		=					ctc_zero_infinity

												# sequence classification
												_lowerCAmelCase    		=					use_weighted_layer_sum
												_lowerCAmelCase    		=					classifier_proj_size




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return functools.reduce(operator.mul       ,  self.conv_stride       ,  1 )





"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
