



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  lowerCamelCase__    (							UpperCamelCase_   ,       unittest.TestCase):




  '''simple docstring'''



  snake_case_			=UnCLIPImageVariationPipeline
  snake_case_			=IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
  snake_case_			=IMAGE_VARIATION_BATCH_PARAMS

  snake_case_			=[
      """generator""",
      """return_dict""",
      """decoder_num_inference_steps""",
      """super_res_num_inference_steps""",
  ]
  snake_case_			=False



  @property
  def 	lowerCAmelCase__   (self				)  ->    int:

      """simple docstring"""






      return 32



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

      """simple docstring"""






      return 32



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      return self.time_input_dim



  @property
  def 	lowerCAmelCase__   (self				)  ->    Dict:

      """simple docstring"""






      return self.time_input_dim * 4



  @property
  def 	lowerCAmelCase__   (self				)  ->    Any:

      """simple docstring"""






      return 1_00



  @property
  def 	lowerCAmelCase__   (self				)  ->    str:

      """simple docstring"""






      lowerCAmelCase__  :       str				=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
      return tokenizer



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      torch.manual_seed(0				)
      lowerCAmelCase__  :       Any				=  CLIPTextConfig(
          bos_token_id=0	,eos_token_id=2	,hidden_size=self.text_embedder_hidden_size	,projection_dim=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=10_00	,)
      return CLIPTextModelWithProjection(_a				)



  @property
  def 	lowerCAmelCase__   (self				)  ->    Dict:

      """simple docstring"""






      torch.manual_seed(0				)
      lowerCAmelCase__  :       str				=  CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size	,projection_dim=self.text_embedder_hidden_size	,num_hidden_layers=5	,num_attention_heads=4	,image_size=32	,intermediate_size=37	,patch_size=1	,)
      return CLIPVisionModelWithProjection(_a				)



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

      """simple docstring"""






      torch.manual_seed(0				)

      lowerCAmelCase__  :       Optional[int]				=  {
          """clip_embeddings_dim""": self.text_embedder_hidden_size,
          """time_embed_dim""": self.time_embed_dim,
          """cross_attention_dim""": self.cross_attention_dim,
      }

      lowerCAmelCase__  :       List[Any]				=  UnCLIPTextProjModel(**_a				)
      return model



  @property
  def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

      """simple docstring"""






      torch.manual_seed(0				)

      lowerCAmelCase__  :       Tuple				=  {
          """sample_size""": 32,
          # RGB in channels
          """in_channels""": 3,
          # Out channels is double in channels because predicts mean and variance
          """out_channels""": 6,
          """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
          """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
          """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
          """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
          """layers_per_block""": 1,
          """cross_attention_dim""": self.cross_attention_dim,
          """attention_head_dim""": 4,
          """resnet_time_scale_shift""": """scale_shift""",
          """class_embed_type""": """identity""",
      }

      lowerCAmelCase__  :       List[str]				=  UNetaDConditionModel(**_a				)
      return model



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      return {
          "sample_size": 64,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }



  @property
  def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

      """simple docstring"""






      torch.manual_seed(0				)

      lowerCAmelCase__  :       List[Any]				=  UNetaDModel(**self.dummy_super_res_kwargs				)
      return model



  @property
  def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

      """simple docstring"""






      torch.manual_seed(1				)

      lowerCAmelCase__  :       List[Any]				=  UNetaDModel(**self.dummy_super_res_kwargs				)
      return model



  def 	lowerCAmelCase__   (self				)  ->    Dict:

      """simple docstring"""






      lowerCAmelCase__  :       str				=  self.dummy_decoder
      lowerCAmelCase__  :       Dict				=  self.dummy_text_proj
      lowerCAmelCase__  :       Any				=  self.dummy_text_encoder
      lowerCAmelCase__  :       Optional[Any]				=  self.dummy_tokenizer
      lowerCAmelCase__  :       Any				=  self.dummy_super_res_first
      lowerCAmelCase__  :       Dict				=  self.dummy_super_res_last

      lowerCAmelCase__  :       int				=  UnCLIPScheduler(
          variance_type='''learned_range'''	,prediction_type='''epsilon'''	,num_train_timesteps=10_00	,)

      lowerCAmelCase__  :       Dict				=  UnCLIPScheduler(
          variance_type='''fixed_small_log'''	,prediction_type='''epsilon'''	,num_train_timesteps=10_00	,)

      lowerCAmelCase__  :       List[Any]				=  CLIPImageProcessor(crop_size=32	,size=32				)

      lowerCAmelCase__  :       Optional[int]				=  self.dummy_image_encoder

      return {
          "decoder": decoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_proj": text_proj,
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }



  def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=0	,__lowerCamelCase=True				)  ->    Dict:

