





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


UpperCAmelCase	:	Any		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




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







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



        '''simple docstring'''




        return 1_2







      @property
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return 1_2







      @property
      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        return 3_2







      @property
      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__	:					Dict										=VQModel(
            block_out_channels=[3_2, 6_4]		,      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    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




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







      @property
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__	:					Union[str, Any]										=CLIPTextConfig(
            bos_token_id=0		,      eos_token_id=2		,      hidden_size=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 CLIPTextModel(lowerCAmelCase__							)







      @property
      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        torch.manual_seed(0							)

        a__	:					str										=1_2
        a__	:					List[str]										=1_2

        a__	:					List[Any]										={
            "attention_bias": True,
            "cross_attention_dim": 3_2,
            "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": 3_2,
            "sample_size": width,
            "activation_fn": "geglu-approximate",
        }

        a__	:					List[str]										=TransformeraDModel(**lowerCAmelCase__							)
        return model







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										="cpu"

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

        a__	:					Any										=VQDiffusionPipeline(
            vqvae=lowerCAmelCase__		,      text_encoder=lowerCAmelCase__		,      tokenizer=lowerCAmelCase__		,      transformer=lowerCAmelCase__		,      scheduler=lowerCAmelCase__		,      learned_classifier_free_sampling_embeddings=lowerCAmelCase__		,      )
        a__	:					str										=pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

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

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

        a__	:					List[str]										=torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
        a__	:					Optional[Any]										=pipe(
            [prompt]		,      generator=lowerCAmelCase__		,      output_type="np"		,      return_dict=lowerCAmelCase__		,      num_inference_steps=2							)[0]

        a__	:					Dict										=image[0, -3:, -3:, -1]
        a__	:					Tuple										=image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 2_4, 2_4, 3)

        a__	:					Dict										=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    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Any										="cpu"

        a__	:					str										=self.dummy_vqvae
        a__	:					str										=self.dummy_text_encoder
        a__	:					Any										=self.dummy_tokenizer
        a__	:					Union[str, Any]										=self.dummy_transformer
        a__	:					str										=VQDiffusionScheduler(self.num_embed							)
        a__	:					Tuple										=LearnedClassifierFreeSamplingEmbeddings(
            learnable=lowerCAmelCase__		,      hidden_size=self.text_embedder_hidden_size		,      length=tokenizer.model_max_length							)

        a__	:					List[str]										=VQDiffusionPipeline(
            vqvae=lowerCAmelCase__		,      text_encoder=lowerCAmelCase__		,      tokenizer=lowerCAmelCase__		,      transformer=lowerCAmelCase__		,      scheduler=lowerCAmelCase__		,      learned_classifier_free_sampling_embeddings=lowerCAmelCase__		,      )
        a__	:					str										=pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="teddy bear playing in the pool"

        a__	:					Tuple										=torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
        a__	:					int										=pipe([prompt]		,      generator=lowerCAmelCase__		,      num_inference_steps=2		,      output_type="np"							)
        a__	:					Any										=output.images

        a__	:					str										=torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
        a__	:					List[str]										=pipe(
            [prompt]		,      generator=lowerCAmelCase__		,      output_type="np"		,      return_dict=lowerCAmelCase__		,      num_inference_steps=2							)[0]

        a__	:					List[str]										=image[0, -3:, -3:, -1]
        a__	:					Optional[Any]										=image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 2_4, 2_4, 3)

        a__	:					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 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




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

        a__	:					Tuple										=VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"							)
        a__	:					Optional[int]										=pipeline.to(lowerCAmelCase__							)
        pipeline.set_progress_bar_config(disable=lowerCAmelCase__							)

        # requires GPU generator for gumbel softmax
        # don't use GPU generator in tests though
        a__	:					Tuple										=torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
        a__	:					List[str]										=pipeline(
            "teddy bear playing in the pool"		,      num_images_per_prompt=1		,      generator=lowerCAmelCase__		,      output_type="np"		,      )

        a__	:					int										=output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)
        assert np.abs(expected_image - image							).max() < 2.0











'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__a          =		logging.get_logger(__name__)

__a          =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__a          =		{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

__a          =		{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


__a          =		{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		VOCAB_FILES_NAMES
    lowercase					=		PRETRAINED_VOCAB_FILES_MAP
    lowercase					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase					=		PRETRAINED_INIT_CONFIGURATION
    lowercase					=		RoFormerTokenizer




    def __init__(    self					:     List[Any] ,     snake_case_					:     List[str]=None ,     snake_case_					:     Dict=None ,     snake_case_					:     Any=True ,     snake_case_					:     str="[UNK]" ,     snake_case_					:     List[str]="[SEP]" ,     snake_case_					:     Optional[Any]="[PAD]" ,     snake_case_					:     Union[str, Any]="[CLS]" ,     snake_case_					:     Union[str, Any]="[MASK]" ,     snake_case_					:     List[Any]=True ,     snake_case_					:     Optional[Any]=None ,     **snake_case_					:     Tuple ,     ):
          super().__init__(
              snake_case_ ,     tokenizer_file=snake_case_ ,     do_lower_case=snake_case_ ,     unk_token=snake_case_ ,     sep_token=snake_case_ ,     pad_token=snake_case_ ,     cls_token=snake_case_ ,     mask_token=snake_case_ ,     tokenize_chinese_chars=snake_case_ ,     strip_accents=snake_case_ ,     **snake_case_ ,     )

          snake_case__  :    str    						=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
          if (
              pre_tok_state.get("""lowercase""" ,     snake_case_						) != do_lower_case
              or pre_tok_state.get("""strip_accents""" ,     snake_case_						) != strip_accents
          ):
                snake_case__  :    str    						=      getattr(snake_case_ ,     pre_tok_state.pop("""type"""						)						)
                snake_case__  :    Optional[int]    						=      do_lower_case
                snake_case__  :    Union[str, Any]    						=      strip_accents
                snake_case__  :    Union[str, Any]    						=      pre_tok_class(**snake_case_						)

          snake_case__  :    str    						=      do_lower_case




    def __getstate__(    self					:     int						):
          snake_case__  :    List[Any]    						=      self.__dict__.copy()
          snake_case__  :    str    						=      BertPreTokenizer()
          return state




    def __setstate__(    self					:     Dict ,     snake_case_					:     Dict						):
          snake_case__  :    List[Any]    						=      d
          snake_case__  :    Union[str, Any]    						=      self.__dict__["""_tokenizer"""].get_vocab()
          snake_case__  :    List[Any]    						=      PreTokenizer.custom(JiebaPreTokenizer(snake_case_						)						)




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[str]=None						):
          snake_case__  :    str    						=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None						):
          snake_case__  :    int    						=      [self.sep_token_id]
          snake_case__  :    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]




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     str ,     snake_case_					:     Optional[str] = None						):
          snake_case__  :    Union[str, Any]    						=      self._tokenizer.model.save(snake_case_ ,     name=snake_case_						)
          return tuple(snake_case_						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     List[str] ,     snake_case_					:     Tuple=None ,     snake_case_					:     List[str]=None ,     snake_case_					:     Union[str, Any]=False ,     **snake_case_					:     Tuple ,     ):
          snake_case__  :    Optional[Any]    						=      BertPreTokenizer()
          return super().save_pretrained(snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     **snake_case_						)
