




"""simple docstring"""




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
			import faiss

@require_faiss
class    UpperCAmelCase_  (       _a						):
		def      _lowerCamelCase				(		self    )		->    Optional[int]:
					__lowercase       :   List[Any]        =    tempfile.mkdtemp()
					__lowercase       :   List[str]        =    8

					# DPR tok
					__lowercase       :   Union[str, Any]        =    [
					    """[UNK]""",
					    """[CLS]""",
					    """[SEP]""",
					    """[PAD]""",
					    """[MASK]""",
					    """want""",
					    """##want""",
					    """##ed""",
					    """wa""",
					    """un""",
					    """runn""",
					    """##ing""",
					    """,""",
					    """low""",
					    """lowest""",
					]
					__lowercase       :   List[Any]        =    os.path.join(self.tmpdirname							,		'''dpr_tokenizer'''    )
					os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase    )
					__lowercase       :   List[str]        =    os.path.join(__lowerCamelCase							,		DPR_VOCAB_FILES_NAMES['''vocab_file''']    )
					with open(self.vocab_file							,		'''w'''							,		encoding='''utf-8'''    ) as vocab_writer:
								vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

					# BART tok
					__lowercase       :   List[str]        =    [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					__lowercase       :   Dict        =    dict(zip(__lowerCamelCase							,		range(len(__lowerCamelCase    )    )    )    )
					__lowercase       :   int        =    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					__lowercase       :   Union[str, Any]        =    {"""unk_token""": """<unk>"""}

					__lowercase       :   Optional[int]        =    os.path.join(self.tmpdirname							,		'''bart_tokenizer'''    )
					os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase    )
					__lowercase       :   List[str]        =    os.path.join(__lowerCamelCase							,		BART_VOCAB_FILES_NAMES['''vocab_file''']    )
					__lowercase       :   Union[str, Any]        =    os.path.join(__lowerCamelCase							,		BART_VOCAB_FILES_NAMES['''merges_file''']    )
					with open(self.vocab_file							,		'''w'''							,		encoding='''utf-8'''    ) as fp:
								fp.write(json.dumps(__lowerCamelCase    ) + '''\n'''    )
					with open(self.merges_file							,		'''w'''							,		encoding='''utf-8'''    ) as fp:
								fp.write('''\n'''.join(__lowerCamelCase    )    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		'''dpr_tokenizer'''    )    )
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		'''dpr_tokenizer'''    )    )
		def      _lowerCamelCase				(		self    )		->    int:
					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		'''bart_tokenizer'''    )    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					shutil.rmtree(self.tmpdirname    )
		def      _lowerCamelCase				(		self    )		->    Any:
					__lowercase       :   Optional[int]        =    Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size    ), 2 * np.ones(self.retrieval_vector_size    )],
					    }    )
					dataset.add_faiss_index('''embeddings'''							,		string_factory='''Flat'''							,		metric_type=faiss.METRIC_INNER_PRODUCT    )
					return dataset
		def      _lowerCamelCase				(		self    )		->    int:
					__lowercase       :   List[str]        =    self.get_dummy_dataset()
					__lowercase       :   List[str]        =    RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		)
					with patch('''transformers.models.rag.retrieval_rag.load_dataset'''    ) as mock_load_dataset:
								__lowercase       :   List[Any]        =    dataset
								__lowercase       :   Dict        =    RagRetriever(
								    __lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
					return retriever
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    List[Any]:
					__lowercase       :   Union[str, Any]        =    self.get_dummy_dataset()
					__lowercase       :   int        =    RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name='''custom'''							,		)
					if from_disk:
								__lowercase       :   Any        =    os.path.join(self.tmpdirname							,		'''dataset'''    )
								__lowercase       :   List[str]        =    os.path.join(self.tmpdirname							,		'''index.faiss'''    )
								dataset.get_index('''embeddings'''    ).save(os.path.join(self.tmpdirname							,		'''index.faiss'''    )    )
								dataset.drop_index('''embeddings'''    )
								dataset.save_to_disk(os.path.join(self.tmpdirname							,		'''dataset'''    )    )
								del dataset
								__lowercase       :   Optional[Any]        =    RagRetriever(
								    __lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
					else:
								__lowercase       :   Any        =    RagRetriever(
								    __lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		index=CustomHFIndex(config.retrieval_vector_size							,		__lowerCamelCase    )							,		)
					return retriever
		def      _lowerCamelCase				(		self    )		->    str:
					__lowercase       :   List[Any]        =    Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size + 1    ), 2 * np.ones(self.retrieval_vector_size + 1    )],
					    }    )
					dataset.add_faiss_index('''embeddings'''							,		string_factory='''Flat'''							,		metric_type=faiss.METRIC_INNER_PRODUCT    )

