'''simple docstring'''




from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__lowercase				   =  getLogger(__name__)


__lowercase				   =  4
__lowercase				   =  7

__lowercase				   =  {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}






def     snake_case__							(      _A: List[Any]			)   ->     int:

		'''simple docstring'''


		if isinstance(_A				,       torch.Tensor			):
				return image
		elif isinstance(_A				,       PIL.Image.Image			):
				lowerCAmelCase        =  [image]

		if isinstance(image[0]				,       PIL.Image.Image			):
				lowerCAmelCase ,					lowerCAmelCase        =  image[0].size
				lowerCAmelCase ,					lowerCAmelCase        =  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

				lowerCAmelCase        =  [np.array(i.resize((w, h)			)			)[None, :] for i in image]
				lowerCAmelCase        =  np.concatenate(_A				,       axis=0			)
				lowerCAmelCase        =  np.array(_A			).astype(np.floataa			) / 255.0
				lowerCAmelCase        =  image.transpose(0				,       3				,       1				,       2			)
				lowerCAmelCase        =  2.0 * image - 1.0
				lowerCAmelCase        =  torch.from_numpy(_A			)
		elif isinstance(image[0]				,       torch.Tensor			):
				lowerCAmelCase        =  torch.cat(_A				,       dim=0			)

		return image






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = 350	,   ):




					"""simple docstring"""


					super().__init__(
					    vae=__lowerCAmelCase	,   text_encoder=__lowerCAmelCase	,   tokenizer=__lowerCAmelCase	,   unet=__lowerCAmelCase	,   low_res_scheduler=__lowerCAmelCase	,   scheduler=__lowerCAmelCase	,   safety_checker=__lowerCAmelCase	,   feature_extractor=__lowerCAmelCase	,   watermarker=__lowerCAmelCase	,   max_noise_level=__lowerCAmelCase	,   )



			def __call__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = 75	,   __lowerCAmelCase = 9.0	,   __lowerCAmelCase = 20	,   __lowerCAmelCase = None	,   __lowerCAmelCase = 1	,   __lowerCAmelCase = 0.0	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = "pil"	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = 1	,   ):




					"""simple docstring"""


					self.check_inputs(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

					# 2. Define call parameters
					if prompt is not None and isinstance(__lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  1
					elif prompt is not None and isinstance(__lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  len(__lowerCAmelCase)
					else:
							lowerCAmelCase        =  prompt_embeds.shape[0]

					lowerCAmelCase        =  self._execution_device
					# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
					# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
					# corresponds to doing no classifier free guidance.
					lowerCAmelCase        =  guidance_scale > 1.0

					# 3. Encode input prompt
					lowerCAmelCase        =  self._encode_prompt(
					    __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   prompt_embeds=__lowerCAmelCase	,   negative_prompt_embeds=__lowerCAmelCase	,   )

					lowerCAmelCase        =  ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

					# 4. Preprocess image
					lowerCAmelCase        =  preprocess(__lowerCAmelCase)
					lowerCAmelCase        =  image.cpu()

					# 5. set timesteps
					self.scheduler.set_timesteps(__lowerCAmelCase	,   device=__lowerCAmelCase)
					lowerCAmelCase        =  self.scheduler.timesteps

					# 5. Add noise to image
					lowerCAmelCase        =  torch.tensor([noise_level]	,   dtype=torch.long	,   device=__lowerCAmelCase)
					lowerCAmelCase        =  torch.randn(image.shape	,   generator=__lowerCAmelCase	,   device=__lowerCAmelCase	,   dtype=__lowerCAmelCase)
					lowerCAmelCase        =  self.low_res_scheduler.add_noise(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

					lowerCAmelCase        =  2 if do_classifier_free_guidance else 1
					lowerCAmelCase        =  np.concatenate([image] * batch_multiplier * num_images_per_prompt)
					lowerCAmelCase        =  np.concatenate([noise_level] * image.shape[0])

					# 6. Prepare latent variables
					lowerCAmelCase ,					lowerCAmelCase        =  image.shape[2:]
					lowerCAmelCase        =  self.prepare_latents(
					    batch_size * num_images_per_prompt	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   )

					# 7. Check that sizes of image and latents match
					lowerCAmelCase        =  image.shape[1]
					if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
							raise ValueError(
							    """Incorrect configuration settings! The config of `pipeline.unet` expects"""
							    f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
							    f" `num_channels_image`: {num_channels_image} "
							    f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
							    """ `pipeline.unet` or your `image` input.""")

					# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
					lowerCAmelCase        =  self.prepare_extra_step_kwargs(__lowerCAmelCase	,   __lowerCAmelCase)

					lowerCAmelCase        =  next(
					    (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")	,   """tensor(float)""")
					lowerCAmelCase        =  ORT_TO_NP_TYPE[timestep_dtype]

					# 9. Denoising loop
					lowerCAmelCase        =  len(__lowerCAmelCase) - num_inference_steps * self.scheduler.order
					with self.progress_bar(total=__lowerCAmelCase) as progress_bar:
							for i, t in enumerate(__lowerCAmelCase):
									# expand the latents if we are doing classifier free guidance
									lowerCAmelCase        =  np.concatenate([latents] * 2) if do_classifier_free_guidance else latents

									# concat latents, mask, masked_image_latents in the channel dimension
									lowerCAmelCase        =  self.scheduler.scale_model_input(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  np.concatenate([latent_model_input, image]	,   axis=1)

									# timestep to tensor
									lowerCAmelCase        =  np.array([t]	,   dtype=__lowerCAmelCase)

									# predict the noise residual
									lowerCAmelCase        =  self.unet(
									    sample=__lowerCAmelCase	,   timestep=__lowerCAmelCase	,   encoder_hidden_states=__lowerCAmelCase	,   class_labels=noise_level.astype(np.intaa)	,   )[0]

									# perform guidance
									if do_classifier_free_guidance:
											lowerCAmelCase ,					lowerCAmelCase        =  np.split(__lowerCAmelCase	,   2)
									lowerCAmelCase        =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									lowerCAmelCase        =  self.scheduler.step(
									    torch.from_numpy(__lowerCAmelCase)	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase).prev_sample

									# call the callback, if provided
									if i == len(__lowerCAmelCase) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
											progress_bar.update()
											if callback is not None and i % callback_steps == 0:
													callback(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

        # 10. Post-processing
					lowerCAmelCase        =  self.decode_latents(latents.float())

					# 11. Convert to PIL
					if output_type == "pil":
							lowerCAmelCase        =  self.numpy_to_pil(__lowerCAmelCase)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  1 / 0.08333 * latents
					lowerCAmelCase        =  self.vae(latent_sample=__lowerCAmelCase)[0]
					lowerCAmelCase        =  np.clip(image / 2 + 0.5	,   0	,   1)
					lowerCAmelCase        =  image.transpose((0, 2, 3, 1))
					return image







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   ):




					"""simple docstring"""


					if prompt is not None and isinstance(__lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  1
					elif prompt is not None and isinstance(__lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  len(__lowerCAmelCase)
					else:
							lowerCAmelCase        =  prompt_embeds.shape[0]

					if prompt_embeds is None:
							lowerCAmelCase        =  self.tokenizer(
							    __lowerCAmelCase	,   padding="""max_length"""	,   max_length=self.tokenizer.model_max_length	,   truncation=__lowerCAmelCase	,   return_tensors="""pt"""	,   )
							lowerCAmelCase        =  text_inputs.input_ids
							lowerCAmelCase        =  self.tokenizer(__lowerCAmelCase	,   padding="""longest"""	,   return_tensors="""pt""").input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
							    __lowerCAmelCase	,   __lowerCAmelCase):
									lowerCAmelCase        =  self.tokenizer.batch_decode(
									    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    f" {self.tokenizer.model_max_length} tokens: {removed_text}")

							# no positional arguments to text_encoder
							lowerCAmelCase        =  self.text_encoder(
							    input_ids=text_input_ids.int().to(__lowerCAmelCase)	,   )
							lowerCAmelCase        =  prompt_embeds[0]

					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  prompt_embeds.shape
					# duplicate text embeddings for each generation per prompt, using mps friendly method
					lowerCAmelCase        =  prompt_embeds.repeat(1	,   __lowerCAmelCase)
					lowerCAmelCase        =  prompt_embeds.reshape(bs_embed * num_images_per_prompt	,   __lowerCAmelCase	,   -1)

					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance and negative_prompt_embeds is None:
							lowerCAmelCase        =  42
							if negative_prompt is None:
									lowerCAmelCase        =  [""""""] * batch_size
							elif type(__lowerCAmelCase) is not type(__lowerCAmelCase):
									raise TypeError(
									    f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCAmelCase)} !="
									    f" {type(__lowerCAmelCase)}.")
							elif isinstance(__lowerCAmelCase	,   __lowerCAmelCase):
									lowerCAmelCase        =  [negative_prompt]
							elif batch_size != len(__lowerCAmelCase):
									raise ValueError(
									    f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCAmelCase)}, but `prompt`:"
									    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
									    """ the batch size of `prompt`.""")
							else:
									lowerCAmelCase        =  negative_prompt

							lowerCAmelCase        =  text_input_ids.shape[-1]
							lowerCAmelCase        =  self.tokenizer(
							    __lowerCAmelCase	,   padding="""max_length"""	,   max_length=__lowerCAmelCase	,   truncation=__lowerCAmelCase	,   return_tensors="""pt"""	,   )

