



"""simple docstring"""







import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class       _a  ( unittest.TestCase):
			__magic_name__     					=     MODEL_FOR_MASKED_LM_MAPPING
			__magic_name__     					=     TF_MODEL_FOR_MASKED_LM_MAPPING



			def 				__lowercase					(			self							:				List[str]		)							->					Optional[int]:
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									if is_torch_available():
															import torch

															torch.cuda.empty_cache()



			@require_tf
			def 				__lowercase					(			self							:				Dict		)							->					Any:
									snake_case   :			Optional[Any]						    = pipeline(task="fill-mask"   ,	model="sshleifer/tiny-distilroberta-base"   ,	top_k=2   ,	framework="tf"		)
									snake_case   :			Tuple						    = unmasker("My name is <mask>"		)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        {"sequence": "My name is grouped", "score": 2.1E-05, "token": 38015, "token_str": " grouped"},
									        {"sequence": "My name is accuser", "score": 2.1E-05, "token": 25506, "token_str": " accuser"},
									    ]   ,	)

									snake_case   :			Union[str, Any]						    = unmasker("The largest city in France is <mask>"		)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        {
									            "sequence": "The largest city in France is grouped",
									            "score": 2.1E-05,
									            "token": 38015,
									            "token_str": " grouped",
									        },
									        {
									            "sequence": "The largest city in France is accuser",
									            "score": 2.1E-05,
									            "token": 25506,
									            "token_str": " accuser",
									        },
									    ]   ,	)

									snake_case   :			int						    = unmasker("My name is <mask>"   ,	targets=[" Patrick", " Clara", " Teven"]   ,	top_k=3		)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        {"sequence": "My name is Clara", "score": 2E-05, "token": 13606, "token_str": " Clara"},
									        {"sequence": "My name is Patrick", "score": 2E-05, "token": 3499, "token_str": " Patrick"},
									        {"sequence": "My name is Te", "score": 1.9E-05, "token": 2941, "token_str": " Te"},
									    ]   ,	)



			@require_torch
			def 				__lowercase					(			self							:				Dict		)							->					Optional[Any]:
									snake_case   :			List[str]						    = pipeline(task="fill-mask"   ,	model="sshleifer/tiny-distilroberta-base"   ,	top_k=2   ,	framework="pt"		)

									snake_case   :			Optional[Any]						    = unmasker("My name is <mask>"		)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        {"sequence": "My name is Maul", "score": 2.2E-05, "token": 35676, "token_str": " Maul"},
									        {"sequence": "My name isELS", "score": 2.2E-05, "token": 16416, "token_str": "ELS"},
									    ]   ,	)

									snake_case   :			List[Any]						    = unmasker("The largest city in France is <mask>"		)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        {
									            "sequence": "The largest city in France is Maul",
									            "score": 2.2E-05,
									            "token": 35676,
									            "token_str": " Maul",
									        },
									        {"sequence": "The largest city in France isELS", "score": 2.2E-05, "token": 16416, "token_str": "ELS"},
									    ]   ,	)

									snake_case   :			Optional[Any]						    = unmasker("My name is <mask>"   ,	targets=[" Patrick", " Clara", " Teven"]   ,	top_k=3		)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        {"sequence": "My name is Patrick", "score": 2.1E-05, "token": 3499, "token_str": " Patrick"},
									        {"sequence": "My name is Te", "score": 2E-05, "token": 2941, "token_str": " Te"},
									        {"sequence": "My name is Clara", "score": 2E-05, "token": 13606, "token_str": " Clara"},
									    ]   ,	)

									snake_case   :			Tuple						    = unmasker("My name is <mask> <mask>"   ,	top_k=2		)

									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=6		)   ,	[
									        [
									            {
									                "score": 2.2E-05,
									                "token": 35676,
									                "token_str": " Maul",
									                "sequence": "<s>My name is Maul<mask></s>",
									            },
									            {"score": 2.2E-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
									        ],
									        [
									            {
									                "score": 2.2E-05,
									                "token": 35676,
									                "token_str": " Maul",
									                "sequence": "<s>My name is<mask> Maul</s>",
									            },
									            {"score": 2.2E-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
									        ],
									    ]   ,	)



			@require_torch_gpu
			def 				__lowercase					(			self							:				List[Any]		)							->					Any:
									snake_case   :			Optional[int]						    = pipeline("fill-mask"   ,	model="hf-internal-testing/tiny-random-distilbert"   ,	device=0   ,	framework="pt"		)

									# convert model to fp16
									pipe.model.half()

									snake_case   :			str						    = pipe("Paris is the [MASK] of France."		)
									# We actually don't care about the result, we just want to make sure
									# it works, meaning the float16 tensor got casted back to float32
									# for postprocessing.
									self.assertIsInstance(_lowercase   ,	_lowercase		)



