

'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase :       Optional[int]			   =     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase :       Optional[int]			   =     2_5_6_0_4_7
UpperCAmelCase :       Union[str, Any]			   =     2_5_6_1_4_5







@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      NllbTokenizer
 lowerCAmelCase__  					=      NllbTokenizerFast
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      {}
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> int:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE     =      NllbTokenizer(__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE			)
      tokenizer.save_pretrained(self.tmpdirname			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      NllbTokenizer(__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      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 [285, 46, 10, 170, 382]]					,							)

      __SCREAMING_SNAKE_CASE     =      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""",
              """.""",
          ]					,							)
      __SCREAMING_SNAKE_CASE     =      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, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      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>""",
              """.""",
          ]					,							)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)
                __SCREAMING_SNAKE_CASE     =      tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
                self.assertSequenceEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)

                # Save tokenizer rust, legacy_format=True
                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE					,							legacy_format=__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # Checks it save with the same files
                self.assertSequenceEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)

                # Save tokenizer rust, legacy_format=False
                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE					,							legacy_format=__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # Checks it saved the tokenizer.json file
                self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Any:


      """simple docstring"""




      if not self.test_seqaseq:
           return

      __SCREAMING_SNAKE_CASE     =      self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'			):
                # Longer text that will definitely require truncation.
                __SCREAMING_SNAKE_CASE     =      [
                    """ UN Chief Says There Is No Military Solution in Syria""",
                    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
                    """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
                    """ will only worsen the violence and misery for millions of people.""",
                ]
                __SCREAMING_SNAKE_CASE     =      [
                    """Şeful ONU declară că nu există o soluţie militară în Siria""",
                    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
                    """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
                    """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
                ]
                try:
                     __SCREAMING_SNAKE_CASE     =      tokenizer.prepare_seqaseq_batch(
                         src_texts=__SCREAMING_SNAKE_CASE					,							tgt_texts=__SCREAMING_SNAKE_CASE					,							max_length=3					,							max_target_length=10					,							return_tensors="""pt"""					,							src_lang="""eng_Latn"""					,							tgt_lang="""ron_Latn"""					,							)
                except NotImplementedError:
                     return
                self.assertEqual(batch.input_ids.shape[1]					,							3			)
                self.assertEqual(batch.labels.shape[1]					,							10			)
                # max_target_length will default to max_length if not specified
                __SCREAMING_SNAKE_CASE     =      tokenizer.prepare_seqaseq_batch(
                    __SCREAMING_SNAKE_CASE					,							tgt_texts=__SCREAMING_SNAKE_CASE					,							max_length=3					,							return_tensors="""pt"""			)
                self.assertEqual(batch.input_ids.shape[1]					,							3			)
                self.assertEqual(batch.labels.shape[1]					,							3			)

                __SCREAMING_SNAKE_CASE     =      tokenizer.prepare_seqaseq_batch(
                    src_texts=__SCREAMING_SNAKE_CASE					,							max_length=3					,							max_target_length=10					,							return_tensors="""pt"""			)
                self.assertEqual(batch_encoder_only.input_ids.shape[1]					,							3			)
                self.assertEqual(batch_encoder_only.attention_mask.shape[1]					,							3			)
                self.assertNotIn("""decoder_input_ids"""					,							__SCREAMING_SNAKE_CASE			)
 @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[int]:


      """simple docstring"""




      pass


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Dict:


      """simple docstring"""




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                __SCREAMING_SNAKE_CASE     =      [AddedToken("""<special>"""					,							lstrip=__SCREAMING_SNAKE_CASE			)]

                __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(
                    __SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.encode("""Hey this is a <special> token"""			)

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.encode("""<special>"""					,							add_special_tokens=__SCREAMING_SNAKE_CASE			)[0]

                self.assertTrue(special_token_id in r_output			)

                if self.test_slow_tokenizer:
                     __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(
                         __SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE					,							)
                     __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(
                         __SCREAMING_SNAKE_CASE					,							additional_special_tokens=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

                     __SCREAMING_SNAKE_CASE     =      tokenizer_p.encode("""Hey this is a <special> token"""			)

                     __SCREAMING_SNAKE_CASE     =      tokenizer_cr.encode("""Hey this is a <special> token"""			)

                     self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
                     self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
                     self.assertTrue(special_token_id in p_output			)
                     self.assertTrue(special_token_id in cr_output			)







