





'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE    =			{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			['''SegformerFeatureExtractor''']
	_SCREAMING_SNAKE_CASE    =			['''SegformerImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			[
	    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SegformerDecodeHead''',
	    '''SegformerForImageClassification''',
	    '''SegformerForSemanticSegmentation''',
	    '''SegformerLayer''',
	    '''SegformerModel''',
	    '''SegformerPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			[
	    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSegformerDecodeHead''',
	    '''TFSegformerForImageClassification''',
	    '''TFSegformerForSemanticSegmentation''',
	    '''TFSegformerModel''',
	    '''TFSegformerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_segformer import SegformerFeatureExtractor
		from .image_processing_segformer import SegformerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_segformer import (
		    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SegformerDecodeHead,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerLayer,
		    SegformerModel,
		    SegformerPreTrainedModel,
		)
	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_segformer import (
		    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSegformerDecodeHead,
		    TFSegformerForImageClassification,
		    TFSegformerForSemanticSegmentation,
		    TFSegformerModel,
		    TFSegformerPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE    =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 	snake_case_      (__A     :						Any					,   __A     :						Union[str, Any]     )			->	int:
   assert isinstance(__A					,   __A     )
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,   [False, True]     )
def 	snake_case_      (__A     :						int					,   __A     :						int					,   __A     :						List[str]     )			->	Tuple:
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize(
    """features"""					,   [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,   )
def 	snake_case_      (__A     :						Dict					,   __A     :						List[Any]					,   __A     :						List[str]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			List[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader(__A					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						str					,   __A     :						Optional[int]					,   __A     :						int     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[Any]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   split=__A     ).read()
   _check_csv_dataset(__A					,   __A     )
   assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""					,   [str, list]     )
def 	snake_case_      (__A     :						Union[str, Any]					,   __A     :						Any					,   __A     :						List[str]     )			->	Optional[int]:
   if issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Dict					    =						csv_path
   elif issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Tuple					    =						[csv_path]
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Dict					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





def 	snake_case_      (__A     :						int					,   __A     :						Optional[int]					,   __A     :						Any=("train",)     )			->	Optional[int]:
   assert isinstance(__A					,   __A     )
   for split in splits:
      __lowerCAmelCase  :			int					    =						dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,   [False, True]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						List[str]					,   __A     :						Any     )			->	List[str]:
   __lowerCAmelCase  :			int					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Optional[Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize(
    """features"""					,   [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,   )
def 	snake_case_      (__A     :						List[str]					,   __A     :						List[Any]					,   __A     :						Optional[int]     )			->	Optional[int]:
   __lowerCAmelCase  :			Optional[int]					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Union[str, Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[int]					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			Optional[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Union[str, Any]					    =						CsvDatasetReader({"""train""": csv_path}					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						Optional[Any]					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   if split:
      __lowerCAmelCase  :			Any					    =						{split: csv_path}
   else:
      __lowerCAmelCase  :			Dict					    =						{"""train""": csv_path, """test""": csv_path}
   __lowerCAmelCase  :			List[str]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A					,   splits=list(path.keys()     )     )
   assert all(dataset[split].split == split for split in path.keys()     )





def 	snake_case_      (__A     :						List[str]     )			->	Optional[Any]:
   with open(__A					,   encoding="""utf-8"""     ) as csvfile:
      yield from csv.reader(__A     )





def 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			Optional[Any]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=1     ).write()

   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						List[str]     )			->	str:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[str]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Dict					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=2     ).write()

   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						Optional[Any]     )			->	List[str]:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[Any]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   with pytest.raises(__A     ):
      CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=0     )


