"""simple docstring"""



import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class 				snake_case__     (   snake_case_,		snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			str           =		StableUnCLIPPipeline
 _snake_case   :			Optional[int]           =		TEXT_TO_IMAGE_PARAMS
 _snake_case   :			List[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
 _snake_case   :			List[Any]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
 _snake_case   :			int           =		TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 _snake_case   :			Optional[int]           =		False


 def 		a__ (				self		):
        __a     				=  32
        __a     				=  embedder_hidden_size

        # prior components

        torch.manual_seed(0		)
        __a     				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

        torch.manual_seed(0		)
        __a     				=  CLIPTextModelWithProjection(
            CLIPTextConfig(
                bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=lowerCamelCase  ,						projection_dim=lowerCamelCase  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=1000  ,						)		)

        torch.manual_seed(0		)
        __a     				=  PriorTransformer(
            num_attention_heads=2  ,						attention_head_dim=12  ,						embedding_dim=lowerCamelCase  ,						num_layers=1  ,						)

        torch.manual_seed(0		)
        __a     				=  DDPMScheduler(
            variance_type="fixed_small_log"  ,						prediction_type="sample"  ,						num_train_timesteps=1000  ,						clip_sample=lowerCamelCase  ,						clip_sample_range=5.0  ,						beta_schedule="squaredcos_cap_v2"  ,						)

        # regular denoising components

        torch.manual_seed(0		)
        __a     				=  StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase		)
        __a     				=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"		)

        torch.manual_seed(0		)
        __a     				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

        torch.manual_seed(0		)
        __a     				=  CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=lowerCamelCase  ,						projection_dim=32  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=1000  ,						)		)

        torch.manual_seed(0		)
        __a     				=  UNetaDConditionModel(
            sample_size=32  ,						in_channels=4  ,						out_channels=4  ,						down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")  ,						up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")  ,						block_out_channels=(32, 64)  ,						attention_head_dim=(2, 4)  ,						class_embed_type="projection"  ,						projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,						cross_attention_dim=lowerCamelCase  ,						layers_per_block=1  ,						upcast_attention=lowerCamelCase  ,						use_linear_projection=lowerCamelCase  ,						)

        torch.manual_seed(0		)
        __a     				=  DDIMScheduler(
            beta_schedule="scaled_linear"  ,						beta_start=0.0_0085  ,						beta_end=0.012  ,						prediction_type="v_prediction"  ,						set_alpha_to_one=lowerCamelCase  ,						steps_offset=1  ,						)

        torch.manual_seed(0		)
        __a     				=  AutoencoderKL()

        __a     				=  {
            # prior components
            "prior_tokenizer": prior_tokenizer,
            "prior_text_encoder": prior_text_encoder,
            "prior": prior,
            "prior_scheduler": prior_scheduler,
            # image noising components
            "image_normalizer": image_normalizer,
            "image_noising_scheduler": image_noising_scheduler,
            # regular denoising components
            "tokenizer": tokenizer,
            "text_encoder": text_encoder,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
        }

        return components


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=0		):
        if str(lowerCamelCase		).startswith("mps"		):
               __a     				=  torch.manual_seed(lowerCamelCase		)
        else:
               __a     				=  torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)
        __a     				=  {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "prior_num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs


 def 		a__ (				self		):
        __a     				=  torch_device == "cpu"

        self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase		)






 def 		a__ (				self		):
        __a     				=  torch_device in ["cpu", "mps"]

        self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase		)






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


 def 		a__ (				self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 		a__ (				self		):
        __a     				=  load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"		)

        __a     				=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"  ,						torch_dtype=torch.floataa		)
        pipe.to(lowerCamelCase		)
        pipe.set_progress_bar_config(disable=lowerCamelCase		)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __a     				=  torch.Generator(device="cpu"		).manual_seed(0		)
        __a     				=  pipe("anime turle"  ,						generator=lowerCamelCase  ,						output_type="np"		)

        __a     				=  output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(lowerCamelCase  ,						lowerCamelCase		)






 def 		a__ (				self		):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __a     				=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"  ,						torch_dtype=torch.floataa		)
        __a     				=  pipe.to(lowerCamelCase		)
        pipe.set_progress_bar_config(disable=lowerCamelCase		)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __a     				=  pipe(
            "anime turtle"  ,						prior_num_inference_steps=2  ,						num_inference_steps=2  ,						output_type="np"  ,						)

