

'''simple docstring'''

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase				: Optional[int]							    =				get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Any	=	XLMRobertaTokenizer
				__UpperCamelCase    :		str	=	XLMRobertaTokenizerFast
				__UpperCamelCase    :		Union[str, Any]	=	True
				__UpperCamelCase    :		Optional[int]	=	True




				def 					_snake_case  (self		):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCAmelCase							       =				XLMRobertaTokenizer(__lowercase       ,			keep_accents=__lowercase		)
										tokenizer.save_pretrained(self.tmpdirname		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''<pad>'''
										__lowerCAmelCase							       =				1

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]       ,			'''<s>'''		)
										self.assertEqual(vocab_keys[1]       ,			'''<pad>'''		)
										self.assertEqual(vocab_keys[-1]       ,			'''<mask>'''		)
										self.assertEqual(len(__lowercase		)       ,			10_02		)




				def 					_snake_case  (self		):
										self.assertEqual(self.get_tokenizer().vocab_size       ,			10_02		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				XLMRobertaTokenizer(__lowercase       ,			keep_accents=__lowercase		)

										__lowerCAmelCase							       =				tokenizer.tokenize('''This is a test'''		)
										self.assertListEqual(__lowercase       ,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']		)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowercase		)       ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]       ,			)

										__lowerCAmelCase							       =				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''		)
										self.assertListEqual(
										    __lowercase       ,			[
										        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''',
										        '''.''',
										    ]       ,			)
										__lowerCAmelCase							       =				tokenizer.convert_tokens_to_ids(__lowercase		)
										self.assertListEqual(
										    __lowercase       ,			[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]       ,			)

										__lowerCAmelCase							       =				tokenizer.convert_ids_to_tokens(__lowercase		)
										self.assertListEqual(
										    __lowercase       ,			[
										        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>''',
										        '''.''',
										    ]       ,			)




				def 					_snake_case  (self		):
										if not self.test_slow_tokenizer:
																# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																return

										__lowerCAmelCase							       =				(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
																						__lowerCAmelCase							       =				self.rust_tokenizer_class.from_pretrained(__lowercase       ,			**__lowercase		)
																						__lowerCAmelCase							       =				self.tokenizer_class.from_pretrained(__lowercase       ,			**__lowercase		)

																						__lowerCAmelCase							       =				tempfile.mkdtemp()

																						__lowerCAmelCase							       =				tokenizer_r.save_pretrained(__lowercase		)
																						__lowerCAmelCase							       =				tokenizer_p.save_pretrained(__lowercase		)

																						# Checks it save with the same files + the tokenizer.json file for the fast one
																						self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files		)		)
																						__lowerCAmelCase							       =				tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f		)
																						self.assertSequenceEqual(__lowercase       ,			__lowercase		)

																						# Checks everything loads correctly in the same way
																						__lowerCAmelCase							       =				tokenizer_r.from_pretrained(__lowercase		)
																						__lowerCAmelCase							       =				tokenizer_p.from_pretrained(__lowercase		)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																												self.assertTrue(hasattr(__lowercase       ,			__lowercase		)		)
																												# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																												# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																						shutil.rmtree(__lowercase		)

																						# Save tokenizer rust, legacy_format=True
																						__lowerCAmelCase							       =				tempfile.mkdtemp()

																						__lowerCAmelCase							       =				tokenizer_r.save_pretrained(__lowercase       ,			legacy_format=__lowercase		)
																						__lowerCAmelCase							       =				tokenizer_p.save_pretrained(__lowercase		)

																						# Checks it save with the same files
																						self.assertSequenceEqual(__lowercase       ,			__lowercase		)

																						# Checks everything loads correctly in the same way
																						__lowerCAmelCase							       =				tokenizer_r.from_pretrained(__lowercase		)
																						__lowerCAmelCase							       =				tokenizer_p.from_pretrained(__lowercase		)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																												self.assertTrue(hasattr(__lowercase       ,			__lowercase		)		)

																						shutil.rmtree(__lowercase		)

