from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase					=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase					=				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''


def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	List[str]		,						lowercase_:	Optional[Any]=8				):
       _UpperCamelCase     :       Optional[int]  						=  height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _UpperCamelCase     :       Union[str, Any]  						=  width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor


def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Optional[int]=512		,						lowercase_:	str=512				):
       _UpperCamelCase     :       str  						=  pil_image.resize((w, h)		,						resample=Image.BICUBIC		,						reducing_gap=1				)
       _UpperCamelCase     :       List[str]  						=  np.array(pil_image.convert('RGB'				)				)
       _UpperCamelCase     :       Tuple  						=  arr.astype(np.floataa				) / 127.5 - 1
       _UpperCamelCase     :       List[Any]  						=  np.transpose(lowercase_		,						[2, 0, 1]				)
       _UpperCamelCase     :       Optional[int]  						=  torch.from_numpy(lowercase_				).unsqueeze(0				)
       return image





class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''

 def __init__(			self       :		Tuple       ,			lowerCamelCase       :		UNetaDConditionModel       ,			lowerCamelCase       :		DDPMScheduler       ,			lowerCamelCase       :		VQModel       ,			) ->							str:
        super().__init__()

        self.register_modules(
            unet=lowerCamelCase       ,			scheduler=lowerCamelCase       ,			movq=lowerCamelCase       ,			)
        _UpperCamelCase     :       Dict  						=  2 ** (len(self.movq.config.block_out_channels  ) - 1)

 def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		Optional[int]       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		Union[str, Any]  ) ->							int:
        # get the original timestep using init_timestep
        _UpperCamelCase     :       Dict  						=  min(int(num_inference_steps * strength  )       ,			lowerCamelCase  )

        _UpperCamelCase     :       Tuple  						=  max(num_inference_steps - init_timestep       ,			0  )
        _UpperCamelCase     :       List[Any]  						=  self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

 def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Dict       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		str=None  ) ->							Any:
        if not isinstance(lowerCamelCase       ,			(torch.Tensor, PIL.Image.Image, list)  ):
               raise ValueError(
                   f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase  )}"  )

        _UpperCamelCase     :       Union[str, Any]  						=  image.to(device=lowerCamelCase       ,			dtype=lowerCamelCase  )

        _UpperCamelCase     :       Dict  						=  batch_size * num_images_per_prompt

        if image.shape[1] == 4:
               _UpperCamelCase     :       int  						=  image

        else:
               if isinstance(lowerCamelCase       ,			lowerCamelCase  ) and len(lowerCamelCase  ) != batch_size:
                      raise ValueError(
                          f"You have passed a list of generators of length {len(lowerCamelCase  )}, but requested an effective batch"
                          f" size of {batch_size}. Make sure the batch size matches the length of the generators."  )

               elif isinstance(lowerCamelCase       ,			lowerCamelCase  ):
                      _UpperCamelCase     :       Union[str, Any]  						=  [
                          self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(lowerCamelCase  )
                      ]
                      _UpperCamelCase     :       Optional[int]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
               else:
                      _UpperCamelCase     :       int  						=  self.movq.encode(lowerCamelCase  ).latent_dist.sample(lowerCamelCase  )

               _UpperCamelCase     :       Any  						=  self.movq.config.scaling_factor * init_latents

        _UpperCamelCase     :       List[str]  						=  torch.cat([init_latents]       ,			dim=0  )

        _UpperCamelCase     :       Union[str, Any]  						=  init_latents.shape
        _UpperCamelCase     :       Any  						=  randn_tensor(lowerCamelCase       ,			generator=lowerCamelCase       ,			device=lowerCamelCase       ,			dtype=lowerCamelCase  )

        # get latents
        _UpperCamelCase     :       Tuple  						=  self.scheduler.add_noise(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )

