





"""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  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							UNetaDModel
					UpperCamelCase								=							'''sample'''


					@property
					def      lowerCamelCase	( self      :Tuple		):
												A		=  4
												A		=  14
												A		=  16

												A		=  floats_tensor((batch_size, num_features, seq_len)		).to(__UpperCamelCase		)
												A		=  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      :Optional[Any]		):
												return (4, 14, 16)


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


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


					@unittest.skipIf(torch_device == "mps"					,	"mish op not supported in MPS"		)
					def      lowerCamelCase	( self      :Any		):
												super().test_determinism()


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


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


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


					def      lowerCamelCase	( self      :List[str]		):
												A		=  {
												    "block_out_channels": (32, 64, 1_28, 2_56),
												    "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",
												}
												A		=  self.dummy_input
												return init_dict, inputs_dict


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

												A		=  model.config.in_channels
												A		=  16
												A		=  torch.randn((1, seq_len, num_features)		).permute(
												    0					,	2					,	1		)  # match original, we can update values and remove
												A		=  torch.full((num_features,)					,	0		)

												with torch.no_grad():
																			A		=  model(__UpperCamelCase					,	__UpperCamelCase		).sample.permute(0					,	2					,	1		)

												A		=  output[0, -3:, -3:].flatten()
												# fmt: off
												A		=  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      :int		):
												# Not implemented yet for this UNet
												pass


					@slow
					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  "harmonai/maestro-150k"
												A		=  UNetaDModel.from_pretrained(__UpperCamelCase					,	subfolder="unet"		)
												model.to(__UpperCamelCase		)

												A		=  6_55_36
												A		=  torch.sin(torch.arange(__UpperCamelCase		)[None, None, :].repeat(1					,	2					,	1		)		).to(__UpperCamelCase		)
												A		=  torch.tensor([1]		).to(__UpperCamelCase		)

												with torch.no_grad():
																			A		=  model(__UpperCamelCase					,	__UpperCamelCase		).sample

												A		=  output.abs().sum()
												A		=  output.abs().max()

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







class  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							UNetaDModel
					UpperCamelCase								=							'''sample'''


					@property
					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  4
												A		=  14
												A		=  16

												A		=  floats_tensor((batch_size, num_features, seq_len)		).to(__UpperCamelCase		)
												A		=  torch.tensor([10] * batch_size		).to(__UpperCamelCase		)

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


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


					@property
					def      lowerCamelCase	( self      :List[Any]		):
												return (4, 14, 1)


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


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


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


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

												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)

																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  output.sample

												self.assertIsNotNone(__UpperCamelCase		)
												A		=  torch.Size((inputs_dict["sample"].shape[0], 1)		)
												self.assertEqual(output.shape					,	__UpperCamelCase					,	"Input and output shapes do not match"		)


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


					def      lowerCamelCase	( self      :List[str]		):
												pass


					def      lowerCamelCase	( self      :Any		):
												A		=  {
												    "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, 1_28, 2_56],
												    "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",
												}
												A		=  self.dummy_input
												return init_dict, inputs_dict


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

												A		=  value_function.config.in_channels
												A		=  14
												A		=  torch.randn((1, seq_len, num_features)		).permute(
												    0					,	2					,	1		)  # match original, we can update values and remove
												A		=  torch.full((num_features,)					,	0		)

												with torch.no_grad():
																			A		=  value_function(__UpperCamelCase					,	__UpperCamelCase		).sample

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


					def      lowerCamelCase	( self      :Any		):
												# Not implemented yet for this UNet
												pass









"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."		)

												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


