import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase	:	Optional[int]				=   '▁'

lowerCamelCase	:	List[str]				=   get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 						__lowercase		(snake_case__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						BigBirdTokenizer
      _snake_case												=						BigBirdTokenizerFast
      _snake_case												=						True
      _snake_case												=						True

      def      UpperCAmelCase				(				self     )		->    List[str]:
         super().setUp()

         snake_case				:    List[Any]           =	self.tokenizer_class(_A    ,				keep_accents=_A     )
         tokenizer.save_pretrained(self.tmpdirname     )

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Dict           =	"""<s>"""
         snake_case				:    Any           =	1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A     )    ,				_A     )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A     )    ,				_A     )

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Union[str, Any]           =	list(self.get_tokenizer().get_vocab().keys()     )

         self.assertEqual(vocab_keys[0]    ,				"""<unk>"""     )
         self.assertEqual(vocab_keys[1]    ,				"""<s>"""     )
         self.assertEqual(vocab_keys[-1]    ,				"""[MASK]"""     )
         self.assertEqual(len(_A     )    ,				1_0_0_4     )

      def      UpperCAmelCase				(				self     )		->    Dict:
         self.assertEqual(self.get_tokenizer().vocab_size    ,				1_0_0_0     )

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:



         if not self.test_rust_tokenizer:
            return

         snake_case				:    str           =	self.get_tokenizer()
         snake_case				:    Any           =	self.get_rust_tokenizer()

         snake_case				:    Tuple           =	"""I was born in 92000, and this is falsé."""

         snake_case				:    int           =	tokenizer.tokenize(_A     )
         snake_case				:    Any           =	rust_tokenizer.tokenize(_A     )
         self.assertListEqual(_A    ,				_A     )

         snake_case				:    List[Any]           =	tokenizer.encode(_A    ,				add_special_tokens=_A     )
         snake_case				:    Union[str, Any]           =	rust_tokenizer.encode(_A    ,				add_special_tokens=_A     )
         self.assertListEqual(_A    ,				_A     )

         snake_case				:    Union[str, Any]           =	self.get_rust_tokenizer()
         snake_case				:    Tuple           =	tokenizer.encode(_A     )
         snake_case				:    Any           =	rust_tokenizer.encode(_A     )
         self.assertListEqual(_A    ,				_A     )

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         snake_case				:    Union[str, Any]           =	BigBirdTokenizer(_A    ,				keep_accents=_A     )

         snake_case				:    Tuple           =	tokenizer.tokenize("""This is a test"""     )
         self.assertListEqual(_A    ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]     )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_A     )    ,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]    ,				)

         snake_case				:    Union[str, Any]           =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
         self.assertListEqual(
             _A    ,				[
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """9""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """é""",
                 """.""",
             ]    ,				)
         snake_case				:    Optional[int]           =	tokenizer.convert_tokens_to_ids(_A     )
         self.assertListEqual(
             _A    ,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]    ,				)

         snake_case				:    Tuple           =	tokenizer.convert_ids_to_tokens(_A     )
         self.assertListEqual(
             _A    ,				[
                 SPIECE_UNDERLINE + """I""",
                 SPIECE_UNDERLINE + """was""",
                 SPIECE_UNDERLINE + """b""",
                 """or""",
                 """n""",
                 SPIECE_UNDERLINE + """in""",
                 SPIECE_UNDERLINE + """""",
                 """<unk>""",
                 """2""",
                 """0""",
                 """0""",
                 """0""",
                 """,""",
                 SPIECE_UNDERLINE + """and""",
                 SPIECE_UNDERLINE + """this""",
                 SPIECE_UNDERLINE + """is""",
                 SPIECE_UNDERLINE + """f""",
                 """al""",
                 """s""",
                 """<unk>""",
                 """.""",
             ]    ,				)

      @cached_property
      def      UpperCAmelCase				(				self     )		->    Dict:
         return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""     )

      @slow
      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    Tuple           =	"""Hello World!"""
         snake_case				:    str           =	[6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]

         self.assertListEqual(_A    ,				self.big_tokenizer.encode(_A     )     )

      @slow
      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    Any           =	(
             """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
             """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
         )
         # fmt: off
         snake_case				:    int           =	[6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6]  # noqa: E231
         # fmt: on
         self.assertListEqual(_A    ,				self.big_tokenizer.encode(_A     )     )

