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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase					:	str       =  "▁"

__lowerCamelCase					:	List[str]       =  {"vocab_file": "spiece.model"}

__lowerCamelCase					:	List[Any]       =  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

__lowerCamelCase					:	Optional[int]       =  {
    "google/pegasus-xsum": 5_12,
}


__lowerCamelCase					:	str       =  logging.get_logger(__name__)






class 		a__						(					SCREAMING_SNAKE_CASE__					):
					A	       =							VOCAB_FILES_NAMES

					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['''input_ids''', '''attention_mask''']

					def __init__(    self      :       Any,_A      :       Dict,_A      :       int="<pad>",_A      :       int="</s>",_A      :       Tuple="<unk>",_A      :       Dict="<mask_2>",_A      :       Tuple="<mask_1>",_A      :       int=None,_A      :       Any=103,_A      :       Optional[int] = None,**_A      :       Any,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    offset
							if additional_special_tokens is not None:
									if not isinstance(A__,A__    ):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A__    )}, but is'
											    F' {type(A__    )}'    )

									SCREAMING_SNAKE_CASE_  :		int				      =    (
									    ([mask_token_sent] + additional_special_tokens)
									    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
									    else additional_special_tokens
									)
									# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
									additional_special_tokens_extended += [
									    F'<unk_{i}>' for i in range(len(A__    ),self.offset - 1    )
									]

									if len(set(A__    )    ) != len(A__    ):
											raise ValueError(
											    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    additional_special_tokens_extended
							else:
									SCREAMING_SNAKE_CASE_  :		str				      =    [mask_token_sent] if mask_token_sent is not None else []
									additional_special_tokens += [F'<unk_{i}>' for i in range(2,self.offset    )]

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=A__,unk_token=A__,mask_token=A__,pad_token=A__,mask_token_sent=A__,offset=A__,additional_special_tokens=A__,sp_model_kwargs=self.sp_model_kwargs,**A__,)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    mask_token_sent
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    vocab_file
							SCREAMING_SNAKE_CASE_  :		int				      =    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(A__    )

							# add special tokens to encoder dict
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
									self.encoder.update(
									    {
									        2: self.mask_token_sent,
									        3: self.mask_token,
									    }    )

							if self.offset > 0:
									# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
									# mask_token_sent is already added to list -> so start at 1
									self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,self.offset - 1    )}    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {v: k for k, v in self.encoder.items()}

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return len(self.sp_model    ) + self.offset

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {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      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.__dict__.copy()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    None
							return state

					def __setstate__(    self      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    d

							# for backward compatibility
							if not hasattr(self,"sp_model_kwargs"    ):
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {}

							SCREAMING_SNAKE_CASE_  :		int				      =    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any    ):







							"""simple docstring"""






							return self.sp_model.encode(A__,out_type=A__    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       List[str]    ):







							"""simple docstring"""






							if token in self.decoder:
									return self.decoder[token]
							elif token in self.added_tokens_decoder:
									return self.added_tokens_decoder[token]
							SCREAMING_SNAKE_CASE_  :		int				      =    self.sp_model.piece_to_id(A__    )
							return sp_id + self.offset

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							if index in self.encoder:
									return self.encoder[index]
							elif index in self.added_tokens_encoder:
									return self.added_tokens_encoder[index]
							else:
									SCREAMING_SNAKE_CASE_  :		int				      =    self.sp_model.IdToPiece(index - self.offset    )
							return token

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    []
							SCREAMING_SNAKE_CASE_  :		Dict				      =    ""
							for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
											out_string += self.sp_model.decode(A__    ) + token
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    []
									else:
											current_sub_tokens.append(A__    )
							out_string += self.sp_model.decode(A__    )
							return out_string.strip()

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[str]=False    ):







							"""simple docstring"""






							return 1

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    set(self.all_special_ids    )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Optional[Any],_A      :       Optional[int] = None,_A      :       List[str] = False    ):







							"""simple docstring"""






							if already_has_special_tokens:
									return self._special_token_mask(A__    )
							elif token_ids_a is None:
									return self._special_token_mask(A__    ) + [1]
							else:
									return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Dict,_A      :       Any=None    ):







							"""simple docstring"""






							if token_ids_a is None:
									return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]






					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Tuple,_A      :       Optional[int] = None    ):







							"""simple docstring"""






							if not os.path.isdir(A__    ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
									return
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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:
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.sp_model.serialized_model_proto()
											fi.write(A__    )

							return (out_vocab_file,)


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=KandinskyVaaPriorPipeline
							lowercase_       :       str       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=['''prompt''', '''negative_prompt''']
							lowercase_       :       Dict       					=[
							    '''num_images_per_prompt''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       List[str]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						CLIPTextConfig(
												    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_2,
												    '''embedding_dim''': self.text_embedder_hidden_size,
												    '''num_layers''': 1,
												}

												lowercase							=						PriorTransformer(**A__)
												# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
												lowercase							=						nn.Parameter(torch.ones(model.clip_std.shape))
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size   ,image_size=2_2_4   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=1_4   ,)

												lowercase							=						CLIPVisionModelWithProjection(A__)
												return model




							@property
							def  A__      (				self):
												lowercase							=						CLIPImageProcessor(
												    crop_size=2_2_4   ,do_center_crop=A__   ,do_normalize=A__   ,do_resize=A__   ,image_mean=[0.48145466, 0.4578275, 0.40821073]   ,image_std=[0.26862954, 0.26130258, 0.27577711]   ,resample=3   ,size=2_2_4   ,)

												return image_processor




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_image_encoder
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_image_processor

												lowercase							=						UnCLIPScheduler(
												    variance_type='''fixed_small_log'''   ,prediction_type='''sample'''   ,num_train_timesteps=1_0_0_0   ,clip_sample=A__   ,clip_sample_range=10.0   ,)

												lowercase							=						{
												    '''prior''': prior,
												    '''image_encoder''': image_encoder,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''scheduler''': scheduler,
												    '''image_processor''': image_processor,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.image_embeds

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[0]

												lowercase							=						image[0, -1_0:]
												lowercase							=						image_from_tuple[0, -1_0:]

												assert image.shape == (1, 3_2)

												lowercase							=						np.array(
												    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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




							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True
												lowercase							=						False

												self._test_inference_batch_single_identical(
												    test_max_difference=A__   ,relax_max_difference=A__   ,test_mean_pixel_difference=A__   ,)





							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						False

												self._test_attention_slicing_forward_pass(
												    test_max_difference=A__   ,test_mean_pixel_difference=A__   ,)




