import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil


class     lowercase     (  UpperCamelCase__  ):




							def 						__snake_case(    self :       List[Any]   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([1, 2, 3]   )   )
												self.assertEqual(arr.type						,				pa.intaa()   )




							def 						__snake_case(    self :       int   )       ->					List[str]:



												'''simple docstring'''

												with self.assertRaises(__snake_case   ):
																	SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([1, 2, 3]   )						,				type=pa.intaa()   )




							def 						__snake_case(    self :       Tuple   )       ->					Tuple:



												'''simple docstring'''

												with self.assertRaises(__snake_case   ):
																	SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([1, 2, 3]						,				try_type=Value("bool"   )						,				type=Value("int64"   )   )   )




							def 						__snake_case(    self :       Union[str, Any]   )       ->					int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([1, 2, 3]						,				type=Value("int32"   )   )   )
												self.assertEqual(arr.type						,				pa.intaa()   )




							def 						__snake_case(    self :       str   )       ->					Optional[int]:



												'''simple docstring'''

												with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
																	SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence(["foo", "bar"]						,				type=Value("int64"   )   )   )




							def 						__snake_case(    self :       List[str]   )       ->					Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([1, 2, 3]						,				try_type=Value("int32"   )   )   )
												self.assertEqual(arr.type						,				pa.intaa()   )




							def 						__snake_case(    self :       List[str]   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence(["foo", "bar"]						,				try_type=Value("int64"   )   )   )
												self.assertEqual(arr.type						,				pa.string()   )




							def 						__snake_case(    self :       Optional[Any]   )       ->					Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([[[1, 2, 3]]]						,				type=ArrayaD((1, 3)						,				"int64"   )   )   )
												self.assertEqual(arr.type						,				ArrayaDExtensionType((1, 3)						,				"int64"   )   )




							def 						__snake_case(    self :       int   )       ->					Dict:



												'''simple docstring'''

												with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
																	SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence(["foo", "bar"]						,				type=ArrayaD((1, 3)						,				"int64"   )   )   )




							def 						__snake_case(    self :       int   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([[[1, 2, 3]]]						,				try_type=ArrayaD((1, 3)						,				"int64"   )   )   )
												self.assertEqual(arr.type						,				ArrayaDExtensionType((1, 3)						,				"int64"   )   )




							def 						__snake_case(    self :       Union[str, Any]   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence(["foo", "bar"]						,				try_type=ArrayaD((1, 3)						,				"int64"   )   )   )
												self.assertEqual(arr.type						,				pa.string()   )





							@require_pil
							def 						__snake_case(    self :       Optional[Any]   )       ->					Dict:



												'''simple docstring'''

												import PIL.Image

												SCREAMING_SNAKE_CASE           =							PIL.Image.fromarray(np.arange(10						,				dtype=np.uinta   ).reshape(2						,				5   )   )
												with patch(
												    "datasets.arrow_writer.cast_to_python_objects"						,				side_effect=__snake_case   ) as mock_cast_to_python_objects:
																	SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]						,				type=Image()   )   )
																	SCREAMING_SNAKE_CASE           =							mock_cast_to_python_objects.call_args_list[-1]
																	self.assertIn("optimize_list_casting"						,				__snake_case   )
																	self.assertFalse(kwargs["optimize_list_casting"]   )





def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				Union[str, Any]	):
					SCREAMING_SNAKE_CASE           =							pa.BufferReader(UpperCAmelCase__	) if isinstance(UpperCAmelCase__   ,			pa.Buffer	) else pa.memory_map(UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							pa.ipc.open_stream(UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							f.read_all()
					assert len(pa_table.to_batches()	) == expected_num_chunks
					assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
					del pa_table





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 1, 1_0]	)
@pytest.mark.parametrize(
    "fields"   ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				Optional[Any]	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					SCREAMING_SNAKE_CASE           =							pa.schema(UpperCAmelCase__	) if fields else None
					with ArrowWriter(stream=UpperCAmelCase__   ,			schema=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__	) as writer:
										writer.write({"col_1": "foo", "col_2": 1}	)
										writer.write({"col_1": "bar", "col_2": 2}	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										SCREAMING_SNAKE_CASE           =							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(UpperCAmelCase__   ,			metadata=writer._schema.metadata	)
					_check_output(output.getvalue()   ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)





def 						__lowerCamelCase				():
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					SCREAMING_SNAKE_CASE           =							Features({"labels": ClassLabel(names=["neg", "pos"]	)}	)
					with ArrowWriter(stream=UpperCAmelCase__   ,			features=UpperCAmelCase__	) as writer:
										writer.write({"labels": 0}	)
										writer.write({"labels": 1}	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert writer._schema == features.arrow_schema
					assert writer._schema.metadata == features.arrow_schema.metadata
					SCREAMING_SNAKE_CASE           =							pa.BufferReader(output.getvalue()	)
					SCREAMING_SNAKE_CASE           =							pa.ipc.open_stream(UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							f.read_all()
					SCREAMING_SNAKE_CASE           =							pa_table.schema
					assert pa_table.num_rows == 2
					assert schema == features.arrow_schema
					assert schema.metadata == features.arrow_schema.metadata
					assert features == Features.from_arrow_schema(UpperCAmelCase__	)