      """simple docstring"""






      lowerCAmelCase__  :       Dict				=  floats_tensor((1, 3, 32, 32)	,rng=random.Random(_a				)				).to(_a				)
      if str(_a				).startswith('''mps'''				):
          lowerCAmelCase__  :       Optional[Any]				=  torch.manual_seed(_a				)
      else:
          lowerCAmelCase__  :       Tuple				=  torch.Generator(device=_a				).manual_seed(_a				)

      if pil_image:
          lowerCAmelCase__  :       Union[str, Any]				=  input_image * 0.5 + 0.5
          lowerCAmelCase__  :       int				=  input_image.clamp(0	,1				)
          lowerCAmelCase__  :       int				=  input_image.cpu().permute(0	,2	,3	,1				).float().numpy()
          lowerCAmelCase__  :       str				=  DiffusionPipeline.numpy_to_pil(_a				)[0]

      return {
          "image": input_image,
          "generator": generator,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "np",
      }



  def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

      """simple docstring"""






      lowerCAmelCase__  :       Tuple				=  """cpu"""

      lowerCAmelCase__  :       str				=  self.get_dummy_components()

      lowerCAmelCase__  :       Optional[int]				=  self.pipeline_class(**_a				)
      lowerCAmelCase__  :       str				=  pipe.to(_a				)

      pipe.set_progress_bar_config(disable=_a				)

      lowerCAmelCase__  :       Dict				=  self.get_dummy_inputs(_a	,pil_image=_a				)

      lowerCAmelCase__  :       Optional[int]				=  pipe(**_a				)
      lowerCAmelCase__  :       List[Any]				=  output.images

      lowerCAmelCase__  :       int				=  self.get_dummy_inputs(_a	,pil_image=_a				)

      lowerCAmelCase__  :       Union[str, Any]				=  pipe(
          **_a	,return_dict=_a	,)[0]

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

      assert image.shape == (1, 64, 64, 3)

      lowerCAmelCase__  :       str				=  np.array(
          [
              0.9997,
              0.0002,
              0.9997,
              0.9997,
              0.9969,
              0.0023,
              0.9997,
              0.9969,
              0.9970,
          ]				)

      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 	lowerCAmelCase__   (self				)  ->    Tuple:

      """simple docstring"""






      lowerCAmelCase__  :       Optional[int]				=  """cpu"""

      lowerCAmelCase__  :       List[Any]				=  self.get_dummy_components()

      lowerCAmelCase__  :       Optional[Any]				=  self.pipeline_class(**_a				)
      lowerCAmelCase__  :       Optional[Any]				=  pipe.to(_a				)

      pipe.set_progress_bar_config(disable=_a				)

      lowerCAmelCase__  :       int				=  self.get_dummy_inputs(_a	,pil_image=_a				)

      lowerCAmelCase__  :       str				=  pipe(**_a				)
      lowerCAmelCase__  :       List[str]				=  output.images

      lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs(_a	,pil_image=_a				)

      lowerCAmelCase__  :       Any				=  pipe(
          **_a	,return_dict=_a	,)[0]

      lowerCAmelCase__  :       List[Any]				=  image[0, -3:, -3:, -1]
      lowerCAmelCase__  :       List[Any]				=  image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCAmelCase__  :       Any				=  np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]				)

      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 	lowerCAmelCase__   (self				)  ->    int:

      """simple docstring"""






      lowerCAmelCase__  :       Dict				=  """cpu"""

      lowerCAmelCase__  :       Dict				=  self.get_dummy_components()

      lowerCAmelCase__  :       str				=  self.pipeline_class(**_a				)
      lowerCAmelCase__  :       Dict				=  pipe.to(_a				)

      pipe.set_progress_bar_config(disable=_a				)

      lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs(_a	,pil_image=_a				)
      lowerCAmelCase__  :       int				=  [
          pipeline_inputs["""image"""],
          pipeline_inputs["""image"""],
      ]

      lowerCAmelCase__  :       int				=  pipe(**_a				)
      lowerCAmelCase__  :       List[Any]				=  output.images

      lowerCAmelCase__  :       List[str]				=  self.get_dummy_inputs(_a	,pil_image=_a				)
      lowerCAmelCase__  :       List[str]				=  [
          tuple_pipeline_inputs["""image"""],
          tuple_pipeline_inputs["""image"""],
      ]

      lowerCAmelCase__  :       int				=  pipe(
          **_a	,return_dict=_a	,)[0]

      lowerCAmelCase__  :       List[Any]				=  image[0, -3:, -3:, -1]
      lowerCAmelCase__  :       Union[str, Any]				=  image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (2, 64, 64, 3)

      lowerCAmelCase__  :       Union[str, Any]				=  np.array(
          [
              0.9997,
              0.9989,
              0.0008,
              0.0021,
              0.9960,
              0.0018,
              0.0014,
              0.0002,
              0.9933,
          ]				)

