
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	ShapEPipeline
	snake_case_          =	["prompt"]
	snake_case_          =	["prompt"]
	snake_case_          =	[
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	snake_case_          =	False






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return 32






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return 32






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					return self.time_input_dim * 4






	@property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return 8






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
					return tokenizer






	@property
	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)
					__A       =					CLIPTextConfig(
					    bos_token_id=0				,eos_token_id=2				,hidden_size=self.text_embedder_hidden_size				,projection_dim=self.text_embedder_hidden_size				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
					return CLIPTextModelWithProjection(A					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					torch.manual_seed(0					)

					__A       =					{
					    "num_attention_heads": 2,
					    "attention_head_dim": 16,
					    "embedding_dim": self.time_input_dim,
					    "num_embeddings": 32,
					    "embedding_proj_dim": self.text_embedder_hidden_size,
					    "time_embed_dim": self.time_embed_dim,
					    "num_layers": 1,
					    "clip_embed_dim": self.time_input_dim * 2,
					    "additional_embeddings": 0,
					    "time_embed_act_fn": "gelu",
					    "norm_in_type": "layer",
					    "encoder_hid_proj_type": None,
					    "added_emb_type": None,
					}

					__A       =					PriorTransformer(**A					)
					return model






	@property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					torch.manual_seed(0					)

					__A       =					{
					    "param_shapes": (
					        (self.renderer_dim, 93),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					    ),
					    "d_latent": self.time_input_dim,
					    "d_hidden": self.renderer_dim,
					    "n_output": 12,
					    "background": (
					        0.1,
					        0.1,
					        0.1,
					    ),
					}
					__A       =					ShapERenderer(**A					)
					return model






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					self.dummy_prior
					__A       =					self.dummy_text_encoder
					__A       =					self.dummy_tokenizer
					__A       =					self.dummy_renderer

					__A       =					HeunDiscreteScheduler(
					    beta_schedule="exp"				,num_train_timesteps=10_24				,prediction_type="sample"				,use_karras_sigmas=A				,clip_sample=A				,clip_sample_range=1.0				,)
					__A       =					{
					    "prior": prior,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					Optional[Any]				,A   :					Any=0					):
					if str(A					).startswith("mps"					):
									__A       =					torch.manual_seed(A					)
					else:
									__A       =					torch.Generator(device=A					).manual_seed(A					)
					__A       =					{
					    "prompt": "horse",
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 32,
					    "output_type": "np",
					}
					return inputs






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					"cpu"

					__A       =					self.get_dummy_components()

					__A       =					self.pipeline_class(**A					)
					__A       =					pipe.to(A					)

					pipe.set_progress_bar_config(disable=A					)

					__A       =					pipe(**self.get_dummy_inputs(A					)					)
					__A       =					output.images[0]
					__A       =					image[0, -3:, -3:, -1]

					assert image.shape == (20, 32, 32, 3)

					__A       =					np.array(
					    [
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					    ]					)

					assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					torch_device == "cpu"
					__A       =					True

					self._test_inference_batch_single_identical(
					    batch_size=2				,test_max_difference=A				,relax_max_difference=A				,)







	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					__A       =					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					1
					__A       =					2

					__A       =					self.get_dummy_inputs(A					)

					for key in inputs.keys():
									if key in self.batch_params:
													__A       =					batch_size * [inputs[key]]

					__A       =					pipe(**A				,num_images_per_prompt=A					)[0]

					assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Dict					):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_np_out.npy"					)
					__A       =					ShapEPipeline.from_pretrained("openai/shap-e"					)
					__A       =					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					torch.Generator(device=A					).manual_seed(0					)

					__A       =					pipe(
					    "a shark"				,generator=A				,guidance_scale=15.0				,num_inference_steps=64				,frame_size=64				,output_type="np"				,).images[0]

					assert images.shape == (20, 64, 64, 3)

					assert_mean_pixel_difference(A				,A					)







"""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


__UpperCamelCase     :						Optional[int]						       =  pytest.mark.integration







@require_faiss
class       a							(   a__  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_snake_case     ) for x in np.arange(30     ).tolist()]}     )
  return dset



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

  lowerCAmelCase    				= self._create_dummy_dataset()
  lowerCAmelCase    				= dset.map(
      lambda _snake_case				,       _snake_case     : {"vecs": i * np.ones(5				,       dtype=np.floataa     )}				,       with_indices=_snake_case				,       keep_in_memory=_snake_case     )
  lowerCAmelCase    				= dset.add_faiss_index('vecs'				,       batch_size=1_00				,       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    UpperCamelCase__  (     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=1_00				,       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    UpperCamelCase__  (     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=_snake_case     ) 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    UpperCamelCase__  (     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(_snake_case				,       partial(dset.get_nearest_examples				,       'vecs2'				,       np.ones(5				,       dtype=np.floataa     )     )     )



 def    UpperCamelCase__  (     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=_snake_case     )
   lowerCAmelCase				,lowerCAmelCase    				= dset.get_nearest_examples('filename'				,       'my_name-train_29'     )
   self.assertEqual(examples['filename'][0]				,       'my_name-train_29'     )







@require_faiss
class       a							(   a__  ):



 def    UpperCamelCase__  (     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(_snake_case     )
  self.assertRaises(_snake_case				,       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(_snake_case     )
  self.assertRaises(_snake_case				,       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(_snake_case     )				,       0     )
  self.assertListEqual([4, 3, 2, 1, 0]				,       _snake_case     )



 def    UpperCamelCase__  (     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(_snake_case     ):
   lowerCAmelCase    				= FaissIndex(string_factory='Flat'				,       custom_index=faiss.IndexFlat(5     )     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  import faiss

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



 def    UpperCamelCase__  (     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=_snake_case     ) 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(_snake_case     )
  self.assertGreater(scores[0]				,       0     )
  self.assertEqual(indices[0]				,       1     )


@require_faiss
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Dict			):
 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(_UpperCAmelCase  ,     storage_options=mockfs.storage_options			)
 lowerCAmelCase    				= FaissIndex.load(_UpperCAmelCase  ,     storage_options=mockfs.storage_options			)

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







@require_elasticsearch
class       a							(   a__  ):



 def    UpperCamelCase__  (     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=_snake_case     )
   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(_snake_case     )
   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(_snake_case				,       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(_snake_case     )
   lowerCAmelCase    				= [scores[0] for scores in total_scores]
   lowerCAmelCase    				= [indices[0] for indices in total_indices]
   self.assertGreater(np.min(_snake_case     )				,       0     )
   self.assertListEqual([1, 1, 1]				,       _snake_case     )

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





