




import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				assert isinstance(__lowerCAmelCase						,  __lowerCAmelCase )
				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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						int										=tmp_path / """cache"""
				__magic_name__						:						Union[str, Any]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__						:						Dict										=JsonDatasetReader(__lowerCAmelCase						,  cache_dir=__lowerCAmelCase						,  keep_in_memory=__lowerCAmelCase ).read()
				_check_json_dataset(__lowerCAmelCase						,  __lowerCAmelCase )




@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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Any										=tmp_path / """cache"""
				__magic_name__						:						str										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						Optional[Any]										=features.copy() if features else default_expected_features
				__magic_name__						:						Dict										=(
				    Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				__magic_name__						:						List[str]										=JsonDatasetReader(__lowerCAmelCase						,  features=__lowerCAmelCase						,  cache_dir=__lowerCAmelCase ).read()
				_check_json_dataset(__lowerCAmelCase						,  __lowerCAmelCase )




@pytest.mark.parametrize(
    """features"""						,  [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]						,  )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						List[str]										=tmp_path / """cache"""
				__magic_name__						:						Dict										={"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
				__magic_name__						:						Any										=features.copy() if features else default_expected_features
				__magic_name__						:						Optional[int]										=(
				    Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				__magic_name__						:						Tuple										=JsonDatasetReader(__lowerCAmelCase						,  features=__lowerCAmelCase						,  cache_dir=__lowerCAmelCase ).read()
				assert isinstance(__lowerCAmelCase						,  __lowerCAmelCase )
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_3", "col_1", "col_2"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
				__magic_name__						:						str										={"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
				__magic_name__						:						Union[str, Any]										=features.copy()
				__magic_name__						:						Dict										=(
				    Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				__magic_name__						:						Optional[Any]										=tmp_path / """cache"""
				__magic_name__						:						Any										=JsonDatasetReader(__lowerCAmelCase						,  features=__lowerCAmelCase						,  cache_dir=__lowerCAmelCase ).read()
				assert isinstance(__lowerCAmelCase						,  __lowerCAmelCase )
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_2", "col_3", "col_1"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""split"""						,  [None, NamedSplit("""train""" ), """train""", """test"""] )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=tmp_path / """cache"""
				__magic_name__						:						List[str]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						List[Any]										=JsonDatasetReader(__lowerCAmelCase						,  cache_dir=__lowerCAmelCase						,  split=__lowerCAmelCase ).read()
				_check_json_dataset(__lowerCAmelCase						,  __lowerCAmelCase )
				assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""						,  [str, list] )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				if issubclass(__lowerCAmelCase						,  __lowerCAmelCase ):
								__magic_name__						:						List[Any]										=jsonl_path
				elif issubclass(__lowerCAmelCase						,  __lowerCAmelCase ):
								__magic_name__						:						List[Any]										=[jsonl_path]
				__magic_name__						:						Optional[int]										=tmp_path / """cache"""
				__magic_name__						:						Any										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						int										=JsonDatasetReader(__lowerCAmelCase						,  cache_dir=__lowerCAmelCase ).read()
				_check_json_dataset(__lowerCAmelCase						,  __lowerCAmelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=("train",) ):
				assert isinstance(__lowerCAmelCase						,  __lowerCAmelCase )
				for split in splits:
								__magic_name__						:						Tuple										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=tmp_path / """cache"""
				__magic_name__						:						Union[str, Any]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__						:						Union[str, Any]										=JsonDatasetReader({"""train""": jsonl_path}						,  cache_dir=__lowerCAmelCase						,  keep_in_memory=__lowerCAmelCase ).read()
				_check_json_datasetdict(__lowerCAmelCase						,  __lowerCAmelCase )




@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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=tmp_path / """cache"""
				__magic_name__						:						Optional[int]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						Tuple										=features.copy() if features else default_expected_features
				__magic_name__						:						Optional[Any]										=(
				    Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				__magic_name__						:						Any										=JsonDatasetReader({"""train""": jsonl_path}						,  features=__lowerCAmelCase						,  cache_dir=__lowerCAmelCase ).read()
				_check_json_datasetdict(__lowerCAmelCase						,  __lowerCAmelCase )




@pytest.mark.parametrize("""split"""						,  [None, NamedSplit("""train""" ), """train""", """test"""] )
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				if split:
								__magic_name__						:						Dict										={split: jsonl_path}
				else:
								__magic_name__						:						Tuple										="""train"""
								__magic_name__						:						int										={"""train""": jsonl_path, """test""": jsonl_path}
				__magic_name__						:						Optional[Any]										=tmp_path / """cache"""
				__magic_name__						:						Union[str, Any]										={"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__magic_name__						:						Dict										=JsonDatasetReader(__lowerCAmelCase						,  cache_dir=__lowerCAmelCase ).read()
				_check_json_datasetdict(__lowerCAmelCase						,  __lowerCAmelCase						,  splits=list(path.keys() ) )
				assert all(dataset[split].split == split for split in path.keys() )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return json.load(__lowerCAmelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return [json.loads(__lowerCAmelCase ) for line in buffer]




class 		__A   :

