



"""simple docstring"""
import argparse
import os
import re


__a      =				'src/diffusers'

# Pattern that looks at the indentation in a line.
__a      =				re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a      =				re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__a      =				re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__a      =				re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__a      =				re.compile(r"\[([^\]]+)\]")
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			_re_indent.search(lowercase_	)
						return "" if search is None else search.groups()[0]
def    A_  (			_lowercase,					_lowercase="",					_lowercase=None,					_lowercase=None	):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			0
						snake_case_ :str						=			code.split("""\n"""	)
						if start_prompt is not None:
												while not lines[index].startswith(lowercase_	):
																		index += 1
												snake_case_ :Tuple						=			["""\n""".join(lines[:index]	)]
						else:
												snake_case_ :Optional[Any]						=			[]

						# We split into blocks until we get to the `end_prompt` (or the end of the block).
						snake_case_ :Union[str, Any]						=			[lines[index]]
						index += 1
						while index < len(lowercase_	) and (end_prompt is None or not lines[index].startswith(lowercase_	)):
												if len(lines[index]	) > 0 and get_indent(lines[index]	) == indent_level:
																		if len(lowercase_	) > 0 and get_indent(current_block[-1]	).startswith(indent_level + """ """	):
																								current_block.append(lines[index]	)
																								blocks.append("""\n""".join(lowercase_	)	)
																								if index < len(lowercase_	) - 1:
																														snake_case_ :Union[str, Any]						=			[lines[index + 1]]
																														index += 1
																								else:
																														snake_case_ :List[Any]						=			[]
																		else:
																								blocks.append("""\n""".join(lowercase_	)	)
																								snake_case_ :int						=			[lines[index]]
												else:
																		current_block.append(lines[index]	)
												index += 1

						# Adds current block if it's nonempty.
						if len(lowercase_	) > 0:
												blocks.append("""\n""".join(lowercase_	)	)

						# Add final block after end_prompt if provided.
						if end_prompt is not None and index < len(lowercase_	):
												blocks.append("""\n""".join(lines[index:]	)	)

						return blocks
def    A_  (			_lowercase	):







						'''simple docstring'''


						def _inner(_lowercase	):
												return key(lowercase_	).lower().replace("""_""",					""""""	)

						return _inner
def    A_  (			_lowercase,					_lowercase=None	):







						'''simple docstring'''


						def noop(_lowercase	):
												return x

						if key is None:
												snake_case_ :Optional[Any]						=			noop
						# Constants are all uppercase, they go first.
						snake_case_ :Optional[int]						=			[obj for obj in objects if key(lowercase_	).isupper()]
						# Classes are not all uppercase but start with a capital, they go second.
						snake_case_ :List[Any]						=			[obj for obj in objects if key(lowercase_	)[0].isupper() and not key(lowercase_	).isupper()]
						# Functions begin with a lowercase, they go last.
						snake_case_ :Tuple						=			[obj for obj in objects if not key(lowercase_	)[0].isupper()]

						snake_case_ :Any						=			ignore_underscore(lowercase_	)
						return sorted(lowercase_,					key=lowercase_	) + sorted(lowercase_,					key=lowercase_	) + sorted(lowercase_,					key=lowercase_	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						def _replace(_lowercase	):
												snake_case_ :Tuple						=			match.groups()[0]
												if "," not in imports:
																		return f"""[{imports}]"""
												snake_case_ :Optional[int]						=			[part.strip().replace("""\"""",					""""""	) for part in imports.split(""","""	)]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]	) == 0:
																		snake_case_ :Any						=			keys[:-1]
												return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(lowercase_	)]	) + "]"

						snake_case_ :Dict						=			import_statement.split("""\n"""	)
						if len(lowercase_	) > 3:
												# Here we have to sort internal imports that are on several lines (one per name):
												# key: [
												#     "object1",
												#     "object2",
												#     ...
												# ]