																						# Save tokenizer rust, legacy_format=False
																						__lowerCAmelCase							       =				tempfile.mkdtemp()

																						__lowerCAmelCase							       =				tokenizer_r.save_pretrained(__lowercase       ,			legacy_format=__lowercase		)
																						__lowerCAmelCase							       =				tokenizer_p.save_pretrained(__lowercase		)

																						# Checks it saved the tokenizer.json file
																						self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files		)		)

																						# Checks everything loads correctly in the same way
																						__lowerCAmelCase							       =				tokenizer_r.from_pretrained(__lowercase		)
																						__lowerCAmelCase							       =				tokenizer_p.from_pretrained(__lowercase		)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																												self.assertTrue(hasattr(__lowercase       ,			__lowercase		)		)

																						shutil.rmtree(__lowercase		)




				@cached_property
				def 					_snake_case  (self		):
										return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''		)




				def 					_snake_case  (self		):
										with tempfile.NamedTemporaryFile() as f:
																shutil.copyfile(__lowercase       ,			f.name		)
																__lowerCAmelCase							       =				XLMRobertaTokenizer(f.name       ,			keep_accents=__lowercase		)
																__lowerCAmelCase							       =				pickle.dumps(__lowercase		)
										pickle.loads(__lowercase		)




				def 					_snake_case  (self		):
										if not self.test_rust_tokenizer:
																return

										__lowerCAmelCase							       =				self.get_tokenizer()
										__lowerCAmelCase							       =				self.get_rust_tokenizer()

										__lowerCAmelCase							       =				'''I was born in 92000, and this is falsé.'''

										__lowerCAmelCase							       =				tokenizer.tokenize(__lowercase		)
										__lowerCAmelCase							       =				rust_tokenizer.tokenize(__lowercase		)
										self.assertListEqual(__lowercase       ,			__lowercase		)

										__lowerCAmelCase							       =				tokenizer.encode(__lowercase       ,			add_special_tokens=__lowercase		)
										__lowerCAmelCase							       =				rust_tokenizer.encode(__lowercase       ,			add_special_tokens=__lowercase		)
										self.assertListEqual(__lowercase       ,			__lowercase		)

										__lowerCAmelCase							       =				self.get_rust_tokenizer()
										__lowerCAmelCase							       =				tokenizer.encode(__lowercase		)
										__lowerCAmelCase							       =				rust_tokenizer.encode(__lowercase		)
										self.assertListEqual(__lowercase       ,			__lowercase		)




				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''Hello World!'''
										__lowerCAmelCase							       =				[0, 3_53_78, 66_61, 38, 2]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

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




				@slow
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				(
										    '''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'''
										)
										__lowerCAmelCase							       =				[
										    0,
										    32_93,
										    83,
										    10,
										    45_52,
										    49_89,
										    79_86,
										    6_78,
										    10,
										    59_15,
										    1_11,
										    17_94_59,
										    12_48_50,
										    4,
										    60_44,
										    2_37,
										    12,
										    6,
										    5,
										    6,
										    4,
										    67_80,
										    7_05,
										    15,
										    13_88,
										    44,
										    3_78,
										    1_01_14,
										    7_11,
										    1_52,
										    20,
										    6,
										    5,
										    2_23_76,
										    6_42,
										    12_21,
										    1_51_90,
										    3_41_53,
										    4_50,
										    56_08,
										    9_59,
										    11_19,
										    5_77_02,
										    1_36,
										    1_86,
										    47,
										    10_98,
										    2_93_67,
										    47,
										    # 4426, # What fairseq tokenizes from "<unk>": "_<"
										    # 3678, # What fairseq tokenizes from "<unk>": "unk"
										    # 2740, # What fairseq tokenizes from "<unk>": ">"
										    3,  # What we tokenize from "<unk>": "<unk>"
										    6,  # Residue from the tokenization: an extra sentencepiece underline
										    4,
										    60_44,
										    2_37,
										    62_84,
										    5_09_01,
										    5_28,
										    31,
										    90,
										    34,
										    9_27,
										    2,
										]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

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




				@slow
				def 					_snake_case  (self		):
										# fmt: off
										__lowerCAmelCase							       =				{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], '''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, 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, 0, 0, 0, 0, 0], [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, 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=__lowercase       ,			model_name='''xlm-roberta-base'''       ,			revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''       ,			)





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