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 1, 1_0]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					with ArrowWriter(
					    stream=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__   ,			hash_salt="split_name"   ,			check_duplicates=UpperCAmelCase__   ,			) as writer:
										with pytest.raises(UpperCAmelCase__	):
															writer.write({"col_1": "foo", "col_2": 1}   ,			key=[1, 2]	)
															SCREAMING_SNAKE_CASE           =							writer.finalize()





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 2, 1_0]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					with ArrowWriter(
					    stream=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__   ,			hash_salt="split_name"   ,			check_duplicates=UpperCAmelCase__   ,			) as writer:
										with pytest.raises(UpperCAmelCase__	):
															writer.write({"col_1": "foo", "col_2": 1}   ,			key=1_0	)
															writer.write({"col_1": "bar", "col_2": 2}   ,			key=1_0	)
															SCREAMING_SNAKE_CASE           =							writer.finalize()





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 2, 1_0]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					with ArrowWriter(
					    stream=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__   ,			hash_salt="split_name"   ,			check_duplicates=UpperCAmelCase__   ,			) as writer:
										writer.write({"col_1": "foo", "col_2": 1}   ,			key=1	)
										writer.write({"col_1": "bar", "col_2": 2}   ,			key=2	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					_check_output(output.getvalue()   ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 1, 1_0]	)
@pytest.mark.parametrize(
    "fields"   ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				str	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					SCREAMING_SNAKE_CASE           =							pa.schema(UpperCAmelCase__	) if fields else None
					with ArrowWriter(stream=UpperCAmelCase__   ,			schema=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__	) as writer:
										writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
										writer.write_batch({"col_1": [], "col_2": []}	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										SCREAMING_SNAKE_CASE           =							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(UpperCAmelCase__   ,			metadata=writer._schema.metadata	)
					_check_output(output.getvalue()   ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 1, 1_0]	)
@pytest.mark.parametrize(
    "fields"   ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Dict	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					SCREAMING_SNAKE_CASE           =							pa.schema(UpperCAmelCase__	) if fields else None
					with ArrowWriter(stream=UpperCAmelCase__   ,			schema=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__	) as writer:
										writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										SCREAMING_SNAKE_CASE           =							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(UpperCAmelCase__   ,			metadata=writer._schema.metadata	)
					_check_output(output.getvalue()   ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)





@pytest.mark.parametrize("writer_batch_size"   ,			[None, 1, 1_0]	)
@pytest.mark.parametrize(
    "fields"   ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					SCREAMING_SNAKE_CASE           =							pa.schema(UpperCAmelCase__	) if fields else None
					with ArrowWriter(stream=UpperCAmelCase__   ,			schema=UpperCAmelCase__   ,			writer_batch_size=UpperCAmelCase__	) as writer:
										writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}	)	)
										writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}	)	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										SCREAMING_SNAKE_CASE           =							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
					assert writer._schema == pa.schema(UpperCAmelCase__   ,			metadata=writer._schema.metadata	)
					_check_output(output.getvalue()   ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)





def 						__lowerCamelCase				():
					with tempfile.TemporaryDirectory() as tmp_dir:
										SCREAMING_SNAKE_CASE           =							{"""col_1""": pa.string(), """col_2""": pa.intaa()}
										SCREAMING_SNAKE_CASE           =							os.path.join(UpperCAmelCase__   ,			"test.arrow"	)
										with ArrowWriter(path=UpperCAmelCase__   ,			schema=pa.schema(UpperCAmelCase__	)	) as writer:
															writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
															SCREAMING_SNAKE_CASE           =							writer.finalize()
										assert num_examples == 2
										assert num_bytes > 0
										assert writer._schema == pa.schema(UpperCAmelCase__   ,			metadata=writer._schema.metadata	)
										_check_output(UpperCAmelCase__   ,			1	)





def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]	):
					if pa.types.is_list(UpperCAmelCase__	):
										return get_base_dtype(arr_type.value_type	)
					else:
										return arr_type





def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[Any]   ,			UpperCAmelCase__  :				Dict	):
					if isinstance(lst[0]   ,			UpperCAmelCase__	):
										change_first_primitive_element_in_list(lst[0]   ,			UpperCAmelCase__	)
					else:
										SCREAMING_SNAKE_CASE           =							value





