

'''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


__lowerCamelCase									=     get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		XGLMTokenizer
    lowercase						       =		XGLMTokenizerFast
    lowercase						       =		True
    lowercase						       =		True



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           super().setUp()

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



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   """<pad>"""
           A_						       =   1

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



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   list(self.get_tokenizer().get_vocab().keys()							)

           self.assertEqual(vocab_keys[0]		,					"""<s>"""							)
           self.assertEqual(vocab_keys[1]		,					"""<pad>"""							)
           self.assertEqual(len(UpperCamelCase__							)		,					1008							)



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           self.assertEqual(self.get_tokenizer().vocab_size		,					1008							)



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   XGLMTokenizer(UpperCamelCase__		,					keep_accents=UpperCamelCase__							)

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

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__							)		,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,					)

           A_						       =   tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
           self.assertListEqual(
               UpperCamelCase__		,					[
                   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(UpperCamelCase__							)
           self.assertListEqual(
               UpperCamelCase__		,					[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]		,					)

           A_						       =   tokenizer.convert_ids_to_tokens(UpperCamelCase__							)
           self.assertListEqual(
               UpperCamelCase__		,					[
                   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							)    ->   Optional[Any]:





           '''simple docstring'''
           return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)



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





           '''simple docstring'''
           with tempfile.NamedTemporaryFile() as f:
                  shutil.copyfile(UpperCamelCase__		,					f.name							)
                  A_						       =   XGLMTokenizer(f.name		,					keep_accents=UpperCamelCase__							)
                  A_						       =   pickle.dumps(UpperCamelCase__							)
           pickle.loads(UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           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(UpperCamelCase__							)
           A_						       =   rust_tokenizer.tokenize(UpperCamelCase__							)
           self.assertListEqual(UpperCamelCase__		,					UpperCamelCase__							)

           A_						       =   tokenizer.encode(UpperCamelCase__		,					add_special_tokens=UpperCamelCase__							)
           A_						       =   rust_tokenizer.encode(UpperCamelCase__		,					add_special_tokens=UpperCamelCase__							)
           self.assertListEqual(UpperCamelCase__		,					UpperCamelCase__							)

           A_						       =   self.get_rust_tokenizer()
           A_						       =   tokenizer.encode(UpperCamelCase__							)
           A_						       =   rust_tokenizer.encode(UpperCamelCase__							)
           self.assertListEqual(UpperCamelCase__		,					UpperCamelCase__							)



    @slow
    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """Hello World!"""
           A_						       =   [2, 31227, 4447, 35]

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



    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           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, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
           # fmt: on

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







    @slow
    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           # fmt: off
           A_						       =   {
               """input_ids""": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 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=UpperCamelCase__		,					model_name="""facebook/xglm-564M"""		,					padding=UpperCamelCase__		,					)



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           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







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

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

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

           assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2


@slow
@require_torch
class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

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

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

           assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
