





'''simple docstring'''



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				str											=       PriorTransformer
	a    :				Optional[Any]											=       "hidden_states"


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							4
				__lowercase										=							8
				__lowercase										=							7

				__lowercase										=							floats_tensor((batch_size, embedding_dim) ).to(_lowerCamelCase )

				__lowercase										=							floats_tensor((batch_size, embedding_dim) ).to(_lowerCamelCase )
				__lowercase										=							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(_lowerCamelCase )

				return {
				    "hidden_states": hidden_states,
				    "timestep": 2,
				    "proj_embedding": proj_embedding,
				    "encoder_hidden_states": encoder_hidden_states,
				}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=0 )							->							Optional[int]:







				'''simple docstring'''





				torch.manual_seed(_lowerCamelCase )
				__lowercase										=							4
				__lowercase										=							8
				__lowercase										=							7

				__lowercase										=							torch.randn((batch_size, embedding_dim) ).to(_lowerCamelCase )

				__lowercase										=							torch.randn((batch_size, embedding_dim) ).to(_lowerCamelCase )
				__lowercase										=							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_lowerCamelCase )

				return {
				    "hidden_states": hidden_states,
				    "timestep": 2,
				    "proj_embedding": proj_embedding,
				    "encoder_hidden_states": encoder_hidden_states,
				}


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







				'''simple docstring'''





				return (4, 8)


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				return (4, 8)


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







				'''simple docstring'''





				__lowercase										=							{
				    '''num_attention_heads''': 2,
				    '''attention_head_dim''': 4,
				    '''num_layers''': 2,
				    '''embedding_dim''': 8,
				    '''num_embeddings''': 7,
				    '''additional_embeddings''': 4,
				}
				__lowercase										=							self.dummy_input
				return init_dict, inputs_dict


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							PriorTransformer.from_pretrained(
				    '''hf-internal-testing/prior-dummy'''     ,output_loading_info=_lowerCamelCase )
				self.assertIsNotNone(_lowerCamelCase )
				self.assertEqual(len(loading_info['''missing_keys'''] )     ,0 )

				model.to(_lowerCamelCase )
				__lowercase										=							model(**self.dummy_input )[0]

				assert hidden_states is not None, "Make sure output is not None"


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							self.prepare_init_args_and_inputs_for_common()

				__lowercase										=							self.model_class(**_lowerCamelCase )
				__lowercase										=							inspect.signature(model.forward )
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowercase										=							[*signature.parameters.keys()]

				__lowercase										=							['''hidden_states''', '''timestep''']
				self.assertListEqual(arg_names[:2]     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy''' )
				__lowercase										=							model.to(_lowerCamelCase )

				if hasattr(_lowerCamelCase     ,'''set_default_attn_processor''' ):
							model.set_default_attn_processor()

				__lowercase										=							self.get_dummy_seed_input()

				with torch.no_grad():
							__lowercase										=							model(**_lowerCamelCase )[0]

				__lowercase										=							output[0, :5].flatten().cpu()
				print(_lowerCamelCase )

				# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
				# the expected output slices are not the same for CPU and GPU.
				__lowercase										=							torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9] )
				self.assertTrue(torch_all_close(_lowerCamelCase     ,_lowerCamelCase     ,rtol=1E-2 ) )






@slow
class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=1     ,_lowerCamelCase=768     ,_lowerCamelCase=77     ,_lowerCamelCase=0 )							->							Optional[Any]:







				'''simple docstring'''





				torch.manual_seed(_lowerCamelCase )
				__lowercase										=							batch_size
				__lowercase										=							embedding_dim
				__lowercase										=							num_embeddings

				__lowercase										=							torch.randn((batch_size, embedding_dim) ).to(_lowerCamelCase )

				__lowercase										=							torch.randn((batch_size, embedding_dim) ).to(_lowerCamelCase )
				__lowercase										=							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_lowerCamelCase )

				return {
				    "hidden_states": hidden_states,
				    "timestep": 2,
				    "proj_embedding": proj_embedding,
				    "encoder_hidden_states": encoder_hidden_states,
				}


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







				'''simple docstring'''





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


	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
	        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
	        # fmt: on
	    ] )
	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''     ,subfolder='''prior''' )
				model.to(_lowerCamelCase )
				__lowercase										=							self.get_dummy_seed_input(seed=_lowerCamelCase )

				with torch.no_grad():
							__lowercase										=							model(**_lowerCamelCase )[0]

				assert list(sample.shape ) == [1, 768]

				__lowercase										=							sample[0, :8].flatten().cpu()
				print(_lowerCamelCase )
				__lowercase										=							torch.tensor(_lowerCamelCase )

				assert torch_all_close(_lowerCamelCase     ,_lowerCamelCase     ,atol=1E-3 )






'''simple docstring'''



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = "▁"     ,_lowerCamelCase = True     ,_lowerCamelCase = "<unk>"     ,_lowerCamelCase = "</s>"     ,_lowerCamelCase = "<pad>"     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{
				    '''pad''': {'''id''': 0, '''token''': pad_token},
				    '''eos''': {'''id''': 1, '''token''': eos_token},
				    '''unk''': {'''id''': 2, '''token''': unk_token},
				}

				__lowercase										=							[None] * len(self.special_tokens )
				for token_dict in self.special_tokens.values():
							__lowercase										=							token_dict['''token''']

				__lowercase										=							Tokenizer(Unigram() )

				__lowercase										=							normalizers.Sequence(
				    [
				        normalizers.Nmt(),
				        normalizers.NFKC(),
				        normalizers.Replace(Regex(''' {2,}''' )     ,''' ''' ),
				        normalizers.Lowercase(),
				    ] )
				__lowercase										=							pre_tokenizers.Sequence(
				    [
				        pre_tokenizers.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase ),
				        pre_tokenizers.Digits(individual_digits=_lowerCamelCase ),
				        pre_tokenizers.Punctuation(),
				    ] )
				__lowercase										=							decoders.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase )

				__lowercase										=							TemplateProcessing(
				    single=f"$A {self.special_tokens['eos']['token']}"     ,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]     ,)

				__lowercase										=							{
				    '''model''': '''SentencePieceUnigram''',
				    '''replacement''': replacement,
				    '''add_prefix_space''': add_prefix_space,
				}

				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							[files]
				self._tokenizer.train(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				self._tokenizer.train_from_iterator(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							json.loads(self._tokenizer.to_str() )

				__lowercase										=							self.special_tokens['''unk''']['''id''']

				__lowercase										=							Tokenizer.from_str(json.dumps(_lowerCamelCase ) )





