

"""simple docstring"""




import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A	:       Union[str, Any]        =						logging.get_logger(__name__)
__A	:       List[str]        =						{'vocab_file': 'spiece.model'}

__A	:       Tuple        =						{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

__A	:       int        =						{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

__A	:       List[str]        =						'▁'





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE														=       PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE														=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE														=       ["input_ids", "token_type_ids"]


		def __init__(self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Any=False	,	__SCREAMING_SNAKE_CASE					:						List[Any]=True	,	__SCREAMING_SNAKE_CASE					:						int=True	,	__SCREAMING_SNAKE_CASE					:						Tuple="<unk>"	,	__SCREAMING_SNAKE_CASE					:						List[str]="[SEP]"	,	__SCREAMING_SNAKE_CASE					:						int="<pad>"	,	__SCREAMING_SNAKE_CASE					:						int="[CLS]"	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]="[MASK]"	,	__SCREAMING_SNAKE_CASE					:						Optional[Dict[str, Any]] = None	,	**__SCREAMING_SNAKE_CASE					:						Dict	,	):
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							A     =   (
							    AddedToken(__SCREAMING_SNAKE_CASE	,	lstrip=__SCREAMING_SNAKE_CASE	,	rstrip=__SCREAMING_SNAKE_CASE	,	normalized=__SCREAMING_SNAKE_CASE)
							    if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							    else mask_token
							)

							A     =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=__SCREAMING_SNAKE_CASE	,	remove_space=__SCREAMING_SNAKE_CASE	,	keep_accents=__SCREAMING_SNAKE_CASE	,	unk_token=__SCREAMING_SNAKE_CASE	,	sep_token=__SCREAMING_SNAKE_CASE	,	pad_token=__SCREAMING_SNAKE_CASE	,	cls_token=__SCREAMING_SNAKE_CASE	,	mask_token=__SCREAMING_SNAKE_CASE	,	sp_model_kwargs=self.sp_model_kwargs	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   do_lower_case
							A     =   remove_space
							A     =   keep_accents
							A     =   vocab_file

							A     =   spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							return len(self.sp_model)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
							vocab.update(self.added_tokens_encoder)
							return vocab


		def __getstate__(self					:						int):
							A     =   self.__dict__.copy()
							A     =   None
							return state


		def __setstate__(self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]):
							A     =   d

							# for backward compatibility
							if not hasattr(self	,	"sp_model_kwargs"):
												A     =   {}

							A     =   spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(self.vocab_file)


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						Any):
							if self.remove_space:
												A     =   " ".join(inputs.strip().split())
							else:
												A     =   inputs
							A     =   outputs.replace("``"	,	"\"").replace("''"	,	"\"")

							if not self.keep_accents:
												A     =   unicodedata.normalize("NFKD"	,	__SCREAMING_SNAKE_CASE)
												A     =   "".join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
							if self.do_lower_case:
												A     =   outputs.lower()

							return outputs


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						str):
							A     =   self.preprocess_text(__SCREAMING_SNAKE_CASE)
							A     =   self.sp_model.encode(__SCREAMING_SNAKE_CASE	,	out_type=__SCREAMING_SNAKE_CASE)
							A     =   []
							for piece in pieces:
												if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
																	A     =   self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE	,	""))
																	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0]) == 1:
																											A     =   cur_pieces[1:]
																						else:
																											A     =   cur_pieces[0][1:]
																	cur_pieces.append(piece[-1])
																	new_pieces.extend(__SCREAMING_SNAKE_CASE)
												else:
																	new_pieces.append(__SCREAMING_SNAKE_CASE)

							return new_pieces


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	__SCREAMING_SNAKE_CASE					:						List[Any]):
							return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						int):
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						List[Any]):
							A     =   []
							A     =   ""
							A     =   False
							for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																	if not prev_is_special:
																						out_string += " "
																	out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE) + token
																	A     =   True
																	A     =   []
												else:
																	current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
																	A     =   False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
							return out_string.strip()


