






'''simple docstring'''







import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class _lowercase      (							_lowercase							, _lowercase							, unittest.TestCase						):
							a							 =			UNetaDModel
							a							 =			"""sample"""




							@property
							def 						lowerCamelCase_							(				self:							List[str]	):
														lowerCamelCase__   :      Optional[Any]			    =			4
														lowerCamelCase__   :      Tuple			    =			14
														lowerCamelCase__   :      List[Any]			    =			16

														lowerCamelCase__   :      Optional[int]			    =			floats_tensor((batch_size, num_features, seq_len)	).to(UpperCamelCase__	)
														lowerCamelCase__   :      str			    =			torch.tensor([10] * batch_size	).to(UpperCamelCase__	)

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




							@property
							def 						lowerCamelCase_							(				self:							List[Any]	):
														return (4, 14, 16)




							@property
							def 						lowerCamelCase_							(				self:							Tuple	):
														return (4, 14, 16)




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														pass




							def 						lowerCamelCase_							(				self:							Dict	):
														pass




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							List[str]	):
														super().test_determinism()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Tuple	):
														super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							List[Any]	):
														super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														super().test_output()




							def 						lowerCamelCase_							(				self:							str	):
														lowerCamelCase__   :      str			    =			{
														    """block_out_channels""": (32, 64, 128, 256),
														    """in_channels""": 14,
														    """out_channels""": 14,
														    """time_embedding_type""": """positional""",
														    """use_timestep_embedding""": True,
														    """flip_sin_to_cos""": False,
														    """freq_shift""": 1.0,
														    """out_block_type""": """OutConv1DBlock""",
														    """mid_block_type""": """MidResTemporalBlock1D""",
														    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
														    """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
														    """act_fn""": """mish""",
														}
														lowerCamelCase__   :      List[Any]			    =			self.dummy_input
														return init_dict, inputs_dict




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Any	):
														lowerCamelCase__    ,						lowerCamelCase__   :      Tuple			    =			UNetaDModel.from_pretrained(
														    """bglick13/hopper-medium-v2-value-function-hor32"""     ,				output_loading_info=UpperCamelCase__     ,				subfolder="""unet"""	)
														self.assertIsNotNone(UpperCamelCase__	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)     ,				0	)

														model.to(UpperCamelCase__	)
														lowerCamelCase__   :      List[str]			    =			model(**self.dummy_input	)

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




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							int	):
														lowerCamelCase__   :      int			    =			UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""     ,				subfolder="""unet"""	)
														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														lowerCamelCase__   :      Any			    =			model.config.in_channels
														lowerCamelCase__   :      Any			    =			16
														lowerCamelCase__   :      List[Any]			    =			torch.randn((1, seq_len, num_features)	).permute(
														    0     ,				2     ,				1	)  # match original, we can update values and remove
														lowerCamelCase__   :      str			    =			torch.full((num_features,)     ,				0	)

														with torch.no_grad():
																					lowerCamelCase__   :      Dict			    =			model(UpperCamelCase__     ,				UpperCamelCase__	).sample.permute(0     ,				2     ,				1	)

														lowerCamelCase__   :      Optional[Any]			    =			output[0, -3:, -3:].flatten()
														# fmt: off
														lowerCamelCase__   :      Union[str, Any]			    =			torch.tensor([-2.137_172, 1.1_426_016, 0.3_688_687, -0.766_922, 0.7_303_146, 0.11_038_864, -0.4_760_633, 0.13_270_172, 0.02_591_348]	)
														# fmt: on
														self.assertTrue(torch.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				rtol=1e-3	)	)




							def 						lowerCamelCase_							(				self:							List[str]	):
														# Not implemented yet for this UNet
														pass




