

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


__A     :					List[str]     = get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
	from transformers.models.mbart.modeling_mbart import shift_tokens_right

__A     :					int     = 25_00_04
__A     :					Optional[Any]     = 25_00_20


@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE	(   _UpperCAmelCase      ,  unittest.TestCase):
						_UpperCamelCase:List[Any]     =						MBartTokenizer
						_UpperCamelCase:Any     =						MBartTokenizerFast
						_UpperCamelCase:Optional[int]     =						True
						_UpperCamelCase:Optional[int]     =						True







						def     _snake_case							(       self				)->				str:
													super().setUp()

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







						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =MBartTokenizer(A_			,		keep_accents=A_				)

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

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

													lowerCamelCase_				     =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""",
													        """.""",
													    ]			,		)
													lowerCamelCase_				     =tokenizer.convert_tokens_to_ids(A_				)
													self.assertListEqual(
													    A_			,		[
													        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]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]			,		)

													lowerCamelCase_				     =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>""",
													        """.""",
													    ]			,		)







						def     _snake_case							(       self				)->				Dict:





													if not self.test_slow_tokenizer:
																				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																				return

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

																											lowerCamelCase_				     =tempfile.mkdtemp()

																											lowerCamelCase_				     =tokenizer_r.save_pretrained(A_				)
																											lowerCamelCase_				     =tokenizer_p.save_pretrained(A_				)

																											# 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				)				)
																											lowerCamelCase_				     =tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
																											self.assertSequenceEqual(A_			,		A_				)

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

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(A_			,		A_				)				)
																																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																											shutil.rmtree(A_				)

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

																											lowerCamelCase_				     =tokenizer_r.save_pretrained(A_			,		legacy_format=A_				)
																											lowerCamelCase_				     =tokenizer_p.save_pretrained(A_				)

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

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

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

																											shutil.rmtree(A_				)

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

																											lowerCamelCase_				     =tokenizer_r.save_pretrained(A_			,		legacy_format=A_				)
																											lowerCamelCase_				     =tokenizer_p.save_pretrained(A_				)

																											# 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
																											lowerCamelCase_				     =tokenizer_r.from_pretrained(A_				)
																											lowerCamelCase_				     =tokenizer_p.from_pretrained(A_				)

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

																											shutil.rmtree(A_				)


@require_torch
@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase):
						_UpperCamelCase:Optional[int]     =						"facebook/mbart-large-en-ro"
						_UpperCamelCase:List[str]     =						[
						    " 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.",
						]
						_UpperCamelCase:Union[str, Any]     =						[
						    "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.",
						]
						_UpperCamelCase:Optional[int]     =						[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]







						@classmethod
						def     _snake_case							(       cls				)->				Tuple:
													lowerCamelCase_				     =MBartTokenizer.from_pretrained(
													    cls.checkpoint_name			,		src_lang="""en_XX"""			,		tgt_lang="""ro_RO"""				)
													lowerCamelCase_				     =1
													return cls







						def     _snake_case							(       self				)->				List[Any]:
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]			,		25_0001				)
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]			,		25_0004				)
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]			,		25_0020				)







						def     _snake_case							(       self				)->				Any:
													lowerCamelCase_				     =self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
													self.assertListEqual(self.expected_src_tokens			,		A_				)







						def     _snake_case							(       self				)->				List[Any]:
													self.assertIn(A_			,		self.tokenizer.all_special_ids				)
													lowerCamelCase_				     =[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
													lowerCamelCase_				     =self.tokenizer.decode(A_			,		skip_special_tokens=A_				)
													lowerCamelCase_				     =self.tokenizer.decode(generated_ids[1:]			,		skip_special_tokens=A_				)
													self.assertEqual(A_			,		A_				)
													self.assertNotIn(self.tokenizer.eos_token			,		A_				)







