






'''simple docstring'''

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							="\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"



def 							snake_case					(     a_							: str    ,   a_							: Any    ,   a_							: int=8	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Tuple					    =					height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      UpperCamelCase_				:   List[str]					    =					width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor



def 							snake_case					(     a_							: List[Any]    ,   a_							: str=512    ,   a_							: Optional[Any]=512	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   int					    =					pil_image.resize((w, h)    ,   resample=Image.BICUBIC    ,   reducing_gap=1	)
      UpperCamelCase_				:   int					    =					np.array(pil_image.convert("""RGB"""	)	)
      UpperCamelCase_				:   Optional[int]					    =					arr.astype(np.floataa	) / 127.5 - 1
      UpperCamelCase_				:   Any					    =					np.transpose(a_    ,   [2, 0, 1]	)
      UpperCamelCase_				:   Tuple					    =					torch.from_numpy(a_	).unsqueeze(0	)
      return image




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     ):
       super().__init__()

       self.register_modules(
           unet=__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     movq=__lowerCAmelCase			,     )
       UpperCamelCase_				:   str					    =					2 ** (len(self.movq.config.block_out_channels ) - 1)
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       # get the original timestep using init_timestep
       UpperCamelCase_				:   Tuple					    =					min(int(num_inference_steps * strength )			,     __lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =					max(num_inference_steps - init_timestep			,     0 )
       UpperCamelCase_				:   Optional[int]					    =					self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       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_				:   Any					    =					image.to(device=__lowerCAmelCase			,     dtype=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =					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_				:   Any					    =					[
                       self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__lowerCAmelCase )
                   ]
                   UpperCamelCase_				:   List[Any]					    =					torch.cat(__lowerCAmelCase			,     dim=0 )
             else:
                   UpperCamelCase_				:   str					    =					self.movq.encode(__lowerCAmelCase ).latent_dist.sample(__lowerCAmelCase )

             UpperCamelCase_				:   Optional[Any]					    =					self.movq.config.scaling_factor * init_latents

       UpperCamelCase_				:   Optional[Any]					    =					torch.cat([init_latents]			,     dim=0 )

       UpperCamelCase_				:   Tuple					    =					init_latents.shape
       UpperCamelCase_				:   Union[str, Any]					    =					randn_tensor(__lowerCAmelCase			,     generator=__lowerCAmelCase			,     device=__lowerCAmelCase			,     dtype=__lowerCAmelCase )

       # get latents
       UpperCamelCase_				:   Tuple					    =					self.scheduler.add_noise(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_				:   int					    =					init_latents

       return latents
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=0 ):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError("""Please install accelerate via `pip install accelerate`""" )

       UpperCamelCase_				:   int					    =					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   _UpperCAmelCase       (							self			,     __lowerCAmelCase=0 ):
       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_				:   str					    =					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_				:   List[Any]					    =					None
       for cpu_offloaded_model in [self.unet, self.movq]:
             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   _UpperCAmelCase       (							self ):
       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			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 1_00			,     __lowerCAmelCase = 4.0			,     __lowerCAmelCase = 0.3			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = None			,     __lowerCAmelCase = "pil"			,     __lowerCAmelCase = True			,     ):
       UpperCamelCase_				:   Optional[Any]					    =					self._execution_device

       UpperCamelCase_				:   Tuple					    =					guidance_scale > 1.0

       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =					torch.cat(__lowerCAmelCase			,     dim=0 )
       UpperCamelCase_				:   List[Any]					    =					image_embeds.shape[0]
       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[Any]					    =					torch.cat(__lowerCAmelCase			,     dim=0 )

       if do_classifier_free_guidance:
             UpperCamelCase_				:   Optional[int]					    =					image_embeds.repeat_interleave(__lowerCAmelCase			,     dim=0 )
             UpperCamelCase_				:   str					    =					negative_image_embeds.repeat_interleave(__lowerCAmelCase			,     dim=0 )