			@slow
			@require_torch
			def 				__lowercase					(			self							:				str		)							->					List[str]:
									snake_case   :			Union[str, Any]						    = pipeline(task="fill-mask"   ,	model="distilroberta-base"   ,	top_k=2   ,	framework="pt"		)
									self.run_large_test(_lowercase		)



			@slow
			@require_tf
			def 				__lowercase					(			self							:				List[str]		)							->					Tuple:
									snake_case   :			Dict						    = pipeline(task="fill-mask"   ,	model="distilroberta-base"   ,	top_k=2   ,	framework="tf"		)
									self.run_large_test(_lowercase		)



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				str		)							->					int:
									snake_case   :			Union[str, Any]						    = unmasker("My name is <mask>"		)
									self.assertEqual(
									    nested_simplify(_lowercase		)   ,	[
									        {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
									        {"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
									    ]   ,	)
									snake_case   :			Dict						    = unmasker("The largest city in France is <mask>"		)
									self.assertEqual(
									    nested_simplify(_lowercase		)   ,	[
									        {
									            "sequence": "The largest city in France is Paris",
									            "score": 0.251,
									            "token": 2201,
									            "token_str": " Paris",
									        },
									        {
									            "sequence": "The largest city in France is Lyon",
									            "score": 0.214,
									            "token": 12790,
									            "token_str": " Lyon",
									        },
									    ]   ,	)

									snake_case   :			Union[str, Any]						    = unmasker("My name is <mask>"   ,	targets=[" Patrick", " Clara", " Teven"]   ,	top_k=3		)
									self.assertEqual(
									    nested_simplify(_lowercase		)   ,	[
									        {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
									        {"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
									        {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
									    ]   ,	)



			@require_torch
			def 				__lowercase					(			self							:				Tuple		)							->					Optional[Any]:
									snake_case   :			Optional[int]						    = pipeline(task="fill-mask"   ,	model="sshleifer/tiny-distilroberta-base"   ,	framework="pt"		)
									snake_case   :			Optional[Any]						    = None
									snake_case   :			List[str]						    = None
									self.run_pipeline_test(_lowercase   ,	[]		)



			@require_tf
			def 				__lowercase					(			self							:				Tuple		)							->					List[Any]:
									snake_case   :			Union[str, Any]						    = pipeline(task="fill-mask"   ,	model="sshleifer/tiny-distilroberta-base"   ,	framework="tf"		)
									snake_case   :			Tuple						    = None
									snake_case   :			str						    = None
									self.run_pipeline_test(_lowercase   ,	[]		)



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				int   ,	_lowercase							:				Optional[int]		)							->					str:
									if tokenizer is None or tokenizer.mask_token_id is None:
															self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"		)

									snake_case   :			Union[str, Any]						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase		)
									snake_case   :			str						    = [
									    F'''This is another {tokenizer.mask_token} test''',
									]
									return fill_masker, examples



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Union[str, Any]		)							->					Tuple:
									snake_case   :			Any						    = fill_masker.tokenizer
									snake_case   :			List[Any]						    = fill_masker.model

									snake_case   :			Union[str, Any]						    = fill_masker(
									    F'''This is a {tokenizer.mask_token}'''   ,	)
									self.assertEqual(
									    _lowercase   ,	[
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									    ]   ,	)

									snake_case   :			str						    = fill_masker([F'''This is a {tokenizer.mask_token}''']		)
									self.assertEqual(
									    _lowercase   ,	[
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									    ]   ,	)

									snake_case   :			Dict						    = fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.''']		)
									self.assertEqual(
									    _lowercase   ,	[
									        [
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        ],
									        [
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        ],
									    ]   ,	)

									with self.assertRaises(_lowercase		):
															fill_masker([None]		)
									# No mask_token is not supported
									with self.assertRaises(_lowercase		):
															fill_masker("This is"		)

									self.run_test_top_k(_lowercase   ,	_lowercase		)
									self.run_test_targets(_lowercase   ,	_lowercase		)
									self.run_test_top_k_targets(_lowercase   ,	_lowercase		)
									self.fill_mask_with_duplicate_targets_and_top_k(_lowercase   ,	_lowercase		)
									self.fill_mask_with_multiple_masks(_lowercase   ,	_lowercase		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Optional[Any]		)							->					List[Any]:
									snake_case   :			Any						    = tokenizer.get_vocab()
									snake_case   :			Optional[int]						    = sorted(vocab.keys()		)[:2]
									# Pipeline argument
									snake_case   :			Any						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase   ,	targets=_lowercase		)
									snake_case   :			Union[str, Any]						    = fill_masker(F'''This is a {tokenizer.mask_token}'''		)
									self.assertEqual(
									    _lowercase   ,	[
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									    ]   ,	)
									snake_case   :			List[Any]						    = {vocab[el] for el in targets}
									self.assertEqual({el["token"] for el in outputs}   ,	_lowercase		)
									snake_case   :			Union[str, Any]						    = [tokenizer.decode([x]		) for x in target_ids]
									self.assertEqual({el["token_str"] for el in outputs}   ,	set(_lowercase		)		)

