

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": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}

class 				a  (	UpperCAmelCase_				):


      def __init__(       self						,					A_						,					A_=False						,					A_=True						,					A_=False						,					A_="<s>"						,					A_="</s>"						,					A_="<unk>"						,					A_="<sep>"						,					A_="<pad>"						,					A_="<cls>"						,					A_="<mask>"						,					A_=["<eop>", "<eod>"]						,					A_ = None						,					**A_						,					):







          '''simple docstring'''


          _UpperCAmelCase       :		Dict 		=    AddedToken(_lowercase						,					lstrip=_lowercase						,					rstrip=_lowercase	) if isinstance(_lowercase						,					_lowercase	) else mask_token

          _UpperCAmelCase       :		Dict 		=    {} if sp_model_kwargs is None else sp_model_kwargs

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

          _UpperCAmelCase       :		List[str] 		=    3

          _UpperCAmelCase       :		List[str] 		=    do_lower_case
          _UpperCAmelCase       :		str 		=    remove_space
          _UpperCAmelCase       :		Optional[int] 		=    keep_accents
          _UpperCAmelCase       :		List[Any] 		=    vocab_file

          _UpperCAmelCase       :		Optional[int] 		=    spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(_lowercase	)

          try:
              import jieba
          except ModuleNotFoundError as error:
              raise error.__class__(
                  "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                  "See https://pypi.org/project/jieba/ for installation."	)
          _UpperCAmelCase       :		int 		=    jieba
          _UpperCAmelCase       :		List[Any] 		=    str.maketrans(" \n"						,					"\u2582\u2583"	)


      @property
      # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
      def        _UpperCAmelCase				(       self	):







          '''simple docstring'''


          return len(self.sp_model	)


      def        _UpperCAmelCase				(       self	):







          '''simple docstring'''


          _UpperCAmelCase       :		str 		=    {self.convert_ids_to_tokens(_lowercase	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab


      def __getstate__(       self	):







          '''simple docstring'''


          _UpperCAmelCase       :		Tuple 		=    self.__dict__.copy()
          _UpperCAmelCase       :		List[str] 		=    None
          return state


      def __setstate__(       self						,					A_	):







          '''simple docstring'''


          _UpperCAmelCase       :		Dict 		=    d

          # for backward compatibility
          if not hasattr(self						,					"sp_model_kwargs"	):
              _UpperCAmelCase       :		List[str] 		=    {}

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


      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


          if self.remove_space:
              _UpperCAmelCase       :		Optional[int] 		=    " ".join(inputs.strip().split()	)
          else:
              _UpperCAmelCase       :		List[str] 		=    inputs
          _UpperCAmelCase       :		Optional[Any] 		=    outputs.replace("``"						,					"\""	).replace("\'\'"						,					"\""	)

          if not self.keep_accents:
              _UpperCAmelCase       :		Tuple 		=    unicodedata.normalize("NFKD"						,					_lowercase	)
              _UpperCAmelCase       :		Dict 		=    "".join([c for c in outputs if not unicodedata.combining(_lowercase	)]	)
          if self.do_lower_case:
              _UpperCAmelCase       :		Optional[Any] 		=    outputs.lower()

          return outputs


      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


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

          return new_pieces


      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


          return self.sp_model.PieceToId(_lowercase	)


      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


          return self.sp_model.IdToPiece(_lowercase	)


      def        _UpperCAmelCase				(       self						,					A_	):







          '''simple docstring'''


          _UpperCAmelCase       :		str 		=    "".join(_lowercase	).replace(_lowercase						,					" "	).strip()
          return out_string


      def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







          '''simple docstring'''


          _UpperCAmelCase       :		Tuple 		=    [self.sep_token_id]
          _UpperCAmelCase       :		List[Any] 		=    [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						,					A_						,					A_ = None						,					A_ = False	):







          '''simple docstring'''


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

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


      def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







          '''simple docstring'''


          _UpperCAmelCase       :		List[Any] 		=    [self.sep_token_id]
          _UpperCAmelCase       :		Optional[int] 		=    [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						,					A_						,					A_ = None	):







          '''simple docstring'''


          if not os.path.isdir(_lowercase	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
          _UpperCAmelCase       :		Optional[int] 		=    os.path.join(
              _lowercase						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

          if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	) and os.path.isfile(self.vocab_file	):
              copyfile(self.vocab_file						,					_lowercase	)
          elif not os.path.isfile(self.vocab_file	):
              with open(_lowercase						,					"wb"	) as fi:
                  _UpperCAmelCase       :		Optional[Any] 		=    self.sp_model.serialized_model_proto()
                  fi.write(_lowercase	)

          return (out_vocab_file,)





      def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







          '''simple docstring'''


          _UpperCAmelCase       :		Optional[Any] 		=    super()._decode(*_lowercase						,					**_lowercase	)
          _UpperCAmelCase       :		str 		=    text.replace(" "						,					""	).replace("\u2582"						,					" "	).replace("\u2583"						,					"\n"	)
          return text





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE_        =      False

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 32


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    VQModel(
										    block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=3						,					num_vq_embeddings=self.num_embed						,					vq_embed_dim=3						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=self.text_embedder_hidden_size						,					intermediate_size=37						,					layer_norm_eps=1e-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					)
										return CLIPTextModel(A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)

										_UpperCAmelCase       :		Optional[int] 		=    12
										_UpperCAmelCase       :		Union[str, Any] 		=    12

										_UpperCAmelCase       :		str 		=    {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										_UpperCAmelCase       :		Any 		=    TransformeraDModel(**A_	)
										return model


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    "cpu"

										_UpperCAmelCase       :		List[str] 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Any 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Optional[int] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Union[str, Any] 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Union[str, Any] 		=    LearnedClassifierFreeSamplingEmbeddings(learnable=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Optional[int] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		List[Any] 		=    output.images

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Optional[Any] 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		int 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		List[str] 		=    np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]	)

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    "cpu"

										_UpperCAmelCase       :		Tuple 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		List[str] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Dict 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Dict 		=    LearnedClassifierFreeSamplingEmbeddings(
										    learnable=A_						,					hidden_size=self.text_embedder_hidden_size						,					length=tokenizer.model_max_length	)

										_UpperCAmelCase       :		Optional[int] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		int 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		Optional[Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Any 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Optional[int] 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		Union[str, Any] 		=    np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"	)

										_UpperCAmelCase       :		Dict 		=    VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
										_UpperCAmelCase       :		Optional[int] 		=    pipeline.to(A_	)
										pipeline.set_progress_bar_config(disable=A_	)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										_UpperCAmelCase       :		Optional[int] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipeline(
										    "teddy bear playing in the pool"						,					num_images_per_prompt=1						,					generator=A_						,					output_type="np"						,					)

										_UpperCAmelCase       :		str 		=    output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image	).max() < 2.0