       UpperCamelCase_				:   int					    =					torch.cat([negative_image_embeds, image_embeds]			,     dim=0 ).to(dtype=self.unet.dtype			,     device=__lowerCAmelCase )

       if not isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =					[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_				:   Optional[int]					    =					torch.cat([prepare_image(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ) for i in image]			,     dim=0 )
       UpperCamelCase_				:   Optional[Any]					    =					image.to(dtype=image_embeds.dtype			,     device=__lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =					self.movq.encode(__lowerCAmelCase )["""latents"""]
       UpperCamelCase_				:   Tuple					    =					latents.repeat_interleave(__lowerCAmelCase			,     dim=0 )
       self.scheduler.set_timesteps(__lowerCAmelCase			,     device=__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =					self.get_timesteps(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =					timesteps[:1].repeat(batch_size * num_images_per_prompt )
       UpperCamelCase_				:   List[Any]					    =					downscale_height_and_width(__lowerCAmelCase			,     __lowerCAmelCase			,     self.movq_scale_factor )
       UpperCamelCase_				:   Union[str, 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_				:   str					    =					torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

             UpperCamelCase_				:   Tuple					    =					{"""image_embeds""": image_embeds}
             UpperCamelCase_				:   List[str]					    =					self.unet(
                 sample=__lowerCAmelCase			,     timestep=__lowerCAmelCase			,     encoder_hidden_states=__lowerCAmelCase			,     added_cond_kwargs=__lowerCAmelCase			,     return_dict=__lowerCAmelCase			,     )[0]

             if do_classifier_free_guidance:
                   UpperCamelCase_				:   str					    =					noise_pred.split(latents.shape[1]			,     dim=1 )
                   UpperCamelCase_				:   str					    =					noise_pred.chunk(2 )
                   UpperCamelCase_				:   int					    =					variance_pred.chunk(2 )
                   UpperCamelCase_				:   Tuple					    =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   UpperCamelCase_				:   str					    =					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_				:   Optional[int]					    =					noise_pred.split(latents.shape[1]			,     dim=1 )

             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase_				:   Tuple					    =					self.scheduler.step(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     generator=__lowerCAmelCase			,     )[0]

       # post-processing
       UpperCamelCase_				:   Any					    =					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_				:   Tuple					    =					image * 0.5 + 0.5
             UpperCamelCase_				:   Optional[Any]					    =					image.clamp(0			,     1 )
             UpperCamelCase_				:   Tuple					    =					image.cpu().permute(0			,     2			,     3			,     1 ).float().numpy()

       if output_type == "pil":
             UpperCamelCase_				:   Dict					    =					self.numpy_to_pil(__lowerCAmelCase )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=__lowerCAmelCase )














'''simple docstring'''

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      UpperCamelCase							="pt"
elif is_tf_available():
      UpperCamelCase							="tf"
else:
      UpperCamelCase							="jax"




class     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						int      =						PerceiverTokenizer
 __a			:						List[str]      =						False
 def   _UpperCAmelCase       (							self ):
       super().setUp()
       UpperCamelCase_				:   Optional[int]					    =							PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def   _UpperCAmelCase       (							self ):
       return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=False			,     __lowerCAmelCase=20			,     __lowerCAmelCase=5 ):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for Perceiver because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       UpperCamelCase_				:   str					    =							[]
       for i in range(len(__lowerCAmelCase ) ):
             try:
                   UpperCamelCase_				:   Tuple					    =							tokenizer.decode([i]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             except UnicodeDecodeError:
                   pass
             toks.append((i, tok) )

       UpperCamelCase_				:   Optional[Any]					    =							list(filter(lambda __lowerCAmelCase : re.match(R"""^[ a-zA-Z]+$"""			,     t[1] )			,     __lowerCAmelCase ) )
       UpperCamelCase_				:   int					    =							list(filter(lambda __lowerCAmelCase : [t[0]] == tokenizer.encode(t[1]			,     add_special_tokens=__lowerCAmelCase )			,     __lowerCAmelCase ) )
       if max_length is not None and len(__lowerCAmelCase ) > max_length:
             UpperCamelCase_				:   List[Any]					    =							toks[:max_length]
       if min_length is not None and len(__lowerCAmelCase ) < min_length and len(__lowerCAmelCase ) > 0:
             while len(__lowerCAmelCase ) < min_length:
                   UpperCamelCase_				:   Tuple					    =							toks + toks
        # toks_str = [t[1] for t in toks]
       UpperCamelCase_				:   List[Any]					    =							[t[0] for t in toks]