        _UpperCamelCase     :       Tuple  						=  init_latents

        return latents

 def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		int=0  ) ->							Any:
        if is_accelerate_available():
               from accelerate import cpu_offload
        else:
               raise ImportError('Please install accelerate via `pip install accelerate`'  )

        _UpperCamelCase     :       Tuple  						=  torch.device(f"cuda:{gpu_id}"  )

        _UpperCamelCase     :       int  						=  [
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                      cpu_offload(lowerCamelCase       ,			lowerCamelCase  )

 def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		Any=0  ) ->							Union[str, Any]:
        if is_accelerate_available() and is_accelerate_version('>='       ,			'0.17.0.dev0'  ):
               from accelerate import cpu_offload_with_hook
        else:
               raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'  )

        _UpperCamelCase     :       Union[str, Any]  						=  torch.device(f"cuda:{gpu_id}"  )

        if self.device.type != "cpu":
               self.to('cpu'       ,			silence_dtype_warnings=lowerCamelCase  )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _UpperCamelCase     :       Union[str, Any]  						=  None
        for cpu_offloaded_model in [self.unet, self.movq]:
               _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  cpu_offload_with_hook(lowerCamelCase       ,			lowerCamelCase       ,			prev_module_hook=lowerCamelCase  )

        # We'll offload the last model manually.
        _UpperCamelCase     :       List[str]  						=  hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 					__snake_case					(			self       :		List[str]  ) ->							Any:
        if not hasattr(self.unet       ,			'_hf_hook'  ):
               return self.device
        for module in self.unet.modules():
               if (
                   hasattr(lowerCamelCase       ,			'_hf_hook'  )
                   and hasattr(module._hf_hook       ,			'execution_device'  )
                   and module._hf_hook.execution_device is not None
               ):
                      return torch.device(module._hf_hook.execution_device  )
        return self.device

 @torch.no_grad()
 @replace_example_docstring(lowerCamelCase  )
 def __call__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[torch.FloatTensor, List[torch.FloatTensor]]       ,			lowerCamelCase       :		Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       ,			lowerCamelCase       :		Union[torch.FloatTensor, List[torch.FloatTensor]]       ,			lowerCamelCase       :		torch.FloatTensor       ,			lowerCamelCase       :		int = 5_1_2       ,			lowerCamelCase       :		int = 5_1_2       ,			lowerCamelCase       :		int = 1_0_0       ,			lowerCamelCase       :		float = 4.0       ,			lowerCamelCase       :		float = 0.3       ,			lowerCamelCase       :		int = 1       ,			lowerCamelCase       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,			lowerCamelCase       :		Optional[str] = "pil"       ,			lowerCamelCase       :		bool = True       ,			) ->							Optional[int]:
        _UpperCamelCase     :       Optional[Any]  						=  self._execution_device

        _UpperCamelCase     :       Optional[Any]  						=  guidance_scale > 1.0

        if isinstance(lowerCamelCase       ,			lowerCamelCase  ):
               _UpperCamelCase     :       List[Any]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
        if isinstance(lowerCamelCase       ,			lowerCamelCase  ):
               _UpperCamelCase     :       List[str]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
        if isinstance(lowerCamelCase       ,			lowerCamelCase  ):
               _UpperCamelCase     :       Optional[Any]  						=  torch.cat(lowerCamelCase       ,			dim=0  )

        _UpperCamelCase     :       Optional[int]  						=  image_embeds.shape[0]

        if do_classifier_free_guidance:
               _UpperCamelCase     :       List[str]  						=  image_embeds.repeat_interleave(lowerCamelCase       ,			dim=0  )
               _UpperCamelCase     :       List[Any]  						=  negative_image_embeds.repeat_interleave(lowerCamelCase       ,			dim=0  )
               _UpperCamelCase     :       Union[str, Any]  						=  hint.repeat_interleave(lowerCamelCase       ,			dim=0  )