							@slow
							def 						lowerCamelCase_							(				self:							str	):
														lowerCamelCase__   :      List[str]			    =			"""harmonai/maestro-150k"""
														lowerCamelCase__   :      Union[str, Any]			    =			UNetaDModel.from_pretrained(UpperCamelCase__     ,				subfolder="""unet"""	)
														model.to(UpperCamelCase__	)

														lowerCamelCase__   :      Optional[Any]			    =			65_536
														lowerCamelCase__   :      int			    =			torch.sin(torch.arange(UpperCamelCase__	)[None, None, :].repeat(1     ,				2     ,				1	)	).to(UpperCamelCase__	)
														lowerCamelCase__   :      Dict			    =			torch.tensor([1]	).to(UpperCamelCase__	)

														with torch.no_grad():
																					lowerCamelCase__   :      int			    =			model(UpperCamelCase__     ,				UpperCamelCase__	).sample

														lowerCamelCase__   :      Optional[int]			    =			output.abs().sum()
														lowerCamelCase__   :      Tuple			    =			output.abs().max()

														assert (output_sum - 224.0_896).abs() < 0.5
														assert (output_max - 0.0_607).abs() < 4e-4



class _lowercase      (							_lowercase							, _lowercase							, unittest.TestCase						):
							a							 =			UNetaDModel
							a							 =			"""sample"""




							@property
							def 						lowerCamelCase_							(				self:							List[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			4
														lowerCamelCase__   :      Tuple			    =			14
														lowerCamelCase__   :      Union[str, Any]			    =			16

														lowerCamelCase__   :      Union[str, Any]			    =			floats_tensor((batch_size, num_features, seq_len)	).to(UpperCamelCase__	)
														lowerCamelCase__   :      str			    =			torch.tensor([10] * batch_size	).to(UpperCamelCase__	)

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




							@property
							def 						lowerCamelCase_							(				self:							List[str]	):
														return (4, 14, 16)




							@property
							def 						lowerCamelCase_							(				self:							List[str]	):
														return (4, 14, 1)




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														super().test_determinism()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Dict	):
														super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							List[Any]	):
														super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														# UNetRL is a value-function is different output shape
														lowerCamelCase__    ,						lowerCamelCase__   :      int			    =			self.prepare_init_args_and_inputs_for_common()
														lowerCamelCase__   :      List[Any]			    =			self.model_class(**UpperCamelCase__	)
														model.to(UpperCamelCase__	)
														model.eval()

														with torch.no_grad():
																					lowerCamelCase__   :      List[str]			    =			model(**UpperCamelCase__	)

																					if isinstance(UpperCamelCase__     ,				UpperCamelCase__	):
																												lowerCamelCase__   :      str			    =			output.sample

														self.assertIsNotNone(UpperCamelCase__	)
														lowerCamelCase__   :      Union[str, Any]			    =			torch.Size((inputs_dict["""sample"""].shape[0], 1)	)
														self.assertEqual(output.shape     ,				UpperCamelCase__     ,				"""Input and output shapes do not match"""	)




							def 						lowerCamelCase_							(				self:							Any	):
														pass




							def 						lowerCamelCase_							(				self:							List[Any]	):
														pass




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			{
														    """in_channels""": 14,
														    """out_channels""": 14,
														    """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
														    """up_block_types""": [],
														    """out_block_type""": """ValueFunction""",
														    """mid_block_type""": """ValueFunctionMidBlock1D""",
														    """block_out_channels""": [32, 64, 128, 256],
														    """layers_per_block""": 1,
														    """downsample_each_block""": True,
														    """use_timestep_embedding""": True,
														    """freq_shift""": 1.0,
														    """flip_sin_to_cos""": False,
														    """time_embedding_type""": """positional""",
														    """act_fn""": """mish""",
														}
														lowerCamelCase__   :      Dict			    =			self.dummy_input
														return init_dict, inputs_dict




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Any	):
														lowerCamelCase__    ,						lowerCamelCase__   :      int			    =			UNetaDModel.from_pretrained(
														    """bglick13/hopper-medium-v2-value-function-hor32"""     ,				output_loading_info=UpperCamelCase__     ,				subfolder="""value_function"""	)
														self.assertIsNotNone(UpperCamelCase__	)
														self.assertEqual(len(vf_loading_info["""missing_keys"""]	)     ,				0	)

														value_function.to(UpperCamelCase__	)
														lowerCamelCase__   :      List[str]			    =			value_function(**self.dummy_input	)

