





import functools
import operator

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


__A							   =							logging.get_logger(__name__)

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



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "sew-d"

 def __init__(self							:  Optional[int]			,					UpperCAmelCase_							:  str=32			,					UpperCAmelCase_							:  str=768			,					UpperCAmelCase_							:  Any=12			,					UpperCAmelCase_							:  Optional[Any]=12			,					UpperCAmelCase_							:  Optional[Any]=3_072			,					UpperCAmelCase_							:  Tuple=2			,					UpperCAmelCase_							:  List[Any]=512			,					UpperCAmelCase_							:  Union[str, Any]=256			,					UpperCAmelCase_							:  int=True			,					UpperCAmelCase_							:  List[Any]=True			,					UpperCAmelCase_							:  int=("p2c", "c2p")			,					UpperCAmelCase_							:  Tuple="layer_norm"			,					UpperCAmelCase_							:  Any="gelu_python"			,					UpperCAmelCase_							:  Tuple=0.1			,					UpperCAmelCase_							:  Any=0.1			,					UpperCAmelCase_							:  Optional[Any]=0.1			,					UpperCAmelCase_							:  List[Any]=0.0			,					UpperCAmelCase_							:  Dict=0.1			,					UpperCAmelCase_							:  int=0.02			,					UpperCAmelCase_							:  List[str]=1E-7			,					UpperCAmelCase_							:  List[Any]=1E-5			,					UpperCAmelCase_							:  Union[str, Any]="group"			,					UpperCAmelCase_							:  List[Any]="gelu"			,					UpperCAmelCase_							:  Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)			,					UpperCAmelCase_							:  str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)			,					UpperCAmelCase_							:  Optional[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)			,					UpperCAmelCase_							:  Optional[Any]=False			,					UpperCAmelCase_							:  Tuple=128			,					UpperCAmelCase_							:  Optional[int]=16			,					UpperCAmelCase_							:  int=True			,					UpperCAmelCase_							:  Tuple=0.05			,					UpperCAmelCase_							:  Union[str, Any]=10			,					UpperCAmelCase_							:  int=2			,					UpperCAmelCase_							:  List[Any]=0.0			,					UpperCAmelCase_							:  Any=10			,					UpperCAmelCase_							:  Union[str, Any]=0			,					UpperCAmelCase_							:  List[str]="mean"			,					UpperCAmelCase_							:  Dict=False			,					UpperCAmelCase_							:  Optional[int]=False			,					UpperCAmelCase_							:  Dict=256			,					UpperCAmelCase_							:  Union[str, Any]=0			,					UpperCAmelCase_							:  Tuple=1			,					UpperCAmelCase_							:  int=2			,					**UpperCAmelCase_							:  Tuple			,					)			->str:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_			,					pad_token_id=UpperCAmelCase_			,					bos_token_id=UpperCAmelCase_			,					eos_token_id=UpperCAmelCase_)
   lowerCamelCase__: str         =hidden_size
   lowerCamelCase__: int         =feat_extract_norm
   lowerCamelCase__: str         =feat_extract_activation
   lowerCamelCase__: List[Any]         =list(UpperCAmelCase_)
   lowerCamelCase__: Dict         =list(UpperCAmelCase_)
   lowerCamelCase__: Tuple         =list(UpperCAmelCase_)
   lowerCamelCase__: str         =conv_bias
   lowerCamelCase__: Any         =num_conv_pos_embeddings
   lowerCamelCase__: List[Any]         =num_conv_pos_embedding_groups
   lowerCamelCase__: Optional[int]         =len(self.conv_dim)
   lowerCamelCase__: Tuple         =num_hidden_layers
   lowerCamelCase__: Tuple         =intermediate_size
   lowerCamelCase__: Optional[Any]         =squeeze_factor
   lowerCamelCase__: int         =max_position_embeddings
   lowerCamelCase__: Dict         =position_buckets
   lowerCamelCase__: List[Any]         =share_att_key
   lowerCamelCase__: Optional[Any]         =relative_attention
   lowerCamelCase__: List[str]         =norm_rel_ebd
   lowerCamelCase__: List[Any]         =list(UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =hidden_act
   lowerCamelCase__: str         =num_attention_heads
   lowerCamelCase__: str         =hidden_dropout
   lowerCamelCase__: List[str]         =attention_dropout
   lowerCamelCase__: str         =activation_dropout
   lowerCamelCase__: int         =feat_proj_dropout
   lowerCamelCase__: Tuple         =final_dropout
   lowerCamelCase__: Any         =layer_norm_eps
   lowerCamelCase__: Optional[Any]         =feature_layer_norm_eps
   lowerCamelCase__: Dict         =initializer_range
   lowerCamelCase__: int         =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__: Any         =apply_spec_augment
   lowerCamelCase__: List[Any]         =mask_time_prob
   lowerCamelCase__: Optional[int]         =mask_time_length
   lowerCamelCase__: Optional[Any]         =mask_time_min_masks
   lowerCamelCase__: Tuple         =mask_feature_prob
   lowerCamelCase__: int         =mask_feature_length
   lowerCamelCase__: Optional[int]         =mask_feature_min_masks

   # ctc loss
   lowerCamelCase__: Dict         =ctc_loss_reduction
   lowerCamelCase__: Any         =ctc_zero_infinity

   # sequence classification
   lowerCamelCase__: Any         =use_weighted_layer_sum
   lowerCamelCase__: Optional[Any]         =classifier_proj_size







 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->Optional[Any]:



   '''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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			str						    = tmp_path / "file.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






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

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






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

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "csv_with_label.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        label\n        good\n        bad\n        good\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Tuple						    = Csv()
						snake_case   :			str						    = csv._generate_tables([[csv_file, malformed_csv_file]] )
						with pytest.raises(lowerCamelCase_     ,		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(lowerCamelCase_ ) in record.message
						    for record in caplog.records )

@require_pil
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






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