        _UpperCamelCase     :       List[Any]  						=  torch.cat([negative_image_embeds, image_embeds]       ,			dim=0  ).to(dtype=self.unet.dtype       ,			device=lowerCamelCase  )
        _UpperCamelCase     :       int  						=  torch.cat([hint, hint]       ,			dim=0  ).to(dtype=self.unet.dtype       ,			device=lowerCamelCase  )

        if not isinstance(lowerCamelCase       ,			lowerCamelCase  ):
               _UpperCamelCase     :       int  						=  [image]
        if not all(isinstance(lowerCamelCase       ,			(PIL.Image.Image, torch.Tensor)  ) for i in image  ):
               raise ValueError(
                   f"Input is in incorrect format: {[type(lowerCamelCase  ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"  )

        _UpperCamelCase     :       Any  						=  torch.cat([prepare_image(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ) for i in image]       ,			dim=0  )
        _UpperCamelCase     :       Any  						=  image.to(dtype=image_embeds.dtype       ,			device=lowerCamelCase  )

        _UpperCamelCase     :       int  						=  self.movq.encode(lowerCamelCase  )['latents']
        _UpperCamelCase     :       Optional[Any]  						=  latents.repeat_interleave(lowerCamelCase       ,			dim=0  )
        self.scheduler.set_timesteps(lowerCamelCase       ,			device=lowerCamelCase  )
        _UpperCamelCase						,      _UpperCamelCase     :       Tuple  						=  self.get_timesteps(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
        _UpperCamelCase     :       Optional[int]  						=  timesteps[:1].repeat(batch_size * num_images_per_prompt  )
        _UpperCamelCase						,      _UpperCamelCase     :       List[Any]  						=  downscale_height_and_width(lowerCamelCase       ,			lowerCamelCase       ,			self.movq_scale_factor  )
        _UpperCamelCase     :       Any  						=  self.prepare_latents(
            lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			image_embeds.dtype       ,			lowerCamelCase       ,			lowerCamelCase  )
        for i, t in enumerate(self.progress_bar(lowerCamelCase  )  ):
               # expand the latents if we are doing classifier free guidance
               _UpperCamelCase     :       List[Any]  						=  torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

               _UpperCamelCase     :       str  						=  {'image_embeds': image_embeds, 'hint': hint}
               _UpperCamelCase     :       Any  						=  self.unet(
                   sample=lowerCamelCase       ,			timestep=lowerCamelCase       ,			encoder_hidden_states=lowerCamelCase       ,			added_cond_kwargs=lowerCamelCase       ,			return_dict=lowerCamelCase       ,			)[0]

               if do_classifier_free_guidance:
                      _UpperCamelCase						,      _UpperCamelCase     :       Optional[int]  						=  noise_pred.split(latents.shape[1]       ,			dim=1  )
                      _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  noise_pred.chunk(2  )
                      _UpperCamelCase						,      _UpperCamelCase     :       int  						=  variance_pred.chunk(2  )
                      _UpperCamelCase     :       int  						=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      _UpperCamelCase     :       Tuple  						=  torch.cat([noise_pred, variance_pred_text]       ,			dim=1  )

               if not (
                   hasattr(self.scheduler.config       ,			'variance_type'  )
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                      _UpperCamelCase						,      _UpperCamelCase     :       List[Any]  						=  noise_pred.split(latents.shape[1]       ,			dim=1  )

               # compute the previous noisy sample x_t -> x_t-1

               _UpperCamelCase     :       List[Any]  						=  self.scheduler.step(
                   lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			generator=lowerCamelCase       ,			)[0]

        # post-processing
        _UpperCamelCase     :       Optional[int]  						=  self.movq.decode(lowerCamelCase       ,			force_not_quantize=lowerCamelCase  )['sample']

        if output_type not in ["pt", "np", "pil"]:
               raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"  )

        if output_type in ["np", "pil"]:
               _UpperCamelCase     :       Any  						=  image * 0.5 + 0.5
               _UpperCamelCase     :       List[str]  						=  image.clamp(0       ,			1  )
               _UpperCamelCase     :       str  						=  image.cpu().permute(0       ,			2       ,			3       ,			1  ).float().numpy()

        if output_type == "pil":
               _UpperCamelCase     :       Optional[Any]  						=  self.numpy_to_pil(lowerCamelCase  )