      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 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      lowerCAmelCase__  :       List[Any]				=  torch.device('''cpu'''				)



      class  lowerCamelCase__    :




        '''simple docstring'''



        snake_case_			=1


      lowerCAmelCase__  :       str				=  self.get_dummy_components()

      lowerCAmelCase__  :       str				=  self.pipeline_class(**_a				)
      lowerCAmelCase__  :       Optional[int]				=  pipe.to(_a				)

      pipe.set_progress_bar_config(disable=_a				)

      lowerCAmelCase__  :       Union[str, Any]				=  torch.Generator(device=_a				).manual_seed(0				)
      lowerCAmelCase__  :       List[Any]				=  pipe.decoder.dtype
      lowerCAmelCase__  :       List[Any]				=  1

      lowerCAmelCase__  :       List[Any]				=  (
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      lowerCAmelCase__  :       Any				=  pipe.prepare_latents(
          _a	,dtype=_a	,device=_a	,generator=_a	,latents=_a	,scheduler=DummyScheduler()				)

      lowerCAmelCase__  :       Tuple				=  (
          batch_size,
          pipe.super_res_first.config.in_channels // 2,
          pipe.super_res_first.config.sample_size,
          pipe.super_res_first.config.sample_size,
      )
      lowerCAmelCase__  :       List[Any]				=  pipe.prepare_latents(
          _a	,dtype=_a	,device=_a	,generator=_a	,latents=_a	,scheduler=DummyScheduler()				)

      lowerCAmelCase__  :       int				=  self.get_dummy_inputs(_a	,pil_image=_a				)

      lowerCAmelCase__  :       List[Any]				=  pipe(
          **_a	,decoder_latents=_a	,super_res_latents=_a				).images

      lowerCAmelCase__  :       Tuple				=  self.get_dummy_inputs(_a	,pil_image=_a				)
      # Don't pass image, instead pass embedding
      lowerCAmelCase__  :       Dict				=  pipeline_inputs.pop('''image'''				)
      lowerCAmelCase__  :       Union[str, Any]				=  pipe.image_encoder(_a				).image_embeds

      lowerCAmelCase__  :       Optional[int]				=  pipe(
          **_a	,decoder_latents=_a	,super_res_latents=_a	,image_embeddings=_a	,).images

      # make sure passing text embeddings manually is identical
      assert np.abs(img_out_a - img_out_a				).max() < 1e-4



  @skip_mps
  def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       List[str]				=  torch_device == """cpu"""

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      lowerCAmelCase__  :       Tuple				=  1e-2

      self._test_attention_slicing_forward_pass(
          test_max_difference=_a	,expected_max_diff=_a				)



  @skip_mps
  def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

      """simple docstring"""






      lowerCAmelCase__  :       List[str]				=  torch_device == """cpu"""
      lowerCAmelCase__  :       List[str]				=  True
      lowerCAmelCase__  :       List[str]				=  [
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=_a	,relax_max_difference=_a	,additional_params_copy_to_batched_inputs=_a	,)



  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      lowerCAmelCase__  :       Optional[int]				=  [
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]

      if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          lowerCAmelCase__  :       List[Any]				=  [2, 3]
          self._test_inference_batch_consistent(
              batch_sizes=_a	,additional_params_copy_to_batched_inputs=_a	,)
      else:
          self._test_inference_batch_consistent(
              additional_params_copy_to_batched_inputs=_a				)



  @skip_mps
  def 	lowerCAmelCase__   (self				)  ->    int:

      """simple docstring"""






      return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

      """simple docstring"""






      return super().test_save_load_local()



  @skip_mps
  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      return super().test_save_load_optional_components()



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




  '''simple docstring'''



  def 	lowerCAmelCase__   (self				)  ->    Tuple:

      """simple docstring"""






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



  def 	lowerCAmelCase__   (self				)  ->    str:

      """simple docstring"""






      lowerCAmelCase__  :       Tuple				=  load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''				)
      lowerCAmelCase__  :       str				=  load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''				)

      lowerCAmelCase__  :       List[Any]				=  UnCLIPImageVariationPipeline.from_pretrained(
          '''kakaobrain/karlo-v1-alpha-image-variations'''	,torch_dtype=torch.floataa				)
      lowerCAmelCase__  :       int				=  pipeline.to(_a				)
      pipeline.set_progress_bar_config(disable=_a				)

      lowerCAmelCase__  :       List[Any]				=  torch.Generator(device='''cpu'''				).manual_seed(0				)
      lowerCAmelCase__  :       Any				=  pipeline(
          _a	,generator=_a	,output_type='''np'''	,)

      lowerCAmelCase__  :       Any				=  output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      assert_mean_pixel_difference(_a	,_a	,15				)






import json
import os
from typing import Optional, Tuple

import regex as re

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


__snake_case       :	int            =logging.get_logger(__name__)

__snake_case       :	List[Any]            ={
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__snake_case       :	Any            ={
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

