



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 				_snake_case			(					UpperCamelCase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[Any]     		=PriorTransformer
				UpperCamelCase__				:					Tuple     		="""hidden_states"""




				@property
				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				4
							lowercase__					  =				8
							lowercase__					  =				7

							lowercase__					  =				floats_tensor((batch_size, embedding_dim)						).to(_a						)

							lowercase__					  =				floats_tensor((batch_size, embedding_dim)						).to(_a						)
							lowercase__					  =				floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(_a						)

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




				def 							A__      (						self					:						List[str],					__lowercase					:						List[Any]=0						):
							torch.manual_seed(_a						)
							lowercase__					  =				4
							lowercase__					  =				8
							lowercase__					  =				7

							lowercase__					  =				torch.randn((batch_size, embedding_dim)						).to(_a						)

							lowercase__					  =				torch.randn((batch_size, embedding_dim)						).to(_a						)
							lowercase__					  =				torch.randn((batch_size, num_embeddings, embedding_dim)						).to(_a						)

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




				@property
				def 							A__      (						self					:						Tuple						):
							return (4, 8)




				@property
				def 							A__      (						self					:						Optional[int]						):
							return (4, 8)




				def 							A__      (						self					:						Optional[Any]						):
							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 							A__      (						self					:						int						):
							lowercase__					  =				PriorTransformer.from_pretrained(
							    "hf-internal-testing/prior-dummy",					output_loading_info=_a						)
							self.assertIsNotNone(_a						)
							self.assertEqual(len(loading_info["missing_keys"]						),					0						)

							model.to(_a						)
							lowercase__					  =				model(**self.dummy_input						)[0]

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




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.prepare_init_args_and_inputs_for_common()

							lowercase__					  =				self.model_class(**_a						)
							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],					_a						)




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"						)
							lowercase__					  =				model.to(_a						)

							if hasattr(_a,					"set_default_attn_processor"						):
										model.set_default_attn_processor()

							lowercase__					  =				self.get_dummy_seed_input()

							with torch.no_grad():
										lowercase__					  =				model(**_a						)[0]

							lowercase__					  =				output[0, :5].flatten().cpu()
							print(_a						)

							# 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([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
							self.assertTrue(torch_all_close(_a,					_a,					rtol=1e-2						)						)
@slow
class 				_snake_case			(					unittest.TestCase):




				def 							A__      (						self					:						Tuple,					__lowercase					:						List[Any]=1,					__lowercase					:						Optional[Any]=768,					__lowercase					:						Optional[int]=77,					__lowercase					:						List[str]=0						):
							torch.manual_seed(_a						)
							lowercase__					  =				batch_size
							lowercase__					  =				embedding_dim
							lowercase__					  =				num_embeddings

							lowercase__					  =				torch.randn((batch_size, embedding_dim)						).to(_a						)

							lowercase__					  =				torch.randn((batch_size, embedding_dim)						).to(_a						)
							lowercase__					  =				torch.randn((batch_size, num_embeddings, embedding_dim)						).to(_a						)

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




				def 							A__      (						self					:						Tuple						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ]						)
				def 							A__      (						self					:						Tuple,					__lowercase					:						Any,					__lowercase					:						Optional[int]						):
							lowercase__					  =				PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior",					subfolder="prior"						)
							model.to(_a						)
							lowercase__					  =				self.get_dummy_seed_input(seed=_a						)

							with torch.no_grad():
										lowercase__					  =				model(**_a						)[0]

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

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

							assert torch_all_close(_a,					_a,					atol=1e-3						)









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 				_snake_case			(					lowercase__):




				def __init__(						self					:						Optional[Any],					__lowercase					:						str = "▁",					__lowercase					:						bool = True,					__lowercase					:						Union[str, AddedToken] = "<unk>",					__lowercase					:						Union[str, AddedToken] = "</s>",					__lowercase					:						Union[str, AddedToken] = "<pad>",					):
							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=__lowercase,					add_prefix_space=__lowercase						),
							        pre_tokenizers.Digits(individual_digits=__lowercase						),
							        pre_tokenizers.Punctuation(),
							    ]						)
							lowercase__					  =				decoders.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						)

							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__(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[str, List[str]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

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

							self.add_unk_id()




				def 							A__      (						self					:						List[Any],					__lowercase					:						Union[Iterator[str], Iterator[Iterator[str]]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

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

							self.add_unk_id()




				def 							A__      (						self					:						str						):
							lowercase__					  =				json.loads(self._tokenizer.to_str()						)

							lowercase__					  =				self.special_tokens["unk"]["id"]

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