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase  )




import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


UpperCAmelCase					=				logging.get_logger(__name__)

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

UpperCAmelCase					=				{
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

UpperCAmelCase					=				{
    '''openai-gpt''': 5_12,
}


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Optional[Any]  						=  text.strip()
       if not text:
              return []
       _UpperCamelCase     :       Optional[Any]  						=  text.split()
       return tokens




class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''

 def __init__(			self       :		str       ,			lowerCamelCase       :		Union[str, Any]=True       ,			lowerCamelCase       :		Optional[Any]=None       ,			lowerCamelCase       :		Dict=True       ,			lowerCamelCase       :		List[Any]=None       ,			lowerCamelCase       :		Optional[Any]=True       ,			) ->							Optional[Any]:
        if never_split is None:
               _UpperCamelCase     :       Union[str, Any]  						=  []
        _UpperCamelCase     :       List[Any]  						=  do_lower_case
        _UpperCamelCase     :       List[Any]  						=  set(lowerCamelCase  )
        _UpperCamelCase     :       int  						=  tokenize_chinese_chars
        _UpperCamelCase     :       Dict  						=  strip_accents
        _UpperCamelCase     :       List[Any]  						=  do_split_on_punc

 def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		List[str]       ,			lowerCamelCase       :		List[Any]=None  ) ->							List[str]:
        _UpperCamelCase     :       Any  						=  self.never_split.union(set(lowerCamelCase  )  ) if never_split else self.never_split
        _UpperCamelCase     :       int  						=  self._clean_text(lowerCamelCase  )

        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        if self.tokenize_chinese_chars:
               _UpperCamelCase     :       Optional[int]  						=  self._tokenize_chinese_chars(lowerCamelCase  )
        # prevents treating the same character with different unicode codepoints as different characters
        _UpperCamelCase     :       int  						=  unicodedata.normalize('NFC'       ,			lowerCamelCase  )
        _UpperCamelCase     :       Tuple  						=  whitespace_tokenize(lowerCamelCase  )
        _UpperCamelCase     :       Dict  						=  []
        for token in orig_tokens:
               if token not in never_split:
                      if self.do_lower_case:
                             _UpperCamelCase     :       Tuple  						=  token.lower()
                             if self.strip_accents is not False:
                                    _UpperCamelCase     :       List[Any]  						=  self._run_strip_accents(lowerCamelCase  )
                      elif self.strip_accents:
                             _UpperCamelCase     :       List[str]  						=  self._run_strip_accents(lowerCamelCase  )
               split_tokens.extend(self._run_split_on_punc(lowerCamelCase       ,			lowerCamelCase  )  )

        _UpperCamelCase     :       str  						=  whitespace_tokenize(' '.join(lowerCamelCase  )  )
        return output_tokens

 def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		Union[str, Any]  ) ->							str:
        _UpperCamelCase     :       Optional[Any]  						=  unicodedata.normalize('NFD'       ,			lowerCamelCase  )
        _UpperCamelCase     :       Dict  						=  []
        for char in text:
               _UpperCamelCase     :       Tuple  						=  unicodedata.category(lowerCamelCase  )
               if cat == "Mn":
                      continue
               output.append(lowerCamelCase  )
        return "".join(lowerCamelCase  )