__snake_case       :	Optional[int]            ={
    'ctrl': 2_5_6,
}

__snake_case       :	Optional[int]            ={
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  set()
				lowerCAmelCase__  :       Tuple				=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char))
								lowerCAmelCase__  :       List[Any]				=  char

				lowerCAmelCase__  :       Dict				=  set(lowerCamelCase_)
				return pairs



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=CONTROL_CODES



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase="<unk>"	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(unk_token=__lowerCamelCase	,**__lowerCamelCase				)

						with open(__lowerCamelCase	,encoding='''utf-8'''				) as vocab_handle:
										lowerCAmelCase__  :       List[str]				=  json.load(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  {v: k for k, v in self.encoder.items()}
						with open(__lowerCamelCase	,encoding='''utf-8'''				) as merges_handle:
										lowerCAmelCase__  :       str				=  merges_handle.read().split('''\n'''				)[1:-1]
						lowerCAmelCase__  :       Optional[Any]				=  [tuple(merge.split()				) for merge in merges]
						lowerCAmelCase__  :       int				=  dict(zip(__lowerCamelCase	,range(len(__lowerCamelCase				)				)				)				)
						lowerCAmelCase__  :       Union[str, Any]				=  {}



		@property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return len(self.encoder				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return dict(self.encoder	,**self.added_tokens_encoder				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						if token in self.cache:
										return self.cache[token]
						lowerCAmelCase__  :       Union[str, Any]				=  tuple(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  tuple(list(word[:-1]				) + [word[-1] + '''</w>''']				)
						lowerCAmelCase__  :       Union[str, Any]				=  get_pairs(__lowerCamelCase				)

						if not pairs:
										return token

						while True:
										lowerCAmelCase__  :       Optional[Any]				=  min(__lowerCamelCase	,key=lambda __lowerCamelCase				: self.bpe_ranks.get(__lowerCamelCase	,float('''inf'''				)				)				)
										if bigram not in self.bpe_ranks:
														break
										lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  bigram
										lowerCAmelCase__  :       Optional[int]				=  []
										lowerCAmelCase__  :       int				=  0
										while i < len(__lowerCamelCase				):
														try:
																		lowerCAmelCase__  :       Optional[Any]				=  word.index(__lowerCamelCase	,__lowerCamelCase				)
														except ValueError:
																		new_word.extend(word[i:]				)
																		break
														else:
																		new_word.extend(word[i:j]				)
																		lowerCAmelCase__  :       int				=  j

														if word[i] == first and i < len(__lowerCamelCase				) - 1 and word[i + 1] == second:
																		new_word.append(first + second				)
																		i += 2
														else:
																		new_word.append(word[i]				)
																		i += 1
										lowerCAmelCase__  :       Optional[Any]				=  tuple(__lowerCamelCase				)
										lowerCAmelCase__  :       Any				=  new_word
										if len(__lowerCamelCase				) == 1:
														break
										else:
														lowerCAmelCase__  :       Optional[Any]				=  get_pairs(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  '''@@ '''.join(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  word[:-4]
						lowerCAmelCase__  :       List[Any]				=  word
						return word



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  []

						lowerCAmelCase__  :       int				=  re.findall(R'''\S+\n?'''	,__lowerCamelCase				)

						for token in words:
										split_tokens.extend(list(self.bpe(__lowerCamelCase				).split(''' '''				)				)				)
						return split_tokens



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						return self.encoder.get(__lowerCamelCase	,self.encoder.get(self.unk_token				)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						return self.decoder.get(__lowerCamelCase	,self.unk_token				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  ''' '''.join(__lowerCamelCase				).replace('''@@ '''	,''''''				).strip()
						return out_string



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Tuple[str]:

						"""simple docstring"""






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

						with open(__lowerCamelCase	,'''w'''	,encoding='''utf-8'''				) as f:
										f.write(json.dumps(self.encoder	,indent=2	,sort_keys=__lowerCamelCase	,ensure_ascii=__lowerCamelCase				) + '''\n'''				)

						lowerCAmelCase__  :       Optional[Any]				=  0
						with open(__lowerCamelCase	,'''w'''	,encoding='''utf-8'''				) as writer:
										writer.write('''#version: 0.2\n'''				)
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,key=lambda __lowerCamelCase				: kv[1]				):
														if index != token_index:
																		logger.warning(
																		    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																		    ''' Please check that the tokenizer is not corrupted!'''				)
																		lowerCAmelCase__  :       Optional[int]				=  token_index
														writer.write(''' '''.join(__lowerCamelCase				) + '''\n'''				)
														index += 1

						return vocab_file, merge_file

		# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
		#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
		#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
		#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
		#     return ''.join(tokens_generated_so_far)

