

from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=13    ,       A__=30    ,       A__=2    ,       A__=3    ,       A__=True    ,       A__=True    ,       A__=32    ,       A__=2    ,       A__=4    ,       A__=37    ,       A__="gelu"    ,       A__=0.1    ,       A__=0.1    ,       A__=10    ,       A__=0.0_2    ,       A__=3    ,       A__=None    ,       ):
						A__							:    List[str]        =		parent
						A__							:    int        =		batch_size
						A__							:    Union[str, Any]        =		image_size
						A__							:    Dict        =		patch_size
						A__							:    List[Any]        =		num_channels
						A__							:    List[Any]        =		is_training
						A__							:    List[str]        =		use_labels
						A__							:    Optional[Any]        =		hidden_size
						A__							:    Union[str, Any]        =		num_hidden_layers
						A__							:    int        =		num_attention_heads
						A__							:    str        =		intermediate_size
						A__							:    int        =		hidden_act
						A__							:    Optional[int]        =		hidden_dropout_prob
						A__							:    List[Any]        =		attention_probs_dropout_prob
						A__							:    Any        =		type_sequence_label_size
						A__							:    Optional[int]        =		initializer_range
						A__							:    int        =		scope

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						A__							:    Optional[Any]        =		(image_size // patch_size) ** 2
						A__							:    Any        =		num_patches + 1






					def   __A			( self			):
						A__							:    str        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

						A__							:    Tuple        =		None
						if self.use_labels:
							A__							:    int        =		ids_tensor([self.batch_size]    ,       self.type_sequence_label_size			)

						A__							:    Tuple        =		self.get_config()

						return config, pixel_values, labels






					def   __A			( self			):
						return ViTConfig(
						    image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       intermediate_size=self.intermediate_size    ,       hidden_act=self.hidden_act    ,       hidden_dropout_prob=self.hidden_dropout_prob    ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,       is_decoder=A__    ,       initializer_range=self.initializer_range    ,       )






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						A__							:    Optional[int]        =		TFViTModel(config=A__			)
						A__							:    List[str]        =		model(A__    ,       training=A__			)
						self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.seq_length, self.hidden_size)			)

						# Test with an image with different size than the one specified in config.
						A__							:    Any        =		self.image_size // 2
						A__							:    List[str]        =		pixel_values[:, :, :image_size, :image_size]
						A__							:    List[str]        =		model(A__    ,       interpolate_pos_encoding=A__    ,       training=A__			)
						A__							:    List[Any]        =		(image_size // self.patch_size) ** 2 + 1
						self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, seq_length, self.hidden_size)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.type_sequence_label_size
						A__							:    Any        =		TFViTForImageClassification(A__			)
						A__							:    int        =		model(A__    ,       labels=A__    ,       training=A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.type_sequence_label_size)			)

						# Test with an image with different size than the one specified in config.
						A__							:    List[str]        =		self.image_size // 2
						A__							:    str        =		pixel_values[:, :, :image_size, :image_size]
						A__							:    List[Any]        =		model(A__    ,       interpolate_pos_encoding=A__    ,       training=A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.type_sequence_label_size)			)

						# test greyscale images
						A__							:    Optional[Any]        =		1
						A__							:    Union[str, Any]        =		TFViTForImageClassification(A__			)
						A__							:    Dict        =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
						A__							:    Any        =		model(A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.type_sequence_label_size)			)






					def   __A			( self			):
						A__							:    Tuple        =		self.prepare_config_and_inputs()
						A__   ,   A__   ,   A__							:    List[Any]        =		config_and_inputs
						A__							:    List[Any]        =		{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_tf
class     _a						(__magic_name__		,   __magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   List[str]  							=       (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
					UpperCAmelCase__:   List[str]  							=       (
					    {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
					    if is_tf_available()
					    else {}
					)

					UpperCAmelCase__:   List[Any]  							=       False
					UpperCAmelCase__:   List[Any]  							=       False
					UpperCAmelCase__:   Union[str, Any]  							=       False






					def   __A			( self			):
						A__							:    List[str]        =		TFViTModelTester(self			)
						A__							:    Tuple        =		ConfigTester(self    ,       config_class=A__    ,       has_text_modality=A__    ,       hidden_size=37			)






					def   __A			( self			):
						self.config_tester.run_common_tests()






					@unittest.skip(reason="""ViT does not use inputs_embeds"""			)
					def   __A			( self			):
						pass






					@unittest.skip(reason="""ViT does not use inputs_embeds"""			)
					def   __A			( self			):
						pass






					def   __A			( self			):
						A__   ,   A__							:    Union[str, Any]        =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:    Optional[int]        =		model_class(A__			)
							self.assertIsInstance(model.get_input_embeddings()    ,       (tf.keras.layers.Layer)			)
							A__							:    Tuple        =		model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(A__    ,       tf.keras.layers.Layer			)			)






					def   __A			( self			):
						A__   ,   A__							:    Optional[int]        =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:    Dict        =		model_class(A__			)
							A__							:    Optional[Any]        =		inspect.signature(model.call			)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A__							:    Any        =		[*signature.parameters.keys()]

							A__							:    Tuple        =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]    ,       A__			)






					def   __A			( self			):
						A__							:    Any        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__			)






					def   __A			( self			):
						A__							:    Any        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__			)






					@slow
					def   __A			( self			):
						A__							:    Optional[Any]        =		TFViTModel.from_pretrained("""google/vit-base-patch16-224"""			)
						self.assertIsNotNone(A__			)



def 						UpperCamelCase					()     ->      Optional[int]:
	A__							:    Any        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image



@require_tf
@require_vision
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@cached_property
					def   __A			( self			):
						return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""			) if is_vision_available() else None






					@slow
					def   __A			( self			):
						A__							:    Optional[Any]        =		TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""			)

						A__							:    Dict        =		self.default_image_processor
						A__							:    Optional[int]        =		prepare_img()
						A__							:    str        =		image_processor(images=A__    ,       return_tensors="""tf"""			)

						# forward pass
						A__							:    int        =		model(**A__			)

						# verify the logits
						A__							:    List[Any]        =		tf.TensorShape((1, 1000)			)
						self.assertEqual(outputs.logits.shape    ,       A__			)

						A__							:    Union[str, Any]        =		tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]			)

						tf.debugging.assert_near(outputs.logits[0, :3]    ,       A__    ,       atol=1e-4			)






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__			)