 def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		List[Any]       ,			lowerCamelCase       :		int=None  ) ->							List[Any]:
        if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
        _UpperCamelCase     :       Optional[int]  						=  list(lowerCamelCase  )
        _UpperCamelCase     :       Dict  						=  0
        _UpperCamelCase     :       Union[str, Any]  						=  True
        _UpperCamelCase     :       int  						=  []
        while i < len(lowerCamelCase  ):
               _UpperCamelCase     :       List[str]  						=  chars[i]
               if _is_punctuation(lowerCamelCase  ):
                      output.append([char]  )
                      _UpperCamelCase     :       str  						=  True
               else:
                      if start_new_word:
                             output.append([]  )
                      _UpperCamelCase     :       List[str]  						=  False
                      output[-1].append(lowerCamelCase  )
               i += 1

        return ["".join(lowerCamelCase  ) for x in output]

 def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		int  ) ->							Optional[Any]:
        _UpperCamelCase     :       Dict  						=  []
        for char in text:
               _UpperCamelCase     :       Optional[int]  						=  ord(lowerCamelCase  )
               if self._is_chinese_char(lowerCamelCase  ):
                      output.append(' '  )
                      output.append(lowerCamelCase  )
                      output.append(' '  )
               else:
                      output.append(lowerCamelCase  )
        return "".join(lowerCamelCase  )

 def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		int  ) ->							Optional[Any]:
        # This defines a "chinese character" as anything in the CJK Unicode block:
        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if (
            (cp >= 0x4_E00 and cp <= 0x9_FFF)
            or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
            or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
            or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
            or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
            or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
            or (cp >= 0xF_900 and cp <= 0xF_AFF)
            or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
        ):  #
               return True

        return False

 def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		int  ) ->							Dict:
        _UpperCamelCase     :       int  						=  []
        for char in text:
               _UpperCamelCase     :       str  						=  ord(lowerCamelCase  )
               if cp == 0 or cp == 0xF_FFD or _is_control(lowerCamelCase  ):
                      continue
               if _is_whitespace(lowerCamelCase  ):
                      output.append(' '  )
               else:
                      output.append(lowerCamelCase  )
        return "".join(lowerCamelCase  )







def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]				):
       _UpperCamelCase     :       List[Any]  						=  set()
       _UpperCamelCase     :       Tuple  						=  word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)				)
              _UpperCamelCase     :       Tuple  						=  char
       return pairs


def SCREAMING_SNAKE_CASE				(     lowercase_:	Dict				):
       _UpperCamelCase     :       Union[str, Any]  						=  text.replace('—'		,						'-'				)
       _UpperCamelCase     :       int  						=  text.replace('–'		,						'-'				)
       _UpperCamelCase     :       Dict  						=  text.replace('―'		,						'-'				)
       _UpperCamelCase     :       Any  						=  text.replace('…'		,						'...'				)
       _UpperCamelCase     :       Dict  						=  text.replace('´'		,						'\''				)
       _UpperCamelCase     :       Union[str, Any]  						=  re.sub(R'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'		,						R' \1 '		,						lowercase_				)
       _UpperCamelCase     :       Dict  						=  re.sub(R'\s*\n\s*'		,						' \n '		,						lowercase_				)
       _UpperCamelCase     :       Union[str, Any]  						=  re.sub(R'[^\S\n]+'		,						' '		,						lowercase_				)
       return text.strip()





class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''
 _lowerCamelCase						:	int				    = VOCAB_FILES_NAMES
 _lowerCamelCase						:	Any				    = PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase						:	Optional[int]				    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase						:	List[str]				    = ['input_ids', 'attention_mask']

 def __init__(			self       :		str       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		Tuple       ,			lowerCamelCase       :		Union[str, Any]="<unk>"       ,			**lowerCamelCase       :		Any  ) ->							Dict:
        super().__init__(unk_token=lowerCamelCase       ,			**lowerCamelCase  )

        try:
               import ftfy
               from spacy.lang.en import English

               _UpperCamelCase     :       Any  						=  English()
               _UpperCamelCase     :       Any  						=  _nlp.tokenizer
               _UpperCamelCase     :       Dict  						=  ftfy.fix_text
        except ImportError:
               logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'  )
               _UpperCamelCase     :       int  						=  BasicTokenizer(do_lower_case=lowerCamelCase  )
               _UpperCamelCase     :       List[Any]  						=  None

