





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort
class     SCREAMING_SNAKE_CASE       (_SCREAMING_SNAKE_CASE      ,     unittest.TestCase				):
   _UpperCamelCase   :		List[str]			 =	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Tuple=0	)->       List[str]:





     """simple docstring"""







     lowercase__           =  np.random.RandomState(a	)
     lowercase__           =  {
         'prompt': 'A painting of a squirrel eating a burger',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 7.5,
         'output_type': 'numpy',
     }
     return inputs


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





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Tuple:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  PNDMScheduler.from_config(pipe.scheduler.config      ,					skip_prk_steps=a	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Any:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Dict:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]	)

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


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





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]	)

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


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





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  pipe(**a	).images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[str]:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  3 * [inputs['prompt']]

     # forward
     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images[0, -3:, -3:, -1]

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  3 * [inputs.pop('prompt'	)]

     lowercase__           =  pipe.tokenizer(
         a      ,					padding='max_length'      ,					max_length=pipe.tokenizer.model_max_length      ,					truncation=a      ,					return_tensors='np'      ,					)
     lowercase__           =  text_inputs['input_ids']

     lowercase__           =  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]

     lowercase__           =  prompt_embeds

     # forward
     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images[0, -3:, -3:, -1]

     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4


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





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  3 * ['this is a negative prompt']
     lowercase__           =  negative_prompt
     lowercase__           =  3 * [inputs['prompt']]

     # forward
     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images[0, -3:, -3:, -1]

     lowercase__           =  self.get_dummy_inputs()
     lowercase__           =  3 * [inputs.pop('prompt'	)]

     lowercase__           =  []
     for p in [prompt, negative_prompt]:
       lowercase__           =  pipe.tokenizer(
           a      ,					padding='max_length'      ,					max_length=pipe.tokenizer.model_max_length      ,					truncation=a      ,					return_tensors='np'      ,					)
       lowercase__           =  text_inputs['input_ids']

       embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]	)

     lowercase__      ,   lowercase__           =  embeds

     # forward
     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images[0, -3:, -3:, -1]

     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4






@nightly
@require_onnxruntime
@require_torch_gpu
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


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





     """simple docstring"""







     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )


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





     """simple docstring"""







     lowercase__           =  ort.SessionOptions()
     lowercase__           =  False
     return options


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





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'      ,					revision='onnx'      ,					safety_checker=a      ,					feature_extractor=a      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
     sd_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  'A painting of a squirrel eating a burger'
     np.random.seed(0	)
     lowercase__           =  sd_pipe([prompt]      ,					guidance_scale=6.0      ,					num_inference_steps=10      ,					output_type='np'	)
     lowercase__           =  output.images

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

     assert image.shape == (1, 512, 512, 3)
     lowercase__           =  np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Any:





     """simple docstring"""







     lowercase__           =  DDIMScheduler.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					subfolder='scheduler'      ,					revision='onnx'	)
     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					revision='onnx'      ,					scheduler=a      ,					safety_checker=a      ,					feature_extractor=a      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
     sd_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  'open neural network exchange'
     lowercase__           =  np.random.RandomState(0	)
     lowercase__           =  sd_pipe([prompt]      ,					guidance_scale=7.5      ,					num_inference_steps=10      ,					generator=a      ,					output_type='np'	)
     lowercase__           =  output.images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__           =  np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					subfolder='scheduler'      ,					revision='onnx'	)
     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					revision='onnx'      ,					scheduler=a      ,					safety_checker=a      ,					feature_extractor=a      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
     sd_pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  'open neural network exchange'
     lowercase__           =  np.random.RandomState(0	)
     lowercase__           =  sd_pipe([prompt]      ,					guidance_scale=7.5      ,					num_inference_steps=10      ,					generator=a      ,					output_type='np'	)
     lowercase__           =  output.images
     lowercase__           =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__           =  np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3


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





     """simple docstring"""







     lowercase__           =  0

     def test_callback_fn(a      :    Optional[int]      ,					a      :    Dict      ,					a      :    Optional[int]	) -> None:
       lowercase__           =  True
       nonlocal number_of_steps
       number_of_steps += 1
       if step == 0:
         assert latents.shape == (1, 4, 64, 64)
         lowercase__           =  latents[0, -3:, -3:, -1]
         lowercase__           =  np.array(
             [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]	)

         assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1E-3
       elif step == 5:
         assert latents.shape == (1, 4, 64, 64)
         lowercase__           =  latents[0, -3:, -3:, -1]
         lowercase__           =  np.array(
             [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]	)

         assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1E-3

     lowercase__           =  False

     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					revision='onnx'      ,					safety_checker=a      ,					feature_extractor=a      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  'Andromeda galaxy in a bottle'

     lowercase__           =  np.random.RandomState(0	)
     pipe(
         prompt=a      ,					num_inference_steps=5      ,					guidance_scale=7.5      ,					generator=a      ,					callback=a      ,					callback_steps=1      ,					)
     assert test_callback_fn.has_been_called
     assert number_of_steps == 6


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					revision='onnx'      ,					safety_checker=a      ,					feature_extractor=a      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
     assert isinstance(a      ,					a	)
     assert pipe.safety_checker is None

     lowercase__           =  pipe('example prompt'      ,					num_inference_steps=2	).images[0]
     assert image is not None

