




'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case   :   str     							=	get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_snake_case   :   Union[str, Any]     							=	256047
_snake_case   :   Tuple     							=	256145

@require_sentencepiece
@require_tokenizers
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       NllbTokenizer
							lowercase_           =       NllbTokenizerFast
							lowercase_           =       True
							lowercase_           =       True
							lowercase_           =       {}







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							int:


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       NllbTokenizer(lowerCAmelCase_							,    keep_accents=lowerCAmelCase_      )
														tokenizer.save_pretrained(self.tmpdirname      )







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


														"""simple docstring"""


														_a			  =       NllbTokenizer(lowerCAmelCase_							,    keep_accents=lowerCAmelCase_      )

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

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

														_a			  =       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
														self.assertListEqual(
														    lowerCAmelCase_							,    [
														        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''',
														        '''.''',
														    ]							,    )
														_a			  =       tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )
														self.assertListEqual(
														    lowerCAmelCase_							,    [
														        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]
														    ]							,    )

														_a			  =       tokenizer.convert_ids_to_tokens(lowerCAmelCase_      )
														self.assertListEqual(
														    lowerCAmelCase_							,    [
														        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      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																												_a			  =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_							,    **lowerCAmelCase_      )
																												_a			  =       self.tokenizer_class.from_pretrained(lowerCAmelCase_							,    **lowerCAmelCase_      )

																												_a			  =       tempfile.mkdtemp()

																												_a			  =       tokenizer_r.save_pretrained(lowerCAmelCase_      )
																												_a			  =       tokenizer_p.save_pretrained(lowerCAmelCase_      )

																												# 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      )      )
																												_a			  =       tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
																												self.assertSequenceEqual(lowerCAmelCase_							,    lowerCAmelCase_      )

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

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

																												shutil.rmtree(lowerCAmelCase_      )

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

																												_a			  =       tokenizer_r.save_pretrained(lowerCAmelCase_							,    legacy_format=lowerCAmelCase_      )
																												_a			  =       tokenizer_p.save_pretrained(lowerCAmelCase_      )

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

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

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

																												shutil.rmtree(lowerCAmelCase_      )

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

																												_a			  =       tokenizer_r.save_pretrained(lowerCAmelCase_							,    legacy_format=lowerCAmelCase_      )
																												_a			  =       tokenizer_p.save_pretrained(lowerCAmelCase_      )

																												# 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
																												_a			  =       tokenizer_r.from_pretrained(lowerCAmelCase_      )
																												_a			  =       tokenizer_p.from_pretrained(lowerCAmelCase_      )

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

																												shutil.rmtree(lowerCAmelCase_      )







							@require_torch
							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Dict:


														"""simple docstring"""


														if not self.test_seqaseq:
																					return

														_a			  =       self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'      ):
																												# Longer text that will definitely require truncation.
																												_a			  =       [
																												    ''' UN Chief Says There Is No Military Solution in Syria''',
																												    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
																												    ''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
																												    ''' will only worsen the violence and misery for millions of people.''',
																												]
																												_a			  =       [
																												    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
																												    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
																												    ''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
																												    ''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
																												]
																												try:
																																			_a			  =       tokenizer.prepare_seqaseq_batch(
																																			    src_texts=lowerCAmelCase_							,    tgt_texts=lowerCAmelCase_							,    max_length=3							,    max_target_length=10							,    return_tensors='''pt'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''ron_Latn'''							,    )
																												except NotImplementedError:
																																			return
																												self.assertEqual(batch.input_ids.shape[1]							,    3      )
																												self.assertEqual(batch.labels.shape[1]							,    10      )
																												# max_target_length will default to max_length if not specified
																												_a			  =       tokenizer.prepare_seqaseq_batch(
																												    lowerCAmelCase_							,    tgt_texts=lowerCAmelCase_							,    max_length=3							,    return_tensors='''pt'''      )
																												self.assertEqual(batch.input_ids.shape[1]							,    3      )
																												self.assertEqual(batch.labels.shape[1]							,    3      )

																												_a			  =       tokenizer.prepare_seqaseq_batch(
																												    src_texts=lowerCAmelCase_							,    max_length=3							,    max_target_length=10							,    return_tensors='''pt'''      )
																												self.assertEqual(batch_encoder_only.input_ids.shape[1]							,    3      )
																												self.assertEqual(batch_encoder_only.attention_mask.shape[1]							,    3      )
																												self.assertNotIn('''decoder_input_ids'''							,    lowerCAmelCase_      )