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

 @property
 def 					__snake_case					(			self       :		str  ) ->							int:
        return True

 @property
 def 					__snake_case					(			self       :		int  ) ->							Dict:
        return len(self.encoder  )

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Union[str, Any]:
        return dict(self.encoder       ,			**self.added_tokens_encoder  )

 def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]  ) ->							Union[str, Any]:
        _UpperCamelCase     :       int  						=  tuple(token[:-1]  ) + (token[-1] + '</w>',)
        if token in self.cache:
               return self.cache[token]
        _UpperCamelCase     :       Dict  						=  get_pairs(lowerCamelCase  )

        if not pairs:
               return token + "</w>"

        while True:
               _UpperCamelCase     :       List[str]  						=  min(lowerCamelCase       ,			key=lambda lowerCamelCase  : self.bpe_ranks.get(lowerCamelCase       ,			float('inf'  )  )  )
               if bigram not in self.bpe_ranks:
                      break
               _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  bigram
               _UpperCamelCase     :       Optional[Any]  						=  []
               _UpperCamelCase     :       Optional[int]  						=  0
               while i < len(lowerCamelCase  ):
                      try:
                             _UpperCamelCase     :       int  						=  word.index(lowerCamelCase       ,			lowerCamelCase  )
                      except ValueError:
                             new_word.extend(word[i:]  )
                             break
                      else:
                             new_word.extend(word[i:j]  )
                             _UpperCamelCase     :       Union[str, Any]  						=  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
               _UpperCamelCase     :       str  						=  tuple(lowerCamelCase  )
               _UpperCamelCase     :       Optional[int]  						=  new_word
               if len(lowerCamelCase  ) == 1:
                      break
               else:
                      _UpperCamelCase     :       List[Any]  						=  get_pairs(lowerCamelCase  )
        _UpperCamelCase     :       Any  						=  ' '.join(lowerCamelCase  )
        if word == "\n  </w>":
               _UpperCamelCase     :       Union[str, Any]  						=  '\n</w>'
        _UpperCamelCase     :       Tuple  						=  word
        return word

 def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		Optional[Any]  ) ->							List[Any]:
        _UpperCamelCase     :       Optional[int]  						=  []
        if self.fix_text is None:
               # Using BERT's BasicTokenizer
               _UpperCamelCase     :       int  						=  self.nlp.tokenize(lowerCamelCase  )
               for token in text:
                      split_tokens.extend(list(self.bpe(lowerCamelCase  ).split(' '  )  )  )
        else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               _UpperCamelCase     :       Optional[Any]  						=  self.nlp(text_standardize(self.fix_text(lowerCamelCase  )  )  )
               for token in text:
                      split_tokens.extend(list(self.bpe(token.text.lower()  ).split(' '  )  )  )
        return split_tokens

 def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		Optional[int]  ) ->							int:
        return self.encoder.get(lowerCamelCase       ,			self.encoder.get(self.unk_token  )  )

 def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		List[str]  ) ->							str:
        return self.decoder.get(lowerCamelCase       ,			self.unk_token  )

 def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Dict  ) ->							Tuple:
        _UpperCamelCase     :       str  						=  ''.join(lowerCamelCase  ).replace('</w>'       ,			' '  ).strip()
        return out_string

 def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		str       ,			lowerCamelCase       :		Optional[str] = None  ) ->							Tuple[str]:
        if not os.path.isdir(lowerCamelCase  ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"  )
               return
        _UpperCamelCase     :       str  						=  os.path.join(
            lowerCamelCase       ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
        _UpperCamelCase     :       List[str]  						=  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'  )

        _UpperCamelCase     :       List[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!'  )
                             _UpperCamelCase     :       str  						=  token_index
                      writer.write(' '.join(lowerCamelCase  ) + '\n'  )
                      index += 1

        return vocab_file, merge_file