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						bool = False	,	__SCREAMING_SNAKE_CASE					:						bool = None	,	__SCREAMING_SNAKE_CASE					:						bool = True	,	**__SCREAMING_SNAKE_CASE					:						Tuple	,	):
							A     =   kwargs.pop("use_source_tokenizer"	,	__SCREAMING_SNAKE_CASE)

							A     =   self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	,	skip_special_tokens=__SCREAMING_SNAKE_CASE)

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							A     =   []
							A     =   []
							for token in filtered_tokens:
												if skip_special_tokens and token in self.all_special_ids:
																	continue
												if token in self.added_tokens_encoder:
																	if current_sub_text:
																						sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE))
																						A     =   []
																	sub_texts.append(__SCREAMING_SNAKE_CASE)
												else:
																	current_sub_text.append(__SCREAMING_SNAKE_CASE)
							if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE))

							# Mimic the behavior of the Rust tokenizer:
							# No space after <unk>
							if spaces_between_special_tokens:
												A     =   re.sub(R"(<unk>) "	,	R"\1"	,	" ".join(__SCREAMING_SNAKE_CASE))
							else:
												A     =   "".join(__SCREAMING_SNAKE_CASE)

							A     =   (
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
												A     =   self.clean_up_tokenization(__SCREAMING_SNAKE_CASE)
												return clean_text
							else:
												return text


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[List[int]] = None):
							A     =   [self.sep_token_id]
							A     =   [self.cls_token_id]
							if token_ids_a is None:
												return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[List[int]] = None	,	__SCREAMING_SNAKE_CASE					:						bool = False):

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__SCREAMING_SNAKE_CASE	,	token_ids_a=__SCREAMING_SNAKE_CASE	,	already_has_special_tokens=__SCREAMING_SNAKE_CASE)

							if token_ids_a is not None:
												return [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[int]	,	__SCREAMING_SNAKE_CASE					:						Optional[List[int]] = None):
							A     =   [self.sep_token_id]
							A     =   [self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep) * [0]
							return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Optional[str] = None):
							if not os.path.isdir(__SCREAMING_SNAKE_CASE):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
												return
							A     =   os.path.join(
							    __SCREAMING_SNAKE_CASE	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

							if os.path.abspath(self.vocab_file) != os.path.abspath(__SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
												copyfile(self.vocab_file	,	__SCREAMING_SNAKE_CASE)
							elif not os.path.isfile(self.vocab_file):
												with open(__SCREAMING_SNAKE_CASE	,	"wb") as fi:
																	A     =   self.sp_model.serialized_model_proto()
																	fi.write(__SCREAMING_SNAKE_CASE)

							return (out_vocab_file,)








"""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					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							pass


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


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


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


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


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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   {
							    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "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    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(loading_info["missing_keys"])	,	0)

							model.to(__SCREAMING_SNAKE_CASE)
							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    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							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     =   1_6
							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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample.permute(0	,	2	,	1)

							A     =   output[0, -3:, -3:].flatten()
							# fmt: off
							A     =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8])
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							# Not implemented yet for this UNet
							pass


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "harmonai/maestro-150k"
							A     =   UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							model.to(__SCREAMING_SNAKE_CASE)

							A     =   6_5_5_3_6
							A     =   torch.sin(torch.arange(__SCREAMING_SNAKE_CASE)[None, None, :].repeat(1	,	2	,	1)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1]).to(__SCREAMING_SNAKE_CASE)

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

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

							assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
							assert (output_max - 0.0_6_0_7).abs() < 4E-4





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							return (4, 1_4, 1)


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


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


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


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


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


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

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

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

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


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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   {
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
							    "up_block_types": [],
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
							    "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    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(vf_loading_info["missing_keys"])	,	0)

							value_function.to(__SCREAMING_SNAKE_CASE)
							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    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	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     =   1_4
							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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							# fmt: off
							A     =   torch.tensor([1_6_5.2_5] * seq_len)
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


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