												# We may have to ignore one or two lines on each side.
												snake_case_ :List[str]						=			2 if lines[1].strip() == """[""" else 1
												snake_case_ :Any						=			[(i, _re_strip_line.search(lowercase_	).groups()[0]) for i, line in enumerate(lines[idx:-idx]	)]
												snake_case_ :Any						=			sort_objects(lowercase_,					key=lambda _lowercase	: x[1]	)
												snake_case_ :int						=			[lines[x[0] + idx] for x in sorted_indices]
												return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]	)
						elif len(lowercase_	) == 3:
												# Here we have to sort internal imports that are on one separate line:
												# key: [
												#     "object1", "object2", ...
												# ]
												if _re_bracket_content.search(lines[1]	) is not None:
																		snake_case_ :Union[str, Any]						=			_re_bracket_content.sub(_replace,					lines[1]	)
												else:
																		snake_case_ :Any						=			[part.strip().replace("""\"""",					""""""	) for part in lines[1].split(""","""	)]
																		# We will have a final empty element if the line finished with a comma.
																		if len(keys[-1]	) == 0:
																								snake_case_ :Tuple						=			keys[:-1]
																		snake_case_ :List[Any]						=			get_indent(lines[1]	) + """, """.join([f"""\"{k}\"""" for k in sort_objects(lowercase_	)]	)
												return "\n".join(lowercase_	)
						else:
												# Finally we have to deal with imports fitting on one line
												snake_case_ :int						=			_re_bracket_content.sub(_replace,					lowercase_	)
												return import_statement
def    A_  (			_lowercase,					_lowercase=True	):







						'''simple docstring'''


						with open(lowercase_,					"""r"""	) as f:
												snake_case_ :Optional[int]						=			f.read()

						if "_import_structure" not in code:
												return

						# Blocks of indent level 0
						snake_case_ :Tuple						=			split_code_in_indented_blocks(
						    lowercase_,					start_prompt="""_import_structure = {""",					end_prompt="""if TYPE_CHECKING:"""	)

						# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
						for block_idx in range(1,					len(lowercase_	) - 1	):
												# Check if the block contains some `_import_structure`s thingy to sort.
												snake_case_ :int						=			main_blocks[block_idx]
												snake_case_ :Optional[Any]						=			block.split("""\n"""	)

												# Get to the start of the imports.
												snake_case_ :Any						=			0
												while line_idx < len(lowercase_	) and "_import_structure" not in block_lines[line_idx]:
																		# Skip dummy import blocks
																		if "import dummy" in block_lines[line_idx]:
																								snake_case_ :Optional[Any]						=			len(lowercase_	)
																		else:
																								line_idx += 1
												if line_idx >= len(lowercase_	):
																		continue

												# Ignore beginning and last line: they don't contain anything.
												snake_case_ :Union[str, Any]						=			"""\n""".join(block_lines[line_idx:-1]	)
												snake_case_ :List[Any]						=			get_indent(block_lines[1]	)
												# Slit the internal block into blocks of indent level 1.
												snake_case_ :Union[str, Any]						=			split_code_in_indented_blocks(lowercase_,					indent_level=lowercase_	)
												# We have two categories of import key: list or _import_structure[key].append/extend
												snake_case_ :Optional[Any]						=			_re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
												# Grab the keys, but there is a trap: some lines are empty or just comments.
												snake_case_ :int						=			[(pattern.search(lowercase_	).groups()[0] if pattern.search(lowercase_	) is not None else None) for b in internal_blocks]
												# We only sort the lines with a key.
												snake_case_ :int						=			[(i, key) for i, key in enumerate(lowercase_	) if key is not None]
												snake_case_ :List[Any]						=			[x[0] for x in sorted(lowercase_,					key=lambda _lowercase	: x[1]	)]

												# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
												snake_case_ :Optional[int]						=			0
												snake_case_ :Any						=			[]
												for i in range(len(lowercase_	)	):
																		if keys[i] is None:
																								reordered_blocks.append(internal_blocks[i]	)
																		else:
																								snake_case_ :Any						=			sort_objects_in_import(internal_blocks[sorted_indices[count]]	)
																								reordered_blocks.append(lowercase_	)
																								count += 1

        # And we put our main block back together with its first and last line.
												snake_case_ :Tuple						=			"""\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]	)

						if code != "\n".join(lowercase_	):
												if check_only:
																		return True
												else:
																		print(f"""Overwriting {file}."""	)
																		with open(lowercase_,					"""w"""	) as f:
																								f.write("""\n""".join(lowercase_	)	)
def    A_  (			_lowercase=True	):