							@unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.'''      )
							def      __lowerCAmelCase    (						self :						Dict      )    ->							Tuple:


														"""simple docstring"""


														pass







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[Any]:


														"""simple docstring"""


														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																												_a			  =       [AddedToken('''<special>'''							,    lstrip=lowerCAmelCase_      )]

																												_a			  =       self.rust_tokenizer_class.from_pretrained(
																												    lowerCAmelCase_							,    additional_special_tokens=lowerCAmelCase_							,    **lowerCAmelCase_      )
																												_a			  =       tokenizer_r.encode('''Hey this is a <special> token'''      )

																												_a			  =       tokenizer_r.encode('''<special>'''							,    add_special_tokens=lowerCAmelCase_      )[0]

																												self.assertTrue(special_token_id in r_output      )

																												if self.test_slow_tokenizer:
																																			_a			  =       self.rust_tokenizer_class.from_pretrained(
																																			    lowerCAmelCase_							,    additional_special_tokens=lowerCAmelCase_							,    **lowerCAmelCase_							,    )
																																			_a			  =       self.tokenizer_class.from_pretrained(
																																			    lowerCAmelCase_							,    additional_special_tokens=lowerCAmelCase_							,    **lowerCAmelCase_      )

																																			_a			  =       tokenizer_p.encode('''Hey this is a <special> token'''      )

																																			_a			  =       tokenizer_cr.encode('''Hey this is a <special> token'''      )

																																			self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
																																			self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
																																			self.assertTrue(special_token_id in p_output      )
																																			self.assertTrue(special_token_id in cr_output      )



@require_torch
@require_sentencepiece
@require_tokenizers
class     A					(	unittest.TestCase					):
							lowercase_           =       'facebook/nllb-200-distilled-600M'
							lowercase_           =       [
							    ' UN Chief Says There Is No Military Solution in Syria',
							    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
							]
							lowercase_           =       [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
							    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
							    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
							]
							lowercase_           =       [
							    25_6047,
							    1_6297,
							    13_4408,
							    8165,
							    24_8066,
							    1_4734,
							    950,
							    1135,
							    10_5721,
							    3573,
							    83,
							    2_7352,
							    108,
							    4_9486,
							    2,
							]







							@classmethod
							def      __lowerCAmelCase    (						cls :						Tuple      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       NllbTokenizer.from_pretrained(
														    cls.checkpoint_name							,    src_lang='''eng_Latn'''							,    tgt_lang='''ron_Latn'''      )
														_a			  =       1
														return cls







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


														"""simple docstring"""


														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab''']							,    25_60_01      )
														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn''']							,    25_60_02      )
														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn''']							,    25_60_57      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
														self.assertListEqual(self.expected_src_tokens							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														self.assertIn(lowerCAmelCase_							,    self.tokenizer.all_special_ids      )
														# fmt: off
														_a			  =       [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
														# fmt: on

														_a			  =       self.tokenizer.decode(lowerCAmelCase_							,    skip_special_tokens=lowerCAmelCase_      )
														_a			  =       self.tokenizer.decode(generated_ids[1:]							,    skip_special_tokens=lowerCAmelCase_      )
														self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
														self.assertNotIn(self.tokenizer.eos_token							,    lowerCAmelCase_      )







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


														"""simple docstring"""


														_a			  =       ['''this is gunna be a long sentence ''' * 20]
														assert isinstance(src_text[0]							,    lowerCAmelCase_      )
														_a			  =       10
														_a			  =       self.tokenizer(lowerCAmelCase_							,    max_length=lowerCAmelCase_							,    truncation=lowerCAmelCase_      ).input_ids[0]
														self.assertEqual(ids[-1]							,    2      )
														self.assertEqual(ids[0]							,    lowerCAmelCase_      )
														self.assertEqual(len(lowerCAmelCase_      )							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      )							,    [25_62_03, 3]      )







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       tempfile.mkdtemp()
														_a			  =       self.tokenizer.fairseq_tokens_to_ids
														self.tokenizer.save_pretrained(lowerCAmelCase_      )
														_a			  =       NllbTokenizer.from_pretrained(lowerCAmelCase_      )
														self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,    lowerCAmelCase_      )