     # check that there's no error when saving a pipeline with one of the models being None
     with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(a	)
       lowercase__           =  OnnxStableDiffusionPipeline.from_pretrained(a	)

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     lowercase__           =  pipe('example prompt'      ,					num_inference_steps=2	).images[0]
     assert image is not None







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_          =		logging.get_logger(__name__)


lowercase_          =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

lowercase_          =		{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

lowercase_          =		{
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCamelCase  ()    ->       Union[str, Any]:
  lowercase__           =  (
      list(range(ord('!'		)		,						ord('~'		) + 1		)		) + list(range(ord('¡'		)		,						ord('¬'		) + 1		)		) + list(range(ord('®'		)		,						ord('ÿ'		) + 1		)		)
  )
  lowercase__           =  bs[:]
  lowercase__           =  0
  for b in range(2**8		):
    if b not in bs:
      bs.append(_SCREAMING_SNAKE_CASE		)
      cs.append(2**8 + n		)
      n += 1
  lowercase__           =  [chr(_SCREAMING_SNAKE_CASE		) for n in cs]
  return dict(zip(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  set()
  lowercase__           =  word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)		)
    lowercase__           =  char
  return pairs


class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Optional[int]			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Union[str, Any]			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Any			 =	['input_ids', 'attention_mask']


   def __init__(   self      :    Dict      ,					a      :    Union[str, Any]      ,					a      :    Optional[Any]      ,					a      :    List[str]="replace"      ,					a      :    Optional[int]="<s>"      ,					a      :    List[str]="</s>"      ,					a      :    List[Any]="</s>"      ,					a      :    Union[str, Any]="<s>"      ,					a      :    Any="<unk>"      ,					a      :    Optional[int]="<pad>"      ,					a      :    Optional[Any]="<mask>"      ,					a      :    Tuple=False      ,					**a      :    List[Any]      ,					)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else bos_token
     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else eos_token
     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else sep_token
     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else cls_token
     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else unk_token
     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     super().__init__(
         errors=a      ,					bos_token=a      ,					eos_token=a      ,					unk_token=a      ,					sep_token=a      ,					cls_token=a      ,					pad_token=a      ,					mask_token=a      ,					add_prefix_space=a      ,					**a      ,					)

     with open(a      ,					encoding='utf-8'	) as vocab_handle:
       lowercase__           =  json.load(a	)
     lowercase__           =  {v: k for k, v in self.encoder.items()}
     lowercase__           =  errors  # how to handle errors in decoding
     lowercase__           =  bytes_to_unicode()
     lowercase__           =  {v: k for k, v in self.byte_encoder.items()}
     with open(a      ,					encoding='utf-8'	) as merges_handle:
       lowercase__           =  merges_handle.read().split('\n'	)[1:-1]
     lowercase__           =  [tuple(merge.split()	) for merge in bpe_merges]
     lowercase__           =  dict(zip(a      ,					range(len(a	)	)	)	)
     lowercase__           =  {}
     lowercase__           =  add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     lowercase__           =  re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Any:





     """simple docstring"""







     return len(self.encoder	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Optional[int]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    List[Any]	)->       Dict:





     """simple docstring"""







     if token in self.cache:
       return self.cache[token]
     lowercase__           =  tuple(a	)
     lowercase__           =  get_pairs(a	)

     if not pairs:
       return token

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

         if word[i] == first and i < len(a	) - 1 and word[i + 1] == second:
           new_word.append(first + second	)
           i += 2
         else:
           new_word.append(word[i]	)
           i += 1
       lowercase__           =  tuple(a	)
       lowercase__           =  new_word
       if len(a	) == 1:
         break
       else:
         lowercase__           =  get_pairs(a	)
     lowercase__           =  ' '.join(a	)
     lowercase__           =  word
     return word


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    str	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  []
     for token in re.findall(self.pat      ,					a	):
       lowercase__           =  ''.join(
           self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a	).split(' '	)	)
     return bpe_tokens


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    List[Any]	)->       Optional[int]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Optional[Any]	)->       Union[str, Any]:





     """simple docstring"""







     return self.decoder.get(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    Optional[int]	)->       Dict:





     """simple docstring"""







     lowercase__           =  ''.join(a	)
     lowercase__           =  bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'      ,					errors=self.errors	)
     return text


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







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

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

     lowercase__           =  0
     with open(a      ,					'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 a	: 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!'	)
           lowercase__           =  token_index
         writer.write(' '.join(a	) + '\n'	)
         index += 1

     return vocab_file, merge_file


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowercase__           =  [self.cls_token_id]
     lowercase__           =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None      ,					a      :    bool = False	)->       List[int]:





     """simple docstring"""







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

     if token_ids_a is None:
       return [1] + ([0] * len(a	)) + [1]
     return [1] + ([0] * len(a	)) + [1, 1] + ([0] * len(a	)) + [1]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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 + sep + token_ids_a + sep	) * [0]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    Dict      ,					a      :    Dict=False      ,					**a      :    Union[str, Any]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  kwargs.pop('add_prefix_space'      ,					self.add_prefix_space	)
     if (is_split_into_words or add_prefix_space) and (len(a	) > 0 and not text[0].isspace()):
       lowercase__           =  ' ' + text
     return (text, kwargs)