						'''simple docstring'''


						snake_case_ :Dict						=			[]
						for root, _, files in os.walk(lowercase_	):
												if "__init__.py" in files:
																		snake_case_ :List[Any]						=			sort_imports(os.path.join(lowercase_,					"""__init__.py"""	),					check_only=lowercase_	)
																		if result:
																								snake_case_ :Optional[int]						=			[os.path.join(lowercase_,					"""__init__.py"""	)]
						if len(lowercase_	) > 0:
												raise ValueError(f"""Would overwrite {len(lowercase_	)} files, run `make style`."""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
						__a      =				parser.parse_args()

						sort_imports_in_all_inits(check_only=args.check_only)



import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


A_       : Any         =			pytest.mark.integration



@require_faiss
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Tuple        =		Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(A__			) for x in np.arange(30			).tolist()]}			)
						return dset






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						A__							:    Union[str, Any]        =		dset.map(
						    lambda A__    ,       A__			: {"vecs": i * np.ones(5    ,       dtype=np.floataa			)}    ,       with_indices=A__    ,       keep_in_memory=A__			)
						A__							:    int        =		dset.add_faiss_index("""vecs"""    ,       batch_size=100    ,       metric_type=faiss.METRIC_INNER_PRODUCT			)
						A__   ,   A__							:    Optional[Any]        =		dset.get_nearest_examples("""vecs"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)
						dset.drop_index("""vecs"""			)






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""    ,       batch_size=100    ,       metric_type=faiss.METRIC_INNER_PRODUCT    ,       )
						A__   ,   A__							:    str        =		dset.get_nearest_examples("""vecs"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""    ,       metric_type=faiss.METRIC_INNER_PRODUCT    ,       )

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__			) as tmp_file:
							dset.save_faiss_index("""vecs"""    ,       tmp_file.name			)
							dset.load_faiss_index("""vecs2"""    ,       tmp_file.name			)
						os.unlink(tmp_file.name			)

						A__   ,   A__							:    Tuple        =		dset.get_nearest_examples("""vecs2"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)






					def   __A			( self			):
						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""			)
						dset.drop_index("""vecs"""			)
						self.assertRaises(A__    ,       partial(dset.get_nearest_examples    ,       """vecs2"""    ,       np.ones(5    ,       dtype=np.floataa			)			)			)






					def   __A			( self			):
						from elasticsearch import Elasticsearch

						A__							:    Dataset        =		self._create_dummy_dataset()
						with patch("""elasticsearch.Elasticsearch.search"""			) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""			) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""			) as mocked_bulk:
							A__							:    List[Any]        =		{"""acknowledged""": True}
							mocked_bulk.return_value([(True, None)] * 30			)
							A__							:    List[Any]        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
							A__							:    Any        =		Elasticsearch()

							dset.add_elasticsearch_index("""filename"""    ,       es_client=A__			)
							A__   ,   A__							:    Any        =		dset.get_nearest_examples("""filename"""    ,       """my_name-train_29"""			)
							self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)



@require_faiss
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						import faiss

						A__							:    Optional[int]        =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT			)

						# add vectors
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsNotNone(index.faiss_index			)
						self.assertEqual(index.faiss_index.ntotal    ,       5			)
						index.add_vectors(np.zeros((5, 5)    ,       dtype=np.floataa			)			)
						self.assertEqual(index.faiss_index.ntotal    ,       10			)

						# single query
						A__							:    Any        =		np.zeros(5    ,       dtype=np.floataa			)
						A__							:    str        =		1
						A__   ,   A__							:    Optional[Any]        =		index.search(A__			)
						self.assertRaises(A__    ,       index.search    ,       query.reshape(-1    ,       1			)			)
						self.assertGreater(scores[0]    ,       0			)
						self.assertEqual(indices[0]    ,       1			)