      @require_torch
      @slow
      def      UpperCAmelCase				(				self     )		->    List[Any]:
         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         snake_case				:    List[str]           =	list(self.big_tokenizer.get_vocab().keys()     )[:1_0]
         snake_case				:    Optional[int]           =	""" """.join(_A     )
         snake_case				:    Union[str, Any]           =	self.big_tokenizer.encode_plus(_A    ,				return_tensors="""pt"""    ,				return_token_type_ids=_A     )
         snake_case				:    Union[str, Any]           =	self.big_tokenizer.batch_encode_plus(
             [sequence + """ """ + sequence]    ,				return_tensors="""pt"""    ,				return_token_type_ids=_A     )

         snake_case				:    Any           =	BigBirdConfig(attention_type="""original_full"""     )
         snake_case				:    List[Any]           =	BigBirdModel(_A     )

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
            model(**_A     )
            model(**_A     )

      @slow
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    str           =	BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""     )
         snake_case				:    str           =	tokenizer.decode(tokenizer("""Paris is the [MASK]."""     ).input_ids     )

         self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""     )

      @slow
      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Dict           =	{"""input_ids""": [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_A    ,				model_name="""google/bigbird-roberta-base"""    ,				revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""    ,				)











import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 						__lowercase		(UpperCamelCase__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						ConsistencyModelPipeline
      _snake_case												=						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      _snake_case												=						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

      # Override required_optional_params to remove num_images_per_prompt
      _snake_case												=						frozenset(
          [
              """num_inference_steps""",
              """generator""",
              """latents""",
              """output_type""",
              """return_dict""",
              """callback""",
              """callback_steps""",
          ]							)

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    Dict           =	UNetaDModel.from_pretrained(
             """diffusers/consistency-models-test"""    ,				subfolder="""test_unet"""    ,				)
         return unet

      @property
      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    Optional[int]           =	UNetaDModel.from_pretrained(
             """diffusers/consistency-models-test"""    ,				subfolder="""test_unet_class_cond"""    ,				)
         return unet

      def      UpperCAmelCase				(				self    ,				A=False     )		->    Optional[Any]:
         if class_cond:
            snake_case				:    List[str]           =	self.dummy_cond_unet
         else:
            snake_case				:    Optional[Any]           =	self.dummy_uncond_unet

         # Default to CM multistep sampler
         snake_case				:    Optional[Any]           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)

         snake_case				:    Dict           =	{
             """unet""": unet,
             """scheduler""": scheduler,
         }

         return components

      def      UpperCAmelCase				(				self    ,				A    ,				A=0     )		->    Optional[int]:
         if str(A     ).startswith("""mps"""     ):
            snake_case				:    Union[str, Any]           =	torch.manual_seed(A     )
         else:
            snake_case				:    Dict           =	torch.Generator(device=A     ).manual_seed(A     )

         snake_case				:    Tuple           =	{
             """batch_size""": 1,
             """num_inference_steps""": None,
             """timesteps""": [2_2, 0],
             """generator""": generator,
             """output_type""": """np""",
         }

         return inputs

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Union[str, Any]           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    List[Any]           =	self.get_dummy_components()
         snake_case				:    Dict           =	ConsistencyModelPipeline(**A     )
         snake_case				:    int           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Any           =	self.get_dummy_inputs(A     )
         snake_case				:    Tuple           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Dict           =	image[0, -3:, -3:, -1]
         snake_case				:    Tuple           =	np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Optional[int]           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    Tuple           =	self.get_dummy_components(class_cond=A     )
         snake_case				:    Dict           =	ConsistencyModelPipeline(**A     )
         snake_case				:    List[Any]           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    str           =	self.get_dummy_inputs(A     )
         snake_case				:    Optional[int]           =	0
         snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Optional[int]           =	image[0, -3:, -3:, -1]
         snake_case				:    Dict           =	np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    str           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    Optional[int]           =	self.get_dummy_components()
         snake_case				:    List[str]           =	ConsistencyModelPipeline(**A     )
         snake_case				:    Dict           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Dict           =	self.get_dummy_inputs(A     )
         snake_case				:    Tuple           =	1
         snake_case				:    Optional[Any]           =	None
         snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Optional[Any]           =	image[0, -3:, -3:, -1]
         snake_case				:    List[str]           =	np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Optional[int]           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    Dict           =	self.get_dummy_components(class_cond=A     )
         snake_case				:    List[Any]           =	ConsistencyModelPipeline(**A     )
         snake_case				:    List[Any]           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Optional[Any]           =	self.get_dummy_inputs(A     )
         snake_case				:    Optional[Any]           =	1
         snake_case				:    Any           =	None
         snake_case				:    Optional[int]           =	0
         snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Optional[int]           =	image[0, -3:, -3:, -1]
         snake_case				:    List[str]           =	np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3




