'''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


SCREAMING_SNAKE_CASE :	List[Any]							     =		get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			XGLMTokenizer
  _SCREAMING_SNAKE_CASE    				=			XGLMTokenizerFast
  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			True

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a       =  XGLMTokenizer(__SCREAMING_SNAKE_CASE     ,							keep_accents=__SCREAMING_SNAKE_CASE       )
   tokenizer.save_pretrained(self.tmpdirname       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  "<pad>"
   __a       =  1

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

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  list(self.get_tokenizer().get_vocab().keys()       )

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

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   self.assertEqual(self.get_tokenizer().vocab_size     ,							10_08       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  XGLMTokenizer(__SCREAMING_SNAKE_CASE     ,							keep_accents=__SCREAMING_SNAKE_CASE       )

   __a       =  tokenizer.tokenize("This is a test"       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							["▁This", "▁is", "▁a", "▁t", "est"]       )

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )     ,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,							)

   __a       =  tokenizer.tokenize("I was born in 92000, and this is falsé."       )
   self.assertListEqual(
       __SCREAMING_SNAKE_CASE     ,							[
           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",
           ".",
       ]     ,							)
   __a       =  tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(
       __SCREAMING_SNAKE_CASE     ,							[
           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]
       ]     ,							)

   __a       =  tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(
       __SCREAMING_SNAKE_CASE     ,							[
           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 		_UpperCAmelCase   (		self :      List[Any]       ):
   return XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   with tempfile.NamedTemporaryFile() as f:
    shutil.copyfile(__SCREAMING_SNAKE_CASE     ,							f.name       )
    __a       =  XGLMTokenizer(f.name     ,							keep_accents=__SCREAMING_SNAKE_CASE       )
    __a       =  pickle.dumps(__SCREAMING_SNAKE_CASE       )
   pickle.loads(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   if not self.test_rust_tokenizer:
    return

   __a       =  self.get_tokenizer()
   __a       =  self.get_rust_tokenizer()

   __a       =  "I was born in 92000, and this is falsé."

   __a       =  tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )
   __a       =  rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  tokenizer.encode(__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE       )
   __a       =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  self.get_rust_tokenizer()
   __a       =  tokenizer.encode(__SCREAMING_SNAKE_CASE       )
   __a       =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  "Hello World!"
   __a       =  [2, 3_12_27, 44_47, 35]

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

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  (
       "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
   __a       =  [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(__SCREAMING_SNAKE_CASE     ,							self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   # fmt: off
   __a       =  {
       "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=__SCREAMING_SNAKE_CASE     ,							model_name="facebook/xglm-564M"     ,							padding=__SCREAMING_SNAKE_CASE     ,							)



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  UNetaDModel(
       block_out_channels=(32, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,							up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  VQModel(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=3     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

   __a       =  image[0, -3:, -3:, -1]
   __a       =  image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

   __a       =  image[0, -3:, -3:, -1]

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