						# batched queries
						A__							:    Optional[int]        =		np.eye(5    ,       dtype=np.floataa			)[::-1]
						A__   ,   A__							:    str        =		index.search_batch(A__			)
						self.assertRaises(A__    ,       index.search_batch    ,       queries[0]			)
						A__							:    str        =		[scores[0] for scores in total_scores]
						A__							:    Tuple        =		[indices[0] for indices in total_indices]
						self.assertGreater(np.min(A__			)    ,       0			)
						self.assertListEqual([4, 3, 2, 1, 0]    ,       A__			)






					def   __A			( self			):
						import faiss

						A__							:    Dict        =		FaissIndex(string_factory="""Flat"""			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexFlat			)
						A__							:    Dict        =		FaissIndex(string_factory="""LSH"""			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexLSH			)
						with self.assertRaises(A__			):
							A__							:    List[Any]        =		FaissIndex(string_factory="""Flat"""    ,       custom_index=faiss.IndexFlat(5			)			)






					def   __A			( self			):
						import faiss

						A__							:    List[Any]        =		faiss.IndexFlat(5			)
						A__							:    Union[str, Any]        =		FaissIndex(custom_index=A__			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexFlat			)






					def   __A			( self			):
						import faiss

						A__							:    Any        =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__			) as tmp_file:
							index.save(tmp_file.name			)
							A__							:    int        =		FaissIndex.load(tmp_file.name			)
						os.unlink(tmp_file.name			)

						A__							:    Optional[Any]        =		np.zeros(5    ,       dtype=np.floataa			)
						A__							:    Optional[int]        =		1
						A__   ,   A__							:    List[Any]        =		index.search(A__			)
						self.assertGreater(scores[0]    ,       0			)
						self.assertEqual(indices[0]    ,       1			)



@require_faiss
def 						UpperCamelCase					(lowercase_: Dict					)     ->      Optional[Any]:
	import faiss

	A__							:    Dict        =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT					)
	index.add_vectors(np.eye(5    ,      dtype=np.floataa					)					)

	A__							:    Optional[Any]        =		"""index.faiss"""
	A__							:    Any        =		f"""mock://{index_name}"""
	index.save(lowercase_    ,      storage_options=mockfs.storage_options					)
	A__							:    str        =		FaissIndex.load(lowercase_    ,      storage_options=mockfs.storage_options					)

	A__							:    int        =		np.zeros(5    ,      dtype=np.floataa					)
	A__							:    Union[str, Any]        =		1
	A__   ,   A__							:    Any        =		index.search(lowercase_					)
	assert scores[0] > 0
	assert indices[0] == 1



@require_elasticsearch
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						from elasticsearch import Elasticsearch

						with patch("""elasticsearch.Elasticsearch.search"""			) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""			) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""			) as mocked_bulk:
							A__							:    List[str]        =		Elasticsearch()
							A__							:    List[Any]        =		{"""acknowledged""": True}
							A__							:    int        =		ElasticSearchIndex(es_client=A__			)
							mocked_bulk.return_value([(True, None)] * 3			)
							index.add_documents(["""foo""", """bar""", """foobar"""]			)

							# single query
							A__							:    Dict        =		"""foo"""
							A__							:    Tuple        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
							A__   ,   A__							:    Tuple        =		index.search(A__			)
							self.assertEqual(scores[0]    ,       1			)
							self.assertEqual(indices[0]    ,       0			)

							# single query with timeout
							A__							:    List[str]        =		"""foo"""
							A__							:    str        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
							A__   ,   A__							:    Dict        =		index.search(A__    ,       request_timeout=30			)
							self.assertEqual(scores[0]    ,       1			)
							self.assertEqual(indices[0]    ,       0			)

							# batched queries
							A__							:    Union[str, Any]        =		["""foo""", """bar""", """foobar"""]
							A__							:    Tuple        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
							A__   ,   A__							:    Dict        =		index.search_batch(A__			)
							A__							:    Tuple        =		[scores[0] for scores in total_scores]
							A__							:    Optional[int]        =		[indices[0] for indices in total_indices]
							self.assertGreater(np.min(A__			)    ,       0			)
							self.assertListEqual([1, 1, 1]    ,       A__			)

							# batched queries with timeout
							A__							:    int        =		["""foo""", """bar""", """foobar"""]
							A__							:    List[Any]        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
							A__   ,   A__							:    Tuple        =		index.search_batch(A__    ,       request_timeout=30			)
							A__							:    List[Any]        =		[scores[0] for scores in total_scores]
							A__							:    Optional[int]        =		[indices[0] for indices in total_indices]
							self.assertGreater(np.min(A__			)    ,       0			)
							self.assertListEqual([1, 1, 1]    ,       A__			)



