





import os
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 SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{"""vocab_file""": """spiece.model"""}

_SCREAMING_SNAKE_CASE      	=		{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

_SCREAMING_SNAKE_CASE      	=		{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE      	=		0
_SCREAMING_SNAKE_CASE      	=		1
_SCREAMING_SNAKE_CASE      	=		2
_SCREAMING_SNAKE_CASE      	=		3
_SCREAMING_SNAKE_CASE      	=		4
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Union[str, Any]       =       VOCAB_FILES_NAMES
						__magic_name__:			Dict       =       PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:			Optional[Any]       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:			Optional[Any]       =       "left"






						def __init__(      self      :      Optional[int]		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]=False		,   _A      :      Optional[Any]=True		,   _A      :      Dict=False		,   _A      :      Any="<s>"		,   _A      :      Union[str, Any]="</s>"		,   _A      :      Dict="<unk>"		,   _A      :      Optional[int]="<sep>"		,   _A      :      Optional[int]="<pad>"		,   _A      :      str="<cls>"		,   _A      :      Any="<mask>"		,   _A      :      Optional[Any]=["<eop>", "<eod>"]		,   _A      :      Optional[Dict[str, Any]] = None		,   **_A      :      Tuple		,   )							->    None:







							"""simple docstring"""


							snake_case_		:			str											=	AddedToken(_A		,   lstrip=_A		,   rstrip=_A	) if isinstance(_A		,   _A	) else mask_token

							snake_case_		:			str											=	{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=_A		,   remove_space=_A		,   keep_accents=_A		,   bos_token=_A		,   eos_token=_A		,   unk_token=_A		,   sep_token=_A		,   pad_token=_A		,   cls_token=_A		,   mask_token=_A		,   additional_special_tokens=_A		,   sp_model_kwargs=self.sp_model_kwargs		,   **_A		,   )

							snake_case_		:			Dict											=	3

							snake_case_		:			List[str]											=	do_lower_case
							snake_case_		:			Tuple											=	remove_space
							snake_case_		:			List[str]											=	keep_accents
							snake_case_		:			Any											=	vocab_file

							snake_case_		:			Optional[Any]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(_A	)






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[int]:







							"""simple docstring"""


							return len(self.sp_model	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    int:







							"""simple docstring"""


							snake_case_		:			Any											=	{self.convert_ids_to_tokens(_A	): i for i in range(self.vocab_size	)}
							vocab.update(self.added_tokens_encoder	)
							return vocab






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







							"""simple docstring"""


							snake_case_		:			str											=	self.__dict__.copy()
							snake_case_		:			int											=	None
							return state






						def __setstate__(      self      :      Dict		,   _A      :      int	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			List[str]											=	d

							# for backward compatibility
							if not hasattr(self		,   'sp_model_kwargs'	):
								snake_case_		:			Optional[int]											=	{}

							snake_case_		:			List[str]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(self.vocab_file	)






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      List[str]	)							->    Tuple:







							"""simple docstring"""


							if self.remove_space:
								snake_case_		:			Dict											=	' '.join(inputs.strip().split()	)
							else:
								snake_case_		:			Tuple											=	inputs
							snake_case_		:			str											=	outputs.replace('``'		,   '"'	).replace('\'\''		,   '"'	)

							if not self.keep_accents:
								snake_case_		:			List[str]											=	unicodedata.normalize('NFKD'		,   _A	)
								snake_case_		:			str											=	''.join([c for c in outputs if not unicodedata.combining(_A	)]	)
							if self.do_lower_case:
								snake_case_		:			Optional[int]											=	outputs.lower()

							return outputs






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      str	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.preprocess_text(_A	)
							snake_case_		:			Union[str, Any]											=	self.sp_model.encode(_A		,   out_type=_A	)
							snake_case_		:			Optional[int]											=	[]
							for piece in pieces:
								if len(_A	) > 1 and piece[-1] == str(','	) and piece[-2].isdigit():
									snake_case_		:			Optional[int]											=	self.sp_model.EncodeAsPieces(piece[:-1].replace(_A		,   ''	)	)
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
										if len(cur_pieces[0]	) == 1:
											snake_case_		:			Dict											=	cur_pieces[1:]
										else:
											snake_case_		:			Optional[Any]											=	cur_pieces[0][1:]
									cur_pieces.append(piece[-1]	)
									new_pieces.extend(_A	)
								else:
									new_pieces.append(_A	)

							return new_pieces






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Dict	)							->    Optional[int]:







							"""simple docstring"""


							return self.sp_model.PieceToId(_A	)






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      Union[str, Any]	)							->    List[str]:







							"""simple docstring"""


							return self.sp_model.IdToPiece(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      int	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			int											=	''.join(_A	).replace(_A		,   ' '	).strip()
							return out_string






						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[int]		,   _A      :      bool = False		,   _A      :      bool = None		,   _A      :      bool = True		,   **_A      :      List[str]		,   )							->    str:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	kwargs.pop('use_source_tokenizer'		,   _A	)

							snake_case_		:			Optional[Any]											=	self.convert_ids_to_tokens(_A		,   skip_special_tokens=_A	)

							# 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
							snake_case_		:			Union[str, Any]											=	[]
							snake_case_		:			List[Any]											=	[]
							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(_A	)	)
										snake_case_		:			Dict											=	[]
									sub_texts.append(_A	)
								else:
									current_sub_text.append(_A	)
							if current_sub_text:
								sub_texts.append(self.convert_tokens_to_string(_A	)	)

							# Mimic the behavior of the Rust tokenizer:
							# By default, there are no spaces between special tokens
							snake_case_		:			Dict											=	''.join(_A	)

							snake_case_		:			Dict											=	(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
								snake_case_		:			str											=	self.clean_up_tokenization(_A	)
								return clean_text
							else:
								return text






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			Dict											=	[self.sep_token_id]
							snake_case_		:			str											=	[self.cls_token_id]
							if token_ids_a is None:
								return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None		,   _A      :      bool = False	)							->    List[int]:







							"""simple docstring"""


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

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






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	[self.sep_token_id]
							snake_case_		:			Tuple											=	[2]

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






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      str		,   _A      :      Optional[str] = None	)							->    Tuple[str]:







							"""simple docstring"""


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

							if os.path.abspath(self.vocab_file	) != os.path.abspath(_A	) and os.path.isfile(self.vocab_file	):
								copyfile(self.vocab_file		,   _A	)
							elif not os.path.isfile(self.vocab_file	):
								with open(_A		,   'wb'	) as fi:
									snake_case_		:			Optional[int]											=	self.sp_model.serialized_model_proto()
									fi.write(_A	)

							return (out_vocab_file,)








import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






						@property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	{
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							snake_case_		:			Tuple											=	self.dummy_input
							return init_dict, inputs_dict






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_						,snake_case_		:			str											=	PriorTransformer.from_pretrained(
							    'hf-internal-testing/prior-dummy'		,   output_loading_info=_A	)
							self.assertIsNotNone(_A	)
							self.assertEqual(len(loading_info['missing_keys']	)		,   0	)

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

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






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[int]											=	self.prepare_init_args_and_inputs_for_common()

							snake_case_		:			Tuple											=	self.model_class(**_A	)
							snake_case_		:			Any											=	inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case_		:			List[str]											=	[*signature.parameters.keys()]

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
							snake_case_		:			str											=	model.to(_A	)

							if hasattr(_A		,   'set_default_attn_processor'	):
								model.set_default_attn_processor()

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							snake_case_		:			str											=	torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
							self.assertTrue(torch_all_close(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






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







							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
						        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
						        # fmt: on
						    ]	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'		,   subfolder='prior'	)
							model.to(_A	)
							snake_case_		:			Optional[Any]											=	self.get_dummy_seed_input(seed=_A	)

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

							assert list(sample.shape	) == [1, 768]

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

							assert torch_all_close(_A		,   _A		,   atol=1E-3	)