									# Call argument
									snake_case   :			int						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase		)
									snake_case   :			Any						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	targets=_lowercase		)
									self.assertEqual(
									    _lowercase   ,	[
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									    ]   ,	)
									snake_case   :			str						    = {vocab[el] for el in targets}
									self.assertEqual({el["token"] for el in outputs}   ,	_lowercase		)
									snake_case   :			Any						    = [tokenizer.decode([x]		) for x in target_ids]
									self.assertEqual({el["token_str"] for el in outputs}   ,	set(_lowercase		)		)

									# Score equivalence
									snake_case   :			str						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	targets=_lowercase		)
									snake_case   :			Union[str, Any]						    = [top_mask["token_str"] for top_mask in outputs]
									snake_case   :			Dict						    = [top_mask["score"] for top_mask in outputs]

									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(_lowercase		) == set(_lowercase		):
															snake_case   :			Union[str, Any]						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	targets=_lowercase		)
															snake_case   :			Union[str, Any]						    = [top_mask["score"] for top_mask in unmasked_targets]
															self.assertEqual(nested_simplify(_lowercase		)   ,	nested_simplify(_lowercase		)		)

									# Raises with invalid
									with self.assertRaises(_lowercase		):
															snake_case   :			Optional[Any]						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	targets=[]		)
									# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
									if "" not in tokenizer.get_vocab():
															with self.assertRaises(_lowercase		):
																					snake_case   :			Any						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	targets=[""]		)
															with self.assertRaises(_lowercase		):
																					snake_case   :			Optional[int]						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	targets=""		)



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				List[Any]		)							->					Any:
									snake_case   :			Optional[Any]						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase   ,	top_k=2		)
									snake_case   :			Union[str, Any]						    = fill_masker(F'''This is a {tokenizer.mask_token}'''		)
									self.assertEqual(
									    _lowercase   ,	[
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									    ]   ,	)

									snake_case   :			str						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase		)
									snake_case   :			Tuple						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	top_k=2		)
									self.assertEqual(
									    _lowercase   ,	[
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									    ]   ,	)
									self.assertEqual(nested_simplify(_lowercase		)   ,	nested_simplify(_lowercase		)		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				int   ,	_lowercase							:				List[Any]		)							->					Any:
									snake_case   :			Optional[Any]						    = tokenizer.get_vocab()
									snake_case   :			str						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase		)

									# top_k=2, ntargets=3
									snake_case   :			List[Any]						    = sorted(vocab.keys()		)[:3]
									snake_case   :			str						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	top_k=2   ,	targets=_lowercase		)

									# If we use the most probably targets, and filter differently, we should still
									# have the same results
									snake_case   :			Optional[int]						    = [el["token_str"] for el in sorted(_lowercase   ,	key=lambda _lowercase		: x["score"]   ,	reverse=_lowercase		)]
									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(_lowercase		).issubset(_lowercase		):
															snake_case   :			Optional[Any]						    = fill_masker(F'''This is a {tokenizer.mask_token}'''   ,	top_k=3   ,	targets=_lowercase		)
															# They should yield exactly the same result
															self.assertEqual(nested_simplify(_lowercase		)   ,	nested_simplify(_lowercase		)		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				Dict   ,	_lowercase							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			Dict						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase		)
									snake_case   :			Optional[Any]						    = tokenizer.get_vocab()
									# String duplicates + id duplicates
									snake_case   :			List[str]						    = sorted(vocab.keys()		)[:3]
									snake_case   :			Any						    = [targets[0], targets[1], targets[0], targets[2], targets[1]]
									snake_case   :			str						    = fill_masker(F'''My name is {tokenizer.mask_token}'''   ,	targets=_lowercase   ,	top_k=10		)

									# The target list contains duplicates, so we can't output more
									# than them
									self.assertEqual(len(_lowercase		)   ,	3		)



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str   ,	_lowercase							:				List[Any]		)							->					Dict:
									snake_case   :			int						    = FillMaskPipeline(model=_lowercase   ,	tokenizer=_lowercase		)

									snake_case   :			Tuple						    = fill_masker(
									    F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''   ,	top_k=2		)
									self.assertEqual(
									    _lowercase   ,	[
									        [
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        ],
									        [
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        ],
									        [
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									            {"sequence": ANY(_lowercase		), "score": ANY(_lowercase		), "token": ANY(_lowercase		), "token_str": ANY(_lowercase		)},
									        ],
									    ]   ,	)








"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = VQModel(
									    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=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=1000   ,	)
									return CLIPTextModel(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									snake_case   :			Tuple						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			Any						    = image[0, -3:, -3:, -1]
									snake_case   :			Any						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



