


"""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       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							4
         lowerCAmelCase     :     str								=							8
         lowerCAmelCase     :     Optional[Any]								=							7

         lowerCAmelCase     :     Tuple								=							floats_tensor((batch_size, embedding_dim)						).to(snake_case__						)

         lowerCAmelCase     :     Tuple								=							floats_tensor((batch_size, embedding_dim)						).to(snake_case__						)
         lowerCAmelCase     :     Tuple								=							floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(snake_case__						)

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

       def        lowercase__							(						self		,     snake_case__=0						):






         """simple docstring"""





         torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							4
         lowerCAmelCase     :     List[Any]								=							8
         lowerCAmelCase     :     Tuple								=							7

         lowerCAmelCase     :     Optional[Any]								=							torch.randn((batch_size, embedding_dim)						).to(snake_case__						)

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

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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return (4, 8)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return (4, 8)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							{
             "num_attention_heads": 2,
             "attention_head_dim": 4,
             "num_layers": 2,
             "embedding_dim": 8,
             "num_embeddings": 7,
             "additional_embeddings": 4,
         }
         lowerCAmelCase     :     Optional[Any]								=							self.dummy_input
         return init_dict, inputs_dict

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							PriorTransformer.from_pretrained(
             "hf-internal-testing/prior-dummy"		,     output_loading_info=snake_case__						)
         self.assertIsNotNone(snake_case__						)
         self.assertEqual(len(loading_info["missing_keys"]						)		,     0						)

         model.to(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							model(**self.dummy_input						)[0]

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							self.prepare_init_args_and_inputs_for_common()

         lowerCAmelCase     :     int								=							self.model_class(**snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase     :     List[str]								=							[*signature.parameters.keys()]

         lowerCAmelCase     :     List[str]								=							["hidden_states", "timestep"]
         self.assertListEqual(arg_names[:2]		,     snake_case__						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"						)
         lowerCAmelCase     :     int								=							model.to(snake_case__						)

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

         lowerCAmelCase     :     Union[str, Any]								=							self.get_dummy_seed_input()

         with torch.no_grad():
           lowerCAmelCase     :     List[Any]								=							model(**snake_case__						)[0]

         lowerCAmelCase     :     Optional[Any]								=							output[0, :5].flatten().cpu()
         print(snake_case__						)

         # 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(snake_case__		,     snake_case__		,     rtol=1e-2						)						)


@slow
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self		,     snake_case__=1		,     snake_case__=768		,     snake_case__=77		,     snake_case__=0						):






         """simple docstring"""





         torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							batch_size
         lowerCAmelCase     :     Optional[Any]								=							embedding_dim
         lowerCAmelCase     :     int								=							num_embeddings

         lowerCAmelCase     :     Union[str, Any]								=							torch.randn((batch_size, embedding_dim)						).to(snake_case__						)

         lowerCAmelCase     :     Dict								=							torch.randn((batch_size, embedding_dim)						).to(snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							torch.randn((batch_size, num_embeddings, embedding_dim)						).to(snake_case__						)

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

       def        lowercase__							(						self						):






         """simple docstring"""





         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        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





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

         with torch.no_grad():
           lowerCAmelCase     :     Tuple								=							model(**snake_case__						)[0]

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

         lowerCAmelCase     :     Optional[int]								=							sample[0, :8].flatten().cpu()
         print(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							torch.tensor(snake_case__						)

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







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case :		str							      =  "▁"

snake_case :		List[Any]							      =  get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    BigBirdTokenizer
			UpperCamelCase__          =    BigBirdTokenizerFast
			UpperCamelCase__          =    True
			UpperCamelCase__          =    True







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

				__magic_name__		:		Optional[Any]	   =     self.tokenizer_class(_a						,   keep_accents=_a		)
				tokenizer.save_pretrained(self.tmpdirname		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     "<s>"
				__magic_name__		:		Dict	   =     1