					__lowercase       :   Optional[Any]        =    os.path.join(self.tmpdirname							,		'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''    )
					dataset.save_faiss_index('''embeddings'''							,		index_file_name + '''.index.dpr'''    )
					pickle.dump(dataset['''id''']							,		open(index_file_name + '''.index_meta.dpr'''							,		'''wb'''    )    )

					__lowercase       :   Union[str, Any]        =    os.path.join(self.tmpdirname							,		'''psgs_w100.tsv.pkl'''    )
					__lowercase       :   Optional[int]        =    {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
					pickle.dump(__lowerCamelCase							,		open(__lowerCamelCase							,		'''wb'''    )    )

					__lowercase       :   Dict        =    RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name='''legacy'''							,		index_path=self.tmpdirname							,		)
					__lowercase       :   List[Any]        =    RagRetriever(
					    __lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()    )
					return retriever
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   Union[str, Any]        =    1
					__lowercase       :   Dict        =    self.get_dummy_canonical_hf_index_retriever()
					__lowercase       :   Union[str, Any]        =    np.array(
					    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
					__lowercase       :   List[str]        =    retriever.retrieve(__lowerCamelCase							,		n_docs=__lowerCamelCase    )
					self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)    )
					self.assertEqual(len(__lowerCamelCase    )							,		2    )
					self.assertEqual(sorted(doc_dicts[0]    )							,		['''embeddings''', '''id''', '''text''', '''title''']    )
					self.assertEqual(len(doc_dicts[0]['''id''']    )							,		__lowerCamelCase    )
					self.assertEqual(doc_dicts[0]['''id'''][0]							,		'''1'''    )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]							,		'''0'''    )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()							,		[[1], [0]]    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					__lowercase       :   List[Any]        =    self.get_dummy_canonical_hf_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
								with patch('''transformers.models.rag.retrieval_rag.load_dataset'''    ) as mock_load_dataset:
											__lowercase       :   List[str]        =    self.get_dummy_dataset()
											retriever.save_pretrained(__lowerCamelCase    )
											__lowercase       :   int        =    RagRetriever.from_pretrained(__lowerCamelCase    )
											self.assertIsInstance(__lowerCamelCase							,		__lowerCamelCase    )
											__lowercase       :   List[Any]        =    np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
											__lowercase       :   Dict        =    retriever.retrieve(__lowerCamelCase							,		n_docs=1    )
											self.assertTrue(out is not None    )
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   int        =    1
					__lowercase       :   Tuple        =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
					__lowercase       :   Union[str, Any]        =    np.array(
					    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
					__lowercase       :   Union[str, Any]        =    retriever.retrieve(__lowerCamelCase							,		n_docs=__lowerCamelCase    )
					self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)    )
					self.assertEqual(len(__lowerCamelCase    )							,		2    )
					self.assertEqual(sorted(doc_dicts[0]    )							,		['''embeddings''', '''id''', '''text''', '''title''']    )
					self.assertEqual(len(doc_dicts[0]['''id''']    )							,		__lowerCamelCase    )
					self.assertEqual(doc_dicts[0]['''id'''][0]							,		'''1'''    )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]							,		'''0'''    )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()							,		[[1], [0]]    )
		def      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   Union[str, Any]        =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
					with tempfile.TemporaryDirectory() as tmp_dirname:
								retriever.save_pretrained(__lowerCamelCase    )
								__lowercase       :   str        =    RagRetriever.from_pretrained(__lowerCamelCase    )
								self.assertIsInstance(__lowerCamelCase							,		__lowerCamelCase    )
								__lowercase       :   List[str]        =    np.array(
								    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
								__lowercase       :   Optional[int]        =    retriever.retrieve(__lowerCamelCase							,		n_docs=1    )
								self.assertTrue(out is not None    )