						@pytest.mark.parametrize("""lines, load_json_function"""     ,			[(True, load_json_lines), (False, load_json)]			)
						def      A__    (		self :Optional[Any]     ,			__snake_case :List[Any]     ,			__snake_case :List[str]     ,			__snake_case :int			):
										'''simple docstring'''

										with io.BytesIO() as buffer:
														JsonDatasetWriter(__snake_case     ,			__snake_case     ,			lines=__snake_case			).write()
														buffer.seek(0			)
														__magic_name__						:						Union[str, Any]										=load_json_function(__snake_case			)
										assert isinstance(__snake_case     ,			__snake_case			)
										assert isinstance(exported_content[0]     ,			__snake_case			)
										assert len(__snake_case			) == 10

						@pytest.mark.parametrize(
						    """orient, container, keys, len_at"""     ,			[
						        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
						        ("""split""", dict, {"""columns""", """data"""}, """data"""),
						        ("""index""", dict, set("""0123456789"""			), None),
						        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
						        ("""values""", list, None, None),
						        ("""table""", dict, {"""schema""", """data"""}, """data"""),
						    ]     ,			)
						def      A__    (		self :Tuple     ,			__snake_case :Optional[Any]     ,			__snake_case :int     ,			__snake_case :Union[str, Any]     ,			__snake_case :Dict     ,			__snake_case :int			):
										'''simple docstring'''

										with io.BytesIO() as buffer:
														JsonDatasetWriter(__snake_case     ,			__snake_case     ,			lines=__snake_case     ,			orient=__snake_case			).write()
														buffer.seek(0			)
														__magic_name__						:						Union[str, Any]										=load_json(__snake_case			)
										assert isinstance(__snake_case     ,			__snake_case			)
										if keys:
														if container is dict:
																		assert exported_content.keys() == keys
														else:
																		assert exported_content[0].keys() == keys
										else:
														assert not hasattr(__snake_case     ,			"""keys"""			) and not hasattr(exported_content[0]     ,			"""keys"""			)
										if len_at:
														assert len(exported_content[len_at]			) == 10
										else:
														assert len(__snake_case			) == 10

						@pytest.mark.parametrize("""lines, load_json_function"""     ,			[(True, load_json_lines), (False, load_json)]			)
						def      A__    (		self :Union[str, Any]     ,			__snake_case :Any     ,			__snake_case :Optional[Any]     ,			__snake_case :Optional[int]			):
										'''simple docstring'''

										with io.BytesIO() as buffer:
														JsonDatasetWriter(__snake_case     ,			__snake_case     ,			lines=__snake_case     ,			num_proc=2			).write()
														buffer.seek(0			)
														__magic_name__						:						int										=load_json_function(__snake_case			)
										assert isinstance(__snake_case     ,			__snake_case			)
										assert isinstance(exported_content[0]     ,			__snake_case			)
										assert len(__snake_case			) == 10

						@pytest.mark.parametrize(
						    """orient, container, keys, len_at"""     ,			[
						        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
						        ("""split""", dict, {"""columns""", """data"""}, """data"""),
						        ("""index""", dict, set("""0123456789"""			), None),
						        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
						        ("""values""", list, None, None),
						        ("""table""", dict, {"""schema""", """data"""}, """data"""),
						    ]     ,			)
						def      A__    (		self :Union[str, Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :Any     ,			__snake_case :List[str]     ,			__snake_case :Dict     ,			__snake_case :Any			):
										'''simple docstring'''

										with io.BytesIO() as buffer:
														JsonDatasetWriter(__snake_case     ,			__snake_case     ,			lines=__snake_case     ,			orient=__snake_case     ,			num_proc=2			).write()
														buffer.seek(0			)
														__magic_name__						:						str										=load_json(__snake_case			)
										assert isinstance(__snake_case     ,			__snake_case			)
										if keys:
														if container is dict:
																		assert exported_content.keys() == keys
														else:
																		assert exported_content[0].keys() == keys
										else:
														assert not hasattr(__snake_case     ,			"""keys"""			) and not hasattr(exported_content[0]     ,			"""keys"""			)
										if len_at:
														assert len(exported_content[len_at]			) == 10
										else:
														assert len(__snake_case			) == 10

						def      A__    (		self :Union[str, Any]     ,			__snake_case :Any			):
										'''simple docstring'''

										with pytest.raises(__snake_case			):
														with io.BytesIO() as buffer:
																		JsonDatasetWriter(__snake_case     ,			__snake_case     ,			num_proc=0			)


						@pytest.mark.parametrize("""compression, extension"""     ,			[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]			)
						def      A__    (		self :Optional[int]     ,			__snake_case :Optional[Any]     ,			__snake_case :int     ,			__snake_case :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=tmp_path_factory.mktemp("""data"""			) / f"test.json.{extension}"
										__magic_name__						:						List[Any]										=str(shared_datadir / f"test_file.json.{extension}"			)
										JsonDatasetWriter(__snake_case     ,			__snake_case     ,			compression=__snake_case			).write()

										with fsspec.open(__snake_case     ,			"""rb"""     ,			compression="""infer"""			) as f:
														__magic_name__						:						int										=f.read()
										with fsspec.open(__snake_case     ,			"""rb"""     ,			compression="""infer"""			) as f:
														__magic_name__						:						Union[str, Any]										=f.read()
										assert exported_content == original_content






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
     from transformers import TvltImageProcessor

if is_speech_available():
     from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

          for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

          for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          self.assertListEqual(list(inputs.keys()			)		,						["audio_values", "audio_mask", "pixel_values", "pixel_mask"]			)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          self.assertListEqual(
              processor.model_input_names		,						image_processor.model_input_names + feature_extractor.model_input_names		,						msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"		,						)