        __a     				=  torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9

"""simple docstring"""



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

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


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.txt"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

SCREAMING_SNAKE_CASE__:Tuple			=	{
    """openbmb/cpm-ant-10b""": 1024,
}



def 					_lowerCamelCase(					a							):
       __a     				=  collections.OrderedDict()
       with open(a ,  "r" ,  encoding="utf-8"							) as reader:
              __a     				=  reader.readlines()
       for index, token in enumerate(a							):
              __a     				=  token.rstrip("\n"							)
              __a     				=  index
       return vocab






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase=200		):
        __a     				=  vocab
        __a     				=  unk_token
        __a     				=  max_input_chars_per_word






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  list(lowerCamelCase		)
        if len(lowerCamelCase		) > self.max_input_chars_per_word:
               return [self.unk_token]

        __a     				=  0
        __a     				=  []
        while start < len(lowerCamelCase		):
               __a     				=  len(lowerCamelCase		)
               __a     				=  None
               while start < end:
                      __a     				=  "".join(chars[start:end]		)
                      if substr in self.vocab:
                             __a     				=  substr
                             break
                      end -= 1
               if cur_substr is None:
                      sub_tokens.append(self.unk_token		)
                      start += 1
               else:
                      sub_tokens.append(lowerCamelCase		)
                      __a     				=  end

        return sub_tokens






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[int]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			int           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			int           =		False


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<d>"  ,						lowerCamelCase="</d>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="</n>"  ,						lowerCamelCase="</_>"  ,						lowerCamelCase="left"  ,						**lowerCamelCase  ,						):
        requires_backends(self  ,						["jieba"]		)
        super().__init__(
            bod_token=lowerCamelCase  ,						eod_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						line_token=lowerCamelCase  ,						space_token=lowerCamelCase  ,						padding_side=lowerCamelCase  ,						**lowerCamelCase  ,						)
        __a     				=  bod_token
        __a     				=  eod_token
        __a     				=  load_vocab(lowerCamelCase		)
        __a     				=  self.encoder[space_token]
        __a     				=  self.encoder[line_token]

        del self.encoder[space_token]
        del self.encoder[line_token]

        __a     				=  collections.OrderedDict(sorted(self.encoder.items()  ,						key=lambda lowerCamelCase		: x[1]		)		)
        __a     				=  {v: k for k, v in self.encoder.items()}

        __a     				=  WordpieceTokenizer(vocab=self.encoder  ,						unk_token=self.unk_token		)


 @property
 def 		a__ (				self		):
        return self.encoder[self.bod_token]


 @property
 def 		a__ (				self		):
        return self.encoder[self.eod_token]


 @property
 def 		a__ (				self		):
        return self.encoder["\n"]


 @property
 def 		a__ (				self		):
        return len(self.encoder		)


 def 		a__ (				self		):
        return dict(self.encoder  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for x in jieba.cut(lowerCamelCase  ,						cut_all=lowerCamelCase		):
               output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase		)		)
        return output_tokens


 def 		a__ (				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  [i for i in token_ids if i >= 0]
        __a     				=  [
            x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
        ]
        return super()._decode(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return token in self.encoder


 def 		a__ (				self  ,						lowerCamelCase		):
        return "".join(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.encoder.get(lowerCamelCase  ,						self.encoder.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase  ,						self.unk_token		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if os.path.isdir(lowerCamelCase		):
               __a     				=  os.path.join(
                   lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
        else:
               __a     				=  (filename_prefix + "-" if filename_prefix else "") + save_directory
        __a     				=  0
        if " " in self.encoder:
               __a     				=  self.encoder[" "]
               del self.encoder[" "]
        if "\n" in self.encoder:
               __a     				=  self.encoder["\n"]
               del self.encoder["\n"]
        __a     				=  collections.OrderedDict(sorted(self.encoder.items()  ,						key=lambda lowerCamelCase		: x[1]		)		)
        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as writer:
               for token, token_index in self.encoder.items():
                      if index != token_index:
                             logger.warning(
                                 F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                                 " Please check that the vocabulary is not corrupted!"		)
                             __a     				=  token_index
                      writer.write(token + "\n"		)
                      index += 1
        return (vocab_file,)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if token_ids_a is None:
               return [self.bos_token_id] + token_ids_a
        return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = False		):

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

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