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					__lowercase       :   List[str]        =    1
					__lowercase       :   Dict        =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
					__lowercase       :   Union[str, Any]        =    np.array(
					    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
					__lowercase       :   str        =    retriever.retrieve(__lowerCamelCase							,		n_docs=__lowerCamelCase    )
					self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)    )
					self.assertEqual(len(__lowerCamelCase    )							,		2    )
					self.assertEqual(sorted(doc_dicts[0]    )							,		['''embeddings''', '''id''', '''text''', '''title''']    )
					self.assertEqual(len(doc_dicts[0]['''id''']    )							,		__lowerCamelCase    )
					self.assertEqual(doc_dicts[0]['''id'''][0]							,		'''1'''    )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]							,		'''0'''    )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()							,		[[1], [0]]    )
		def      _lowerCamelCase				(		self    )		->    Any:
					__lowercase       :   List[Any]        =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
					with tempfile.TemporaryDirectory() as tmp_dirname:
								retriever.save_pretrained(__lowerCamelCase    )
								__lowercase       :   Optional[int]        =    RagRetriever.from_pretrained(__lowerCamelCase    )
								self.assertIsInstance(__lowerCamelCase							,		__lowerCamelCase    )
								__lowercase       :   Tuple        =    np.array(
								    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
								__lowercase       :   Any        =    retriever.retrieve(__lowerCamelCase							,		n_docs=1    )
								self.assertTrue(out is not None    )
		def      _lowerCamelCase				(		self    )		->    str:
					__lowercase       :   Any        =    1
					__lowercase       :   List[Any]        =    self.get_dummy_legacy_index_retriever()
					__lowercase       :   Dict        =    np.array(
					    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
					__lowercase       :   Any        =    retriever.retrieve(__lowerCamelCase							,		n_docs=__lowerCamelCase    )
					self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)    )
					self.assertEqual(len(__lowerCamelCase    )							,		2    )
					self.assertEqual(sorted(doc_dicts[0]    )							,		['''text''', '''title''']    )
					self.assertEqual(len(doc_dicts[0]['''text''']    )							,		__lowerCamelCase    )
					self.assertEqual(doc_dicts[0]['''text'''][0]							,		'''bar'''    )  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''text'''][0]							,		'''foo'''    )  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()							,		[[1], [0]]    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					__lowercase       :   Optional[int]        =    self.get_dummy_legacy_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
								retriever.save_pretrained(__lowerCamelCase    )
								__lowercase       :   List[Any]        =    RagRetriever.from_pretrained(__lowerCamelCase    )
								self.assertIsInstance(__lowerCamelCase							,		__lowerCamelCase    )
								__lowercase       :   Optional[Any]        =    np.array(
								    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
								__lowercase       :   Tuple        =    retriever.retrieve(__lowerCamelCase							,		n_docs=1    )
								self.assertTrue(out is not None    )
		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def      _lowerCamelCase				(		self    )		->    List[str]:
					import torch

					__lowercase       :   List[Any]        =    1
					__lowercase       :   List[Any]        =    self.get_dummy_canonical_hf_index_retriever()
					__lowercase       :   int        =    [[5, 7], [10, 11]]
					__lowercase       :   Dict        =    np.array(
					    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
					__lowercase       :   Optional[int]        =    retriever(__lowerCamelCase							,		__lowerCamelCase							,		prefix=retriever.config.generator.prefix							,		n_docs=__lowerCamelCase    )
					__lowercase       :   Optional[int]        =    (
					    out["""context_input_ids"""],
					    out["""context_attention_mask"""],
					    out["""retrieved_doc_embeds"""],
					)
					self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)    )
					self.assertIsInstance(__lowerCamelCase							,		__lowerCamelCase    )
					self.assertIsInstance(__lowerCamelCase							,		__lowerCamelCase    )
					self.assertIsInstance(__lowerCamelCase							,		np.ndarray    )

