




"""simple docstring"""




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def 					a__				(       snake_case__=None ,				snake_case__=None       )		->      int:
			return field(default_factory=lambda: default ,				metadata=snake_case__       )






@dataclass
class 					__magic_name__							:






				'''simple docstring'''
				__UpperCamelCase    						=		field(
				    metadata={"help": "The csv file to plot."}		,      )
				__UpperCamelCase    						=		field(
				    default=UpperCAmelCase__		,      metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}		,      )
				__UpperCamelCase    						=		field(
				    default=UpperCAmelCase__		,      metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}		,      )
				__UpperCamelCase    						=		field(
				    default=UpperCAmelCase__		,      metadata={"help": "Disable logarithmic scale when plotting"}		,      )
				__UpperCamelCase    						=		field(
				    default=UpperCAmelCase__		,      metadata={
				        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
				    }		,      )
				__UpperCamelCase    						=		field(
				    default=UpperCAmelCase__		,      metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}		,      )
				__UpperCamelCase    						=		list_field(
				    default=UpperCAmelCase__		,      metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )




def 					a__				(       snake_case__       )		->      Dict:
			try:
						int(snake_case__       )
						return True
			except ValueError:
						return False



def 					a__				(       snake_case__       )		->      Any:
			try:
						float(snake_case__       )
						return True
			except ValueError:
						return False






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      args
							lowerCamelCase       =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

							with open(self.args.csv_file	,				newline=""""""				) as csv_file:
										lowerCamelCase       =      csv.DictReader(_a				)
										for row in reader:
													lowerCamelCase       =      row["""model"""]
													self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]				)				)
													self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]				)				)
													if can_convert_to_int(row["""result"""]				):
																# value is not None
																lowerCamelCase       =      int(row["""result"""]				)
													elif can_convert_to_float(row["""result"""]				):
																# value is not None
																lowerCamelCase       =      float(row["""result"""]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      plt.subplots()
							lowerCamelCase       =      """Time usage""" if self.args.is_time else """Memory usage"""
							lowerCamelCase       =      title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

							if not self.args.no_log_scale:
										# set logarithm scales
										ax.set_xscale("""log"""				)
										ax.set_yscale("""log"""				)

							for axis in [ax.xaxis, ax.yaxis]:
										axis.set_major_formatter(ScalarFormatter()				)

							for model_name_idx, model_name in enumerate(self.result_dict.keys()				):
										lowerCamelCase       =      sorted(set(self.result_dict[model_name]["""bsz"""]				)				)
										lowerCamelCase       =      sorted(set(self.result_dict[model_name]["""seq_len"""]				)				)
										lowerCamelCase       =      self.result_dict[model_name]["""result"""]

										((lowerCamelCase)   ,     (lowerCamelCase))       =      (
										    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
										)

										lowerCamelCase       =      (
										    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
										)

										for inner_loop_value in inner_loop_array:
													if self.args.plot_along_batch:
																lowerCamelCase       =      np.asarray(
																    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,				dtype=_a	,				)
													else:
																lowerCamelCase       =      np.asarray(
																    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,				dtype=np.floataa	,				)

													((lowerCamelCase)   ,     (lowerCamelCase))       =      (
													    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
													)

													lowerCamelCase       =      np.asarray(_a	,				_a				)[: len(_a				)]
													plt.scatter(
													    _a	,				_a	,				label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'				)
													plt.plot(_a	,				_a	,				"""--"""				)

										title_str += f' {label_model_name} vs.'

							lowerCamelCase       =      title_str[:-4]
							lowerCamelCase       =      """Time in s""" if self.args.is_time else """Memory in MB"""

							# plot
							plt.title(_a				)
							plt.xlabel(_a				)
							plt.ylabel(_a				)
							plt.legend()

							if self.args.figure_png_file is not None:
										plt.savefig(self.args.figure_png_file				)
							else:
										plt.show()




def 					a__				(       )		->      Optional[int]:
			lowerCamelCase       =      HfArgumentParser(snake_case__       )
			lowerCamelCase       =      parser.parse_args_into_dataclasses()[0]
			lowerCamelCase       =      Plot(args=snake_case__       )
			plot.plot()


if __name__ == "__main__":
					main()









"""simple docstring"""




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


lowerCAmelCase							:    List[Any]  							=							pytest.mark.integration






@require_faiss
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(_a				) for x in np.arange(30				).tolist()]}				)
							return dset