							@require_torch
							def      __lowerCAmelCase    (						self :						str      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.tokenizer(
														    self.src_text							,    text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=len(self.expected_src_tokens      )							,    return_tensors='''pt'''							,    )
														_a			  =       shift_tokens_right(
														    batch['''labels''']							,    self.tokenizer.pad_token_id							,    self.tokenizer.lang_code_to_id['''ron_Latn''']      )

														self.assertIsInstance(lowerCAmelCase_							,    lowerCAmelCase_      )

														self.assertEqual((2, 15)							,    batch.input_ids.shape      )
														self.assertEqual((2, 15)							,    batch.attention_mask.shape      )
														_a			  =       batch.input_ids.tolist()[0]
														self.assertListEqual(self.expected_src_tokens							,    lowerCAmelCase_      )
														self.assertEqual(lowerCAmelCase_							,    batch.decoder_input_ids[0, 0]      )  # EOS
														# Test that special tokens are reset
														self.assertEqual(self.tokenizer.prefix_tokens							,    [EN_CODE]      )
														self.assertEqual(self.tokenizer.suffix_tokens							,    [self.tokenizer.eos_token_id]      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														_a			  =       self.tokenizer(self.src_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=3							,    return_tensors='''pt'''      )
														_a			  =       self.tokenizer(
														    text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=10							,    return_tensors='''pt'''      )
														_a			  =       targets['''input_ids''']
														_a			  =       shift_tokens_right(
														    lowerCAmelCase_							,    self.tokenizer.pad_token_id							,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]							,    )

														self.assertEqual(batch.input_ids.shape[1]							,    3      )
														self.assertEqual(batch.decoder_input_ids.shape[1]							,    10      )







							@require_torch
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       self.tokenizer._build_translation_inputs(
														    '''A test'''							,    return_tensors='''pt'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''fra_Latn'''      )

														self.assertEqual(
														    nested_simplify(lowerCAmelCase_      )							,    {
														        # A, test, EOS, en_XX
														        '''input_ids''': [[25_60_47, 70, 73_56, 2]],
														        '''attention_mask''': [[1, 1, 1, 1]],
														        # ar_AR
														        '''forced_bos_token_id''': 25_60_57,
														    }							,    )







							@require_torch
							def      __lowerCAmelCase    (						self :						Any      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       True
														_a			  =       self.tokenizer(
														    '''UN Chief says there is no military solution in Syria'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''fra_Latn'''      )
														self.assertEqual(
														    inputs.input_ids							,    [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]      )

														_a			  =       False
														_a			  =       self.tokenizer(
														    '''UN Chief says there is no military solution in Syria'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''fra_Latn'''      )
														self.assertEqual(
														    inputs.input_ids							,    [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]      )







'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       UnCLIPImageVariationPipeline
							lowercase_           =       IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
							lowercase_           =       IMAGE_VARIATION_BATCH_PARAMS

							lowercase_           =       [
							    'generator',
							    'return_dict',
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]
							lowercase_           =       False







							@property
							def      __lowerCAmelCase    (						self :						int      )    ->							str:


														"""simple docstring"""


														return 32







							@property
							def      __lowerCAmelCase    (						self :						Any      )    ->							str:


														"""simple docstring"""


														return 32







							@property
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Optional[int]:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Any:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def      __lowerCAmelCase    (						self :						int      )    ->							Optional[Any]:


														"""simple docstring"""


														return 1_00







							@property
							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
														return tokenizer







							@property
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							str:


														"""simple docstring"""


														torch.manual_seed(0      )
														_a			  =       CLIPTextConfig(
														    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    projection_dim=self.text_embedder_hidden_size							,    intermediate_size=37							,    layer_norm_eps=1e-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=10_00							,    )
														return CLIPTextModelWithProjection(lowerCAmelCase_      )







							@property
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							List[str]:


														"""simple docstring"""


														torch.manual_seed(0      )
														_a			  =       CLIPVisionConfig(
														    hidden_size=self.text_embedder_hidden_size							,    projection_dim=self.text_embedder_hidden_size							,    num_hidden_layers=5							,    num_attention_heads=4							,    image_size=32							,    intermediate_size=37							,    patch_size=1							,    )
														return CLIPVisionModelWithProjection(lowerCAmelCase_      )







							@property
							def      __lowerCAmelCase    (						self :						Any      )    ->							List[Any]:


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       {
														    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
														    '''time_embed_dim''': self.time_embed_dim,
														    '''cross_attention_dim''': self.cross_attention_dim,
														}

														_a			  =       UnCLIPTextProjModel(**lowerCAmelCase_      )
														return model







							@property
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							str:


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       {
														    '''sample_size''': 32,
														    # RGB in channels
														    '''in_channels''': 3,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 6,
														    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
														    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
														    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
														    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
														    '''layers_per_block''': 1,
														    '''cross_attention_dim''': self.cross_attention_dim,
														    '''attention_head_dim''': 4,
														    '''resnet_time_scale_shift''': '''scale_shift''',
														    '''class_embed_type''': '''identity''',
														}

														_a			  =       UNetaDConditionModel(**lowerCAmelCase_      )
														return model







							@property
							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							str:


														"""simple docstring"""


														return {
														    "sample_size": 64,
														    "layers_per_block": 1,
														    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
														    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
														    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
														    "in_channels": 6,
														    "out_channels": 3,
														}







							@property
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							str:


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       UNetaDModel(**self.dummy_super_res_kwargs      )
														return model







							@property
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							int:


														"""simple docstring"""


														torch.manual_seed(1      )

														_a			  =       UNetaDModel(**self.dummy_super_res_kwargs      )
														return model







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.dummy_decoder
														_a			  =       self.dummy_text_proj
														_a			  =       self.dummy_text_encoder
														_a			  =       self.dummy_tokenizer
														_a			  =       self.dummy_super_res_first
														_a			  =       self.dummy_super_res_last

														_a			  =       UnCLIPScheduler(
														    variance_type='''learned_range'''							,    prediction_type='''epsilon'''							,    num_train_timesteps=10_00							,    )

														_a			  =       UnCLIPScheduler(
														    variance_type='''fixed_small_log'''							,    prediction_type='''epsilon'''							,    num_train_timesteps=10_00							,    )

														_a			  =       CLIPImageProcessor(crop_size=32							,    size=32      )

														_a			  =       self.dummy_image_encoder

														return {
														    "decoder": decoder,
														    "text_encoder": text_encoder,
														    "tokenizer": tokenizer,
														    "text_proj": text_proj,
														    "feature_extractor": feature_extractor,
														    "image_encoder": image_encoder,
														    "super_res_first": super_res_first,
														    "super_res_last": super_res_last,
														    "decoder_scheduler": decoder_scheduler,
														    "super_res_scheduler": super_res_scheduler,
														}







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Optional[Any]=0							,    lowerCAmelCase_ :						Dict=True      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       floats_tensor((1, 3, 32, 32)							,    rng=random.Random(lowerCAmelCase_      )      ).to(lowerCAmelCase_      )
														if str(lowerCAmelCase_      ).startswith('''mps'''      ):
																					_a			  =       torch.manual_seed(lowerCAmelCase_      )
														else:
																					_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(lowerCAmelCase_      )

														if pil_image:
																					_a			  =       input_image * 0.5 + 0.5
																					_a			  =       input_image.clamp(0							,    1      )
																					_a			  =       input_image.cpu().permute(0							,    2							,    3							,    1      ).float().numpy()
																					_a			  =       DiffusionPipeline.numpy_to_pil(lowerCAmelCase_      )[0]

														return {
														    "image": input_image,
														    "generator": generator,
														    "decoder_num_inference_steps": 2,
														    "super_res_num_inference_steps": 2,
														    "output_type": "np",
														}







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


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(**lowerCAmelCase_      )
														_a			  =       output.images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(
														    **lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

														_a			  =       image[0, -3:, -3:, -1]
														_a			  =       image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														_a			  =       np.array(
														    [
														        0.9_9_9_7,
														        0.0_0_0_2,
														        0.9_9_9_7,
														        0.9_9_9_7,
														        0.9_9_6_9,
														        0.0_0_2_3,
														        0.9_9_9_7,
														        0.9_9_6_9,
														        0.9_9_7_0,
														    ]      )

														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      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(**lowerCAmelCase_      )
														_a			  =       output.images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(
														    **lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

														_a			  =       image[0, -3:, -3:, -1]
														_a			  =       image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														_a			  =       np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]      )

														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      __lowerCAmelCase    (						self :						Optional[int]      )    ->							str:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )
														_a			  =       [
														    pipeline_inputs['''image'''],
														    pipeline_inputs['''image'''],
														]

														_a			  =       pipe(**lowerCAmelCase_      )
														_a			  =       output.images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )
														_a			  =       [
														    tuple_pipeline_inputs['''image'''],
														    tuple_pipeline_inputs['''image'''],
														]

														_a			  =       pipe(
														    **lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

														_a			  =       image[0, -3:, -3:, -1]
														_a			  =       image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (2, 64, 64, 3)

