

"""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    snake_case     (    _UpperCamelCase  ):

				def __init__(     self							:    List[Any]					,  UpperCamelCase__							:    str = "▁"					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    Union[str, AddedToken] = "<unk>"					,  UpperCamelCase__							:    Union[str, AddedToken] = "</s>"					,  UpperCamelCase__							:    Union[str, AddedToken] = "<pad>"					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      {
						    'pad': {'id': 0, 'token': pad_token},
						    'eos': {'id': 1, 'token': eos_token},
						    'unk': {'id': 2, 'token': unk_token},
						}

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

						__lowerCAmelCase:	Optional[int]   				=      Tokenizer(Unigram())

						__lowerCAmelCase:	List[Any]   				=      normalizers.Sequence(
						    [
						        normalizers.Nmt(),
						        normalizers.NFKC(),
						        normalizers.Replace(Regex(" {2,}")					,  " "),
						        normalizers.Lowercase(),
						    ])
						__lowerCAmelCase:	Optional[int]   				=      pre_tokenizers.Sequence(
						    [
						        pre_tokenizers.Metaspace(replacement=_UpperCAmelCase					,  add_prefix_space=_UpperCAmelCase),
						        pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
						        pre_tokenizers.Punctuation(),
						    ])
						__lowerCAmelCase:	int   				=      decoders.Metaspace(replacement=_UpperCAmelCase					,  add_prefix_space=_UpperCAmelCase)

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

						__lowerCAmelCase:	str   				=      {
						    'model': 'SentencePieceUnigram',
						    'replacement': replacement,
						    'add_prefix_space': add_prefix_space,
						}

						super().__init__(_UpperCAmelCase					,  _UpperCAmelCase)

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Union[str, List[str]]					,  UpperCamelCase__							:    int = 8_0_0_0					,  UpperCamelCase__							:    bool = True					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      trainers.UnigramTrainer(
						    vocab_size=_UpperCAmelCase					,  special_tokens=self.special_tokens_list					,  show_progress=_UpperCAmelCase					,  )

						if isinstance(_UpperCAmelCase					,  _UpperCAmelCase):
								__lowerCAmelCase:	Union[str, Any]   				=      [files]
						self._tokenizer.train(_UpperCAmelCase					,  trainer=_UpperCAmelCase)

						self.add_unk_id()

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Union[Iterator[str], Iterator[Iterator[str]]]					,  UpperCamelCase__							:    int = 8_0_0_0					,  UpperCamelCase__							:    bool = True					,  )->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      trainers.UnigramTrainer(
						    vocab_size=_UpperCAmelCase					,  special_tokens=self.special_tokens_list					,  show_progress=_UpperCAmelCase					,  )

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

						self.add_unk_id()




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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      json.loads(self._tokenizer.to_str())

						__lowerCAmelCase:	Any   				=      self.special_tokens['unk']['id']

						__lowerCAmelCase:	int   				=      Tokenizer.from_str(json.dumps(_UpperCAmelCase))





"""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    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  PriorTransformer
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """hidden_states"""

				@property
				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      4
						__lowerCAmelCase:	int   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	str   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str=0)->       str:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      4
						__lowerCAmelCase:	Dict   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	List[str]   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

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



						'''simple docstring'''


						return (4, 8)

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



						'''simple docstring'''


						return (4, 8)

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      {
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						__lowerCAmelCase:	Any   				=      self.dummy_input
						return init_dict, inputs_dict

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



						'''simple docstring'''


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

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      model(**self.dummy_input)[0]

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

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



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.prepare_init_args_and_inputs_for_common()

						__lowerCAmelCase:	Tuple   				=      self.model_class(**UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__lowerCAmelCase:	List[Any]   				=      [*signature.parameters.keys()]

						__lowerCAmelCase:	Any   				=      ["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]					,  UpperCamelCase__)




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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
						__lowerCAmelCase:	Union[str, Any]   				=      model.to(UpperCamelCase__)

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

						__lowerCAmelCase:	str   				=      self.get_dummy_seed_input()

						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(**UpperCamelCase__)[0]

						__lowerCAmelCase:	Dict   				=      output[0, :5].flatten().cpu()
						print(UpperCamelCase__)

						# 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.
						__lowerCAmelCase:	List[str]   				=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
						self.assertTrue(torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  rtol=1e-2))





@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    int=7_7					,  UpperCamelCase__							:    Any=0)->       Union[str, Any]:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Any   				=      embedding_dim
						__lowerCAmelCase:	Dict   				=      num_embeddings

						__lowerCAmelCase:	Dict   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

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



						'''simple docstring'''


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




				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ])
				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"					,  subfolder="prior")
						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.get_dummy_seed_input(seed=UpperCamelCase__)

						with torch.no_grad():
								__lowerCAmelCase:	Optional[Any]   				=      model(**UpperCamelCase__)[0]

						assert list(sample.shape) == [1, 7_6_8]

						__lowerCAmelCase:	Dict   				=      sample[0, :8].flatten().cpu()
						print(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(UpperCamelCase__)

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