							# if hasattr(uncond_input, "attention_mask"):
							#     attention_mask = uncond_input.attention_mask.to(device)
							# else:
							#     attention_mask = None

							lowerCAmelCase        =  self.text_encoder(
							    input_ids=uncond_input.input_ids.int().to(__lowerCAmelCase)	,   )
							lowerCAmelCase        =  uncond_embeddings[0]

					if do_classifier_free_guidance:
							lowerCAmelCase        =  uncond_embeddings.shape[1]
							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							lowerCAmelCase        =  uncond_embeddings.repeat(1	,   __lowerCAmelCase)
							lowerCAmelCase        =  uncond_embeddings.reshape(batch_size * num_images_per_prompt	,   __lowerCAmelCase	,   -1)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							lowerCAmelCase        =  np.concatenate([uncond_embeddings, prompt_embeds])

					return prompt_embeds





'''simple docstring'''




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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__lowercase				   =  {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

__lowercase				   =  {
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}






@lru_cache()
def     snake_case__							(      )   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  (
		    list(range(ord("""!"""			)				,       ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)				,       ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)				,       ord("""ÿ"""			) + 1			)			)
		)
		lowerCAmelCase        =  bs[:]
		lowerCAmelCase        =  0
		for b in range(2**8			):
				if b not in bs:
						bs.append(_A			)
						cs.append(2**8 + n			)
						n += 1
		lowerCAmelCase        =  [chr(_A			) for n in cs]
		return dict(zip(_A				,       _A			)			)






def     snake_case__							(      _A: int			)   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  set()
		lowerCAmelCase        =  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)			)
				lowerCAmelCase        =  char
		return pairs






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Tuple								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					List[str]								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Any								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					str								=	['''input_ids''', '''attention_mask''']



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase="replace"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase=None	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else bos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else eos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else unk_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else pad_token
					super().__init__(
					    errors=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   add_bos_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
					lowerCAmelCase        =  add_bos_token

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

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



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return len(self.encoder)



			def 							a_	(       self):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					if token in self.cache:
							return self.cache[token]
					lowerCAmelCase        =  tuple(__lowerCAmelCase)
					lowerCAmelCase        =  get_pairs(__lowerCAmelCase)

					if not pairs:
							return token

					while True:
							lowerCAmelCase        =  min(__lowerCAmelCase	,   key=lambda __lowerCAmelCase: self.bpe_ranks.get(__lowerCAmelCase	,   float("""inf""")))
							if bigram not in self.bpe_ranks:
									break
							lowerCAmelCase ,					lowerCAmelCase        =  bigram
							lowerCAmelCase        =  []
							lowerCAmelCase        =  0
							while i < len(__lowerCAmelCase):
									try:
											lowerCAmelCase        =  word.index(__lowerCAmelCase	,   __lowerCAmelCase)
									except ValueError:
											new_word.extend(word[i:])
											break
									else:
											new_word.extend(word[i:j])
											lowerCAmelCase        =  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        =  tuple(__lowerCAmelCase)
							lowerCAmelCase        =  new_word
							if len(__lowerCAmelCase) == 1:
									break
							else:
									lowerCAmelCase        =  get_pairs(__lowerCAmelCase)
					lowerCAmelCase        =  """ """.join(__lowerCAmelCase)
					lowerCAmelCase        =  word
					return word



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=None):




					"""simple docstring"""


					if self.add_bos_token:
							lowerCAmelCase        =  [self.bos_token_id]
					else:
							lowerCAmelCase        =  []

					lowerCAmelCase        =  bos_token_ids + token_ids_a

					if token_ids_a is None:
							return output

					return output + bos_token_ids + token_ids_a



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					for token in re.findall(self.pat	,   __lowerCAmelCase):
							lowerCAmelCase        =  """""".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(__lowerCAmelCase).split(""" """))
					return bpe_tokens



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.decoder.get(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """""".join(__lowerCAmelCase)
					lowerCAmelCase        =  bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""	,   errors=self.errors)
					return text



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase        =  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        =  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        =  token_index
									writer.write(""" """.join(__lowerCAmelCase) + """\n""")
									index += 1

					return vocab_file, merge_file



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""add_prefix_space"""	,   self.add_prefix_space)
					if is_split_into_words or add_prefix_space:
							lowerCAmelCase        =  """ """ + text
					return (text, kwargs)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  super()._decode(
					    token_ids=__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

					if truncate_before_pattern is not None and len(__lowerCAmelCase) > 0:
							lowerCAmelCase        =  self.truncate(__lowerCAmelCase	,   __lowerCAmelCase)

					return decoded_text







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					def find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  pattern.search(__lowerCAmelCase	,   __lowerCAmelCase)
							return m.start() if m else -1

					lowerCAmelCase        =  [re.compile(__lowerCAmelCase	,   re.MULTILINE) for pattern in truncate_before_pattern]

					lowerCAmelCase        =  list(re.finditer("""^print"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: prints[1].start()]

					lowerCAmelCase        =  list(re.finditer("""^def"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: defs[1].start()]

					lowerCAmelCase        =  0

					lowerCAmelCase        =  [
					    pos for pos in [find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase) for terminal in terminals] if pos != -1
					]

					if len(__lowerCAmelCase) > 0:
							return completion[: min(__lowerCAmelCase)]
					else:
							return completion