						def     _snake_case							(       self				)->				str:
													lowerCamelCase_				     =['''this is gunna be a long sentence ''' * 20]
													assert isinstance(src_text[0]			,		A_				)
													lowerCamelCase_				     =10
													lowerCamelCase_				     =self.tokenizer(A_			,		max_length=A_			,		truncation=A_				).input_ids[0]
													self.assertEqual(ids[-2]			,		2				)
													self.assertEqual(ids[-1]			,		A_				)
													self.assertEqual(len(A_				)			,		A_				)







						def     _snake_case							(       self				)->				str:
													self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]				)			,		[25_0026, 25_0001]				)







						def     _snake_case							(       self				)->				Tuple:
													lowerCamelCase_				     =tempfile.mkdtemp()
													lowerCamelCase_				     =self.tokenizer.fairseq_tokens_to_ids
													self.tokenizer.save_pretrained(A_				)
													lowerCamelCase_				     =MBartTokenizer.from_pretrained(A_				)
													self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,		A_				)







						@require_torch
						def     _snake_case							(       self				)->				Dict:
													lowerCamelCase_				     =self.tokenizer(self.src_text			,		text_target=self.tgt_text			,		padding=A_			,		return_tensors="""pt"""				)
													lowerCamelCase_				     =shift_tokens_right(batch["""labels"""]			,		self.tokenizer.pad_token_id				)

													# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
													assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
													assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
													assert batch.decoder_input_ids[1][-1] == 2
													assert batch.labels[1][-2:].tolist() == [2, RO_CODE]







						@require_torch
						def     _snake_case							(       self				)->				Dict:
													lowerCamelCase_				     =self.tokenizer(
													    self.src_text			,		text_target=self.tgt_text			,		padding=A_			,		truncation=A_			,		max_length=len(self.expected_src_tokens				)			,		return_tensors="""pt"""			,		)

													lowerCamelCase_				     =shift_tokens_right(batch["""labels"""]			,		self.tokenizer.pad_token_id				)

													self.assertIsInstance(A_			,		A_				)

													self.assertEqual((2, 14)			,		batch.input_ids.shape				)
													self.assertEqual((2, 14)			,		batch.attention_mask.shape				)
													lowerCamelCase_				     =batch.input_ids.tolist()[0]
													self.assertListEqual(self.expected_src_tokens			,		A_				)
													self.assertEqual(2			,		batch.decoder_input_ids[0, -1]				)  # EOS
													# Test that special tokens are reset
													self.assertEqual(self.tokenizer.prefix_tokens			,		[]				)
													self.assertEqual(self.tokenizer.suffix_tokens			,		[self.tokenizer.eos_token_id, EN_CODE]				)







						def     _snake_case							(       self				)->				Dict:
													lowerCamelCase_				     =self.tokenizer(self.src_text			,		padding=A_			,		truncation=A_			,		max_length=3			,		return_tensors="""pt"""				)
													lowerCamelCase_				     =self.tokenizer(
													    text_target=self.tgt_text			,		padding=A_			,		truncation=A_			,		max_length=10			,		return_tensors="""pt"""				)
													lowerCamelCase_				     =targets['''input_ids''']
													lowerCamelCase_				     =shift_tokens_right(A_			,		self.tokenizer.pad_token_id				)

													self.assertEqual(batch.input_ids.shape[1]			,		3				)
													self.assertEqual(batch.decoder_input_ids.shape[1]			,		10				)







						@require_torch
						def     _snake_case							(       self				)->				List[str]:
													lowerCamelCase_				     =self.tokenizer._build_translation_inputs(
													    """A test"""			,		return_tensors="""pt"""			,		src_lang="""en_XX"""			,		tgt_lang="""ar_AR"""				)

													self.assertEqual(
													    nested_simplify(A_				)			,		{
													        # A, test, EOS, en_XX
													        """input_ids""": [[62, 3034, 2, 25_0004]],
													        """attention_mask""": [[1, 1, 1, 1]],
													        # ar_AR
													        """forced_bos_token_id""": 25_0001,
													    }			,		)



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   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					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					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					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   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''google/ddpm-cifar10-32'''

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

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