					__lowercase       :   List[str]        =    retriever(
					    __lowerCamelCase							,		__lowerCamelCase							,		prefix=retriever.config.generator.prefix							,		n_docs=__lowerCamelCase							,		return_tensors='''pt'''							,		)
					__lowercase       :   Optional[int]        =    (  # noqa: F841
					    out["""context_input_ids"""],
					    out["""context_attention_mask"""],
					    out["""retrieved_doc_embeds"""],
					    out["""doc_ids"""],
					)
					self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)    )
					self.assertIsInstance(__lowerCamelCase							,		torch.Tensor    )
					self.assertIsInstance(__lowerCamelCase							,		torch.Tensor    )
					self.assertIsInstance(__lowerCamelCase							,		torch.Tensor    )
		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def      _lowerCamelCase				(		self    )		->    int:
					__lowercase       :   Dict        =    self.get_dpr_ctx_encoder_tokenizer()
					__lowercase       :   Dict        =    1
					__lowercase       :   List[Any]        =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
					retriever.set_ctx_encoder_tokenizer(__lowerCamelCase    )

					__lowercase       :   int        =    [[5, 7], [10, 11]]
					__lowercase       :   List[Any]        =    np.array(
					    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]							,		dtype=np.floataa    )
					__lowercase       :   Optional[Any]        =    retriever(__lowerCamelCase							,		__lowerCamelCase							,		prefix=retriever.config.generator.prefix							,		n_docs=__lowerCamelCase    )

					self.assertEqual(
					    len(__lowerCamelCase    )							,		6    )  # check whether the retriever output consist of 6 attributes including tokenized docs
					self.assertEqual(
					    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')    )							,		__lowerCamelCase    )  # check for doc token related keys in dictionary.











import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Tuple     	=  ShapEImgaImgPipeline
	snake_case__							:     Optional[Any]     	=  ["""image"""]
	snake_case__							:     Union[str, Any]     	=  ["""image"""]
	snake_case__							:     Optional[Any]     	=  [
	    """num_images_per_prompt""",
	    """num_inference_steps""",
	    """generator""",
	    """latents""",
	    """guidance_scale""",
	    """frame_size""",
	    """output_type""",
	    """return_dict""",
	]
	snake_case__							:     List[str]     	=  False





	@property
	def 						_A (		self				:					Any	):
		return 32





	@property
	def 						_A (		self				:					Any	):
		return 32





	@property
	def 						_A (		self				:					Optional[Any]	):
		return self.time_input_dim * 4





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





	@property
	def 						_A (		self				:					int	):
		torch.manual_seed(0	)
		UpperCamelCase     :Union[str, Any]          =	CLIPVisionConfig(
		    hidden_size=self.text_embedder_hidden_size ,  image_size=64 ,  projection_dim=self.text_embedder_hidden_size ,  intermediate_size=37 ,  num_attention_heads=4 ,  num_channels=3 ,  num_hidden_layers=5 ,  patch_size=1 ,  )

		UpperCamelCase     :Optional[int]          =	CLIPVisionModel(__lowerCamelCase	)
		return model





	@property
	def 						_A (		self				:					str	):
		UpperCamelCase     :Optional[int]          =	CLIPImageProcessor(
		    crop_size=224 ,  do_center_crop=__lowerCamelCase ,  do_normalize=__lowerCamelCase ,  do_resize=__lowerCamelCase ,  image_mean=[0.48145466, 0.4578275, 0.40821073] ,  image_std=[0.26862954, 0.26130258, 0.27577711] ,  resample=3 ,  size=224 ,  )

		return image_processor





	@property
	def 						_A (		self				:					Tuple	):
		torch.manual_seed(0	)

		UpperCamelCase     :Dict          =	{
		    """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""",
		    """embedding_proj_norm_type""": """layer""",
		    """encoder_hid_proj_type""": None,
		    """added_emb_type""": None,
		}

		UpperCamelCase     :int          =	PriorTransformer(**__lowerCamelCase	)
		return model