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      "facebook/nllb-200-distilled-600M"
 lowerCAmelCase__  					=      [
     " UN Chief Says There Is No Military Solution in Syria",
     " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
 ]
 lowerCAmelCase__  					=      [
     "Şeful ONU declară că nu există o soluţie militară în Siria",
     "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
     " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
     " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
 ]
 lowerCAmelCase__  					=      [
     256047,
     16297,
     134408,
     8165,
     248066,
     14734,
     950,
     1135,
     105721,
     3573,
     83,
     27352,
     108,
     49486,
     2,
 ]
 @classmethod
 def       UpperCAmelCase__		(		cls					:       List[Any]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      NllbTokenizer.from_pretrained(
          cls.checkpoint_name					,							src_lang="""eng_Latn"""					,							tgt_lang="""ron_Latn"""			)
      __SCREAMING_SNAKE_CASE     =      1
      return cls
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> List[str]:


      """simple docstring"""




      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]					,							256_001			)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]					,							256_002			)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]					,							256_057			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
      self.assertListEqual(self.expected_src_tokens					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      self.assertIn(__SCREAMING_SNAKE_CASE					,							self.tokenizer.all_special_ids			)
      # fmt: off
      __SCREAMING_SNAKE_CASE     =      [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
      # fmt: on

      __SCREAMING_SNAKE_CASE     =      self.tokenizer.decode(__SCREAMING_SNAKE_CASE					,							skip_special_tokens=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.decode(generated_ids[1:]					,							skip_special_tokens=__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      self.assertNotIn(self.tokenizer.eos_token					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ["""this is gunna be a long sentence """ * 20]
      assert isinstance(src_text[0]					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      10
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE			).input_ids[0]
      self.assertEqual(ids[-1]					,							2			)
      self.assertEqual(ids[0]					,							__SCREAMING_SNAKE_CASE			)
      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int			)  -> List[Any]:


      """simple docstring"""




      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)					,							[256_203, 3]			)
 def       UpperCAmelCase__		(		self					:       str			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      NllbTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,							__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          self.src_text					,							text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=len(self.expected_src_tokens			)					,							return_tensors="""pt"""					,							)
      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(
          batch["""labels"""]					,							self.tokenizer.pad_token_id					,							self.tokenizer.lang_code_to_id["""ron_Latn"""]			)

      self.assertIsInstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

      self.assertEqual((2, 15)					,							batch.input_ids.shape			)
      self.assertEqual((2, 15)					,							batch.attention_mask.shape			)
      __SCREAMING_SNAKE_CASE     =      batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens					,							__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							batch.decoder_input_ids[0, 0]			)  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens					,							[EN_CODE]			)
      self.assertEqual(self.tokenizer.suffix_tokens					,							[self.tokenizer.eos_token_id]			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(self.src_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=3					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=10					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      targets["""input_ids"""]
      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(
          __SCREAMING_SNAKE_CASE					,							self.tokenizer.pad_token_id					,							decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]					,							)

      self.assertEqual(batch.input_ids.shape[1]					,							3			)
      self.assertEqual(batch.decoder_input_ids.shape[1]					,							10			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       int			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer._build_translation_inputs(
          """A test"""					,							return_tensors="""pt"""					,							src_lang="""eng_Latn"""					,							tgt_lang="""fra_Latn"""			)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE			)					,							{
              # A, test, EOS, en_XX
              """input_ids""": [[256_047, 70, 7_356, 2]],
              """attention_mask""": [[1, 1, 1, 1]],
              # ar_AR
              """forced_bos_token_id""": 256_057,
          }					,							)


 @require_torch
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      True
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          """UN Chief says there is no military solution in Syria"""					,							src_lang="""eng_Latn"""					,							tgt_lang="""fra_Latn"""			)
      self.assertEqual(
          inputs.input_ids					,							[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]			)

      __SCREAMING_SNAKE_CASE     =      False
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          """UN Chief says there is no military solution in Syria"""					,							src_lang="""eng_Latn"""					,							tgt_lang="""fra_Latn"""			)
      self.assertEqual(
          inputs.input_ids					,							[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]			)









'''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 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @property
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

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

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

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

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

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      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 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """google/ddpm-cifar10-32"""

      __SCREAMING_SNAKE_CASE     =      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

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

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

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      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