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]						,   "<unk>"		)
				self.assertEqual(vocab_keys[1]						,   "<s>"		)
				self.assertEqual(vocab_keys[-1]						,   "[MASK]"		)
				self.assertEqual(len(_a		)						,   1_004		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self.assertEqual(self.get_tokenizer().vocab_size						,   1_000		)







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

				__magic_name__		:		Dict	   =     self.get_tokenizer()
				__magic_name__		:		str	   =     self.get_rust_tokenizer()

				__magic_name__		:		Any	   =     "I was born in 92000, and this is falsé."

				__magic_name__		:		Dict	   =     tokenizer.tokenize(_a		)
				__magic_name__		:		Any	   =     rust_tokenizer.tokenize(_a		)
				self.assertListEqual(_a						,   _a		)

				__magic_name__		:		List[Any]	   =     tokenizer.encode(_a						,   add_special_tokens=_a		)
				__magic_name__		:		List[str]	   =     rust_tokenizer.encode(_a						,   add_special_tokens=_a		)
				self.assertListEqual(_a						,   _a		)

				__magic_name__		:		str	   =     self.get_rust_tokenizer()
				__magic_name__		:		Dict	   =     tokenizer.encode(_a		)
				__magic_name__		:		Optional[int]	   =     rust_tokenizer.encode(_a		)
				self.assertListEqual(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     BigBirdTokenizer(_a						,   keep_accents=_a		)

				__magic_name__		:		str	   =     tokenizer.tokenize("This is a test"		)
				self.assertListEqual(_a						,   ["▁This", "▁is", "▁a", "▁t", "est"]		)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(_a		)						,   [285, 46, 10, 170, 382]						,   )

				__magic_name__		:		Dict	   =     tokenizer.tokenize("I was born in 92000, and this is falsé."		)
				self.assertListEqual(
				    _a						,   [
				        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",
				        ".",
				    ]						,   )
				__magic_name__		:		Union[str, Any]	   =     tokenizer.convert_tokens_to_ids(_a		)
				self.assertListEqual(
				    _a						,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,   )

				__magic_name__		:		int	   =     tokenizer.convert_ids_to_tokens(_a		)
				self.assertListEqual(
				    _a						,   [
				        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>",
				        ".",
				    ]						,   )







			@cached_property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     "Hello World!"
				__magic_name__		:		Dict	   =     [65, 18_536, 2_260, 101, 66]

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







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     (
				    "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"
				)
				# fmt: off
				__magic_name__		:		List[str]	   =     [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
				# fmt: on
				self.assertListEqual(_a						,   self.big_tokenizer.encode(_a		)		)







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				import torch

				from transformers import BigBirdConfig, BigBirdModel

				# Build sequence
				__magic_name__		:		Optional[Any]	   =     list(self.big_tokenizer.get_vocab().keys()		)[:10]
				__magic_name__		:		List[Any]	   =     " ".join(_a		)
				__magic_name__		:		Any	   =     self.big_tokenizer.encode_plus(_a						,   return_tensors="pt"						,   return_token_type_ids=_a		)
				__magic_name__		:		Union[str, Any]	   =     self.big_tokenizer.batch_encode_plus(
				    [sequence + " " + sequence]						,   return_tensors="pt"						,   return_token_type_ids=_a		)

				__magic_name__		:		List[str]	   =     BigBirdConfig(attention_type="original_full"		)
				__magic_name__		:		Optional[int]	   =     BigBirdModel(_a		)

				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

				with torch.no_grad():
					model(**_a		)
					model(**_a		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)
				__magic_name__		:		int	   =     tokenizer.decode(tokenizer("Paris is the [MASK]."		).input_ids		)

				self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				# fmt: off
				__magic_name__		:		Optional[Any]	   =     {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 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], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 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]], "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, 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, 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=_a						,   model_name="google/bigbird-roberta-base"						,   revision="215c99f1600e06f83acce68422f2035b2b5c3510"						,   )