	@property
	def 						_A (		self				:					Optional[int]	):
		torch.manual_seed(0	)

		UpperCamelCase     :str          =	{
		    """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,
		    ),
		}
		UpperCamelCase     :List[str]          =	ShapERenderer(**__lowerCamelCase	)
		return model





	def 						_A (		self				:					str	):
		UpperCamelCase     :int          =	self.dummy_prior
		UpperCamelCase     :Any          =	self.dummy_image_encoder
		UpperCamelCase     :Dict          =	self.dummy_image_processor
		UpperCamelCase     :List[Any]          =	self.dummy_renderer

		UpperCamelCase     :int          =	HeunDiscreteScheduler(
		    beta_schedule="""exp""" ,  num_train_timesteps=1_024 ,  prediction_type="""sample""" ,  use_karras_sigmas=__lowerCamelCase ,  clip_sample=__lowerCamelCase ,  clip_sample_range=1.0 ,  )
		UpperCamelCase     :Optional[Any]          =	{
		    """prior""": prior,
		    """image_encoder""": image_encoder,
		    """image_processor""": image_processor,
		    """renderer""": renderer,
		    """scheduler""": scheduler,
		}

		return components





	def 						_A (		self				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Any=0	):
		UpperCamelCase     :Any          =	floats_tensor((1, 3, 64, 64) ,  rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

		if str(__lowerCamelCase	).startswith("""mps"""	):
			UpperCamelCase     :List[Any]          =	torch.manual_seed(__lowerCamelCase	)
		else:
			UpperCamelCase     :Optional[int]          =	torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	{
		    """image""": input_image,
		    """generator""": generator,
		    """num_inference_steps""": 1,
		    """frame_size""": 32,
		    """output_type""": """np""",
		}
		return inputs





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Dict          =	"""cpu"""

		UpperCamelCase     :List[Any]          =	self.get_dummy_components()

		UpperCamelCase     :Optional[int]          =	self.pipeline_class(**__lowerCamelCase	)
		UpperCamelCase     :int          =	pipe.to(__lowerCamelCase	)

		pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	pipe(**self.get_dummy_inputs(__lowerCamelCase	)	)
		UpperCamelCase     :Dict          =	output.images[0]
		UpperCamelCase     :List[Any]          =	image[0, -3:, -3:, -1]

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

		UpperCamelCase     :Dict          =	np.array(
		    [
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		    ]	)

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





	def 						_A (		self				:					List[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 						_A (		self				:					List[Any]	):
		UpperCamelCase     :str          =	torch_device == """cpu"""
		UpperCamelCase     :int          =	True
		self._test_inference_batch_single_identical(
		    batch_size=2 ,  test_max_difference=__lowerCamelCase ,  relax_max_difference=__lowerCamelCase ,  )





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :List[Any]          =	self.get_dummy_components()
		UpperCamelCase     :Optional[int]          =	self.pipeline_class(**__lowerCamelCase	)
		UpperCamelCase     :List[Any]          =	pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Any          =	1
		UpperCamelCase     :int          =	2

		UpperCamelCase     :Union[str, Any]          =	self.get_dummy_inputs(__lowerCamelCase	)

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

		UpperCamelCase     :Optional[int]          =	pipe(**__lowerCamelCase ,  num_images_per_prompt=__lowerCamelCase	)[0]

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

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





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





	def 						_A (		self				:					Any	):
		UpperCamelCase     :Optional[Any]          =	load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""	)
		UpperCamelCase     :Any          =	load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/shap_e/test_shap_e_img2img_out.npy"""	)
		UpperCamelCase     :Union[str, Any]          =	ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""	)
		UpperCamelCase     :List[str]          =	pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	torch.Generator(device=__lowerCamelCase	).manual_seed(0	)

		UpperCamelCase     :Optional[int]          =	pipe(
		    __lowerCamelCase ,  generator=__lowerCamelCase ,  guidance_scale=3.0 ,  num_inference_steps=64 ,  frame_size=64 ,  output_type="""np""" ,  ).images[0]

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

		assert_mean_pixel_difference(__lowerCamelCase ,  __lowerCamelCase	)