       # Ensure consistency
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase			,     clean_up_tokenization_spaces=__lowerCAmelCase )
       if " " not in output_txt and len(__lowerCAmelCase ) > 1:
             UpperCamelCase_				:   Tuple					    =							(
                 tokenizer.decode([toks_ids[0]]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
                 + """ """
                 + tokenizer.decode(toks_ids[1:]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             )
       if with_prefix_space:
             UpperCamelCase_				:   Tuple					    =							""" """ + output_txt
       UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
       return output_txt, output_ids
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[int]					    =							"""Unicode €."""
       UpperCamelCase_				:   Dict					    =							tokenizer(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )

       # decoding
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[CLS]Unicode €.[SEP]""" )

       UpperCamelCase_				:   str					    =							tokenizer("""e è é ê ë""" )
       UpperCamelCase_				:   List[str]					    =							[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )
       # decoding
       UpperCamelCase_				:   List[str]					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[CLS]e è é ê ë[SEP]""" )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) )			,     """[CLS]e è é ê ë[SEP]""" )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       # fmt: off
       UpperCamelCase_				:   int					    =							[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
       # fmt: on
       UpperCamelCase_				:   Any					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )

       if FRAMEWORK != "jax":
             UpperCamelCase_				:   Optional[Any]					    =							list(batch.input_ids.numpy()[0] )
       else:
             UpperCamelCase_				:   Optional[int]					    =							list(batch.input_ids.tolist()[0] )

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

       self.assertEqual((2, 38)			,     batch.input_ids.shape )
       self.assertEqual((2, 38)			,     batch.attention_mask.shape )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Union[str, Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       UpperCamelCase_				:   int					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn("""input_ids"""			,     __lowerCAmelCase )
       self.assertIn("""attention_mask"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_input_ids"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_attention_mask"""			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   List[str]					    =							[
           """Summary of the text.""",
           """Another summary.""",
       ]
       UpperCamelCase_				:   Dict					    =							tokenizer(
           text_target=__lowerCAmelCase			,     max_length=32			,     padding="""max_length"""			,     truncation=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertEqual(32			,     targets["""input_ids"""].shape[1] )
 def   _UpperCAmelCase       (							self ):
       # safety check on max_len default value so we are sure the test works
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   self.assertNotEqual(tokenizer.model_max_length			,     42 )

        # Now let's start the test
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCamelCase_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   Optional[int]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   UpperCamelCase_				:   Dict					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   Union[str, Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

                   shutil.rmtree(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizers(model_max_length=42 )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCamelCase_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   List[str]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   tokenizer.add_tokens(["""bim""", """bambam"""] )
                   UpperCamelCase_				:   Any					    =							tokenizer.additional_special_tokens
                   additional_special_tokens.append("""new_additional_special_token""" )
                   tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   List[Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
                   self.assertIn("""new_additional_special_token"""			,     after_tokenizer.additional_special_tokens )
                   self.assertEqual(after_tokenizer.model_max_length			,     42 )

                   UpperCamelCase_				:   int					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase			,     model_max_length=43 )
                   self.assertEqual(tokenizer.model_max_length			,     43 )

                   shutil.rmtree(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							[]
       if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

       if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   Union[str, Any]					    =							json.load(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   List[str]					    =							json.load(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							[F"<extra_id_{i}>" for i in range(1_25 )]

                   UpperCamelCase_				:   Union[str, Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]
                   UpperCamelCase_				:   List[Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     """w"""			,     encoding="""utf-8""" ) as outfile:
                         json.dump(__lowerCAmelCase			,     __lowerCAmelCase )
                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     """w"""			,     encoding="""utf-8""" ) as outfile:
                         json.dump(__lowerCAmelCase			,     __lowerCAmelCase )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   UpperCamelCase_				:   Optional[int]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     )
                   self.assertIn(
                       """an_additional_special_token"""			,     tokenizer_without_change_in_init.additional_special_tokens )
                   self.assertEqual(
                       ["""an_additional_special_token"""]			,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) )			,     )

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   UpperCamelCase_				:   Optional[Any]					    =							added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,     lstrip=__lowerCAmelCase )]
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     additional_special_tokens=__lowerCAmelCase			,     )

                   self.assertIn("""a_new_additional_special_token"""			,     tokenizer.additional_special_tokens )
                   self.assertEqual(
                       ["""a_new_additional_special_token"""]			,     tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) )			,     )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([1_78] )			,     """�""" )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass





 def   _UpperCAmelCase       (							self ):
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       UpperCamelCase_				:   Dict					    =							self.get_tokenizers(fast=__lowerCAmelCase			,     do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   str					    =							["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.convert_tokens_to_string(__lowerCAmelCase )

                   self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )