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




      """simple docstring"""





      def      UpperCAmelCase				(				self     )		->    Dict:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

      def      UpperCAmelCase				(				self    ,				A=0    ,				A=False    ,				A="cpu"    ,				A=torch.floataa    ,				A=(1, 3, 6_4, 6_4)     )		->    int:
         snake_case				:    Union[str, Any]           =	torch.manual_seed(A     )

         snake_case				:    Tuple           =	{
             """num_inference_steps""": None,
             """timesteps""": [2_2, 0],
             """class_labels""": 0,
             """generator""": generator,
             """output_type""": """np""",
         }

         if get_fixed_latents:
            snake_case				:    Optional[int]           =	self.get_fixed_latents(seed=A    ,				device=A    ,				dtype=A    ,				shape=A     )
            snake_case				:    int           =	latents

         return inputs

      def      UpperCAmelCase				(				self    ,				A=0    ,				A="cpu"    ,				A=torch.floataa    ,				A=(1, 3, 6_4, 6_4)     )		->    Any:
         if type(A     ) == str:
            snake_case				:    List[str]           =	torch.device(A     )
         snake_case				:    Any           =	torch.Generator(device=A     ).manual_seed(A     )
         snake_case				:    Dict           =	randn_tensor(A    ,				generator=A    ,				device=A    ,				dtype=A     )
         return latents

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    List[Any]           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    Optional[int]           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    Any           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Tuple           =	self.get_inputs()
         snake_case				:    List[str]           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

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

         snake_case				:    Union[str, Any]           =	np.array([0.08_88, 0.08_81, 0.06_66, 0.04_79, 0.02_92, 0.01_95, 0.02_01, 0.01_63, 0.02_54]     )

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

      def      UpperCAmelCase				(				self     )		->    Tuple:
         snake_case				:    Tuple           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    int           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    List[str]           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Union[str, Any]           =	self.get_inputs()
         snake_case				:    Tuple           =	1
         snake_case				:    List[str]           =	None
         snake_case				:    Any           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

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

         snake_case				:    Union[str, Any]           =	np.array([0.03_40, 0.01_52, 0.00_63, 0.02_67, 0.02_21, 0.01_07, 0.04_16, 0.01_86, 0.02_17]     )

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

      @require_torch_a
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    List[Any]           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    Optional[Any]           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    List[Any]           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A    ,				torch_dtype=torch.floataa     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    List[str]           =	self.get_inputs(get_fixed_latents=A    ,				device=A     )
         # Ensure usage of flash attention in torch 2.0
         with sdp_kernel(enable_flash=A    ,				enable_math=A    ,				enable_mem_efficient=A     ):
            snake_case				:    Tuple           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

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

         snake_case				:    int           =	np.array([0.18_75, 0.14_28, 0.12_89, 0.21_51, 0.20_92, 0.14_77, 0.18_77, 0.16_41, 0.13_53]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      @require_torch_a
      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Optional[Any]           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    Any           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    Dict           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A    ,				torch_dtype=torch.floataa     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Optional[int]           =	self.get_inputs(get_fixed_latents=A    ,				device=A     )
         snake_case				:    Union[str, Any]           =	1
         snake_case				:    Dict           =	None
         # Ensure usage of flash attention in torch 2.0
         with sdp_kernel(enable_flash=A    ,				enable_math=A    ,				enable_mem_efficient=A     ):
            snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

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

         snake_case				:    Dict           =	np.array([0.16_63, 0.19_48, 0.22_75, 0.16_80, 0.12_04, 0.12_45, 0.18_58, 0.13_38, 0.20_95]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3





