
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_											=						get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase_				(	__UpperCAmelCase    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						XGLMTokenizer
 UpperCAmelCase_		    =						XGLMTokenizerFast
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						True





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

     # We have a SentencePiece fixture for testing
     snake_case_ :    Optional[Any]     			=						XGLMTokenizer(lowerCAmelCase_ ,       keep_accents=lowerCAmelCase_)
     tokenizer.save_pretrained(self.tmpdirname)





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						"<pad>"
     snake_case_ :    Tuple     			=						1

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





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						list(self.get_tokenizer().get_vocab().keys())

     self.assertEqual(vocab_keys[0] ,       "<s>")
     self.assertEqual(vocab_keys[1] ,       "<pad>")
     self.assertEqual(len(lowerCAmelCase_) ,       10_08)





 def        snake_case__		(    self):
     self.assertEqual(self.get_tokenizer().vocab_size ,       10_08)





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						XGLMTokenizer(lowerCAmelCase_ ,       keep_accents=lowerCAmelCase_)

     snake_case_ :    Tuple     			=						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]] ,       )

     snake_case_ :    Any     			=						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",
             ".",
         ] ,       )
     snake_case_ :    Any     			=						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]
         ] ,       )

     snake_case_ :    Tuple     			=						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>",
             ".",
         ] ,       )





 @cached_property
 def        snake_case__		(    self):
     return XGLMTokenizer.from_pretrained("facebook/xglm-564M")





 def        snake_case__		(    self):







     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(lowerCAmelCase_ ,       f.name)
         snake_case_ :    str     			=						XGLMTokenizer(f.name ,       keep_accents=lowerCAmelCase_)
         snake_case_ :    str     			=						pickle.dumps(lowerCAmelCase_)
     pickle.loads(lowerCAmelCase_)





 def        snake_case__		(    self):







     if not self.test_rust_tokenizer:
         return

     snake_case_ :    Tuple     			=						self.get_tokenizer()
     snake_case_ :    str     			=						self.get_rust_tokenizer()

     snake_case_ :    List[Any]     			=						"I was born in 92000, and this is falsé."

     snake_case_ :    List[Any]     			=						tokenizer.tokenize(lowerCAmelCase_)
     snake_case_ :    Optional[Any]     			=						rust_tokenizer.tokenize(lowerCAmelCase_)
     self.assertListEqual(lowerCAmelCase_ ,       lowerCAmelCase_)

     snake_case_ :    Union[str, Any]     			=						tokenizer.encode(lowerCAmelCase_ ,       add_special_tokens=lowerCAmelCase_)
     snake_case_ :    Union[str, Any]     			=						rust_tokenizer.encode(lowerCAmelCase_ ,       add_special_tokens=lowerCAmelCase_)
     self.assertListEqual(lowerCAmelCase_ ,       lowerCAmelCase_)

     snake_case_ :    Any     			=						self.get_rust_tokenizer()
     snake_case_ :    str     			=						tokenizer.encode(lowerCAmelCase_)
     snake_case_ :    str     			=						rust_tokenizer.encode(lowerCAmelCase_)
     self.assertListEqual(lowerCAmelCase_ ,       lowerCAmelCase_)





 @slow
 def        snake_case__		(    self):
     snake_case_ :    int     			=						"Hello World!"
     snake_case_ :    Tuple     			=						[2, 3_12_27, 44_47, 35]

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





 @slow
 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						(
         "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
     snake_case_ :    Any     			=						[2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
     # fmt: on

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





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Any     			=						{
         "input_ids": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
         "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, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
     }  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCAmelCase_ ,       model_name="facebook/xglm-564M" ,       padding=lowerCAmelCase_ ,       )






'''simple docstring'''
import gc
import math
import unittest

import torch

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

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a_											=						logging.get_logger(__name__)

enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						4
     snake_case_ :    int     			=						3
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						{
         "block_out_channels": (32, 64),
         "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
         "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
         "attention_head_dim": 3,
         "out_channels": 3,
         "in_channels": 3,
         "layers_per_block": 2,
         "sample_size": 32,
     }
     snake_case_ :    List[Any]     			=						self.dummy_input
     return init_dict, inputs_dict






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    Dict     			=						4
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    Dict     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						{
         "sample_size": 32,
         "in_channels": 4,
         "out_channels": 4,
         "layers_per_block": 2,
         "block_out_channels": (32, 64),
         "attention_head_dim": 32,
         "down_block_types": ("DownBlock2D", "DownBlock2D"),
         "up_block_types": ("UpBlock2D", "UpBlock2D"),
     }
     snake_case_ :    Any     			=						self.dummy_input
     return init_dict, inputs_dict





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Tuple     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)

     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    int     			=						model(**self.dummy_input).sample

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





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[Any]     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model.to(lowercase_)
     snake_case_ :    str     			=						model(**self.dummy_input).sample

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





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
     snake_case_			, snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model_accelerate.to(lowercase_)
     model_accelerate.eval()

     snake_case_ :    Optional[int]     			=						torch.randn(
         1 ,       model_accelerate.config.in_channels ,       model_accelerate.config.sample_size ,       model_accelerate.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     snake_case_ :    Optional[int]     			=						model_accelerate(lowercase_ ,       lowercase_)["sample"]

     # two models don't need to stay in the device at the same time
     del model_accelerate
     torch.cuda.empty_cache()
     gc.collect()

     snake_case_			, snake_case_ :    Union[str, Any]     			=						UNetaDModel.from_pretrained(
         "fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_ ,       low_cpu_mem_usage=lowercase_)
     model_normal_load.to(lowercase_)
     model_normal_load.eval()
     snake_case_ :    Optional[Any]     			=						model_normal_load(lowercase_ ,       lowercase_)["sample"]

     assert torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
     model.eval()
     model.to(lowercase_)

     snake_case_ :    List[str]     			=						torch.randn(
         1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    str     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Tuple     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    Optional[int]     			=						output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     snake_case_ :    Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3))






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self ,       lowercase_=(32, 32)):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Dict     			=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa ,       device=lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						{
         "block_out_channels": [32, 64, 64, 64],
         "in_channels": 3,
         "layers_per_block": 1,
         "out_channels": 3,
         "time_embedding_type": "fourier",
         "norm_eps": 1E-6,
         "mid_block_scale_factor": math.sqrt(2.0),
         "norm_num_groups": None,
         "down_block_types": [
             "SkipDownBlock2D",
             "AttnSkipDownBlock2D",
             "SkipDownBlock2D",
             "SkipDownBlock2D",
         ],
         "up_block_types": [
             "SkipUpBlock2D",
             "SkipUpBlock2D",
             "AttnSkipUpBlock2D",
             "SkipUpBlock2D",
         ],
     }
     snake_case_ :    Optional[Any]     			=						self.dummy_input
     return init_dict, inputs_dict





 @slow
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[int]     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ,       output_loading_info=lowercase_)
     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    Dict     			=						self.dummy_input
     snake_case_ :    Tuple     			=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowercase_)
     snake_case_ :    Tuple     			=						noise
     snake_case_ :    Tuple     			=						model(**lowercase_)

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





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
     model.to(lowercase_)

     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    Dict     			=						(2_56, 2_56)

     snake_case_ :    Tuple     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    int     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[str]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
     model.to(lowercase_)

     snake_case_ :    Dict     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    List[Any]     			=						(32, 32)

     snake_case_ :    int     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     # not required for this model
     pass




