

'''simple docstring'''



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


UpperCAmelCase :       str			   =     logging.get_logger(__name__)

UpperCAmelCase :       Union[str, Any]			   =     '▁'

UpperCAmelCase :       Dict			   =     {'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase :       List[Any]			   =     {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCAmelCase :       Dict			   =     {
    'facebook/xglm-564M': 2_0_4_8,
}







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      VOCAB_FILES_NAMES
 lowerCAmelCase__  					=      PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__  					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__  					=      ["input_ids", "attention_mask"]
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       int="<s>"					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]="</s>"					,							__SCREAMING_SNAKE_CASE					:       int="</s>"					,							__SCREAMING_SNAKE_CASE					:       str="<s>"					,							__SCREAMING_SNAKE_CASE					:       List[str]="<unk>"					,							__SCREAMING_SNAKE_CASE					:       Any="<pad>"					,							__SCREAMING_SNAKE_CASE					:       Optional[Dict[str, Any]] = None					,							**__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							)  -> None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      {} if sp_model_kwargs is None else sp_model_kwargs

      # Compatibility with the original tokenizer
      __SCREAMING_SNAKE_CASE     =      7
      __SCREAMING_SNAKE_CASE     =      [f'<madeupword{i}>' for i in range(self.num_madeup_words			)]

      __SCREAMING_SNAKE_CASE     =      kwargs.get("""additional_special_tokens"""					,							[]			)
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

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

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

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      __SCREAMING_SNAKE_CASE     =      1

      # Mimic fairseq token-to-id alignment for the first 4 token
      __SCREAMING_SNAKE_CASE     =      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

      __SCREAMING_SNAKE_CASE     =      len(self.sp_model			)
      __SCREAMING_SNAKE_CASE     =      {f'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
      self.fairseq_tokens_to_ids.update(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(		self					:       Union[str, Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.__dict__.copy()
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      self.sp_model.serialized_model_proto()
      return state
 def __setstate__(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      d

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

      __SCREAMING_SNAKE_CASE     =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None			)  -> List[int]:


      """simple docstring"""




      if token_ids_a is None:
           return [self.sep_token_id] + token_ids_a
      __SCREAMING_SNAKE_CASE     =      [self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None					,							__SCREAMING_SNAKE_CASE					:       bool = False			)  -> List[int]:


      """simple docstring"""




      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 None:
           return [1] + ([0] * len(__SCREAMING_SNAKE_CASE			))
      return [1] + ([0] * len(__SCREAMING_SNAKE_CASE			)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE			))
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       List[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[List[int]] = None			)  -> List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [self.sep_token_id]

      if token_ids_a is None:
           return len(sep + token_ids_a			) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a			) * [0]
 @property
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Union[str, Any]:


      """simple docstring"""




      return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words
 def       UpperCAmelCase__		(		self					:       str			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      {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       UpperCAmelCase__		(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       str			)  -> List[str]:


      """simple docstring"""




      return self.sp_model.encode(__SCREAMING_SNAKE_CASE					,							out_type=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Dict:


      """simple docstring"""




      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      __SCREAMING_SNAKE_CASE     =      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE			)

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> str:


      """simple docstring"""




      if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset			)
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """""".join(__SCREAMING_SNAKE_CASE			).replace(__SCREAMING_SNAKE_CASE					,							""" """			).strip()
      return out_string


 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[str] = None			)  -> Tuple[str]:


      """simple docstring"""




      if not os.path.isdir(__SCREAMING_SNAKE_CASE			):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
           return
      __SCREAMING_SNAKE_CASE     =      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:
                __SCREAMING_SNAKE_CASE     =      self.sp_model.serialized_model_proto()
                fi.write(__SCREAMING_SNAKE_CASE			)

      return (out_vocab_file,)









'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @property
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=3					,							out_channels=3					,							down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,							up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,							)
      return model


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""					,							return_dict=__SCREAMING_SNAKE_CASE			)[0]

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE     =      image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

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







@slow
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """google/ddpm-cifar10-32"""

      __SCREAMING_SNAKE_CASE     =      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]			)

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






