"""simple docstring"""




import os
import re
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   :	Optional[int] 					=     logging.get_logger(__name__)

_a   :	Optional[Any] 					=     {'vocab_file': 'spiece.model'}

_a   :	Union[str, Any] 					=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

_a   :	Union[str, Any] 					=     {
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    str	      =	VOCAB_FILES_NAMES
     _UpperCamelCase							:    int	      =	PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:    int	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:    str	      =	["input_ids", "attention_mask"]
     _UpperCamelCase							:    List[int]	      =	[]






     def __init__(	self       ,  a__       ,  a__="<unk>"       ,  a__="<s>"       ,  a__="</s>"       ,  a__="<pad>"       ,  a__="[SEP]"       ,  a__="[MASK]"       ,  a__="[CLS]"       ,  a__ = None       ,  **a__       ,  ):
       _lowerCAmelCase		: Union[str, Any]						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else bos_token
       _lowerCAmelCase		: Tuple						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else eos_token
       _lowerCAmelCase		: Any						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else unk_token
       _lowerCAmelCase		: int						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else pad_token
       _lowerCAmelCase		: int						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else cls_token
       _lowerCAmelCase		: Tuple						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase		: Union[str, Any]						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else mask_token

       _lowerCAmelCase		: int						    =  {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=a__       ,  eos_token=a__       ,  unk_token=a__       ,  pad_token=a__       ,  sep_token=a__       ,  mask_token=a__       ,  cls_token=a__       ,  sp_model_kwargs=self.sp_model_kwargs       ,  **a__       ,  )

       _lowerCAmelCase		: Optional[Any]						    =  vocab_file

       _lowerCAmelCase		: Dict						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(a__						)






     @property
     def 					__A						(	self						):
       return self.sp_model.get_piece_size()






     def 					__A						(	self						):
       _lowerCAmelCase		: 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						):
       _lowerCAmelCase		: int						    =  self.__dict__.copy()
       _lowerCAmelCase		: str						    =  None
       return state






     def __setstate__(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  d

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

       _lowerCAmelCase		: Any						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)






     def 					__A						(	self       ,  a__						):
       return self.sp_model.encode(a__       ,  out_type=a__						)






     def 					__A						(	self       ,  a__						):
       return self.sp_model.piece_to_id(a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Any						    =  self.sp_model.IdToPiece(a__						)
       return token






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  []
       _lowerCAmelCase		: List[Any]						    =  """"""
       _lowerCAmelCase		: int						    =  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(a__						) + token
           _lowerCAmelCase		: List[Any]						    =  True
           _lowerCAmelCase		: int						    =  []
         else:
           current_sub_tokens.append(a__						)
           _lowerCAmelCase		: List[str]						    =  False
       out_string += self.sp_model.decode(a__						)
       return out_string.strip()






     def 					__A						(	self       ,  a__       ,  a__ = False       ,  a__ = None       ,  a__ = True       ,  **a__       ,  ):
       _lowerCAmelCase		: Any						    =  kwargs.pop("""use_source_tokenizer"""       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  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
       _lowerCAmelCase		: Optional[int]						    =  []
       _lowerCAmelCase		: Optional[int]						    =  []
       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__						)						)
             _lowerCAmelCase		: str						    =  []
           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:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
         _lowerCAmelCase		: Tuple						    =  re.sub(r""" (\[(MASK|SEP)\])"""       ,  r"""\1"""       ,  """ """.join(a__						)						)
       else:
         _lowerCAmelCase		: Tuple						    =  """""".join(a__						)

       _lowerCAmelCase		: Optional[Any]						    =  (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
         _lowerCAmelCase		: Any						    =  self.clean_up_tokenization(a__						)
         return clean_text
       else:
         return text






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       if not os.path.isdir(a__						):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
         return
       _lowerCAmelCase		: Any						    =  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:
           _lowerCAmelCase		: Tuple						    =  self.sp_model.serialized_model_proto()
           fi.write(a__						)

       return (out_vocab_file,)






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _lowerCAmelCase		: Dict						    =  [self.cls_token_id]
       _lowerCAmelCase		: Dict						    =  [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = False						):
       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 None:
         return [1] + ([0] * len(a__						)) + [1]
       return [1] + ([0] * len(a__						)) + [1] + ([0] * len(a__						)) + [1]






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       _lowerCAmelCase		: int						    =  [self.sep_token_id]
       _lowerCAmelCase		: List[str]						    =  [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]





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[int]	      =	DiTPipeline
     _UpperCamelCase							:    Union[str, Any]	      =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     _UpperCamelCase							:    Dict	      =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:    Union[str, Any]	      =	False






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  TransformeraDModel(
           sample_size=16       ,  num_layers=2       ,  patch_size=4       ,  attention_head_dim=8       ,  num_attention_heads=2       ,  in_channels=4       ,  out_channels=8       ,  attention_bias=a__       ,  activation_fn="""gelu-approximate"""       ,  num_embeds_ada_norm=1000       ,  norm_type="""ada_norm_zero"""       ,  norm_elementwise_affine=a__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  AutoencoderKL()
       _lowerCAmelCase		: Union[str, Any]						    =  DDIMScheduler()
       _lowerCAmelCase		: Optional[Any]						    =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Any						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: Tuple						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Any						    =  {
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  """cpu"""

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_components()
       _lowerCAmelCase		: Optional[int]						    =  self.pipeline_class(**a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Tuple						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: List[str]						    =  pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape       ,  (1, 16, 16, 3)						)
       _lowerCAmelCase		: List[Any]						    =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
       _lowerCAmelCase		: Optional[int]						    =  np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(a__       ,  1e-3						)






     def 					__A						(	self						):
       self._test_inference_batch_single_identical(relax_max_difference=a__       ,  expected_max_diff=1e-3						)






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()       ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,  )
     def 					__A						(	self						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







@require_torch_gpu
@slow
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)

       _lowerCAmelCase		: int						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: Dict						    =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
       _lowerCAmelCase		: Union[str, Any]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: Any						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=40       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
         assert np.abs((expected_image - image).max()						) < 1e-2






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       _lowerCAmelCase		: Dict						    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       _lowerCAmelCase		: List[str]						    =  ["""vase""", """umbrella"""]
       _lowerCAmelCase		: Optional[int]						    =  pipe.get_label_ids(a__						)

       _lowerCAmelCase		: str						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: List[str]						    =  pipe(a__       ,  generator=a__       ,  num_inference_steps=25       ,  output_type="""np"""						).images

       for word, image in zip(a__       ,  a__						):
         _lowerCAmelCase		: str						    =  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             F"/dit/{word}_512.npy"						)

         assert np.abs((expected_image - image).max()						) < 1e-1