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      self._create_dummy_dataset()
							lowerCamelCase       =      dset.map(
							    lambda _a	,				_a				: {"vecs": i * np.ones(5	,				dtype=np.floataa				)}	,				with_indices=_a	,				keep_in_memory=_a				)
							lowerCamelCase       =      dset.add_faiss_index("""vecs"""	,				batch_size=100	,				metric_type=faiss.METRIC_INNER_PRODUCT				)
							lowerCamelCase   ,     lowerCamelCase       =      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      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	,				)
							lowerCamelCase   ,     lowerCamelCase       =      dset.get_nearest_examples("""vecs"""	,				np.ones(5	,				dtype=np.floataa				)				)
							self.assertEqual(examples["""filename"""][0]	,				"""my_name-train_29"""				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      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				)

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							from elasticsearch import Elasticsearch

							lowerCamelCase       =      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:
										lowerCamelCase       =      {"""acknowledged""": True}
										mocked_bulk.return_value([(True, None)] * 30				)
										lowerCamelCase       =      {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
										lowerCamelCase       =      Elasticsearch()

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






@require_faiss
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      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
							lowerCamelCase       =      np.zeros(5	,				dtype=np.floataa				)
							lowerCamelCase       =      1
							lowerCamelCase   ,     lowerCamelCase       =      index.search(_a				)
							self.assertRaises(_a	,				index.search	,				query.reshape(-1	,				1				)				)
							self.assertGreater(scores[0]	,				0				)
							self.assertEqual(indices[0]	,				1				)

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      FaissIndex(string_factory="""Flat"""				)
							index.add_vectors(np.eye(5	,				dtype=np.floataa				)				)
							self.assertIsInstance(index.faiss_index	,				faiss.IndexFlat				)
							lowerCamelCase       =      FaissIndex(string_factory="""LSH"""				)
							index.add_vectors(np.eye(5	,				dtype=np.floataa				)				)
							self.assertIsInstance(index.faiss_index	,				faiss.IndexLSH				)
							with self.assertRaises(_a				):
										lowerCamelCase       =      FaissIndex(string_factory="""Flat"""	,				custom_index=faiss.IndexFlat(5				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      faiss.IndexFlat(5				)
							lowerCamelCase       =      FaissIndex(custom_index=_a				)
							index.add_vectors(np.eye(5	,				dtype=np.floataa				)				)
							self.assertIsInstance(index.faiss_index	,				faiss.IndexFlat				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							import faiss

							lowerCamelCase       =      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				)
										lowerCamelCase       =      FaissIndex.load(tmp_file.name				)
							os.unlink(tmp_file.name				)

							lowerCamelCase       =      np.zeros(5	,				dtype=np.floataa				)
							lowerCamelCase       =      1
							lowerCamelCase   ,     lowerCamelCase       =      index.search(_a				)
							self.assertGreater(scores[0]	,				0				)
							self.assertEqual(indices[0]	,				1				)




@require_faiss
def 					a__				(       snake_case__       )		->      Tuple:
			import faiss

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

			lowerCamelCase       =      """index.faiss"""
			lowerCamelCase       =      F'mock://{index_name}'
			index.save(snake_case__ ,				storage_options=mockfs.storage_options       )
			lowerCamelCase       =      FaissIndex.load(snake_case__ ,				storage_options=mockfs.storage_options       )

			lowerCamelCase       =      np.zeros(5 ,				dtype=np.floataa       )
			lowerCamelCase       =      1
			lowerCamelCase   ,     lowerCamelCase       =      index.search(snake_case__       )
			assert scores[0] > 0
			assert indices[0] == 1






@require_elasticsearch
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							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:
										lowerCamelCase       =      Elasticsearch()
										lowerCamelCase       =      {"""acknowledged""": True}
										lowerCamelCase       =      ElasticSearchIndex(es_client=_a				)
										mocked_bulk.return_value([(True, None)] * 3				)
										index.add_documents(["""foo""", """bar""", """foobar"""]				)

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

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

										# batched queries
										lowerCamelCase       =      ["""foo""", """bar""", """foobar"""]
										lowerCamelCase       =      {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
										lowerCamelCase   ,     lowerCamelCase       =      index.search_batch(_a				)
										lowerCamelCase       =      [scores[0] for scores in total_scores]
										lowerCamelCase       =      [indices[0] for indices in total_indices]
										self.assertGreater(np.min(_a				)	,				0				)
										self.assertListEqual([1, 1, 1]	,				_a				)

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