														_a			  =       np.array(
														    [
														        0.9_9_9_7,
														        0.9_9_8_9,
														        0.0_0_0_8,
														        0.0_0_2_1,
														        0.9_9_6_0,
														        0.0_0_1_8,
														        0.0_0_1_4,
														        0.0_0_0_2,
														        0.9_9_3_3,
														    ]      )

														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      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       torch.device('''cpu'''      )

														class     A					:
																					lowercase_           =       1



														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(0      )
														_a			  =       pipe.decoder.dtype
														_a			  =       1

														_a			  =       (
														    batch_size,
														    pipe.decoder.config.in_channels,
														    pipe.decoder.config.sample_size,
														    pipe.decoder.config.sample_size,
														)
														_a			  =       pipe.prepare_latents(
														    lowerCAmelCase_							,    dtype=lowerCAmelCase_							,    device=lowerCAmelCase_							,    generator=lowerCAmelCase_							,    latents=lowerCAmelCase_							,    scheduler=DummyScheduler()      )

														_a			  =       (
														    batch_size,
														    pipe.super_res_first.config.in_channels // 2,
														    pipe.super_res_first.config.sample_size,
														    pipe.super_res_first.config.sample_size,
														)
														_a			  =       pipe.prepare_latents(
														    lowerCAmelCase_							,    dtype=lowerCAmelCase_							,    device=lowerCAmelCase_							,    generator=lowerCAmelCase_							,    latents=lowerCAmelCase_							,    scheduler=DummyScheduler()      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(
														    **lowerCAmelCase_							,    decoder_latents=lowerCAmelCase_							,    super_res_latents=lowerCAmelCase_      ).images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )
														# Don't pass image, instead pass embedding
														_a			  =       pipeline_inputs.pop('''image'''      )
														_a			  =       pipe.image_encoder(lowerCAmelCase_      ).image_embeds

														_a			  =       pipe(
														    **lowerCAmelCase_							,    decoder_latents=lowerCAmelCase_							,    super_res_latents=lowerCAmelCase_							,    image_embeddings=lowerCAmelCase_							,    ).images

														# make sure passing text embeddings manually is identical
														assert np.abs(img_out_a - img_out_a      ).max() < 1e-4







							@skip_mps
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       torch_device == '''cpu'''

														# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
														_a			  =       1e-2

														self._test_attention_slicing_forward_pass(
														    test_max_difference=lowerCAmelCase_							,    expected_max_diff=lowerCAmelCase_      )







							@skip_mps
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       torch_device == '''cpu'''
														_a			  =       True
														_a			  =       [
														    '''decoder_num_inference_steps''',
														    '''super_res_num_inference_steps''',
														]

														self._test_inference_batch_single_identical(
														    test_max_difference=lowerCAmelCase_							,    relax_max_difference=lowerCAmelCase_							,    additional_params_copy_to_batched_inputs=lowerCAmelCase_							,    )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       [
														    '''decoder_num_inference_steps''',
														    '''super_res_num_inference_steps''',
														]

														if torch_device == "mps":
																					# TODO: MPS errors with larger batch sizes
																					_a			  =       [2, 3]
																					self._test_inference_batch_consistent(
																					    batch_sizes=lowerCAmelCase_							,    additional_params_copy_to_batched_inputs=lowerCAmelCase_							,    )
														else:
																					self._test_inference_batch_consistent(
																					    additional_params_copy_to_batched_inputs=lowerCAmelCase_      )







							@skip_mps
							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														return super().test_dict_tuple_outputs_equivalent()







							@skip_mps
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														return super().test_save_load_local()







							@skip_mps
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							List[str]:


														"""simple docstring"""


														return super().test_save_load_optional_components()



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







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







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


														"""simple docstring"""


														_a			  =       load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''      )
														_a			  =       load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''      )

														_a			  =       UnCLIPImageVariationPipeline.from_pretrained(
														    '''kakaobrain/karlo-v1-alpha-image-variations'''							,    torch_dtype=torch.floataa      )
														_a			  =       pipeline.to(lowerCAmelCase_      )
														pipeline.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       torch.Generator(device='''cpu'''      ).manual_seed(0      )
														_a			  =       pipeline(
														    lowerCAmelCase_							,    generator=lowerCAmelCase_							,    output_type='''np'''							,    )

														_a			  =       output.images[0]

														assert image.shape == (2_56, 2_56, 3)

														assert_mean_pixel_difference(lowerCAmelCase_							,    lowerCAmelCase_							,    15      )