@pytest.mark.parametrize("optimized_int_type, expected_dtype"   ,			[(None, pa.intaa()), (Value("int32"	), pa.intaa())]	)
@pytest.mark.parametrize("sequence"   ,			[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				Optional[Any]   ,			UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							pa.array(TypedSequence(UpperCAmelCase__   ,			optimized_int_type=UpperCAmelCase__	)	)
					assert get_base_dtype(arr.type	) == expected_dtype





@pytest.mark.parametrize(
    "col, expected_dtype"   ,			[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]   ,			)
@pytest.mark.parametrize("sequence"   ,			[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				str	):
					# in range
					SCREAMING_SNAKE_CASE           =							pa.array(OptimizedTypedSequence(UpperCAmelCase__   ,			col=UpperCAmelCase__	)	)
					assert get_base_dtype(arr.type	) == expected_dtype

					# not in range
					if col != "other":
										# avoids errors due to in-place modifications
										SCREAMING_SNAKE_CASE           =							copy.deepcopy(UpperCAmelCase__	)
										SCREAMING_SNAKE_CASE           =							np.iinfo(expected_dtype.to_pandas_dtype()	).max + 1
										change_first_primitive_element_in_list(UpperCAmelCase__   ,			UpperCAmelCase__	)
										SCREAMING_SNAKE_CASE           =							pa.array(OptimizedTypedSequence(UpperCAmelCase__   ,			col=UpperCAmelCase__	)	)
										assert get_base_dtype(arr.type	) == pa.intaa()





@pytest.mark.parametrize("raise_exception"   ,			[False, True]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path / "dataset-train.arrow"	)
					try:
										with ArrowWriter(path=UpperCAmelCase__	) as writer:
															if raise_exception:
																				raise pa.lib.ArrowInvalid()
															else:
																				writer.stream.close()
					except pa.lib.ArrowInvalid:
										pass
					finally:
										assert writer.stream.closed





def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple	):
					SCREAMING_SNAKE_CASE           =							"""mock://dataset-train.arrow"""
					with ArrowWriter(path=UpperCAmelCase__   ,			storage_options=mockfs.storage_options	) as writer:
										assert isinstance(writer._fs   ,			type(UpperCAmelCase__	)	)
										assert writer._fs.storage_options == mockfs.storage_options
										writer.write({"col_1": "foo", "col_2": 1}	)
										writer.write({"col_1": "bar", "col_2": 2}	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert mockfs.exists(UpperCAmelCase__	)





def 						__lowerCamelCase				():
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					with ParquetWriter(stream=UpperCAmelCase__	) as writer:
										writer.write({"col_1": "foo", "col_2": 1}	)
										writer.write({"col_1": "bar", "col_2": 2}	)
										SCREAMING_SNAKE_CASE           =							writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					SCREAMING_SNAKE_CASE           =							pa.BufferReader(output.getvalue()	)
					SCREAMING_SNAKE_CASE           =							pq.read_table(UpperCAmelCase__	)
					assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}





@require_pil
@pytest.mark.parametrize("embed_local_files"   ,			[False, True]	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				Dict	):
					import PIL.Image

					SCREAMING_SNAKE_CASE           =							str(tmp_path / "test_image_rgb.jpg"	)
					PIL.Image.fromarray(np.zeros((5, 5)   ,			dtype=np.uinta	)	).save(UpperCAmelCase__   ,			format="png"	)
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					with ParquetWriter(
					    stream=UpperCAmelCase__   ,			features=Features({"image": Image()}	)   ,			embed_local_files=UpperCAmelCase__	) as writer:
										writer.write({"image": image_path}	)
										writer.finalize()
					SCREAMING_SNAKE_CASE           =							pa.BufferReader(output.getvalue()	)
					SCREAMING_SNAKE_CASE           =							pq.read_table(UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							pa_table.to_pydict()
					if embed_local_files:
										assert isinstance(out["image"][0]["path"]   ,			UpperCAmelCase__	)
										with open(UpperCAmelCase__   ,			"rb"	) as f:
															assert out["image"][0]["bytes"] == f.read()
					else:
										assert out["image"][0]["path"] == image_path
										assert out["image"][0]["bytes"] is None





def 						__lowerCamelCase				():
					SCREAMING_SNAKE_CASE           =							pa.schema([pa.field("col_1"   ,			pa.string()   ,			nullable=UpperCAmelCase__	)]	)
					SCREAMING_SNAKE_CASE           =							pa.BufferOutputStream()
					with ArrowWriter(stream=UpperCAmelCase__	) as writer:
										writer._build_writer(inferred_schema=UpperCAmelCase__	)
										assert writer._schema == pa.schema([pa.field("col_1"   ,			pa.string()	)]	)










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