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




							@unittest.skipIf(torch_device == """mps"""     ,				"""mish op not supported in MPS"""	)
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__    ,						lowerCamelCase__   :      Tuple			    =			UNetaDModel.from_pretrained(
														    """bglick13/hopper-medium-v2-value-function-hor32"""     ,				output_loading_info=UpperCamelCase__     ,				subfolder="""value_function"""	)
														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														lowerCamelCase__   :      Dict			    =			value_function.config.in_channels
														lowerCamelCase__   :      str			    =			14
														lowerCamelCase__   :      Any			    =			torch.randn((1, seq_len, num_features)	).permute(
														    0     ,				2     ,				1	)  # match original, we can update values and remove
														lowerCamelCase__   :      List[str]			    =			torch.full((num_features,)     ,				0	)

														with torch.no_grad():
																					lowerCamelCase__   :      Optional[Any]			    =			value_function(UpperCamelCase__     ,				UpperCamelCase__	).sample

														# fmt: off
														lowerCamelCase__   :      Any			    =			torch.tensor([165.25] * seq_len	)
														# fmt: on
														self.assertTrue(torch.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				rtol=1e-3	)	)




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														# Not implemented yet for this UNet
														pass









'''simple docstring'''







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().setUp()
														lowerCamelCase__   :      int			    =			[
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														lowerCamelCase__   :      List[str]			    =			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__	)	)	)	)
														lowerCamelCase__   :      List[str]			    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														lowerCamelCase__   :      List[Any]			    =			{"""unk_token""": """<unk>"""}

														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""merges_file"""]	)
														with open(self.vocab_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write(json.dumps(UpperCamelCase__	) + """\n"""	)
														with open(self.merges_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(UpperCamelCase__	)	)




							def 						lowerCamelCase_							(				self:							Tuple     ,				**UpperCamelCase__:							Dict	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Tuple	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]	):
														return "lower newer", "lower newer"




							@cached_property
							def 						lowerCamelCase_							(				self:							str	):
														return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




							@cached_property
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Any			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__   :      List[Any]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(UpperCamelCase__     ,				max_length=len(UpperCamelCase__	)     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)

																					self.assertEqual((2, 9)     ,				batch.input_ids.shape	)
																					self.assertEqual((2, 9)     ,				batch.attention_mask.shape	)
																					lowerCamelCase__   :      Optional[Any]			    =			batch.input_ids.tolist()[0]
																					self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__	)
																					# Test that special tokens are reset




							@require_torch
							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				UpperCamelCase__	)
																					self.assertIn("""attention_mask"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""labels"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				UpperCamelCase__	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			[
														    """Summary of the text.""",
														    """Another summary.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Any			    =			tokenizer(text_target=UpperCamelCase__     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							str	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			["""A long paragraph for summarization."""]
														lowerCamelCase__   :      Any			    =			[
														    """Summary of the text.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(text_target=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      List[str]			    =			inputs["""input_ids"""]
																					lowerCamelCase__   :      Optional[int]			    =			targets["""input_ids"""]
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														pass




							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												lowerCamelCase__   :      int			    =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      int			    =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      Dict			    =			"""A, <mask> AllenNLP sentence."""
																												lowerCamelCase__   :      Dict			    =			tokenizer_r.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)
																												lowerCamelCase__   :      Union[str, Any]			    =			tokenizer_p.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)

																												# token_type_ids should put 0 everywhere
																												self.assertEqual(sum(tokens_r["""token_type_ids"""]	)     ,				sum(tokens_p["""token_type_ids"""]	)	)

																												# attention_mask should put 1 everywhere, so sum over length should be 1
																												self.assertEqual(
																												    sum(tokens_r["""attention_mask"""]	) / len(tokens_r["""attention_mask"""]	)     ,				sum(tokens_p["""attention_mask"""]	) / len(tokens_p["""attention_mask"""]	)     ,				)

																												lowerCamelCase__   :      Any			    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																												lowerCamelCase__   :      Any			    =			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)

																												# Rust correctly handles the space before the mask while python doesnt
																												self.assertSequenceEqual(tokens_p["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																												self.assertSequenceEqual(tokens_r["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
																												self.assertSequenceEqual(
																												    UpperCamelCase__     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)

